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Machine Learning and Knowledge Extraction
Number of Followers: 17  Open Access journalISSN (Online) 2504-4990 Published by MDPI  [258 journals]
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- MAKE, Vol. 6, Pages 1389-1412: Prediction of the Behaviour from Discharge
Points for Solid Waste Management
Authors: Sergio De-la-Mata-Moratilla, Jose-Maria Gutierrez-Martinez, Ana Castillo-Martinez, Sergio Caro-Alvaro
First page: 1389
Abstract: This research investigates the behaviour of the Discharge Points in a Municipal Solid Waste Management System to evaluate the feasibility of making individual predictions of every Discharge Point. Such predictions could enhance system management through optimisation, improving their ecological and economic impact. The current approaches consider installations as a whole, but individual predictions may yield better results. This paper follows a methodology that includes analysing data from 200 different Discharge Points over a period of four years and applying twelve forecast algorithms found as more commonly used for these predictions in the literature, including Random Forest, Support Vector Machines, and Decision Tree, to identify predictive patterns. The results are compared and evaluated to determine the accuracy of individual predictions and their potential improvements. As the results show that the algorithms do not capture the individual Discharge Points behaviour, alternative approaches are suggested for further development.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-06-24
DOI: 10.3390/make6030066
Issue No: Vol. 6, No. 3 (2024)
- MAKE, Vol. 6, Pages 1413-1438: Using Deep Q-Learning to Dynamically Toggle
between Push/Pull Actions in Computational Trust Mechanisms
Authors: Zoi Lygizou, Dimitris Kalles
First page: 1413
Abstract: Recent work on decentralized computational trust models for open multi-agent systems has resulted in the development of CA, a biologically inspired model which focuses on the trustee’s perspective. This new model addresses a serious unresolved problem in existing trust and reputation models, namely the inability to handle constantly changing behaviors and agents’ continuous entry and exit from the system. In previous work, we compared CA to FIRE, a well-known trust and reputation model, and found that CA is superior when the trustor population changes, whereas FIRE is more resilient to the trustee population changes. Thus, in this paper, we investigate how the trustors can detect the presence of several dynamic factors in their environment and then decide which trust model to employ in order to maximize utility. We frame this problem as a machine learning problem in a partially observable environment, where the presence of several dynamic factors is not known to the trustor, and we describe how an adaptable trustor can rely on a few measurable features so as to assess the current state of the environment and then use Deep Q-Learning (DQL), in a single-agent reinforcement learning setting, to learn how to adapt to a changing environment. We ran a series of simulation experiments to compare the performance of the adaptable trustor with the performance of trustors using only one model (FIRE or CA) and we show that an adaptable agent is indeed capable of learning when to use each model and, thus, perform consistently in dynamic environments.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-06-27
DOI: 10.3390/make6030067
Issue No: Vol. 6, No. 3 (2024)
- MAKE, Vol. 6, Pages 1439-1465: Using Segmentation to Boost Classification
Performance and Explainability in CapsNets
Authors: Dominik Vranay, Maroš Hliboký, László Kovács, Peter Sinčák
First page: 1439
Abstract: In this paper, we present Combined-CapsNet (C-CapsNet), a novel approach aimed at enhancing the performance and explainability of Capsule Neural Networks (CapsNets) in image classification tasks. Our method involves the integration of segmentation masks as reconstruction targets within the CapsNet architecture. This integration helps in better feature extraction by focusing on significant image parts while reducing the number of parameters required for accurate classification. C-CapsNet combines principles from Efficient-CapsNet and the original CapsNet, introducing several novel improvements such as the use of segmentation masks to reconstruct images and a number of tweaks to the routing algorithm, which enhance both classification accuracy and interoperability. We evaluated C-CapsNet using the Oxford-IIIT Pet and SIIM-ACR Pneumothorax datasets, achieving mean F1 scores of 93% and 67%, respectively. These results demonstrate a significant performance improvement over traditional CapsNet and CNN models. The method’s effectiveness is further highlighted by its ability to produce clear and interpretable segmentation masks, which can be used to validate the network’s focus during classification tasks. Our findings suggest that C-CapsNet not only improves the accuracy of CapsNets but also enhances their explainability, making them more suitable for real-world applications, particularly in medical imaging.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-06-28
DOI: 10.3390/make6030068
Issue No: Vol. 6, No. 3 (2024)
- MAKE, Vol. 6, Pages 1466-1483: Motor PHM on Edge Computing with Anomaly
Detection and Fault Severity Estimation through Compressed Data Using PCA
and Autoencoder
Authors: Jong Hyun Choi, Sung Kyu Jang, Woon Hyung Cho, Seokbae Moon, Hyeongkeun Kim
First page: 1466
Abstract: The motor is essential for manufacturing industries, but wear can cause unexpected failure. Predictive and health management (PHM) for motors is critical in manufacturing sites. In particular, data-driven PHM using deep learning methods has gained popularity because it reduces the need for domain expertise. However, the massive amount of data poses challenges to traditional cloud-based PHM, making edge computing a promising solution. This study proposes a novel approach to motor PHM in edge devices. Our approach integrates principal component analysis (PCA) and an autoencoder (AE) encoder achieving effective data compression while preserving fault detection and severity estimation integrity. The compressed data is visualized using t-SNE, and its ability to retain information is assessed through clustering performance metrics. The proposed method is tested on a custom-made experimental platform dataset, demonstrating robustness across various fault scenarios and providing valuable insights for practical applications in manufacturing.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-06-28
DOI: 10.3390/make6030069
Issue No: Vol. 6, No. 3 (2024)
- MAKE, Vol. 6, Pages 1484-1493: Enhancing Computation-Efficiency of Deep
Neural Network Processing on Edge Devices through Serial/Parallel Systolic
Computing
Authors: Iraj Moghaddasi, Byeong-Gyu Nam
First page: 1484
Abstract: In recent years, deep neural networks (DNNs) have addressed new applications with intelligent autonomy, often achieving higher accuracy than human experts. This capability comes at the expense of the ever-increasing complexity of emerging DNNs, causing enormous challenges while deploying on resource-limited edge devices. Improving the efficiency of DNN hardware accelerators by compression has been explored previously. Existing state-of-the-art studies applied approximate computing to enhance energy efficiency even at the expense of a little accuracy loss. In contrast, bit-serial processing has been used for improving the computational efficiency of neural processing without accuracy loss, exploiting a simple design, dynamic precision adjustment, and computation pruning. This research presents Serial/Parallel Systolic Array (SPSA) and Octet Serial/Parallel Systolic Array (OSPSA) processing elements for edge DNN acceleration, which exploit bit-serial processing on systolic array architecture for improving computational efficiency. For evaluation, all designs were described at the RTL level and synthesized in 28 nm technology. Post-synthesis cycle-accurate simulations of image classification over DNNs illustrated that, on average, a sample 16 × 16 systolic array indicated remarkable improvements of 17.6% and 50.6% in energy efficiency compared to the baseline, with no loss of accuracy.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-07-01
DOI: 10.3390/make6030070
Issue No: Vol. 6, No. 3 (2024)
- MAKE, Vol. 6, Pages 1494-1509: A Cognitive Load Theory (CLT) Analysis of
Machine Learning Explainability, Transparency, Interpretability, and
Shared Interpretability
Authors: Stephen Fox, Vitor Fortes Rey
First page: 1494
Abstract: Information that is complicated and ambiguous entails high cognitive load. Trying to understand such information can involve a lot of cognitive effort. An alternative to expending a lot of cognitive effort is to engage in motivated cognition, which can involve selective attention to new information that matches existing beliefs. In accordance with principles of least action related to management of cognitive effort, another alternative is to give up trying to understand new information with high cognitive load. In either case, high cognitive load can limit potential for understanding of new information and learning from new information. Cognitive Load Theory (CLT) provides a framework for relating the characteristics of information to human cognitive load. Although CLT has been developed through more than three decades of scientific research, it has not been applied comprehensively to improve the explainability, transparency, interpretability, and shared interpretability (ETISI) of machine learning models and their outputs. Here, in order to illustrate the broad relevance of CLT to ETISI, it is applied to analyze a type of hybrid machine learning called Algebraic Machine Learning (AML). This is the example because AML has characteristics that offer high potential for ETISI. However, application of CLT reveals potential for high cognitive load that can limit ETISI even when AML is used in conjunction with decision trees. Following the AML example, the general relevance of CLT to machine learning ETISI is discussed with the examples of SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), and the Contextual Importance and Utility (CIU) method. Overall, it is argued in this Perspective paper that CLT can provide science-based design principles that can contribute to improving the ETISI of all types of machine learning.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-07-02
DOI: 10.3390/make6030071
Issue No: Vol. 6, No. 3 (2024)
- MAKE, Vol. 6, Pages 1510-1530: Learning Experiences and Didactic Needs of
German Healthcare Professions: A Focus Group Study for the Design of
Personalized Interprofessional Further Education in Dementia Healthcare
Authors: Marie Stelter, Manuela Malek, Margareta Halek, Jan Ehlers, Julia Nitsche
First page: 1510
Abstract: Considering the multifaceted nature of neurodegenerative diseases like dementia and the necessity for interprofessional knowledge, this research extends its scope to encompass professionals with diverse levels of training and experience in dementia care. A need analysis for the project “My INdividual Digital EDucation.RUHR” (MINDED.RUHR) is conducted to develop an automatized recommender system for individual learning content using AI. In this sub-study, the aim was to reveal didactic specialties, knowledge gaps, and structural challenges of further education in dementia care of different health professions and to derive learning preference personae. Eight focus group interviews among nine health professions and up to six participants (N = 34) each took place to survey distinct didactic experiences and learning needs. The results reflect various learning preferences, with a propensity to multimedia, practical, and interactive tasks. Health professions are used to digital education but show aversions against synchronous e-learning formats. The derived learning preference personae constitute profound blueprints for a user-centered digital education design process, aiming to establish personalized and representative further education in dementia care applicable to various individual preferences and structural workplace challenges of healthcare professions.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-07-03
DOI: 10.3390/make6030072
Issue No: Vol. 6, No. 3 (2024)
- MAKE, Vol. 6, Pages 1531-1544: Evaluation Metrics for Generative Models:
An Empirical Study
Authors: Eyal Betzalel, Coby Penso, Ethan Fetaya
First page: 1531
Abstract: Generative models such as generative adversarial networks, diffusion models, and variational auto-encoders have become prevalent in recent years. While it is true that these models have shown remarkable results, evaluating their performance is challenging. This issue is of vital importance to push research forward and identify meaningful gains from random noise. Currently, heuristic metrics such as the inception score (IS) and Fréchet inception distance (FID) are the most common evaluation metrics, but what they measure is not entirely clear. Additionally, there are questions regarding how meaningful their score actually is. In this work, we propose a novel evaluation protocol for likelihood-based generative models, based on generating a high-quality synthetic dataset on which we can estimate classical metrics for comparison. This new scheme harnesses the advantages of knowing the underlying likelihood values of the data by measuring the divergence between the model-generated data and the synthetic dataset. Our study shows that while FID and IS correlate with several f-divergences, their ranking of close models can vary considerably, making them problematic when used for fine-grained comparison. We further use this experimental setting to study which evaluation metric best correlates with our probabilistic metrics.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-07-07
DOI: 10.3390/make6030073
Issue No: Vol. 6, No. 3 (2024)
- MAKE, Vol. 6, Pages 1545-1563: Navigating the Multimodal Landscape: A
Review on Integration of Text and Image Data in Machine Learning
Architectures
Authors: Maisha Binte Rashid, Md Shahidur Rahaman, Pablo Rivas
First page: 1545
Abstract: Images and text have become essential parts of the multimodal machine learning (MMML) framework in today’s world because data are always available, and technological breakthroughs bring disparate forms together, and while text adds semantic richness and narrative to images, images capture visual subtleties and emotions. Together, these two media improve knowledge beyond what would be possible with just one revolutionary application. This paper investigates feature extraction and advancement from text and image data using pre-trained models in MMML. It offers a thorough analysis of fusion architectures, outlining text and image data integration and evaluating their overall advantages and effects. Furthermore, it draws attention to the shortcomings and difficulties that MMML currently faces and guides areas that need more research and development. We have gathered 341 research articles from five digital library databases to accomplish this. Following a thorough assessment procedure, we have 88 research papers that enable us to evaluate MMML in detail. Our findings demonstrate that pre-trained models, such as BERT for text and ResNet for images, are predominantly employed for feature extraction due to their robust performance in diverse applications. Fusion techniques, ranging from simple concatenation to advanced attention mechanisms, are extensively adopted to enhance the representation of multimodal data. Despite these advancements, MMML models face significant challenges, including handling noisy data, optimizing dataset size, and ensuring robustness against adversarial attacks. Our findings highlight the necessity for further research to address these challenges, particularly in developing methods to improve the robustness of MMML models.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-07-09
DOI: 10.3390/make6030074
Issue No: Vol. 6, No. 3 (2024)
- MAKE, Vol. 6, Pages 1564-1578: Climate Change and Soil Health: Explainable
Artificial Intelligence Reveals Microbiome Response to Warming
Authors: Pierfrancesco Novielli, Michele Magarelli, Donato Romano, Lorenzo de Trizio, Pierpaolo Di Bitonto, Alfonso Monaco, Nicola Amoroso, Anna Maria Stellacci, Claudia Zoani, Roberto Bellotti, Sabina Tangaro
First page: 1564
Abstract: Climate change presents an unprecedented global challenge, demanding collective action to both mitigate its effects and adapt to its consequences. Soil health and function are profoundly impacted by climate change, particularly evident in the sensitivity of soil microbial respiration to warming, known as Q10. Q10 measures the rate of microbial respiration’s increase with a temperature rise of 10 degrees Celsius, playing a pivotal role in understanding soil carbon dynamics in response to climate change. Leveraging machine learning techniques, particularly explainable artificial intelligence (XAI), offers a promising avenue to analyze complex data and identify biomarkers crucial for developing innovative climate change mitigation strategies. This research aims to evaluate the extent to which chemical, physical, and microbiological soil characteristics are associated with high or low Q10 values, utilizing XAI approaches. The Extra Trees Classifier algorithm was employed, yielding an average accuracy of 0.923±0.009, an average AUCROC of 0.964±0.004, and an average AUCPRC of 0.963±0.006. Additionally, through XAI techniques, we elucidate the significant features contributing to the prediction of Q10 classes. The XAI analysis shows that the temperature sensitivity of soil respiration increases with microbiome variables but decreases with non-microbiome variables beyond a threshold. Our findings underscore the critical role of the soil microbiome in predicting soil Q10 dynamics, providing valuable insights for developing targeted climate change mitigation strategies.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-07-10
DOI: 10.3390/make6030075
Issue No: Vol. 6, No. 3 (2024)
- MAKE, Vol. 6, Pages 1579-1596: Deep Learning-Powered Optical Microscopy
for Steel Research
Authors: Šárka Mikmeková, Martin Zouhar, Jan Čermák, Ondřej Ambrož, Patrik Jozefovič, Ivo Konvalina, Eliška Materna Mikmeková, Jiří Materna
First page: 1579
Abstract: The success of machine learning (ML) models in object or pattern recognition naturally leads to ML being employed in the classification of the microstructure of steel surfaces. Light optical microscopy (LOM) is the traditional imaging process in this field. However, the increasing use of ML to extract or relate more aspects of the aforementioned materials and the limitations of LOM motivated us to provide an improvement to the established image acquisition process. In essence, we perform style transfer from LOM to scanning electron microscopy (SEM) combined with “intelligent” upscaling. This is achieved by employing an ML model trained on a multimodal dataset to generate an SEM-like image from the corresponding LOM image. This transformation, in our opinion, which is corroborated by a detailed analysis of the source, target and prediction, successfully pushes the limits of LOM in the case of steel surfaces. The expected consequence is the improvement of the precise characterization of advanced multiphase steels’ structure based on these transformed LOM images.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-07-11
DOI: 10.3390/make6030076
Issue No: Vol. 6, No. 3 (2024)
- MAKE, Vol. 6, Pages 1597-1618: Learning Effective Good Variables from
Physical Data
Authors: Giulio Barletta, Giovanni Trezza, Eliodoro Chiavazzo
First page: 1597
Abstract: We assume that a sufficiently large database is available, where a physical property of interest and a number of associated ruling primitive variables or observables are stored. We introduce and test two machine learning approaches to discover possible groups or combinations of primitive variables, regardless of data origin, being it numerical or experimental: the first approach is based on regression models, whereas the second on classification models. The variable group (here referred to as the new effective good variable) can be considered as successfully found when the physical property of interest is characterized by the following effective invariant behavior: in the first method, invariance of the group implies invariance of the property up to a given accuracy; in the other method, upon partition of the physical property values into two or more classes, invariance of the group implies invariance of the class. For the sake of illustration, the two methods are successfully applied to two popular empirical correlations describing the convective heat transfer phenomenon and to the Newton’s law of universal gravitation.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-07-12
DOI: 10.3390/make6030077
Issue No: Vol. 6, No. 3 (2024)
- MAKE, Vol. 6, Pages 1619-1632: Examining the Global Patent Landscape of
Artificial Intelligence-Driven Solutions for COVID-19
Authors: Fabio Mota, Luiza Amara Maciel Braga, Bernardo Pereira Cabral, Natiele Carla da Silva Ferreira, Cláudio Damasceno Pinto, José Aguiar Coelho, Luiz Anastacio Alves
First page: 1619
