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Abstract: Abstract This study includes a panoramic view of various existing techniques and approaches of Metaheuristic Optimization Algorithms (MOAs), specifically applied in solving decision-making problems. The synergy of MOAs and Multi-Criteria Decision-Making (MCDM) methods has already established many milestones in the literature. However, the review papers existing in the literature mostly segregates MOAs and MCDM, lacking behind a comprehensive exploration of their integration. This paper bridges the aforesaid gap by providing the recent publications of these two intricate domains arranged and explored with respect to their key contributions. The paper emphasizes on four highly cited Evolutionary Algorithms (EAs) to reduce the information overload. It provides in-depth exploration of practical applications, highlighting instances where synthesis of past achievements and current trends lay the groundwork for future explorations. The study claims that more than 85% of this work has been performed in the last decade only with Genetic Algorithm (GA)-MCDM leading this realm. It offers valuable insights for scholars and practitioners seeking to navigate the intricate developments in this interdisciplinary field. PubDate: 2024-08-02
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Abstract: Abstract The Architecture, Engineering and Construction (AEC) sector faces severe sustainability and efficiency challenges. In recent years, various initiatives have demonstrated how artificial intelligence can effectively address these challenges and improve sustainability and efficiency in the sector. In the context of retrofit projects, there is a continual rising interest in the deployment of Artificial Intelligence (AI) techniques and applications, but the complex nature of such projects requires critical insight into data, processes, and applications so that value can be maximised. This study aims to review AI applications and techniques that have been used in the context of retrofit projects. A review of existing literature on the use of artificial intelligence in retrofit projects within the construction industry was carried out through a thematic analysis. The analysis revealed the potential advantages and difficulties associated with employing AI techniques in retrofit projects, and also identified the commonly utilised techniques, data sources, and processes involved. This study provides a pathway to realise the broad benefits of AI applications for retrofit projects. This study adds to the AI body of knowledge domain by synthesizing the state-of-the-art of AI applications for Retrofit and revealing future research opportunities in this field to enhance the sustainability and efficiency of the AEC sector. PubDate: 2024-08-01
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Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Numerous studies have investigated the coupled multi-field processes in frozen soils, focusing on the variation in frozen soils and addressing the influences of climate change, hydrological processes, and ecosystems in cold regions. The investigation of coupled multi-physics field processes in frozen soils has emerged as a prominent research area, leading to significant advancements in coupling models and simulation solvers. However, substantial differences remain among various coupled models due to the insufficient observations and in-depth understanding of multi-field coupling processes. Therefore, this study comprehensively reviews the latest research process on multi-field models and numerical simulation methods, including thermo-hydraulic (TH) coupling, thermo-mechanical (TM) coupling, hydro-mechanical (HM) coupling, thermo–hydro-mechanical (THM) coupling, thermo–hydro-chemical (THC) coupling and thermo–hydro-mechanical–chemical (THMC) coupling. Furthermore, the primary simulation methods are summarised, including the continuum mechanics method, discrete or discontinuous mechanics method, and simulators specifically designed for heat and mass transfer modelling. Finally, this study outlines critical findings and proposes future research directions on multi-physical field modelling of frozen soils. This study provides the theoretical basis for in-depth mechanism analyses