Abstract: Artificial Intelligence (AI) technologies have been widely applied to tackle Coronavirus Disease 2019 (COVID-19) challenges, from diagnosis to prevention. Patents are a valuable source for understanding the AI technologies used in the COVID-19 context, allowing the identification of the current technological scenario, fields of application, and research, development, and innovation trends. This study aimed to analyze the global patent landscape of AI applications related to COVID-19. To do so, we analyzed AI-related COVID-19 patent metadata collected in the Derwent Innovations Index using systematic review, bibliometrics, and network analysis., Our results show diagnosis as the most frequent application field, followed by prevention. Deep Learning algorithms, such as Convolutional Neural Network (CNN), were predominantly used for diagnosis, while Machine Learning algorithms, such as Support Vector Machine (SVM), were mainly used for prevention. The most frequent International Patent Classification Codes were related to computing arrangements based on specific computational models, information, and communication technology for detecting, monitoring, or modeling epidemics or pandemics, and methods or arrangements for pattern recognition using electronic means. The most central algorithms of the two-mode network were CNN, SVM, and Random Forest (RF), while the most central application fields were diagnosis, prevention, and forecast. The most significant connection between algorithms and application fields occurred between CNN and diagnosis. Our findings contribute to a better understanding of the technological landscape involving AI and COVID-19, and we hope they can inform future research and development’s decision making and planning.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-07-16
DOI: 10.3390/make6030078
Issue No: Vol. 6, No. 3 (2024)
- MAKE, Vol. 6, Pages 1633-1652: Extending Multi-Output Methods for
Long-Term Aboveground Biomass Time Series Forecasting Using Convolutional
Neural Networks
Authors: Efrain Noa-Yarasca, Javier M. Osorio Leyton, Jay P. Angerer
First page: 1633
Abstract: Accurate aboveground vegetation biomass forecasting is essential for livestock management, climate impact assessments, and ecosystem health. While artificial intelligence (AI) techniques have advanced time series forecasting, a research gap in predicting aboveground biomass time series beyond single values persists. This study introduces RECMO and DirRecMO, two multi-output methods for forecasting aboveground vegetation biomass. Using convolutional neural networks, their efficacy is evaluated across short-, medium-, and long-term horizons on six Kenyan grassland biomass datasets, and compared with that of existing single-output methods (Recursive, Direct, and DirRec) and multi-output methods (MIMO and DIRMO). The results indicate that single-output methods are superior for short-term predictions, while both single-output and multi-output methods exhibit a comparable effectiveness in long-term forecasts. RECMO and DirRecMO outperform established multi-output methods, demonstrating a promising potential for biomass forecasting. This study underscores the significant impact of multi-output size on forecast accuracy, highlighting the need for optimal size adjustments and showcasing the proposed methods’ flexibility in long-term forecasts. Short-term predictions show less significant differences among methods, complicating the identification of the best performer. However, clear distinctions emerge in medium- and long-term forecasts, underscoring the greater importance of method choice for long-term predictions. Moreover, as the forecast horizon extends, errors escalate across all methods, reflecting the challenges of predicting distant future periods. This study suggests advancing hybrid models (e.g., RECMO and DirRecMO) to improve extended horizon forecasting. Future research should enhance adaptability, investigate multi-output impacts, and conduct comparative studies across diverse domains, datasets, and AI algorithms for robust insights.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-07-17
DOI: 10.3390/make6030079
Issue No: Vol. 6, No. 3 (2024)
- MAKE, Vol. 6, Pages 1653-1666: Global and Local Interpretable Machine
Learning Allow Early Prediction of Unscheduled Hospital Readmission
Authors: Rafael Ruiz de San Martín, Catalina Morales-Hernández, Carmen Barberá, Carlos Martínez-Cortés, Antonio Jesús Banegas-Luna, Francisco José Segura-Méndez, Horacio Pérez-Sánchez, Isabel Morales-Moreno, Juan José Hernández-Morante
First page: 1653
Abstract: Nowadays, most of the health expenditure is due to chronic patients who are readmitted several times for their pathologies. Personalized prevention strategies could be developed to improve the management of these patients. The aim of the present work was to develop local predictive models using interpretable machine learning techniques to early identify individual unscheduled hospital readmissions. To do this, a retrospective, case-control study, based on information regarding patient readmission in 2018–2019, was conducted. After curation of the initial dataset (n = 76,210), the final number of participants was n = 29,026. A machine learning analysis was performed following several algorithms using unscheduled hospital readmissions as dependent variable. Local model-agnostic interpretability methods were also performed. We observed a 13% rate of unscheduled hospital readmissions cases. There were statistically significant differences regarding age and days of stay (p < 0.001 in both cases). A logistic regression model revealed chronic therapy (odds ratio: 3.75), diabetes mellitus history (odds ratio: 1.14), and days of stay (odds ratio: 1.02) as relevant factors. Machine learning algorithms yielded better results regarding sensitivity and other metrics. Following, this procedure, days of stay and age were the most important factors to predict unscheduled hospital readmissions. Interestingly, other variables like allergies and adverse drug reaction antecedents were relevant. Individualized prediction models also revealed a high sensitivity. In conclusion, our study identified significant factors influencing unscheduled hospital readmissions, emphasizing the impact of age and length of stay. We introduced a personalized risk model for predicting hospital readmissions with notable accuracy. Future research should include more clinical variables to refine this model further.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-07-17
DOI: 10.3390/make6030080
Issue No: Vol. 6, No. 3 (2024)
- MAKE, Vol. 6, Pages 1667-1669: Comment on Novozhilova et al. More Capable,
Less Benevolent: Trust Perceptions of AI Systems across Societal Contexts.
Mach. Learn. Knowl. Extr. 2024, 6, 342–366
Authors: Robertas Damaševičius
First page: 1667
Abstract: The referenced article [...]
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-07-22
DOI: 10.3390/make6030081
Issue No: Vol. 6, No. 3 (2024)
- MAKE, Vol. 6, Pages 1670-1672: Reply to Damaševičius, R.
Comment on “Novozhilova et al. More Capable, Less Benevolent: Trust
Perceptions of AI Systems across Societal Contexts. Mach. Learn. Knowl.
Extr. 2024, 6, 342–366”
Authors: Ekaterina Novozhilova, Kate Mays, Sejin Paik, James Katz
First page: 1670
Abstract: We would like to thank Dr [...]
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-07-22
DOI: 10.3390/make6030082
Issue No: Vol. 6, No. 3 (2024)
- MAKE, Vol. 6, Pages 1673-1698: Enhancing Visitor Forecasting with
Target-Concatenated Autoencoder and Ensemble Learning
Authors: Ray-I Chang, Chih-Yung Tsai, Yu-Wei Chang
First page: 1673
Abstract: Accurate forecasting of inbound visitor numbers is crucial for effective planning and resource allocation in the tourism industry. Preceding forecasting algorithms primarily focused on time series analysis, often overlooking influential factors such as economic conditions. Regression models, on the other hand, face challenges when dealing with high-dimensional data. Previous autoencoders for feature selection do not simultaneously incorporate feature and target information simultaneously, potentially limiting their effectiveness in improving predictive performance. This study presents a novel approach that combines a target-concatenated autoencoder (TCA) with ensemble learning to enhance the accuracy of tourism demand predictions. The TCA method integrates the prediction target into the training process, ensuring that the learned feature representations are optimized for specific forecasting tasks. Extensive experiments conducted on the Taiwan and Hawaii datasets demonstrate that the proposed TCA method significantly outperforms traditional feature selection techniques and other advanced algorithms in terms of the mean absolute percentage error (MAPE), mean absolute error (MAE), and coefficient of determination (R2). The results show that TCA combined with XGBoost achieves MAPE values of 3.3947% and 4.0059% for the Taiwan and Hawaii datasets, respectively, indicating substantial improvements over existing methods. Additionally, the proposed approach yields better R2 and MAE metrics than existing methods, further demonstrating its effectiveness. This study highlights the potential of TCA in providing reliable and accurate forecasts, thereby supporting strategic planning, infrastructure development, and sustainable growth in the tourism sector. Future research is advised to explore real-time data integration, expanded feature sets, and hybrid modeling approaches to further enhance the capabilities of the proposed framework.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-07-25
DOI: 10.3390/make6030083
Issue No: Vol. 6, No. 3 (2024)
- MAKE, Vol. 6, Pages 1699-1719: Assessing the Value of Transfer Learning
Metrics for Radio Frequency Domain Adaptation
Authors: Lauren J. Wong, Braeden P. Muller, Sean McPherson, Alan J. Michaels
First page: 1699
Abstract: The use of transfer learning (TL) techniques has become common practice in fields such as computer vision (CV) and natural language processing (NLP). Leveraging prior knowledge gained from data with different distributions, TL offers higher performance and reduced training time, but has yet to be fully utilized in applications of machine learning (ML) and deep learning (DL) techniques and applications related to wireless communications, a field loosely termed radio frequency machine learning (RFML). This work examines whether existing transferability metrics, used in other modalities, might be useful in the context of RFML. Results show that the two existing metrics tested, Log Expected Empirical Prediction (LEEP) and Logarithm of Maximum Evidence (LogME), correlate well with post-transfer accuracy and can therefore be used to select source models for radio frequency (RF) domain adaptation and to predict post-transfer accuracy.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-07-25
DOI: 10.3390/make6030084
Issue No: Vol. 6, No. 3 (2024)
- MAKE, Vol. 6, Pages 1720-1761: Towards AI Dashboards in Financial
Services: Design and Implementation of an AI Development Dashboard for
Credit Assessment
Authors: Mustafa Pamuk, Matthias Schumann
First page: 1720
Abstract: Financial institutions are increasingly turning to artificial intelligence (AI) to improve their decision-making processes and gain a competitive edge. Due to the iterative process of AI development, it is mandatory to have a structured process in place, from the design to the deployment of AI-based services in the finance industry. This process must include the required validation and coordination with regulatory authorities. An appropriate dashboard can help to shape and structure the process of model development, e.g., for credit assessment in the finance industry. In addition, the analysis of datasets must be included as an important part of the dashboard to understand the reasons for changes in model performance. Furthermore, a dashboard can undertake documentation tasks to make the process of model development traceable, explainable, and transparent, as required by regulatory authorities in the finance industry. This can offer a comprehensive solution for financial companies to optimize their models, improve regulatory compliance, and ultimately foster sustainable growth in an increasingly competitive market. In this study, we investigate the requirements and provide a prototypical dashboard to create, manage, compare, and validate AI models to be used in the credit assessment of private customers.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-07-27
DOI: 10.3390/make6030085
Issue No: Vol. 6, No. 3 (2024)
- MAKE, Vol. 6, Pages 1762-1781: Diverse Machine Learning for Forecasting
Goal-Scoring Likelihood in Elite Football Leagues
Authors: Christina Markopoulou, George Papageorgiou, Christos Tjortjis
First page: 1762
Abstract: The field of sports analytics has grown rapidly, with a primary focus on performance forecasting, enhancing the understanding of player capabilities, and indirectly benefiting team strategies and player development. This work aims to forecast and comparatively evaluate players’ goal-scoring likelihood in four elite football leagues (Premier League, Bundesliga, La Liga, and Serie A) by mining advanced statistics from 2017 to 2023. Six types of machine learning (ML) models were developed and tested individually through experiments on the comprehensive datasets collected for these leagues. We also tested the upper 30th percentile of the best-performing players based on their performance in the last season, with varied features evaluated to enhance prediction accuracy in distinct scenarios. The results offer insights into the forecasting abilities of those leagues, identifying the best forecasting methodologies and the factors that most significantly contribute to the prediction of players’ goal-scoring. XGBoost consistently outperformed other models in most experiments, yielding the most accurate results and leading to a well-generalized model. Notably, when applied to Serie A, it achieved a mean absolute error (MAE) of 1.29. This study provides insights into ML-based performance prediction, advancing the field of player performance forecasting.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-07-28
DOI: 10.3390/make6030086
Issue No: Vol. 6, No. 3 (2024)
- MAKE, Vol. 6, Pages 1782-1797: Insights from Augmented Data Integration
and Strong Regularization in Drug Synergy Prediction with SynerGNet
Authors: Mengmeng Liu, Gopal Srivastava, J. Ramanujam, Michal Brylinski
First page: 1782
Abstract: SynerGNet is a novel approach to predicting drug synergy against cancer cell lines. In this study, we discuss in detail the construction process of SynerGNet, emphasizing its comprehensive design tailored to handle complex data patterns. Additionally, we investigate a counterintuitive phenomenon when integrating more augmented data into the training set results in an increase in testing loss alongside improved predictive accuracy. This sheds light on the nuanced dynamics of model learning. Further, we demonstrate the effectiveness of strong regularization techniques in mitigating overfitting, ensuring the robustness and generalization ability of SynerGNet. Finally, the continuous performance enhancements achieved through the integration of augmented data are highlighted. By gradually increasing the amount of augmented data in the training set, we observe substantial improvements in model performance. For instance, compared to models trained exclusively on the original data, the integration of the augmented data can lead to a 5.5% increase in the balanced accuracy and a 7.8% decrease in the false positive rate. Through rigorous benchmarks and analyses, our study contributes valuable insights into the development and optimization of predictive models in biomedical research.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-07-29
DOI: 10.3390/make6030087
Issue No: Vol. 6, No. 3 (2024)
- MAKE, Vol. 6, Pages 1798-1817: Learning Optimal Dynamic Treatment Regime
from Observational Clinical Data through Reinforcement Learning
Authors: Seyum Abebe, Irene Poli, Roger D. Jones, Debora Slanzi
First page: 1798
Abstract: In medicine, dynamic treatment regimes (DTRs) have emerged to guide personalized treatment decisions for patients, accounting for their unique characteristics. However, existing methods for determining optimal DTRs face limitations, often due to reliance on linear models unsuitable for complex disease analysis and a focus on outcome prediction over treatment effect estimation. To overcome these challenges, decision tree-based reinforcement learning approaches have been proposed. Our study aims to evaluate the performance and feasibility of such algorithms: tree-based reinforcement learning (T-RL), DTR-Causal Tree (DTR-CT), DTR-Causal Forest (DTR-CF), stochastic tree-based reinforcement learning (SL-RL), and Q-learning with Random Forest. Using real-world clinical data, we conducted experiments to compare algorithm performances. Evaluation metrics included the proportion of correctly assigned patients to recommended treatments and the empirical mean with standard deviation of expected counterfactual outcomes based on estimated optimal treatment strategies. This research not only highlights the potential of decision tree-based reinforcement learning for dynamic treatment regimes but also contributes to advancing personalized medicine by offering nuanced and effective treatment recommendations.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-07-30
DOI: 10.3390/make6030088
Issue No: Vol. 6, No. 3 (2024)
- MAKE, Vol. 6, Pages 1818-1839: Enhanced Graph Representation Convolution:
Effective Inferring Gene Regulatory Network Using Graph Convolution
Network with Self-Attention Graph Pooling Layer
Authors: Duaa Mohammad Alawad, Ataur Katebi, Md Tamjidul Hoque
First page: 1818
Abstract: Studying gene regulatory networks (GRNs) is paramount for unraveling the complexities of biological processes and their associated disorders, such as diabetes, cancer, and Alzheimer’s disease. Recent advancements in computational biology have aimed to enhance the inference of GRNs from gene expression data, a non-trivial task given the networks’ intricate nature. The challenge lies in accurately identifying the myriad interactions among transcription factors and target genes, which govern cellular functions. This research introduces a cutting-edge technique, EGRC (Effective GRN Inference applying Graph Convolution with Self-Attention Graph Pooling), which innovatively conceptualizes GRN reconstruction as a graph classification problem, where the task is to discern the links within subgraphs that encapsulate pairs of nodes. By leveraging Spearman’s correlation, we generate potential subgraphs that bring nonlinear associations between transcription factors and their targets to light. We use mutual information to enhance this, capturing a broader spectrum of gene interactions. Our methodology bifurcates these subgraphs into ‘Positive’ and ‘Negative’ categories. ‘Positive’ subgraphs are those where a transcription factor and its target gene are connected, including interactions among their neighbors. ‘Negative’ subgraphs, conversely, denote pairs without a direct connection. EGRC utilizes dual graph convolution network (GCN) models that exploit node attributes from gene expression profiles and graph embedding techniques to classify these. The performance of EGRC is substantiated by comprehensive evaluations using the DREAM5 datasets. Notably, EGRC attained an AUROC of 0.856 and an AUPR of 0.841 on the E. coli dataset. In contrast, the in silico dataset achieved an AUROC of 0.5058 and an AUPR of 0.958. Furthermore, on the S. cerevisiae dataset, EGRC recorded an AUROC of 0.823 and an AUPR of 0.822. These results underscore the robustness of EGRC in accurately inferring GRNs across various organisms. The advanced performance of EGRC represents a substantial advancement in the field, promising to deepen our comprehension of the intricate biological processes and their implications in both health and disease.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-08-01
DOI: 10.3390/make6030089
Issue No: Vol. 6, No. 3 (2024)
- MAKE, Vol. 6, Pages 1840-1856: A Parallel Approach to Enhance the
Performance of Supervised Machine Learning Realized in a Multicore
Environment
Authors: Ashutosh Ghimire, Fathi Amsaad
First page: 1840
Abstract: Machine learning models play a critical role in applications such as image recognition, natural language processing, and medical diagnosis, where accuracy and efficiency are paramount. As datasets grow in complexity, so too do the computational demands of classification techniques. Previous research has achieved high accuracy but required significant computational time. This paper proposes a parallel architecture for Ensemble Machine Learning Models, harnessing multicore CPUs to expedite performance. The primary objective is to enhance machine learning efficiency without compromising accuracy through parallel computing. This study focuses on benchmark ensemble models including Random Forest, XGBoost, ADABoost, and K Nearest Neighbors. These models are applied to tasks such as wine quality classification and fraud detection in credit card transactions. The results demonstrate that, compared to single-core processing, machine learning tasks run 1.7 times and 3.8 times faster for small and large datasets on quad-core CPUs, respectively.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-08-02
DOI: 10.3390/make6030090
Issue No: Vol. 6, No. 3 (2024)
- MAKE, Vol. 6, Pages 1857-1870: Accuracy Improvement of Debonding Damage
Detection Technology in Composite Blade Joints for 20 kW Class Wind
Turbine
Authors: Hakgeun Kim, Hyeongjin Kim, Kiweon Kang
First page: 1857
Abstract: Securing the structural safety of blades has become crucial, owing to the increasing size and weight of blades resulting from the recent development of large wind turbines. Composites are primarily used for blade manufacturing because of their high specific strength and specific stiffness. However, in composite blades, joints may experience fractures from the loads generated during wind turbine operation, leading to deformation caused by changes in structural stiffness. In this study, 7132 debonding damage data, classified by damage type, position, and size, were selected to predict debonding damage based on natural frequency. The change in the natural frequency caused by debonding damage was acquired through finite element (FE) modeling and modal analysis. Synchronization between the FE analysis model and manufactured blades was achieved through modal testing and data analysis. Finally, the relationship between debonding damage and the change in natural frequency was examined using artificial neural network techniques.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-08-07
DOI: 10.3390/make6030091
Issue No: Vol. 6, No. 3 (2024)
- MAKE, Vol. 6, Pages 1871-1893: Balancing Results from AI-Based
Geostatistics versus Fuzzy Inference by Game Theory Bargaining to Improve
a Groundwater Monitoring Network
Authors: Masoumeh Hashemi, Richard C. Peralta, Matt Yost
First page: 1871
Abstract: An artificial intelligence-based geostatistical optimization algorithm was developed to upgrade a test Iranian aquifer’s existing groundwater monitoring network. For that aquifer, a preliminary study revealed that a Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) more accurately determined temporally average water table elevations than geostatistical kriging, spline, and inverse distance weighting. Because kriging is usually used in that area for water table estimation, the developed algorithm used MLP-ANN to guide kriging, and Genetic Algorithm (GA) to determine locations for new monitoring well location(s). For possible annual fiscal budgets allowing 1–12 new wells, 12 sets of optimal new well locations are reported. Each set has the locations of new wells that would minimize the squared difference between the time-averaged heads developed by kriging versus MLP-ANN. Also, to simultaneously consider local expertise, the algorithm used fuzzy inference to quantify an expert’s satisfaction with the number of new wells. Then, the algorithm used symmetric bargaining (Nash, Kalai–Smorodinsky, and area monotonic) to present an upgradation strategy that balanced professional judgment and heuristic optimization. In essence, the algorithm demonstrates the systematic application of relatively new computational practices to a common situation worldwide.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-08-09
DOI: 10.3390/make6030092
Issue No: Vol. 6, No. 3 (2024)
- MAKE, Vol. 6, Pages 1894-1920: Visual Reasoning and Multi-Agent Approach
in Multimodal Large Language Models (MLLMs): Solving TSP and mTSP
Combinatorial Challenges
Authors: Mohammed Elhenawy, Ahmad Abutahoun, Taqwa I. Alhadidi, Ahmed Jaber, Huthaifa I. Ashqar, Shadi Jaradat, Ahmed Abdelhay, Sebastien Glaser, Andry Rakotonirainy
First page: 1894
Abstract: Multimodal Large Language Models (MLLMs) harness comprehensive knowledge spanning text, images, and audio to adeptly tackle complex problems. This study explores the ability of MLLMs in visually solving the Traveling Salesman Problem (TSP) and Multiple Traveling Salesman Problem (mTSP) using images that portray point distributions on a two-dimensional plane. We introduce a novel approach employing multiple specialized agents within the MLLM framework, each dedicated to optimizing solutions for these combinatorial challenges. We benchmarked our multi-agent model solutions against the Google OR tools, which served as the baseline for comparison. The results demonstrated that both multi-agent models—Multi-Agent 1, which includes the initializer, critic, and scorer agents, and Multi-Agent 2, which comprises only the initializer and critic agents—significantly improved the solution quality for TSP and mTSP problems. Multi-Agent 1 excelled in environments requiring detailed route refinement and evaluation, providing a robust framework for sophisticated optimizations. In contrast, Multi-Agent 2, focusing on iterative refinements by the initializer and critic, proved effective for rapid decision-making scenarios. These experiments yield promising outcomes, showcasing the robust visual reasoning capabilities of MLLMs in addressing diverse combinatorial problems. The findings underscore the potential of MLLMs as powerful tools in computational optimization, offering insights that could inspire further advancements in this promising field.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-08-13
DOI: 10.3390/make6030093
Issue No: Vol. 6, No. 3 (2024)
- MAKE, Vol. 6, Pages 1921-1935: Optimal Knowledge Distillation through
Non-Heuristic Control of Dark Knowledge
Authors: Darian Onchis, Codruta Istin, Ioan Samuila
First page: 1921
Abstract: In this paper, a method is introduced to control the dark knowledge values also known as soft targets, with the purpose of improving the training by knowledge distillation for multi-class classification tasks. Knowledge distillation effectively transfers knowledge from a larger model to a smaller model to achieve efficient, fast, and generalizable performance while retaining much of the original accuracy. The majority of deep neural models used for classification tasks append a SoftMax layer to generate output probabilities and it is usual to take the highest score and consider it the inference of the model, while the rest of the probability values are generally ignored. The focus is on those probabilities as carriers of dark knowledge and our aim is to quantify the relevance of dark knowledge, not heuristically as provided in the literature so far, but with an inductive proof on the SoftMax operational limits. These limits are further pushed by using an incremental decision tree with information gain split. The user can set a desired precision and an accuracy level to obtain a maximal temperature setting for a continual classification process. Moreover, by fitting both the hard targets and the soft targets, one obtains an optimal knowledge distillation effect that mitigates better catastrophic forgetting. The strengths of our method come from the possibility of controlling the amount of distillation transferred non-heuristically and the agnostic application of this model-independent study.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-08-22
DOI: 10.3390/make6030094
Issue No: Vol. 6, No. 3 (2024)
- MAKE, Vol. 6, Pages 1936-1952: Forecasting the Right Crop Nutrients for
Specific Crops Based on Collected Data Using an Artificial Neural Network
(ANN)
Authors: Sairoel Amertet, Girma Gebresenbet
First page: 1936
Abstract: In farming technologies, it is difficult to properly provide the accurate crop nutrients for respective crops. For this reason, farmers are experiencing enormous problems. Although various types of machine learning (deep learning and convolutional neural networks) have been used to identify crop diseases, as has crop classification-based image processing, they have failed to forecast accurate crop nutrients for various crops, as crop nutrients are numerical instead of visual. Neural networks represent an opportunity for the precision agriculture sector to more accurately forecast crop nutrition. Recent technological advancements in neural networks have begun to provide greater precision, with an array of opportunities in pattern recognition. Neural networks represent an opportunity to effectively solve numerical data problems. The aim of the current study is to estimate the right crop nutrients for the right crops based on the data collected using an artificial neural network. The crop data were collected from the MNIST dataset. To forecast the precise nutrients for the crops, ANN models were developed. The entire system was simulated in a MATLAB environment. The obtained results for forecasting accurate nutrients were 99.997%, 99.996%, and 99.997% for validation, training, and testing, respectively. Therefore, the proposed algorithm is suitable for forecasting accurate crop nutrients for the crops.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-08-26
DOI: 10.3390/make6030095
Issue No: Vol. 6, No. 3 (2024)
- MAKE, Vol. 6, Pages 1953-1968: Assessing Fine-Tuned NER Models with
Limited Data in French: Automating Detection of New Technologies,
Technological Domains, and Startup Names in Renewable Energy
Authors: Connor MacLean, Denis Cavallucci
First page: 1953
Abstract: Achieving carbon neutrality by 2050 requires unprecedented technological, economic, and sociological changes. With time as a scarce resource, it is crucial to base decisions on relevant facts and information to avoid misdirection. This study aims to help decision makers quickly find relevant information related to companies and organizations in the renewable energy sector. In this study, we propose fine-tuning five RNN and transformer models trained for French on a new category, “TECH”. This category is used to classify technological domains and new products. In addition, as the model is fine-tuned on news related to startups, we note an improvement in the detection of startup and company names in the “ORG” category. We further explore the capacities of the most effective model to accurately predict entities using a small amount of training data. We show the progression of the model from being trained on several hundred to several thousand annotations. This analysis allows us to demonstrate the potential of these models to extract insights without large corpora, allowing us to reduce the long process of annotating custom training data. This approach is used to automatically extract new company mentions as well as to extract technologies and technology domains that are currently being discussed in the news in order to better analyze industry trends. This approach further allows to group together mentions of specific energy domains with the companies that are actively developing new technologies in the field.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-08-27
DOI: 10.3390/make6030096
Issue No: Vol. 6, No. 3 (2024)
- MAKE, Vol. 6, Pages 1969-1996: Black Box Adversarial Reprogramming for
Time Series Feature Classification in Ball Bearings’ Remaining
Useful Life Classification
Authors: Alexander Bott, Felix Schreyer, Alexander Puchta, Jürgen Fleischer
First page: 1969
Abstract: Standard ML relies on ample data, but limited availability poses challenges. Transfer learning offers a solution by leveraging pre-existing knowledge. Yet many methods require access to the model’s internal aspects, limiting applicability to white box models. To address this, Tsai, Chen and Ho introduced Black Box Adversarial Reprogramming for transfer learning with black box models. While tested primarily in image classification, this paper explores its potential in time series classification, particularly predictive maintenance. We develop an adversarial reprogramming concept tailored to black box time series classifiers. Our study focuses on predicting the Remaining Useful Life of rolling bearings. We construct a comprehensive ML pipeline, encompassing feature engineering and model fine-tuning, and compare results with traditional transfer learning. We investigate the impact of hyperparameters and training parameters on model performance, demonstrating the successful application of Black Box Adversarial Reprogramming to time series data. The method achieved a weighted F1-score of 0.77, although it exhibited significant stochastic fluctuations, with scores ranging from 0.3 to 0.77 due to randomness in gradient estimation.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-08-27
DOI: 10.3390/make6030097
Issue No: Vol. 6, No. 3 (2024)
- MAKE, Vol. 6, Pages 736-750: A New and Lightweight R-Peak Detector Using
the TEDA Evolving Algorithm
Authors: Lucileide M. D. da Silva, Sérgio N. Silva, Luísa C. de Souza, Karolayne S. de Azevedo, Luiz Affonso Guedes, Marcelo A. C. Fernandes
First page: 736
Abstract: The literature on ECG delineation algorithms has seen significant growth in recent decades. However, several challenges still need to be addressed. This work aims to propose a lightweight R-peak-detection algorithm that does not require pre-setting and performs classification on a sample-by-sample basis. The novelty of the proposed approach lies in the utilization of the typicality eccentricity detection anomaly (TEDA) algorithm for R-peak detection. The proposed method for R-peak detection consists of three phases. Firstly, the ECG signal is preprocessed by calculating the signal’s slope and applying filtering techniques. Next, the preprocessed signal is inputted into the TEDA algorithm for R-peak estimation. Finally, in the third and last step, the R-peak identification is carried out. To evaluate the effectiveness of the proposed technique, experiments were conducted on the MIT-BIH arrhythmia database (MIT-AD) for R-peak detection and validation. The results of the study demonstrated that the proposed evolutive algorithm achieved a sensitivity (Se in %), positive predictivity (+P in %), and accuracy (ACC in %) of 95.45%, 99.61%, and 95.09%, respectively, with a tolerance (TOL) of 100 milliseconds. One key advantage of the proposed technique is its low computational complexity, as it is based on a statistical framework calculated recursively. It employs the concepts of typicity and eccentricity to determine whether a given sample is normal or abnormal within the dataset. Unlike most traditional methods, it does not require signal buffering or windowing. Furthermore, the proposed technique employs simple decision rules rather than heuristic approaches, further contributing to its computational efficiency.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-03-29
DOI: 10.3390/make6020034
Issue No: Vol. 6, No. 2 (2024)
- MAKE, Vol. 6, Pages 751-769: Soil Sampling Map Optimization with a Dual
Deep Learning Framework
Authors: Tan-Hanh Pham, Kim-Doang Nguyen
First page: 751
Abstract: Soil sampling constitutes a fundamental process in agriculture, enabling precise soil analysis and optimal fertilization. The automated selection of accurate soil sampling locations representative of a given field is critical for informed soil treatment decisions. This study leverages recent advancements in deep learning to develop efficient tools for generating soil sampling maps. We proposed two models, namely UDL and UFN, which are the results of innovations in machine learning architecture design and integration. The models are meticulously trained on a comprehensive soil sampling dataset collected from local farms in South Dakota. The data include five key attributes: aspect, flow accumulation, slope, normalized difference vegetation index, and yield. The inputs to the models consist of multispectral images, and the ground truths are highly unbalanced binary images. To address this challenge, we innovate a feature extraction technique to find patterns and characteristics from the data before using these refined features for further processing and generating soil sampling maps. Our approach is centered around building a refiner that extracts fine features and a selector that utilizes these features to produce prediction maps containing the selected optimal soil sampling locations. Our experimental results demonstrate the superiority of our tools compared to existing methods. During testing, our proposed models exhibit outstanding performance, achieving the highest mean Intersection over Union of 60.82% and mean Dice Coefficient of 73.74%. The research not only introduces an innovative tool for soil sampling but also lays the foundation for the integration of traditional and modern soil sampling methods. This work provides a promising solution for precision agriculture and soil management.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-03-29
DOI: 10.3390/make6020035
Issue No: Vol. 6, No. 2 (2024)
- MAKE, Vol. 6, Pages 770-788: Birthweight Range Prediction and
Classification: A Machine Learning-Based Sustainable Approach
Authors: Dina A. Alabbad, Shahad Y. Ajibi, Raghad B. Alotaibi, Noura K. Alsqer, Rahaf A. Alqahtani, Noor M. Felemban, Atta Rahman, Sumayh S. Aljameel, Mohammed Imran Basheer Ahmed, Mustafa M. Youldash
First page: 770
Abstract: An accurate prediction of fetal birth weight is crucial in ensuring safe delivery without health complications for the mother and baby. The uncertainty surrounding the fetus’s birth situation, including its weight range, can lead to significant risks for both mother and baby. As there is a standard birth weight range, if the fetus exceeds or falls below this range, it can result in considerable health problems. Although ultrasound imaging is commonly used to predict fetal weight, it does not always provide accurate readings, which may lead to unnecessary decisions such as early delivery and cesarian section. Besides that, no supporting system is available to predict the weight range in Saudi Arabia. Therefore, leveraging the available technologies to build a system that can serve as a second opinion for doctors and health professionals is essential. Machine learning (ML) offers significant advantages to numerous fields and can address various issues. As such, this study aims to utilize ML techniques to build a predictive model to predict the birthweight range of infants into low, normal, or high. For this purpose, two datasets were used: one from King Fahd University Hospital (KFHU), Saudi Arabia, and another publicly available dataset from the Institute of Electrical and Electronics Engineers (IEEE) data port. KFUH’s best result was obtained with the Extra Trees model, achieving an accuracy, precision, recall, and F1-score of 98%, with a specificity of 99%. On the other hand, using the Random Forest model, the IEEE dataset attained an accuracy, precision, recall, and F1-score of 96%, respectively, with a specificity of 98%. These results suggest that the proposed ML system can provide reliable predictions, which could be of significant value for doctors and health professionals in Saudi Arabia.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-04-01
DOI: 10.3390/make6020036
Issue No: Vol. 6, No. 2 (2024)
- MAKE, Vol. 6, Pages 789-799: Effective Data Reduction Using Discriminative
Feature Selection Based on Principal Component Analysis
Authors: Faith Nwokoma, Justin Foreman, Cajetan M. Akujuobi
First page: 789
Abstract: Effective data reduction must retain the greatest possible amount of informative content of the data under examination. Feature selection is the default for dimensionality reduction, as the relevant features of a dataset are usually retained through this method. In this study, we used unsupervised learning to discover the top-k discriminative features present in the large multivariate IoT dataset used. We used the statistics of principal component analysis to filter the relevant features based on the ranks of the features along the principal directions while also considering the coefficients of the components. The selected number of principal components was used to decide the number of features to be selected in the SVD process. A number of experiments were conducted using different benchmark datasets, and the effectiveness of the proposed method was evaluated based on the reconstruction error. The potency of the results was verified by subjecting the algorithm to a large IoT dataset, and we compared the performance based on accuracy and reconstruction error to the results of the benchmark datasets. The performance evaluation showed consistency with the results obtained with the benchmark datasets, which were of high accuracy and low reconstruction error.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-04-03
DOI: 10.3390/make6020037
Issue No: Vol. 6, No. 2 (2024)
- MAKE, Vol. 6, Pages 800-826: A Meta Algorithm for Interpretable Ensemble
Learning: The League of Experts
Authors: Richard Vogel, Tobias Schlosser, Robert Manthey, Marc Ritter, Matthias Vodel, Maximilian Eibl, Kristan Alexander Schneider
First page: 800
Abstract: Background. The importance of explainable artificial intelligence and machine learning (XAI/XML) is increasingly being recognized, aiming to understand how information contributes to decisions, the method’s bias, or sensitivity to data pathologies. Efforts are often directed to post hoc explanations of black box models. These approaches add additional sources for errors without resolving their shortcomings. Less effort is directed into the design of intrinsically interpretable approaches. Methods. We introduce an intrinsically interpretable methodology motivated by ensemble learning: the League of Experts (LoE) model. We establish the theoretical framework first and then deduce a modular meta algorithm. In our description, we focus primarily on classification problems. However, LoE applies equally to regression problems. Specific to classification problems, we employ classical decision trees as classifier ensembles as a particular instance. This choice facilitates the derivation of human-understandable decision rules for the underlying classification problem, which results in a derived rule learning system denoted as RuleLoE. Results. In addition to 12 KEEL classification datasets, we employ two standard datasets from particularly relevant domains—medicine and finance—to illustrate the LoE algorithm. The performance of LoE with respect to its accuracy and rule coverage is comparable to common state-of-the-art classification methods. Moreover, LoE delivers a clearly understandable set of decision rules with adjustable complexity, describing the classification problem. Conclusions. LoE is a reliable method for classification and regression problems with an accuracy that seems to be appropriate for situations in which underlying causalities are in the center of interest rather than just accurate predictions or classifications.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-04-09
DOI: 10.3390/make6020038
Issue No: Vol. 6, No. 2 (2024)
- MAKE, Vol. 6, Pages 827-841: Impact of Nature of Medical Data on Machine
and Deep Learning for Imbalanced Datasets: Clinical Validity of SMOTE Is
Questionable
Authors: Seifollah Gholampour
First page: 827
Abstract: Dataset imbalances pose a significant challenge to predictive modeling in both medical and financial domains, where conventional strategies, including resampling and algorithmic modifications, often fail to adequately address minority class underrepresentation. This study theoretically and practically investigates how the inherent nature of medical data affects the classification of minority classes. It employs ten machine and deep learning classifiers, ranging from ensemble learners to cost-sensitive algorithms, across comparably sized medical and financial datasets. Despite these efforts, none of the classifiers achieved effective classification of the minority class in the medical dataset, with sensitivity below 5.0% and area under the curve (AUC) below 57.0%. In contrast, the similar classifiers applied to the financial dataset demonstrated strong discriminative power, with overall accuracy exceeding 95.0%, sensitivity over 73.0%, and AUC above 96.0%. This disparity underscores the unpredictable variability inherent in the nature of medical data, as exemplified by the dispersed and homogeneous distribution of the minority class among other classes in principal component analysis (PCA) graphs. The application of the synthetic minority oversampling technique (SMOTE) introduced 62 synthetic patients based on merely 20 original cases, casting doubt on its clinical validity and the representation of real-world patient variability. Furthermore, post-SMOTE feature importance analysis, utilizing SHapley Additive exPlanations (SHAP) and tree-based methods, contradicted established cerebral stroke parameters, further questioning the clinical coherence of synthetic dataset augmentation. These findings call into question the clinical validity of the SMOTE technique and underscore the urgent need for advanced modeling techniques and algorithmic innovations for predicting minority-class outcomes in medical datasets without depending on resampling strategies. This approach underscores the importance of developing methods that are not only theoretically robust but also clinically relevant and applicable to real-world clinical scenarios. Consequently, this study underscores the importance of future research efforts to bridge the gap between theoretical advancements and the practical, clinical applications of models like SMOTE in healthcare.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-04-15
DOI: 10.3390/make6020039
Issue No: Vol. 6, No. 2 (2024)
- MAKE, Vol. 6, Pages 842-876: A Comprehensive Survey on Deep Learning
Methods in Human Activity Recognition
Authors: Michail Kaseris, Ioannis Kostavelis, Sotiris Malassiotis
First page: 842
Abstract: Human activity recognition (HAR) remains an essential field of research with increasing real-world applications ranging from healthcare to industrial environments. As the volume of publications in this domain continues to grow, staying abreast of the most pertinent and innovative methodologies can be challenging. This survey provides a comprehensive overview of the state-of-the-art methods employed in HAR, embracing both classical machine learning techniques and their recent advancements. We investigate a plethora of approaches that leverage diverse input modalities including, but not limited to, accelerometer data, video sequences, and audio signals. Recognizing the challenge of navigating the vast and ever-growing HAR literature, we introduce a novel methodology that employs large language models to efficiently filter and pinpoint relevant academic papers. This not only reduces manual effort but also ensures the inclusion of the most influential works. We also provide a taxonomy of the examined literature to enable scholars to have rapid and organized access when studying HAR approaches. Through this survey, we aim to inform researchers and practitioners with a holistic understanding of the current HAR landscape, its evolution, and the promising avenues for future exploration.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-04-18
DOI: 10.3390/make6020040
Issue No: Vol. 6, No. 2 (2024)
- MAKE, Vol. 6, Pages 877-897: Enhancing Legal Sentiment Analysis: A
Convolutional Neural Network–Long Short-Term Memory Document-Level
Model
Authors: Bolanle Abimbola, Enrique de La Cal Marin, Qing Tan
First page: 877
Abstract: This research investigates the application of deep learning in sentiment analysis of Canadian maritime case law. It offers a framework for improving maritime law and legal analytic policy-making procedures. The automation of legal document extraction takes center stage, underscoring the vital role sentiment analysis plays at the document level. Therefore, this study introduces a novel strategy for sentiment analysis in Canadian maritime case law, combining sentiment case law approaches with state-of-the-art deep learning techniques. The overarching goal is to systematically unearth hidden biases within case law and investigate their impact on legal outcomes. Employing Convolutional Neural Network (CNN)- and long short-term memory (LSTM)-based models, this research achieves a remarkable accuracy of 98.05% for categorizing instances. In contrast, conventional machine learning techniques such as support vector machine (SVM) yield an accuracy rate of 52.57%, naïve Bayes at 57.44%, and logistic regression at 61.86%. The superior accuracy of the CNN and STM model combination underscores its usefulness in legal sentiment analysis, offering promising future applications in diverse fields like legal analytics and policy design. These findings mark a significant choice for AI-powered legal tools, presenting more sophisticated and sentiment-aware options for the legal profession.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-04-19
DOI: 10.3390/make6020041
Issue No: Vol. 6, No. 2 (2024)
- MAKE, Vol. 6, Pages 898-916: Concept Paper for a Digital Expert:
Systematic Derivation of (Causal) Bayesian Networks Based on Ontologies
for Knowledge-Based Production Steps
Authors: Manja Mai-Ly Pfaff-Kastner, Ken Wenzel, Steffen Ihlenfeldt
First page: 898
Abstract: Despite increasing digitalization and automation, complex production processes often require human judgment/decision-making adaptability. Humans can abstract and transfer knowledge to new situations. People in production are an irreplaceable resource. This paper presents a new concept for digitizing human expertise and their ability to make knowledge-based decisions in the production area based on ontologies and causal Bayesian networks for further research. Dedicated approaches for the ontology-based creation of Bayesian networks exist in the literature. Therefore, we first comprehensively analyze previous studies and summarize the approaches. We then add the causal perspective, which has often not been an explicit subject of consideration. We see a research gap in the systematic and structured approach to ontology-based generation of causal graphs (CGs). At the current state of knowledge, the semantic understanding of a domain formalized in an ontology can contribute to developing a generic approach to derive a CG. The ontology functions as a knowledge base by formally representing knowledge and experience. Causal inference calculations can mathematically imitate the human decision-making process under uncertainty. Therefore, a systematic ontology-based approach to building a CG can allow digitizing the human ability to make decisions based on experience and knowledge.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-04-25
DOI: 10.3390/make6020042
Issue No: Vol. 6, No. 2 (2024)
- MAKE, Vol. 6, Pages 917-943: A Comprehensive Summary of the Application of
Machine Learning Techniques for CO2-Enhanced Oil Recovery Projects
Authors: Xuejia Du, Sameer Salasakar, Ganesh Thakur
First page: 917
Abstract: This paper focuses on the current application of machine learning (ML) in enhanced oil recovery (EOR) through CO2 injection, which exhibits promising economic and environmental benefits for climate-change mitigation strategies. Our comprehensive review explores the diverse use cases of ML techniques in CO2-EOR, including aspects such as minimum miscible pressure (MMP) prediction, well location optimization, oil production and recovery factor prediction, multi-objective optimization, Pressure–Volume–Temperature (PVT) property estimation, Water Alternating Gas (WAG) analysis, and CO2-foam EOR, from 101 reviewed papers. We catalog relative information, including the input parameters, objectives, data sources, train/test/validate information, results, evaluation, and rating score for each area based on criteria such as data quality, ML-building process, and the analysis of results. We also briefly summarized the benefits and limitations of ML methods in petroleum industry applications. Our detailed and extensive study could serve as an invaluable reference for employing ML techniques in the petroleum industry. Based on the review, we found that ML techniques offer great potential in solving problems in the majority of CO2-EOR areas involving prediction and regression. With the generation of massive amounts of data in the everyday oil and gas industry, machine learning techniques can provide efficient and reliable preliminary results for the industry.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-04-29
DOI: 10.3390/make6020043
Issue No: Vol. 6, No. 2 (2024)
- MAKE, Vol. 6, Pages 944-964: Quantum-Enhanced Representation Learning: A
Quanvolutional Autoencoder Approach against DDoS Threats
Authors: Pablo Rivas, Javier Orduz, Tonni Das Jui, Casimer DeCusatis, Bikram Khanal
First page: 944
Abstract: Motivated by the growing threat of distributed denial-of-service (DDoS) attacks and the emergence of quantum computing, this study introduces a novel “quanvolutional autoencoder” architecture for learning representations. The architecture leverages the computational advantages of quantum mechanics to improve upon traditional machine learning techniques. Specifically, the quanvolutional autoencoder employs randomized quantum circuits to analyze time-series data from DDoS attacks, offering a robust alternative to classical convolutional neural networks. Experimental results suggest that the quanvolutional autoencoder performs similarly to classical models in visualizing and learning from DDoS hive plots and leads to faster convergence and learning stability. These findings suggest that quantum machine learning holds significant promise for advancing data analysis and visualization in cybersecurity. The study highlights the need for further research in this fast-growing field, particularly for unsupervised anomaly detection.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-05-01
DOI: 10.3390/make6020044
Issue No: Vol. 6, No. 2 (2024)
- MAKE, Vol. 6, Pages 965-986: VOD: Vision-Based Building Energy Data
Outlier Detection
Authors: Jinzhao Tian, Tianya Zhao, Zhuorui Li, Tian Li, Haipei Bie, Vivian Loftness
First page: 965
Abstract: Outlier detection plays a critical role in building operation optimization and data quality maintenance. However, existing methods often struggle with the complexity and variability of building energy data, leading to poorly generalized and explainable results. To address the gap, this study introduces a novel Vision-based Outlier Detection (VOD) approach, leveraging computer vision models to spot outliers in the building energy records. The models are trained to identify outliers by analyzing the load shapes in 2D time series plots derived from the energy data. The VOD approach is tested on four years of workday time-series electricity consumption data from 290 commercial buildings in the United States. Two distinct models are developed for different usage purposes, namely a classification model for broad-level outlier detection and an object detection model for the demands of precise pinpointing of outliers. The classification model is also interpreted via Grad-CAM to enhance its usage reliability. The classification model achieves an F1 score of 0.88, and the object detection model achieves an Average Precision (AP) of 0.84. VOD is a very efficient path to identifying energy consumption outliers in building operations, paving the way for the enhancement of building energy data quality, operation efficiency, and energy savings.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-05-03
DOI: 10.3390/make6020045
Issue No: Vol. 6, No. 2 (2024)
- MAKE, Vol. 6, Pages 987-1008: Multilayer Perceptron Neural Network with
Arithmetic Optimization Algorithm-Based Feature Selection for
Cardiovascular Disease Prediction
Authors: Fahad A. Alghamdi, Haitham Almanaseer, Ghaith Jaradat, Ashraf Jaradat, Mutasem K. Alsmadi, Sana Jawarneh, Abdullah S. Almurayh, Jehad Alqurni, Hayat Alfagham
First page: 987
Abstract: In the healthcare field, diagnosing disease is the most concerning issue. Various diseases including cardiovascular diseases (CVDs) significantly influence illness or death. On the other hand, early and precise diagnosis of CVDs can decrease chances of death, resulting in a better and healthier life for patients. Researchers have used traditional machine learning (ML) techniques for CVD prediction and classification. However, many of them are inaccurate and time-consuming due to the unavailability of quality data including imbalanced samples, inefficient data preprocessing, and the existing selection criteria. These factors lead to an overfitting or bias issue towards a certain class label in the prediction model. Therefore, an intelligent system is needed which can accurately diagnose CVDs. We proposed an automated ML model for various kinds of CVD prediction and classification. Our prediction model consists of multiple steps. Firstly, a benchmark dataset is preprocessed using filter techniques. Secondly, a novel arithmetic optimization algorithm is implemented as a feature selection technique to select the best subset of features that influence the accuracy of the prediction model. Thirdly, a classification task is implemented using a multilayer perceptron neural network to classify the instances of the dataset into two class labels, determining whether they have a CVD or not. The proposed ML model is trained on the preprocessed data and then tested and validated. Furthermore, for the comparative analysis of the model, various performance evaluation metrics are calculated including overall accuracy, precision, recall, and F1-score. As a result, it has been observed that the proposed prediction model can achieve 88.89% accuracy, which is the highest in a comparison with the traditional ML techniques.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-05-05
DOI: 10.3390/make6020046
Issue No: Vol. 6, No. 2 (2024)
- MAKE, Vol. 6, Pages 1009-1054: Categorical Data Clustering: A Bibliometric
Analysis and Taxonomy
Authors: Maya Cendana, Ren-Jieh Kuo
First page: 1009
Abstract: Numerous real-world applications apply categorical data clustering to find hidden patterns in the data. The K-modes-based algorithm is a popular algorithm for solving common issues in categorical data, from outlier and noise sensitivity to local optima, utilizing metaheuristic methods. Many studies have focused on increasing clustering performance, with new methods now outperforming the traditional K-modes algorithm. It is important to investigate this evolution to help scholars understand how the existing algorithms overcome the common issues of categorical data. Using a research-area-based bibliometric analysis, this study retrieved articles from the Web of Science (WoS) Core Collection published between 2014 and 2023. This study presents a deep analysis of 64 articles to develop a new taxonomy of categorical data clustering algorithms. This study also discusses the potential challenges and opportunities in possible alternative solutions to categorical data clustering.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-05-07
DOI: 10.3390/make6020047
Issue No: Vol. 6, No. 2 (2024)
- MAKE, Vol. 6, Pages 1055-1071: EyeXNet: Enhancing Abnormality Detection
and Diagnosis via Eye-Tracking and X-ray Fusion
Authors: Chihcheng Hsieh, André Luís, José Neves, Isabel Blanco Nobre, Sandra Costa Sousa, Chun Ouyang, Joaquim Jorge, Catarina Moreira
First page: 1055
Abstract: Integrating eye gaze data with chest X-ray images in deep learning (DL) has led to contradictory conclusions in the literature. Some authors assert that eye gaze data can enhance prediction accuracy, while others consider eye tracking irrelevant for predictive tasks. We argue that this disagreement lies in how researchers process eye-tracking data as most remain agnostic to the human component and apply the data directly to DL models without proper preprocessing. We present EyeXNet, a multimodal DL architecture that combines images and radiologists’ fixation masks to predict abnormality locations in chest X-rays. We focus on fixation maps during reporting moments as radiologists are more likely to focus on regions with abnormalities and provide more targeted regions to the predictive models. Our analysis compares radiologist fixations in both silent and reporting moments, revealing that more targeted and focused fixations occur during reporting. Our results show that integrating the fixation masks in a multimodal DL architecture outperformed the baseline model in five out of eight experiments regarding average Recall and six out of eight regarding average Precision. Incorporating fixation masks representing radiologists’ classification patterns in a multimodal DL architecture benefits lesion detection in chest X-ray (CXR) images, particularly when there is a strong correlation between fixation masks and generated proposal regions. This highlights the potential of leveraging fixation masks to enhance multimodal DL architectures for CXR image analysis. This work represents a first step towards human-centered DL, moving away from traditional data-driven and human-agnostic approaches.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-05-09
DOI: 10.3390/make6020048
Issue No: Vol. 6, No. 2 (2024)
- MAKE, Vol. 6, Pages 1072-1086: Improving Time Series Regression Model
Accuracy via Systematic Training Dataset Augmentation and Sampling
Authors: Robin Ströbel, Marcus Mau, Alexander Puchta, Jürgen Fleischer
First page: 1072
Abstract: This study addresses a significant gap in the field of time series regression modeling by highlighting the central role of data augmentation in improving model accuracy. The primary objective is to present a detailed methodology for systematic sampling of training datasets through data augmentation to improve the accuracy of time series regression models. Therefore, different augmentation techniques are compared to evaluate their impact on model accuracy across different datasets and model architectures. In addition, this research highlights the need for a standardized approach to creating training datasets using multiple augmentation methods. The lack of a clear framework hinders the easy integration of data augmentation into time series regression pipelines. Our systematic methodology promotes model accuracy while providing a robust foundation for practitioners to seamlessly integrate data augmentation into their modeling practices. The effectiveness of our approach is demonstrated using process data from two milling machines. Experiments show that the optimized training dataset improves the generalization ability of machine learning models in 86.67% of the evaluated scenarios. However, the prediction accuracy of models trained on a sufficient dataset remains largely unaffected. Based on these results, sophisticated sampling strategies such as Quadratic Weighting of multiple augmentation approaches may be beneficial.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-05-11
DOI: 10.3390/make6020049
Issue No: Vol. 6, No. 2 (2024)
- MAKE, Vol. 6, Pages 1087-1113: Assessment of Software Vulnerability
Contributing Factors by Model-Agnostic Explainable AI
Authors: Ding Li, Yan Liu, Jun Huang
First page: 1087
Abstract: Software vulnerability detection aims to proactively reduce the risk to software security and reliability. Despite advancements in deep-learning-based detection, a semantic gap still remains between learned features and human-understandable vulnerability semantics. In this paper, we present an XAI-based framework to assess program code in a graph context as feature representations and their effect on code vulnerability classification into multiple Common Weakness Enumeration (CWE) types. Our XAI framework is deep-learning-model-agnostic and programming-language-neutral. We rank the feature importance of 40 syntactic constructs for each of the top 20 distributed CWE types from three datasets in Java and C++. By means of four metrics of information retrieval, we measure the similarity of human-understandable CWE types using each CWE type’s feature contribution ranking learned from XAI methods. We observe that the subtle semantic difference between CWE types occurs after the variation in neighboring features’ contribution rankings. Our study shows that the XAI explanation results have approximately 78% Top-1 to 89% Top-5 similarity hit rates and a mean average precision of 0.70 compared with the baseline of CWE similarity identified by the open community experts. Our framework allows for code vulnerability patterns to be learned and contributing factors to be assessed at the same stage.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-05-16
DOI: 10.3390/make6020050
Issue No: Vol. 6, No. 2 (2024)
- MAKE, Vol. 6, Pages 1114-1125: The Human-Centred Design of a Universal
Module for Artificial Intelligence Literacy in Tertiary Education
Institutions
Authors: Daswin De Silva, Shalinka Jayatilleke, Mona El-Ayoubi, Zafar Issadeen, Harsha Moraliyage, Nishan Mills
First page: 1114
Abstract: Generative Artificial Intelligence (AI) is heralding a new era in AI for performing a spectrum of complex tasks that are indistinguishable from humans. Alongside language and text, Generative AI models have been built for all other modalities of digital data, image, video, audio, and code. The full extent of Generative AI and its opportunities, challenges, contributions, and risks are still being explored by academic researchers, industry practitioners, and government policymakers. While this deep understanding of Generative AI continues to evolve, the lack of fluency, literacy, and effective interaction with Generative and conventional AI technologies are common challenges across all domains. Tertiary education institutions are uniquely positioned to address this void. In this article, we present the human-centred design of a universal AI literacy module, followed by its four primary constructs that provide core competence in AI to coursework and research students and academic and professional staff in a tertiary education setting. In comparison to related work in AI literacy, our design is inclusive due to the collaborative approach between multiple stakeholder groups and is comprehensive given the descriptive formulation of the primary constructs of this module with exemplars of how they activate core operational competence across the four groups.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-05-18
DOI: 10.3390/make6020051
Issue No: Vol. 6, No. 2 (2024)
- MAKE, Vol. 6, Pages 1126-1144: Locally-Scaled Kernels and Confidence
Voting
Authors: Elizabeth Hofer, Martin v. Mohrenschildt
First page: 1126
Abstract: Classification, the task of discerning the class of an unlabeled data point using information from a set of labeled data points, is a well-studied area of machine learning with a variety of approaches. Many of these approaches are closely linked to the selection of metrics or the generalizing of similarities defined by kernels. These metrics or similarity measures often require their parameters to be tuned in order to achieve the highest accuracy for each dataset. For example, an extensive search is required to determine the value of K or the choice of distance metric in K-NN classification. This paper explores a method of kernel construction that when used in classification performs consistently over a variety of datasets and does not require the parameters to be tuned. Inspired by dimensionality reduction techniques (DRT), we construct a kernel-based similarity measure that captures the topological structure of the data. This work compares the accuracy of K-NN classifiers, computed with specific operating parameters that obtain the highest accuracy per dataset, to a single trial of the here-proposed kernel classifier with no specialized parameters on standard benchmark sets. The here-proposed kernel used with simple classifiers has comparable accuracy to the ‘best-case’ K-NN classifiers without requiring the tuning of operating parameters.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-05-23
DOI: 10.3390/make6020052
Issue No: Vol. 6, No. 2 (2024)
- MAKE, Vol. 6, Pages 1145-1153: Evaluation of AI ChatBots for the Creation
of Patient-Informed Consent Sheets
Authors: Florian Jürgen Raimann, Vanessa Neef, Marie Charlotte Hennighausen, Kai Zacharowski, Armin Niklas Flinspach
First page: 1145
Abstract: Introduction: Large language models (LLMs), such as ChatGPT, are a topic of major public interest, and their potential benefits and threats are a subject of discussion. The potential contribution of these models to health care is widely discussed. However, few studies to date have examined LLMs. For example, the potential use of LLMs in (individualized) informed consent remains unclear. Methods: We analyzed the performance of the LLMs ChatGPT 3.5, ChatGPT 4.0, and Gemini with regard to their ability to create an information sheet for six basic anesthesiologic procedures in response to corresponding questions. We performed multiple attempts to create forms for anesthesia and analyzed the results checklists based on existing standard sheets. Results: None of the LLMs tested were able to create a legally compliant information sheet for any basic anesthesiologic procedure. Overall, fewer than one-third of the risks, procedural descriptions, and preparations listed were covered by the LLMs. Conclusions: There are clear limitations of current LLMs in terms of practical application. Advantages in the generation of patient-adapted risk stratification within individual informed consent forms are not available at the moment, although the potential for further development is difficult to predict.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-05-24
DOI: 10.3390/make6020053
Issue No: Vol. 6, No. 2 (2024)
- MAKE, Vol. 6, Pages 1154-1169: Fine-Tuning Artificial Neural Networks to
Predict Pest Numbers in Grain Crops: A Case Study in Kazakhstan
Authors: Galiya Anarbekova, Luis Gonzaga Baca Ruiz, Akerke Akanova, Saltanat Sharipova, Nazira Ospanova
First page: 1154
Abstract: This study investigates the application of different ML methods for predicting pest outbreaks in Kazakhstan for grain crops. Comprehensive data spanning from 2005 to 2022, including pest population metrics, meteorological data, and geographical parameters, were employed to train the neural network for forecasting the population dynamics of Phyllotreta vittula pests in Kazakhstan. By evaluating various network configurations and hyperparameters, this research considers the application of MLP, MT-ANN, LSTM, transformer, and SVR. The transformer consistently demonstrates superior predictive accuracy in terms of MSE. Additionally, this work highlights the impact of several training hyperparameters such as epochs and batch size on predictive accuracy. Interestingly, the second season exhibits unique responses, stressing the effect of some features on model performance. By advancing our understanding of fine-tuning ANNs for accurate pest prediction in grain crops, this research contributes to the development of more precise and efficient pest control strategies. In addition, the consistent dominance of the transformer model makes it suitable for its implementation in practical applications. Finally, this work contributes to sustainable agricultural practices by promoting targeted interventions and potentially reducing reliance on chemical pesticides.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-05-26
DOI: 10.3390/make6020054
Issue No: Vol. 6, No. 2 (2024)
- MAKE, Vol. 6, Pages 1170-1192: Uncertainty in XAI: Human Perception and
Modeling Approaches
Authors: Teodor Chiaburu, Frank Haußer, Felix Bießmann
First page: 1170
Abstract: Artificial Intelligence (AI) plays an increasingly integral role in decision-making processes. In order to foster trust in AI predictions, many approaches towards explainable AI (XAI) have been developed and evaluated. Surprisingly, one factor that is essential for trust has been underrepresented in XAI research so far: uncertainty, both with respect to how it is modeled in Machine Learning (ML) and XAI as well as how it is perceived by humans relying on AI assistance. This review paper provides an in-depth analysis of both aspects. We review established and recent methods to account for uncertainty in ML models and XAI approaches and we discuss empirical evidence on how model uncertainty is perceived by human users of XAI systems. We summarize the methodological advancements and limitations of methods and human perception. Finally, we discuss the implications of the current state of the art in model development and research on human perception. We believe highlighting the role of uncertainty in XAI will be helpful to both practitioners and researchers and could ultimately support more responsible use of AI in practical applications.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-05-27
DOI: 10.3390/make6020055
Issue No: Vol. 6, No. 2 (2024)
- MAKE, Vol. 6, Pages 1193-1209: Design and Implementation of a
Self-Supervised Algorithm for Vein Structural Patterns Analysis Using
Advanced Unsupervised Techniques
Authors: Swati Rastogi, Siddhartha Prakash Duttagupta, Anirban Guha
First page: 1193
Abstract: Compared to other identity verification systems applications, vein patterns have the lowest potential for being used fraudulently. The present research examines the practicability of gathering vascular data from NIR images of veins. In this study, we propose a self-supervision learning algorithm that envisions an automated process to retrieve vascular patterns computationally using unsupervised approaches. This new self-learning algorithm sorts the vascular patterns into clusters and then uses 2D image data to recuperate the extracted vascular patterns linked to NIR templates. Our work incorporates multi-scale filtering followed by multi-scale feature extraction, recognition, identification, and matching. We design the ORC, GPO, and RDM algorithms with these inclusions and finally develop the vascular pattern mining model to visualize the computational retrieval of vascular patterns from NIR imageries. As a result, the developed self-supervised learning algorithm shows a 96.7% accuracy rate utilizing appropriate image quality assessment parameters. In our work, we also contend that we provide strategies that are both theoretically sound and practically efficient for concerns such as how many clusters should be used for specific tasks, which clustering technique should be used, how to set the threshold for single linkage algorithms, and how much data should be excluded as outliers. Consequently, we aim to circumvent Kleinberg’s impossibility while attaining significant clustering to develop a self-supervised learning algorithm using unsupervised methodologies.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-05-31
DOI: 10.3390/make6020056
Issue No: Vol. 6, No. 2 (2024)
- MAKE, Vol. 6, Pages 1210-1242: An Analysis of Radio Frequency Transfer
Learning Behavior
Authors: Lauren J. Wong, Braeden Muller, Sean McPherson, Alan J. Michaels
First page: 1210
Abstract: Transfer learning (TL) techniques, which leverage prior knowledge gained from data with different distributions to achieve higher performance and reduced training time, are often used in computer vision (CV) and natural language processing (NLP), but have yet to be fully utilized in the field of radio frequency machine learning (RFML). This work systematically evaluates how the training domain and task, characterized by the transmitter (Tx)/receiver (Rx) hardware and channel environment, impact radio frequency (RF) TL performance for example automatic modulation classification (AMC) and specific emitter identification (SEI) use-cases. Through exhaustive experimentation using carefully curated synthetic and captured datasets with varying signal types, channel types, signal to noise ratios (SNRs), carrier/center frequencys (CFs), frequency offsets (FOs), and Tx and Rx devices, actionable and generalized conclusions are drawn regarding how best to use RF TL techniques for domain adaptation and sequential learning. Consistent with trends identified in other modalities, our results show that RF TL performance is highly dependent on the similarity between the source and target domains/tasks, but also on the relative difficulty of the source and target domains/tasks. Results also discuss the impacts of channel environment and hardware variations on RF TL performance and compare RF TL performance using head re-training and model fine-tuning methods.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-06-03
DOI: 10.3390/make6020057
Issue No: Vol. 6, No. 2 (2024)
- MAKE, Vol. 6, Pages 1243-1262: Bayesian Networks for the Diagnosis and
Prognosis of Diseases: A Scoping Review
Authors: Kristina Polotskaya, Carlos S. Muñoz-Vaqlencia, Alejandro Rabasa, Jose A. Quesada-Rico, Domingo Orozco-Beltrán, Xavier Barber
First page: 1243
Abstract: Bayesian networks (BNs) are probabilistic graphical models that leverage Bayes’ theorem to portray dependencies and cause-and-effect relationships between variables. These networks have gained prominence in the field of health sciences, particularly in diagnostic processes, by allowing the integration of medical knowledge into models and addressing uncertainty in a probabilistic manner. Objectives: This review aims to provide an exhaustive overview of the current state of Bayesian networks in disease diagnosis and prognosis. Additionally, it seeks to introduce readers to the fundamental methodology of BNs, emphasising their versatility and applicability across varied medical domains. Employing a meticulous search strategy with MeSH descriptors in diverse scientific databases, we identified 190 relevant references. These were subjected to a rigorous analysis, resulting in the retention of 60 papers for in-depth review. The robustness of our approach minimised the risk of selection bias. Results: The selected studies encompass a wide range of medical areas, providing insights into the statistical methodology, implementation feasibility, and predictive accuracy of BNs, as evidenced by an average area under the curve (AUC) exceeding 75%. The comprehensive analysis underscores the adaptability and efficacy of Bayesian networks in diverse clinical scenarios. The majority of the examined studies demonstrate the potential of BNs as reliable adjuncts to clinical decision-making. The findings of this review affirm the role of Bayesian networks as accessible and versatile artificial intelligence tools in healthcare. They offer a viable solution to address complex medical challenges, facilitating timely and informed decision-making under conditions of uncertainty. The extensive exploration of Bayesian networks presented in this review highlights their significance and growing impact in the realm of disease diagnosis and prognosis. It underscores the need for further research and development to optimise their capabilities and broaden their applicability in addressing diverse and intricate healthcare challenges.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-06-04
DOI: 10.3390/make6020058
Issue No: Vol. 6, No. 2 (2024)
- MAKE, Vol. 6, Pages 1263-1280: Machine Learning in Geosciences: A Review
of Complex Environmental Monitoring Applications
Authors: Maria Silvia Binetti, Carmine Massarelli, Vito Felice Uricchio
First page: 1263
Abstract: This is a systematic literature review of the application of machine learning (ML) algorithms in geosciences, with a focus on environmental monitoring applications. ML algorithms, with their ability to analyze vast quantities of data, decipher complex relationships, and predict future events, and they offer promising capabilities to implement technologies based on more precise and reliable data processing. This review considers several vulnerable and particularly at-risk themes as landfills, mining activities, the protection of coastal dunes, illegal discharges into water bodies, and the pollution and degradation of soil and water matrices in large industrial complexes. These case studies about environmental monitoring provide an opportunity to better examine the impact of human activities on the environment, with a specific focus on water and soil matrices. The recent literature underscores the increasing importance of ML in these contexts, highlighting a preference for adapted classic models: random forest (RF) (the most widely used), decision trees (DTs), support vector machines (SVMs), artificial neural networks (ANNs), convolutional neural networks (CNNs), principal component analysis (PCA), and much more. In the field of environmental management, the following methodologies offer invaluable insights that can steer strategic planning and decision-making based on more accurate image classification, prediction models, object detection and recognition, map classification, data classification, and environmental variable predictions.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-06-05
DOI: 10.3390/make6020059
Issue No: Vol. 6, No. 2 (2024)
- MAKE, Vol. 6, Pages 1281-1297: Advanced Multi-Label Image Classification
Techniques Using Ensemble Methods
Authors: Tamás Katona, Gábor Tóth, Mátyás Petró, Balázs Harangi
First page: 1281
Abstract: Chest X-rays are vital in healthcare for diagnosing various conditions due to their low Radiation exposure, widespread availability, and rapid interpretation. However, their interpretation requires specialized expertise, which can limit scalability and delay diagnoses. This study addresses the multi-label classification challenge of chest X-ray images using the Chest X-ray14 dataset. We propose a novel online ensemble technique that differs from previous penalty-based methods by focusing on combining individual model losses with the overall ensemble loss. This approach enhances interaction and feedback among models during training. Our method integrates multiple pre-trained CNNs using strategies like combining CNNs through an additional fully connected layer and employing a label-weighted average for outputs. This multi-layered approach leverages the strengths of each model component, improving classification accuracy and generalization. By focusing solely on image data, our ensemble model addresses the challenges posed by null vectors and diverse pathologies, advancing computer-aided radiology.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-06-07
DOI: 10.3390/make6020060
Issue No: Vol. 6, No. 2 (2024)
- MAKE, Vol. 6, Pages 1298-1322: Interaction Difference Hypothesis Test for
Prediction Models
Authors: Thomas Welchowski, Dominic Edelmann
First page: 1298
Abstract: Machine learning research focuses on the improvement of prediction performance. Progress was made with black-box models that flexibly adapt to the given data. However, due to their increased complexity, black-box models are more difficult to interpret. To address this issue, techniques for interpretable machine learning have been developed, yet there is still a lack of methods to reliably identify interaction effects between predictors under uncertainty. In this work, we present a model-agnostic hypothesis test for the identification of interaction effects in black-box machine learning models. The test statistic is based on the difference between the variance of the estimated prediction function and a version of the estimated prediction function without interaction effects derived via partial dependence functions. The properties of the proposed hypothesis test were explored in simulations of linear and nonlinear models. The proposed hypothesis test can be applied to any black-box prediction model, and the null hypothesis of the test can be flexibly specified according to the research question of interest. Furthermore, the test is computationally fast to apply, as the null distribution does not require the resampling or refitting of black-box prediction models.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-06-14
DOI: 10.3390/make6020061
Issue No: Vol. 6, No. 2 (2024)
- MAKE, Vol. 6, Pages 1323-1342: Extracting Interpretable Knowledge from the
Remote Monitoring of COVID-19 Patients
Authors: Melina Tziomaka, Athanasios Kallipolitis, Andreas Menychtas, Parisis Gallos, Christos Panagopoulos, Alice Georgia Vassiliou, Edison Jahaj, Ioanna Dimopoulou, Anastasia Kotanidou, Ilias Maglogiannis
First page: 1323
Abstract: Apart from providing user-friendly applications that support digitized healthcare routines, the use of wearable devices has proven to increase the independence of patients in a healthcare setting. By applying machine learning techniques to real health-related data, important conclusions can be drawn for unsolved issues related to disease prognosis. In this paper, various machine learning techniques are examined and analyzed for the provision of personalized care to COVID-19 patients with mild symptoms based on individual characteristics and the comorbidities they have, while the connection between the stimuli and predictive results are utilized for the evaluation of the system’s transparency. The results, jointly analyzing wearable and electronic health record data for the prediction of a daily dyspnea grade and the duration of fever, are promising in terms of evaluation metrics even in a specified stratum of patients. The interpretability scheme provides useful insight concerning factors that greatly influenced the results. Moreover, it is demonstrated that the use of wearable devices for remote monitoring through cloud platforms is feasible while providing awareness of a patient’s condition, leading to the early detection of undesired changes and reduced visits for patient screening.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-06-18
DOI: 10.3390/make6020062
Issue No: Vol. 6, No. 2 (2024)
- MAKE, Vol. 6, Pages 1343-1360: A Review of Orebody Knowledge Enhancement
Using Machine Learning on Open-Pit Mine Measure-While-Drilling Data
Authors: Daniel M. Goldstein, Chris Aldrich, Louisa O’Connor
First page: 1343
Abstract: Measure while drilling (MWD) refers to the acquisition of real-time data associated with the drilling process, including information related to the geological characteristics encountered in hard-rock mining. The availability of large quantities of low-cost MWD data from blast holes compared to expensive and sparsely collected orebody knowledge (OBK) data from exploration drill holes make the former more desirable for characterizing pre-excavation subsurface conditions. Machine learning (ML) plays a critical role in the real-time or near-real-time analysis of MWD data to enable timely enhancement of OBK for operational purposes. Applications can be categorized into three areas, focused on the mechanical properties of the rock mass, the lithology of the rock, as well as, related to that, the estimation of the geochemical species in the rock mass. From a review of the open literature, the following can be concluded: (i) The most important MWD metrics are the rate of penetration (rop), torque (tor), weight on bit (wob), bit air pressure (bap), and drill rotation speed (rpm). (ii) Multilayer perceptron analysis has mostly been used, followed by Gaussian processes and other methods, mainly to identify rock types. (iii) Recent advances in deep learning methods designed to deal with unstructured data, such as borehole images and vibrational signals, have not yet been fully exploited, although this is an emerging trend. (iv) Significant recent developments in explainable artificial intelligence could also be used to better advantage in understanding the association between MWD metrics and the mechanical and geochemical structure and properties of drilled rock.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-06-18
DOI: 10.3390/make6020063
Issue No: Vol. 6, No. 2 (2024)
- MAKE, Vol. 6, Pages 1361-1377: Image Text Extraction and Natural Language
Processing of Unstructured Data from Medical Reports
Authors: Ivan Malashin, Igor Masich, Vadim Tynchenko, Andrei Gantimurov, Vladimir Nelyub, Aleksei Borodulin
First page: 1361
Abstract: This study presents an integrated approach for automatically extracting and structuring information from medical reports, captured as scanned documents or photographs, through a combination of image recognition and natural language processing (NLP) techniques like named entity recognition (NER). The primary aim was to develop an adaptive model for efficient text extraction from medical report images. This involved utilizing a genetic algorithm (GA) to fine-tune optical character recognition (OCR) hyperparameters, ensuring maximal text extraction length, followed by NER processing to categorize the extracted information into required entities, adjusting parameters if entities were not correctly extracted based on manual annotations. Despite the diverse formats of medical report images in the dataset, all in Russian, this serves as a conceptual example of information extraction (IE) that can be easily extended to other languages.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-06-18
DOI: 10.3390/make6020064
Issue No: Vol. 6, No. 2 (2024)
- MAKE, Vol. 6, Pages 1378-1388: Cross-Validation Visualized: A Narrative
Guide to Advanced Methods
Authors: Johannes Allgaier, Rüdiger Pryss
First page: 1378
Abstract: This study delves into the multifaceted nature of cross-validation (CV) techniques in machine learning model evaluation and selection, underscoring the challenge of choosing the most appropriate method due to the plethora of available variants. It aims to clarify and standardize terminology such as sets, groups, folds, and samples pivotal in the CV domain, and introduces an exhaustive compilation of advanced CV methods like leave-one-out, leave-p-out, Monte Carlo, grouped, stratified, and time-split CV within a hold-out CV framework. Through graphical representations, the paper enhances the comprehension of these methodologies, facilitating more informed decision making for practitioners. It further explores the synergy between different CV strategies and advocates for a unified approach to reporting model performance by consolidating essential metrics. The paper culminates in a comprehensive overview of the CV techniques discussed, illustrated with practical examples, offering valuable insights for both novice and experienced researchers in the field.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-06-20
DOI: 10.3390/make6020065
Issue No: Vol. 6, No. 2 (2024)
- MAKE, Vol. 6, Pages 41-52: An Evaluative Baseline for Sentence-Level
Semantic Division
Authors: Kuangsheng Cai, Zugang Chen, Hengliang Guo, Shaohua Wang, Guoqing Li, Jing Li, Feng Chen, Hang Feng
First page: 41
Abstract: Semantic folding theory (SFT) is an emerging cognitive science theory that aims to explain how the human brain processes and organizes semantic information. The distribution of text into semantic grids is key to SFT. We propose a sentence-level semantic division baseline with 100 grids (SSDB-100), the only dataset we are currently aware of that performs a relevant validation of the sentence-level SFT algorithm, to evaluate the validity of text distribution in semantic grids and divide it using classical division algorithms on SSDB-100. In this article, we describe the construction of SSDB-100. First, a semantic division questionnaire with broad coverage was generated by limiting the uncertainty range of the topics and corpus. Subsequently, through an expert survey, 11 human experts provided feedback. Finally, we analyzed and processed the feedback; the average consistency index for the used feedback was 0.856 after eliminating the invalid feedback. SSDB-100 has 100 semantic grids with clear distinctions between the grids, allowing the dataset to be extended using semantic methods.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-01-02
DOI: 10.3390/make6010003
Issue No: Vol. 6, No. 1 (2024)
- MAKE, Vol. 6, Pages 53-77: Machine Learning for an Enhanced Credit Risk
Analysis: A Comparative Study of Loan Approval Prediction Models
Integrating Mental Health Data
Authors: Adnan Alagic, Natasa Zivic, Esad Kadusic, Dzenan Hamzic, Narcisa Hadzajlic, Mejra Dizdarevic, Elmedin Selmanovic
First page: 53
Abstract: The number of loan requests is rapidly growing worldwide representing a multi-billion-dollar business in the credit approval industry. Large data volumes extracted from the banking transactions that represent customers’ behavior are available, but processing loan applications is a complex and time-consuming task for banking institutions. In 2022, over 20 million Americans had open loans, totaling USD 178 billion in debt, although over 20% of loan applications were rejected. Numerous statistical methods have been deployed to estimate loan risks opening the field to estimate whether machine learning techniques can better predict the potential risks. To study the machine learning paradigm in this sector, the mental health dataset and loan approval dataset presenting survey results from 1991 individuals are used as inputs to experiment with the credit risk prediction ability of the chosen machine learning algorithms. Giving a comprehensive comparative analysis, this paper shows how the chosen machine learning algorithms can distinguish between normal and risky loan customers who might never pay their debts back. The results from the tested algorithms show that XGBoost achieves the highest accuracy of 84% in the first dataset, surpassing gradient boost (83%) and KNN (83%). In the second dataset, random forest achieved the highest accuracy of 85%, followed by decision tree and KNN with 83%. Alongside accuracy, the precision, recall, and overall performance of the algorithms were tested and a confusion matrix analysis was performed producing numerical results that emphasized the superior performance of XGBoost and random forest in the classification tasks in the first dataset, and XGBoost and decision tree in the second dataset. Researchers and practitioners can rely on these findings to form their model selection process and enhance the accuracy and precision of their classification models.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-01-04
DOI: 10.3390/make6010004
Issue No: Vol. 6, No. 1 (2024)
- MAKE, Vol. 6, Pages 78-97: A Data Mining Approach for Health Transport
Demand
Authors: Jorge Blanco Prieto, Marina Ferreras González, Steven Van Vaerenbergh, Oscar Jesús Cosido Cobos
First page: 78
Abstract: Efficient planning and management of health transport services are crucial for improving accessibility and enhancing the quality of healthcare. This study focuses on the choice of determinant variables in the prediction of health transport demand using data mining and analysis techniques. Specifically, health transport services data from Asturias, spanning a seven-year period, are analyzed with the aim of developing accurate predictive models. The problem at hand requires the handling of large volumes of data and multiple predictor variables, leading to challenges in computational cost and interpretation of the results. Therefore, data mining techniques are applied to identify the most relevant variables in the design of predictive models. This approach allows for reducing the computational cost without sacrificing prediction accuracy. The findings of this study underscore that the selection of significant variables is essential for optimizing medical transport resources and improving the planning of emergency services. With the most relevant variables identified, a balance between prediction accuracy and computational efficiency is achieved. As a result, improved service management is observed to lead to increased accessibility to health services and better resource planning.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-01-04
DOI: 10.3390/make6010005
Issue No: Vol. 6, No. 1 (2024)
- MAKE, Vol. 6, Pages 98-125: Predicting Wind Comfort in an Urban Area: A
Comparison of a Regression- with a Classification-CNN for General Wind
Rose Statistics
Authors: Jennifer Werner, Dimitri Nowak, Franziska Hunger, Tomas Johnson, Andreas Mark, Alexander Gösta, Fredrik Edelvik
First page: 98
Abstract: Wind comfort is an important factor when new buildings in existing urban areas are planned. It is common practice to use computational fluid dynamics (CFD) simulations to model wind comfort. These simulations are usually time-consuming, making it impossible to explore a high number of different design choices for a new urban development with wind simulations. Data-driven approaches based on simulations have shown great promise, and have recently been used to predict wind comfort in urban areas. These surrogate models could be used in generative design software and would enable the planner to explore a large number of options for a new design. In this paper, we propose a novel machine learning workflow (MLW) for direct wind comfort prediction. The MLW incorporates a regression and a classification U-Net, trained based on CFD simulations. Furthermore, we present an augmentation strategy focusing on generating more training data independent of the underlying wind statistics needed to calculate the wind comfort criterion. We train the models based on different sets of training data and compare the results. All trained models (regression and classification) yield an F1-score greater than 80% and can be combined with any wind rose statistic.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-01-04
DOI: 10.3390/make6010006
Issue No: Vol. 6, No. 1 (2024)
- MAKE, Vol. 6, Pages 126-142: Knowledge Graph Extraction of Business
Interactions from News Text for Business Networking Analysis
Authors: Didier Gohourou, Kazuhiro Kuwabara
First page: 126
Abstract: Network representation of data is key to a variety of fields and their applications including trading and business. A major source of data that can be used to build insightful networks is the abundant amount of unstructured text data available through the web. The efforts to turn unstructured text data into a network have spawned different research endeavors, including the simplification of the process. This study presents the design and implementation of TraCER, a pipeline that turns unstructured text data into a graph, targeting the business networking domain. It describes the application of natural language processing techniques used to process the text, as well as the heuristics and learning algorithms that categorize the nodes and the links. The study also presents some simple yet efficient methods for the entity-linking and relation classification steps of the pipeline.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-01-07
DOI: 10.3390/make6010007
Issue No: Vol. 6, No. 1 (2024)
- MAKE, Vol. 6, Pages 143-155: What Do the Regulators Mean' A Taxonomy of
Regulatory Principles for the Use of AI in Financial Services
Authors: Mustafa Pamuk, Matthias Schumann, Robert C. Nickerson
First page: 143
Abstract: The intended automation in the financial industry creates a proper area for artificial intelligence usage. However, complex and high regulatory standards and rapid technological developments pose significant challenges in developing and deploying AI-based services in the finance industry. The regulatory principles defined by financial authorities in Europe need to be structured in a fine-granular way to promote understanding and ensure customer safety and the quality of AI-based services in the financial industry. This will lead to a better understanding of regulators’ priorities and guide how AI-based services are built. This paper provides a classification pattern with a taxonomy that clarifies the existing European regulatory principles for researchers, regulatory authorities, and financial services companies. Our study can pave the way for developing compliant AI-based services by bringing out the thematic focus of regulatory principles.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-01-11
DOI: 10.3390/make6010008
Issue No: Vol. 6, No. 1 (2024)
- MAKE, Vol. 6, Pages 156-170: An Ensemble-Based Multi-Classification
Machine Learning Classifiers Approach to Detect Multiple Classes of
Cyberbullying
Authors: Abdulkarim Faraj Alqahtani, Mohammad Ilyas
First page: 156
Abstract: The impact of communication through social media is currently considered a significant social issue. This issue can lead to inappropriate behavior using social media, which is referred to as cyberbullying. Automated systems are capable of efficiently identifying cyberbullying and performing sentiment analysis on social media platforms. This study focuses on enhancing a system to detect six types of cyberbullying tweets. Employing multi-classification algorithms on a cyberbullying dataset, our approach achieved high accuracy, particularly with the TF-IDF (bigram) feature extraction. Our experiment achieved high performance compared with that stated for previous experiments on the same dataset. Two ensemble machine learning methods, employing the N-gram with TF-IDF feature-extraction technique, demonstrated superior performance in classification. Three popular multi-classification algorithms: Decision Trees, Random Forest, and XGBoost, were combined into two varied ensemble methods separately. These ensemble classifiers demonstrated superior performance compared to traditional machine learning classifier models. The stacking classifier reached 90.71% accuracy and the voting classifier 90.44%. The results of the experiments showed that the framework can detect six different types of cyberbullying more efficiently, with an accuracy rate of 0.9071.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-01-12
DOI: 10.3390/make6010009
Issue No: Vol. 6, No. 1 (2024)
- MAKE, Vol. 6, Pages 171-198: GAN-Based Tabular Data Generator for
Constructing Synopsis in Approximate Query Processing: Challenges and
Solutions
Authors: Mohammadali Fallahian, Mohsen Dorodchi, Kyle Kreth
First page: 171
Abstract: In data-driven systems, data exploration is imperative for making real-time decisions. However, big data are stored in massive databases that are difficult to retrieve. Approximate Query Processing (AQP) is a technique for providing approximate answers to aggregate queries based on a summary of the data (synopsis) that closely replicates the behavior of the actual data; this can be useful when an approximate answer to queries is acceptable in a fraction of the real execution time. This study explores the novel utilization of a Generative Adversarial Network (GAN) for the generation of tabular data that can be employed in AQP for synopsis construction. We thoroughly investigate the unique challenges posed by the synopsis construction process, including maintaining data distribution characteristics, handling bounded continuous and categorical data, and preserving semantic relationships, and we then introduce the advancement of tabular GAN architectures that overcome these challenges. Furthermore, we propose and validate a suite of statistical metrics tailored for assessing the reliability of GAN-generated synopses. Our findings demonstrate that advanced GAN variations exhibit a promising capacity to generate high-fidelity synopses, potentially transforming the efficiency and effectiveness of AQP in data-driven systems.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-01-16
DOI: 10.3390/make6010010
Issue No: Vol. 6, No. 1 (2024)
- MAKE, Vol. 6, Pages 199-214: The Impact of Light Conditions on Neural
Affect Classification: A Deep Learning Approach
Authors: Sophie Zentner, Alberto Barradas Chacon, Selina C. Wriessnegger
First page: 199
Abstract: Understanding and detecting human emotions is crucial for enhancing mental health, cognitive performance and human–computer interactions. This field in affective computing is relatively unexplored, and gaining knowledge about which external factors impact emotions could enhance communication between users and machines. Furthermore, it could also help us to manage affective disorders or understand affective physiological responses to human spatial and digital environments. The main objective of the current study was to investigate the influence of external stimulation, specifically the influence of different light conditions, on brain activity while observing affect-eliciting pictures and their classification. In this context, a multichannel electroencephalography (EEG) was recorded in 30 participants as they observed images from the Nencki Affective Picture System (NAPS) database in an art-gallery-style Virtual Reality (VR) environment. The elicited affect states were classified into three affect classes within the two-dimensional valence–arousal plane. Valence (positive/negative) and arousal (high/low) values were reported by participants on continuous scales. The experiment was conducted in two experimental conditions: a warm light condition and a cold light condition. Thus, three classification tasks arose with regard to the recorded brain data: classification of an affect state within a warm-light condition, classification of an affect state within a cold light condition, and warm light vs. cold light classification during observation of affect-eliciting images. For all classification tasks, Linear Discriminant Analysis, a Spatial Filter Model, a Convolutional Neural Network, the EEGNet, and the SincNet were compared. The EEGNet architecture performed best in all tasks. It could significantly classify three affect states with 43.12% accuracy under the influence of warm light. Under the influence of cold light, no model could achieve significant results. The classification between visual stimulus with warm light vs. cold light could be classified significantly with 76.65% accuracy from the EEGNet, well above any other machine learning or deep learning model. No significant differences could be detected between affect recognition in different light conditions, but the results point towards the advantage of gradient-based learning methods for data-driven experimental designs for the problem of affect decoding from EEG, providing modern tools for affective computing in digital spaces. Moreover, the ability to discern externally driven affective states through deep learning not only advances our understanding of the human mind but also opens avenues for developing innovative therapeutic interventions and improving human–computer interaction.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-01-18
DOI: 10.3390/make6010011
Issue No: Vol. 6, No. 1 (2024)
- MAKE, Vol. 6, Pages 215-232: Algorithmic Information Theory for the
Precise Engineering of Flexible Material Mechanics
Authors: Liang Luo, George K. Stylios
First page: 215
Abstract: The structure of fibrous assemblies is highly complex, being both random and regular at the same time, which leads to the complexity of its mechanical behaviour. Using algorithms such as machine learning to process complex mechanical property data requires consideration and understanding of its information principles. There are many different methods and instruments for measuring flexible material mechanics, and many different mechanics models exist. There is a need for an evaluation method to determine how close the results they obtain are to the real material mechanical behaviours. This paper considers and investigates measurements, data, models and simulations of fabric’s low-stress mechanics from an information perspective. The simplification of measurements and models will lead to a loss of information and, ultimately, a loss of authenticity in the results. Kolmogorov complexity is used as a tool to analyse and evaluate the algorithmic information content of multivariate nonlinear relationships of fabric stress and strain. The loss of algorithmic information content resulting from simplified approaches to various material measurements, models and simulations is also evaluated. For example, ignoring the friction hysteresis component in the material mechanical data can cause the model and simulation to lose more than 50% of the algorithm information, whilst the average loss of information using uniaxial measurement data can be as high as 75%. The results of this evaluation can be used to determine the authenticity of measurements and models and to identify the direction for new measurement instrument development and material mechanics modelling. It has been shown that a vast number of models, which use unary relationships to describe fabric behaviour and ignore the presence of frictional hysteresis, are inaccurate because they hold less than 12% of real fabric mechanics data. The paper also explores the possibility of compressing the measurement data of fabric mechanical properties.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-01-22
DOI: 10.3390/make6010012
Issue No: Vol. 6, No. 1 (2024)
- MAKE, Vol. 6, Pages 233-258: A Text-Based Predictive Maintenance Approach
for Facility Management Requests Utilizing Association Rule Mining and
Large Language Models
Authors: Maximilian Lowin
First page: 233
Abstract: Introduction: Due to the lack of labeled data, applying predictive maintenance algorithms for facility management is cumbersome. Most companies are unwilling to share data or do not have time for annotation. In addition, most available facility management data are text data. Thus, there is a need for an unsupervised predictive maintenance algorithm that is capable of handling textual data. Methodology: This paper proposes applying association rule mining on maintenance requests to identify upcoming needs in facility management. By coupling temporal association rule mining with the concept of semantic similarity derived from large language models, the proposed methodology can discover meaningful knowledge in the form of rules suitable for decision-making. Results: Relying on the large German language models works best for the presented case study. Introducing a temporal lift filter allows for reducing the created rules to the most important ones. Conclusions: Only a few maintenance requests are sufficient to mine association rules that show links between different infrastructural failures. Due to the unsupervised manner of the proposed algorithm, domain experts need to evaluate the relevance of the specific rules. Nevertheless, the algorithm enables companies to efficiently utilize their data stored in databases to create interpretable rules supporting decision-making.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-01-26
DOI: 10.3390/make6010013
Issue No: Vol. 6, No. 1 (2024)
- MAKE, Vol. 6, Pages 259-282: Real-Time Droplet Detection for Agricultural
Spraying Systems: A Deep Learning Approach
Authors: Nhut Huynh, Kim-Doang Nguyen
First page: 259
Abstract: Nozzles are ubiquitous in agriculture: they are used to spray and apply nutrients and pesticides to crops. The properties of droplets sprayed from nozzles are vital factors that determine the effectiveness of the spray. Droplet size and other characteristics affect spray retention and drift, which indicates how much of the spray adheres to the crop and how much becomes chemical runoff that pollutes the environment. There is a critical need to measure these droplet properties to improve the performance of crop spraying systems. This paper establishes a deep learning methodology to detect droplets moving across a camera frame to measure their size. This framework is compatible with embedded systems that have limited onboard resources and can operate in real time. The method leverages a combination of techniques including resizing, normalization, pruning, detection head, unified feature map extraction via a feature pyramid network, non-maximum suppression, and optimization-based training. The approach is designed with the capability of detecting droplets of various sizes, shapes, and orientations. The experimental results demonstrate that the model designed in this study, coupled with the right combination of dataset and augmentation, achieved a 97% precision and 96.8% recall in droplet detection. The proposed methodology outperformed previous models, marking a significant advancement in droplet detection for precision agriculture applications.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-01-26
DOI: 10.3390/make6010014
Issue No: Vol. 6, No. 1 (2024)
- MAKE, Vol. 6, Pages 283-315: Distributed Learning in the
IoT–Edge–Cloud Continuum
Authors: Audris Arzovs, Janis Judvaitis, Krisjanis Nesenbergs, Leo Selavo
First page: 283
Abstract: The goal of the IoT–Edge–Cloud Continuum approach is to distribute computation and data loads across multiple types of devices taking advantage of the different strengths of each, such as proximity to the data source, data access, or computing power, while mitigating potential weaknesses. Most current machine learning operations are currently concentrated on remote high-performance computing devices, such as the cloud, which leads to challenges related to latency, privacy, and other inefficiencies. Distributed learning approaches can address these issues by enabling the distribution of machine learning operations throughout the IoT–Edge–Cloud Continuum by incorporating Edge and even IoT layers into machine learning operations more directly. Approaches like transfer learning could help to transfer the knowledge from more performant IoT–Edge–Cloud Continuum layers to more resource-constrained devices, e.g., IoT. The implementation of these methods in machine learning operations, including the related data handling security and privacy approaches, is challenging and actively being researched. In this article the distributed learning and transfer learning domains are researched, focusing on security, robustness, and privacy aspects, and their potential usage in the IoT–Edge–Cloud Continuum, including research on tools to use for implementing these methods. To achieve this, we have reviewed 145 sources and described the relevant methods as well as their relevant attack vectors and provided suggestions on mitigation.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-02-01
DOI: 10.3390/make6010015
Issue No: Vol. 6, No. 1 (2024)
- MAKE, Vol. 6, Pages 316-341: SHapley Additive exPlanations (SHAP) for
Efficient Feature Selection in Rolling Bearing Fault Diagnosis
Authors: Mailson Ribeiro Santos, Affonso Guedes, Ignacio Sanchez-Gendriz
First page: 316
Abstract: This study introduces an efficient methodology for addressing fault detection, classification, and severity estimation in rolling element bearings. The methodology is structured into three sequential phases, each dedicated to generating distinct machine-learning-based models for the tasks of fault detection, classification, and severity estimation. To enhance the effectiveness of fault diagnosis, information acquired in one phase is leveraged in the subsequent phase. Additionally, in the pursuit of attaining models that are both compact and efficient, an explainable artificial intelligence (XAI) technique is incorporated to meticulously select optimal features for the machine learning (ML) models. The chosen ML technique for the tasks of fault detection, classification, and severity estimation is the support vector machine (SVM). To validate the approach, the widely recognized Case Western Reserve University benchmark is utilized. The results obtained emphasize the efficiency and efficacy of the proposal. Remarkably, even with a highly limited number of features, evaluation metrics consistently indicate an accuracy of over 90% in the majority of cases when employing this approach.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-02-05
DOI: 10.3390/make6010016
Issue No: Vol. 6, No. 1 (2024)
- MAKE, Vol. 6, Pages 342-366: More Capable, Less Benevolent: Trust
Perceptions of AI Systems across Societal Contexts
Authors: Ekaterina Novozhilova, Kate Mays, Sejin Paik, James E. Katz
First page: 342
Abstract: Modern AI applications have caused broad societal implications across key public domains. While previous research primarily focuses on individual user perspectives regarding AI systems, this study expands our understanding to encompass general public perceptions. Through a survey (N = 1506), we examined public trust across various tasks within education, healthcare, and creative arts domains. The results show that participants vary in their trust across domains. Notably, AI systems’ abilities were evaluated higher than their benevolence across all domains. Demographic traits had less influence on trust in AI abilities and benevolence compared to technology-related factors. Specifically, participants with greater technological competence, AI familiarity, and knowledge viewed AI as more capable in all domains. These participants also perceived greater systems’ benevolence in healthcare and creative arts but not in education. We discuss the importance of considering public trust and its determinants in AI adoption.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-02-05
DOI: 10.3390/make6010017
Issue No: Vol. 6, No. 1 (2024)
- MAKE, Vol. 6, Pages 367-384: Prompt Engineering or Fine-Tuning' A Case
Study on Phishing Detection with Large Language Models
Authors: Fouad Trad, Ali Chehab
First page: 367
Abstract: Large Language Models (LLMs) are reshaping the landscape of Machine Learning (ML) application development. The emergence of versatile LLMs capable of undertaking a wide array of tasks has reduced the necessity for intensive human involvement in training and maintaining ML models. Despite these advancements, a pivotal question emerges: can these generalized models negate the need for task-specific models' This study addresses this question by comparing the effectiveness of LLMs in detecting phishing URLs when utilized with prompt-engineering techniques versus when fine-tuned. Notably, we explore multiple prompt-engineering strategies for phishing URL detection and apply them to two chat models, GPT-3.5-turbo and Claude 2. In this context, the maximum result achieved was an F1-score of 92.74% by using a test set of 1000 samples. Following this, we fine-tune a range of base LLMs, including GPT-2, Bloom, Baby LLaMA, and DistilGPT-2—all primarily developed for text generation—exclusively for phishing URL detection. The fine-tuning approach culminated in a peak performance, achieving an F1-score of 97.29% and an AUC of 99.56% on the same test set, thereby outperforming existing state-of-the-art methods. These results highlight that while LLMs harnessed through prompt engineering can expedite application development processes, achieving a decent performance, they are not as effective as dedicated, task-specific LLMs.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-02-06
DOI: 10.3390/make6010018
Issue No: Vol. 6, No. 1 (2024)
- MAKE, Vol. 6, Pages 385-401: Explicit Physics-Informed Deep Learning for
Computer-Aided Diagnostic Tasks in Medical Imaging
Authors: Shira Nemirovsky-Rotman, Eyal Bercovich
First page: 385
Abstract: DNN-based systems have demonstrated unprecedented performance in terms of accuracy and speed over the past decade. However, recent work has shown that such models may not be sufficiently robust during the inference process. Furthermore, due to the data-driven learning nature of DNNs, designing interpretable and generalizable networks is a major challenge, especially when considering critical applications such as medical computer-aided diagnostics (CAD) and other medical imaging tasks. Within this context, a line of approaches incorporating prior knowledge domain information into deep learning methods has recently emerged. In particular, many of these approaches utilize known physics-based forward imaging models, aimed at improving the stability and generalization ability of DNNs for medical imaging applications. In this paper, we review recent work focused on such physics-based or physics-prior-based learning for a variety of imaging modalities and medical applications. We discuss how the inclusion of such physics priors to the training process and/or network architecture supports their stability and generalization ability. Moreover, we propose a new physics-based approach, in which an explicit physics prior, which describes the relation between the input and output of the forward imaging model, is included as an additional input into the network architecture. Furthermore, we propose a tailored training process for this extended architecture, for which training data are generated with perturbed physical priors that are also integrated into the network. Within the scope of this approach, we offer a problem formulation for a regression task with a highly nonlinear forward model and highlight possible useful applications for this task. Finally, we briefly discuss future challenges for physics-informed deep learning in the context of medical imaging.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-02-12
DOI: 10.3390/make6010019
Issue No: Vol. 6, No. 1 (2024)
- MAKE, Vol. 6, Pages 402-419: Machine Learning Predictive Analysis of
Liquefaction Resistance for Sandy Soils Enhanced by Chemical Injection
Authors: Yuxin Cong, Toshiyuki Motohashi, Koki Nakao, Shinya Inazumi
First page: 402
Abstract: The objective of this study was to investigate the liquefaction resistance of chemically improved sandy soils in a straightforward and accurate manner. Using only the existing experimental databases and artificial intelligence, the goal was to predict the experimental results as supporting information before performing the physical experiments. Emphasis was placed on the significance of data from 20 loading cycles of cyclic undrained triaxial tests to determine the liquefaction resistance and the contribution of each explanatory variable. Different combinations of explanatory variables were considered. Regarding the predictive model, it was observed that a case with the liquefaction resistance ratio as the dependent variable and other parameters as explanatory variables yielded favorable results. In terms of exploring combinations of explanatory variables, it was found advantageous to include all the variables, as doing so consistently resulted in a high coefficient of determination. The inclusion of the liquefaction resistance ratio in the training data was found to improve the predictive accuracy. In addition, the results obtained when using a linear model for the prediction suggested the potential to accurately predict the liquefaction resistance using historical data.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-02-14
DOI: 10.3390/make6010020
Issue No: Vol. 6, No. 1 (2024)
- MAKE, Vol. 6, Pages 420-434: Overcoming Therapeutic Inertia in Type 2
Diabetes: Exploring Machine Learning-Based Scenario Simulation for
Improving Short-Term Glycemic Control
Authors: Musacchio Nicoletta, Rita Zilich, Davide Masi, Fabio Baccetti, Besmir Nreu, Carlo Bruno Giorda, Giacomo Guaita, Lelio Morviducci, Marco Muselli, Alessandro Ozzello, Federico Pisani, Paola Ponzani, Antonio Rossi, Pierluigi Santin, Damiano Verda, Graziano Di Cianni, Riccardo Candido
First page: 420
Abstract: Background: International guidelines for diabetes care emphasize the urgency of promptly achieving and sustaining adequate glycemic control to reduce the occurrence of micro/macrovascular complications in patients with type 2 diabetes mellitus (T2DM). However, data from the Italian Association of Medical Diabetologists (AMD) Annals reveal that only 47% of T2DM patients reach appropriate glycemic targets, with approximately 30% relying on insulin therapy, either solely or in combination. This artificial intelligence analysis seeks to assess the potential impact of timely insulin initiation in all eligible patients via a “what-if” scenario simulation, leveraging real-world data. Methods: This retrospective cohort study utilized the AMD Annals database, comprising 1,186,247 T2DM patients from 2005 to 2019. Employing the Logic Learning Machine (LLM), we simulated timely insulin use for all eligible patients, estimating its effect on glycemic control after 12 months within a cohort of 85,239 patients. Of these, 20,015 were employed for the machine learning phase and 65,224 for simulation. Results: Within the simulated scenario, the introduction of appropriate insulin therapy led to a noteworthy projected 17% increase in patients meeting the metabolic target after 12 months from therapy initiation within the cohort of 65,224 individuals. The LLM’s projection envisages 32,851 potential patients achieving the target (hemoglobin glycated < 7.5%) after 12 months, compared to 21,453 patients observed in real-world cases. The receiver operating characteristic (ROC) curve analysis for this model demonstrated modest performance, with an area under the curve (AUC) value of 70.4%. Conclusions: This study reaffirms the significance of combatting therapeutic inertia in managing T2DM patients. Early insulinization, when clinically appropriate, markedly enhances patients’ metabolic goals at the 12-month follow-up.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-02-14
DOI: 10.3390/make6010021
Issue No: Vol. 6, No. 1 (2024)
- MAKE, Vol. 6, Pages 435-447: High-Throughput Ensemble-Learning-Driven Band
Gap Prediction of Double Perovskites Solar Cells Absorber
Authors: Sabrina Djeradi, Tahar Dahame, Mohamed Abdelilah Fadla, Bachir Bentria, Mohammed Benali Kanoun, Souraya Goumri-Said
First page: 435
Abstract: Perovskite materials have attracted much attention in recent years due to their high performance, especially in the field of photovoltaics. However, the dark side of these materials is their poor stability, which poses a huge challenge to their practical applications. Double perovskite compounds, on the other hand, can show more stability as a result of their specific structure. One of the key properties of both perovskite and double perovskite is their tunable band gap, which can be determined using different techniques. Density functional theory (DFT), for instance, offers the potential to intelligently direct experimental investigation activities and predict various properties, including band gap. In reality, however, it is still difficult to anticipate the energy band gap from first principles, and accurate results often require more expensive methods such as hybrid functional or GW methods. In this paper, we present our development of high-throughput supervised ensemble learning-based methods: random forest, XGBoost, and Light GBM using a database of 1306 double perovskites materials to predict the energy band gap. Based on elemental properties, characteristics have been vectorized from chemical compositions. Our findings demonstrate the efficiency of ensemble learning methods and imply that scientists would benefit from recently employed methods in materials informatics.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-02-16
DOI: 10.3390/make6010022
Issue No: Vol. 6, No. 1 (2024)
- MAKE, Vol. 6, Pages 448-463: VisFormers—Combining Vision and
Transformers for Enhanced Complex Document Classification
Authors: Subhayu Dutta, Subhrangshu Adhikary, Ashutosh Dhar Dwivedi
First page: 448
Abstract: Complex documents have text, figures, tables, and other elements. The classification of scanned copies of different categories of complex documents like memos, newspapers, letters, and more is essential for rapid digitization. However, this task is very challenging as most scanned complex documents look similar. This is because all documents have similar colors of the page and letters, similar textures for all papers, and very few contrasting features. Several attempts have been made in the state of the art to classify complex documents; however, only a few of these works have addressed the classification of complex documents with similar features, and among these, the performances could be more satisfactory. To overcome this, this paper presents a method to use an optical character reader to extract the texts. It proposes a multi-headed model to combine vision-based transfer learning and natural-language-based Transformers within the same network for simultaneous training for different inputs and optimizers in specific parts of the network. A subset of the Ryers Vision Lab Complex Document Information Processing dataset containing 16 different document classes was used to evaluate the performances. The proposed multi-headed VisFormers network classified the documents with up to 94.2% accuracy, while a regular natural-language-processing-based Transformer network achieved 83%, and vision-based VGG19 transfer learning could achieve only up to 90% accuracy. The model deployment can help sort the scanned copies of various documents into different categories.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-02-16
DOI: 10.3390/make6010023
Issue No: Vol. 6, No. 1 (2024)
- MAKE, Vol. 6, Pages 464-505: Alzheimer’s Disease Detection Using
Deep Learning on Neuroimaging: A Systematic Review
Authors: Mohammed G. Alsubaie, Suhuai Luo, Kamran Shaukat
First page: 464
Abstract: Alzheimer’s disease (AD) is a pressing global issue, demanding effective diagnostic approaches. This systematic review surveys the recent literature (2018 onwards) to illuminate the current landscape of AD detection via deep learning. Focusing on neuroimaging, this study explores single- and multi-modality investigations, delving into biomarkers, features, and preprocessing techniques. Various deep models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative models, are evaluated for their AD detection performance. Challenges such as limited datasets and training procedures persist. Emphasis is placed on the need to differentiate AD from similar brain patterns, necessitating discriminative feature representations. This review highlights deep learning’s potential and limitations in AD detection, underscoring dataset importance. Future directions involve benchmark platform development for streamlined comparisons. In conclusion, while deep learning holds promise for accurate AD detection, refining models and methods is crucial to tackle challenges and enhance diagnostic precision.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-02-21
DOI: 10.3390/make6010024
Issue No: Vol. 6, No. 1 (2024)
- MAKE, Vol. 6, Pages 506-553: Refereeing the Sport of Squash with a Machine
Learning System
Authors: Enqi Ma, Zbigniew J. Kabala
First page: 506
Abstract: Squash is a sport where referee decisions are essential to the game. However, these decisions are very subjective in nature. Disputes, both from the players and the audience, regularly occur because the referee made a controversial call. In this study, we propose automating the referee decision process through machine learning. We trained neural networks to predict such decisions using data from 400 referee decisions acquired through extensive video footage reviewing and labeling. Six positional values were extracted, including the attacking player’s position, the retreating player’s position, the ball’s position in the frame, the ball’s projected first bounce, the ball’s projected second bounce, and the attacking player’s racket head position. We calculated nine additional distance values, such as the distance between players and the distance from the attacking player’s racket head to the ball’s path. Models were trained on Wolfram Mathematica and Python using these values. The best Wolfram Mathematica model and the best Python model achieved accuracies of 86% ± 3.03% and 85.2% ± 5.1%, respectively. These accuracies surpass 85%, demonstrating near-human performance. Our model has great potential for improvement as it is currently trained with limited, unbalanced data (400 decisions) and lacks crucial data points such as time and speed. The performance of our model is almost surely going to improve significantly with a larger training dataset. Unlike human referees, machine learning models follow a consistent standard, have unlimited attention spans, and make decisions instantly. If the accuracy is improved in the future, the model can potentially serve as an extra refereeing official for both professional and amateur squash matches. Both the analysis of referee decisions in squash and the proposal to automate the process using machine learning is unique to this study.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-03-05
DOI: 10.3390/make6010025
Issue No: Vol. 6, No. 1 (2024)
- MAKE, Vol. 6, Pages 554-579: Classification, Regression, and Survival Rule
Induction with Complex and M-of-N Elementary Conditions
Authors: Cezary Maszczyk, Marek Sikora, Łukasz Wróbel
First page: 554
Abstract: Most rule induction algorithms generate rules with simple logical conditions based on equality or inequality relations. This feature limits their ability to discover complex dependencies that may exist in data. This article presents an extension to the sequential covering rule induction algorithm that allows it to generate complex and M-of-N conditions within the premises of rules. The proposed methodology uncovers complex patterns in data that are not adequately expressed by rules with simple conditions. The novel two-phase approach efficiently generates M-of-N conditions by analysing frequent sets in previously induced simple and complex rule conditions. The presented method allows rule induction for classification, regression and survival problems. Extensive experiments on various public datasets show that the proposed method often leads to more concise rulesets compared to those using only simple conditions. Importantly, the inclusion of complex conditions and M-of-N conditions has no statistically significant negative impact on the predictive ability of the ruleset. Experimental results and a ready-to-use implementation are available in the GitHub repository. The proposed algorithm can potentially serve as a valuable tool for knowledge discovery and facilitate the interpretation of rule-based models by making them more concise.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-03-05
DOI: 10.3390/make6010026
Issue No: Vol. 6, No. 1 (2024)
- MAKE, Vol. 6, Pages 580-592: Representing Human Ethical Requirements in
Hybrid Machine Learning Models: Technical Opportunities and Fundamental
Challenges
Authors: Stephen Fox, Vitor Fortes Rey
First page: 580
Abstract: Hybrid machine learning encompasses predefinition of rules and ongoing learning from data. Human organizations can implement hybrid machine learning (HML) to automate some of their operations. Human organizations need to ensure that their HML implementations are aligned with human ethical requirements as defined in laws, regulations, standards, etc. The purpose of the study reported here was to investigate technical opportunities for representing human ethical requirements in HML. The study sought to represent two types of human ethical requirements in HML: locally simple and locally complex. The locally simple case is road traffic regulations. This can be considered to be a relatively simple case because human ethical requirements for road safety, such as stopping at red traffic lights, are defined clearly and have limited scope for personal interpretation. The locally complex case is diagnosis procedures for functional disorders, which can include medically unexplained symptoms. This case can be considered to be locally complex because human ethical requirements for functional disorder healthcare are less well defined and are more subject to personal interpretation. Representations were made in a type of HML called Algebraic Machine Learning. Our findings indicate that there are technical opportunities to represent human ethical requirements in HML because of its combination of human-defined top down rules and bottom up data-driven learning. However, our findings also indicate that there are limitations to representing human ethical requirements: irrespective of what type of machine learning is used. These limitations arise from fundamental challenges in defining complex ethical requirements, and from potential for opposing interpretations of their implementation. Furthermore, locally simple ethical requirements can contribute to wider ethical complexity.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-03-08
DOI: 10.3390/make6010027
Issue No: Vol. 6, No. 1 (2024)
- MAKE, Vol. 6, Pages 593-618: Augmenting Deep Neural Networks with Symbolic
Educational Knowledge: Towards Trustworthy and Interpretable AI for
Education
Authors: Danial Hooshyar, Roger Azevedo, Yeongwook Yang
First page: 593
Abstract: Artificial neural networks (ANNs) have proven to be among the most important artificial intelligence (AI) techniques in educational applications, providing adaptive educational services. However, their educational potential is limited in practice due to challenges such as the following: (i) the difficulties in incorporating symbolic educational knowledge (e.g., causal relationships and practitioners’ knowledge) in their development, (ii) a propensity to learn and reflect biases, and (iii) a lack of interpretability. As education is classified as a ‘high-risk’ domain under recent regulatory frameworks like the EU AI Act—highlighting its influence on individual futures and discrimination risks—integrating educational insights into ANNs is essential. This ensures that AI applications adhere to essential educational restrictions and provide interpretable predictions. This research introduces NSAI, a neural-symbolic AI approach that integrates neural networks with knowledge representation and symbolic reasoning. It injects and extracts educational knowledge into and from deep neural networks to model learners’ computational thinking, aiming to enhance personalized learning and develop computational thinking skills. Our findings revealed that the NSAI approach demonstrates better generalizability compared to deep neural networks trained on both original training data and data enriched by SMOTE and autoencoder methods. More importantly, we found that, unlike traditional deep neural networks, which mainly relied on spurious correlations in their predictions, the NSAI approach prioritizes the development of robust representations that accurately capture causal relationships between inputs and outputs. This focus significantly reduces the reinforcement of biases and prevents misleading correlations in the models. Furthermore, our research showed that the NSAI approach enables the extraction of rules from the trained network, facilitating interpretation and reasoning during the path to predictions, as well as refining the initial educational knowledge. These findings imply that neural-symbolic AI not only overcomes the limitations of ANNs in education but also holds broader potential for transforming educational practices and outcomes through trustworthy and interpretable applications.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-03-10
DOI: 10.3390/make6010028
Issue No: Vol. 6, No. 1 (2024)
- MAKE, Vol. 6, Pages 619-641: Classifying Breast Tumors in Digital
Tomosynthesis by Combining Image Quality-Aware Features and Tumor Texture
Descriptors
Authors: Loay Hassan, Mohamed Abdel-Nasser, Adel Saleh, Domenec Puig
First page: 619
Abstract: Digital breast tomosynthesis (DBT) is a 3D breast cancer screening technique that can overcome the limitations of standard 2D digital mammography. However, DBT images often suffer from artifacts stemming from acquisition conditions, a limited angular range, and low radiation doses. These artifacts have the potential to degrade the performance of automated breast tumor classification tools. Notably, most existing automated breast tumor classification methods do not consider the effect of DBT image quality when designing the classification models. In contrast, this paper introduces a novel deep learning-based framework for classifying breast tumors in DBT images. This framework combines global image quality-aware features with tumor texture descriptors. The proposed approach employs a two-branch model: in the top branch, a deep convolutional neural network (CNN) model is trained to extract robust features from the region of interest that includes the tumor. In the bottom branch, a deep learning model named TomoQA is trained to extract global image quality-aware features from input DBT images. The quality-aware features and the tumor descriptors are then combined and fed into a fully-connected layer to classify breast tumors as benign or malignant. The unique advantage of this model is the combination of DBT image quality-aware features with tumor texture descriptors, which helps accurately classify breast tumors as benign or malignant. Experimental results on a publicly available DBT image dataset demonstrate that the proposed framework achieves superior breast tumor classification results, outperforming all existing deep learning-based methods.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-03-11
DOI: 10.3390/make6010029
Issue No: Vol. 6, No. 1 (2024)
- MAKE, Vol. 6, Pages 642-657: Enhancing Docking Accuracy with PECAN2, a 3D
Atomic Neural Network Trained without Co-Complex Crystal Structures
Authors: Heesung Shim, Jonathan E. Allen, W. F. Drew Bennett
First page: 642
Abstract: Decades of drug development research have explored a vast chemical space for highly active compounds. The exponential growth of virtual libraries enables easy access to billions of synthesizable molecules. Computational modeling, particularly molecular docking, utilizes physics-based calculations to prioritize molecules for synthesis and testing. Nevertheless, the molecular docking process often yields docking poses with favorable scores that prove to be inaccurate with experimental testing. To address these issues, several approaches using machine learning (ML) have been proposed to filter incorrect poses based on the crystal structures. However, most of the methods are limited by the availability of structure data. Here, we propose a new pose classification approach, PECAN2 (Pose Classification with 3D Atomic Network 2), without the need for crystal structures, based on a 3D atomic neural network with Point Cloud Network (PCN). The new approach uses the correlation between docking scores and experimental data to assign labels, instead of relying on the crystal structures. We validate the proposed classifier on multiple datasets including human mu, delta, and kappa opioid receptors and SARS-CoV-2 Mpro. Our results demonstrate that leveraging the correlation between docking scores and experimental data alone enhances molecular docking performance by filtering out false positives and false negatives.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-03-11
DOI: 10.3390/make6010030
Issue No: Vol. 6, No. 1 (2024)
- MAKE, Vol. 6, Pages 658-678: Why Do Tree Ensemble Approximators Not
Outperform the Recursive-Rule eXtraction Algorithm'
Authors: Soma Onishi, Masahiro Nishimura, Ryota Fujimura, Yoichi Hayashi
First page: 658
Abstract: Although machine learning models are widely used in critical domains, their complexity and poor interpretability remain problematic. Decision trees (DTs) and rule-based models are known for their interpretability, and numerous studies have investigated techniques for approximating tree ensembles using DTs or rule sets, even though these approximators often overlook interpretability. These methods generate three types of rule sets: DT based, unordered, and decision list based. However, very few metrics exist that can distinguish and compare these rule sets. Therefore, the present study proposes an interpretability metric to allow for comparisons of interpretability between different rule sets and investigates the interpretability of the rules generated by the tree ensemble approximators. We compare these rule sets with the Recursive-Rule eXtraction algorithm (Re-RX) with J48graft to offer insights into the interpretability gap. The results indicate that Re-RX with J48graft can handle categorical and numerical attributes separately, has simple rules, and achieves a high interpretability, even when the number of rules is large. RuleCOSI+, a state-of-the-art method, showed significantly lower results regarding interpretability, but had the smallest number of rules.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-03-16
DOI: 10.3390/make6010031
Issue No: Vol. 6, No. 1 (2024)
- MAKE, Vol. 6, Pages 679-698: Analyzing the Impact of Oncological Data at
Different Time Points and Tumor Biomarkers on Artificial Intelligence
Predictions for Five-Year Survival in Esophageal Cancer
Authors: Leandra Lukomski, Juan Pisula, Naita Wirsik, Alexander Damanakis, Jin-On Jung, Karl Knipper, Rabi Datta, Wolfgang Schröder, Florian Gebauer, Thomas Schmidt, Alexander Quaas, Katarzyna Bozek, Christiane Bruns, Felix Popp
First page: 679
Abstract: AIM: In this study, we use Artificial Intelligence (AI), including Machine (ML) and Deep Learning (DL), to predict the long-term survival of resectable esophageal cancer (EC) patients in a high-volume surgical center. Our objective is to evaluate the predictive efficacy of AI methods for survival prognosis across different time points of oncological treatment. This involves comparing models trained with clinical data, integrating either Tumor, Node, Metastasis (TNM) classification or tumor biomarker analysis, for long-term survival predictions. METHODS: In this retrospective study, 1002 patients diagnosed with EC between 1996 and 2021 were analyzed. The original dataset comprised 55 pre- and postoperative patient characteristics and 55 immunohistochemically evaluated biomarkers following surgical intervention. To predict the five-year survival status, four AI methods (Random Forest RF, XG Boost XG, Artificial Neural Network ANN, TabNet TN) and Logistic Regression (LR) were employed. The models were trained using three predefined subsets of the training dataset as follows: (I) the baseline dataset (BL) consisting of pre-, intra-, and postoperative data, including the TNM but excluding tumor biomarkers, (II) clinical data accessible at the time of the initial diagnostic workup (primary staging dataset, PS), and (III) the PS dataset including tumor biomarkers from tissue microarrays (PS + biomarkers), excluding TNM status. We used permutation feature importance for feature selection to identify only important variables for AI-driven reduced datasets and subsequent model retraining. RESULTS: Model training on the BL dataset demonstrated similar predictive performances for all models (Accuracy, ACC: 0.73/0.74/0.76/0.75/0.73; AUC: 0.78/0.82/0.83/0.80/0.79 RF/XG/ANN/TN/LR, respectively). The predictive performance and generalizability declined when the models were trained with the PS dataset. Surprisingly, the inclusion of biomarkers in the PS dataset for model training led to improved predictions (PS dataset vs. PS dataset + biomarkers; ACC: 0.70 vs. 0.77/0.73 vs. 0.79/0.71 vs. 0.75/0.69 vs. 0.72/0.63 vs. 0.66; AUC: 0.77 vs. 0.83/0.80 vs. 0.85/0.76 vs. 0.86/0.70 vs. 0.76/0.70 vs. 0.69 RF/XG/ANN/TN/LR, respectively). The AI models outperformed LR when trained with the PS datasets. The important features shared after AI-driven feature selection in all models trained with the BL dataset included histopathological lymph node status (pN), histopathological tumor size (pT), clinical tumor size (cT), age at the time of surgery, and postoperative tracheostomy. Following training with the PS dataset with biomarkers, the important predictive features included patient age at the time of surgery, TP-53 gene mutation, Mesothelin expression, thymidine phosphorylase (TYMP) expression, NANOG homebox protein expression, and indoleamine 2,3-dioxygenase (IDO) expressed on tumor-infiltrating lymphocytes, as well as tumor-infiltrating Mast- and Natural killer cells. CONCLUSION: Different AI methods similarly predict the long-term survival status of patients with EC and outperform LR, the state-of-the-art classification model. Survival status can be predicted with similar predictive performance with patient data at an early stage of treatment when utilizing additional biomarker analysis. This suggests that individual survival predictions can be made early in cancer treatment by utilizing biomarkers, reducing the necessity for the pathological TNM status post-surgery. This study identifies important features for survival predictions that vary depending on the timing of oncological treatment.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-03-19
DOI: 10.3390/make6010032
Issue No: Vol. 6, No. 1 (2024)
- MAKE, Vol. 6, Pages 699-736: Medical Image Classifications Using
Convolutional Neural Networks: A Survey of Current Methods and Statistical
Modeling of the Literature
Authors: Foziya Ahmed Mohammed, Kula Kekeba Tune, Beakal Gizachew Assefa, Marti Jett, Seid Muhie
First page: 699
Abstract: In this review, we compiled convolutional neural network (CNN) methods which have the potential to automate the manual, costly and error-prone processing of medical images. We attempted to provide a thorough survey of improved architectures, popular frameworks, activation functions, ensemble techniques, hyperparameter optimizations, performance metrics, relevant datasets and data preprocessing strategies that can be used to design robust CNN models. We also used machine learning algorithms for the statistical modeling of the current literature to uncover latent topics, method gaps, prevalent themes and potential future advancements. The statistical modeling results indicate a temporal shift in favor of improved CNN designs, such as a shift from the use of a CNN architecture to a CNN-transformer hybrid. The insights from statistical modeling point that the surge of CNN practitioners into the medical imaging field, partly driven by the COVID-19 challenge, catalyzed the use of CNN methods for detecting and diagnosing pathological conditions. This phenomenon likely contributed to the sharp increase in the number of publications on the use of CNNs for medical imaging, both during and after the pandemic. Overall, the existing literature has certain gaps in scope with respect to the design and optimization of CNN architectures and methods specifically for medical imaging. Additionally, there is a lack of post hoc explainability of CNN models and slow progress in adopting CNNs for low-resource medical imaging. This review ends with a list of open research questions that have been identified through statistical modeling and recommendations that can potentially help set up more robust, improved and reproducible CNN experiments for medical imaging.
Citation: Machine Learning and Knowledge Extraction
PubDate: 2024-03-21
DOI: 10.3390/make6010033
Issue No: Vol. 6, No. 1 (2024)
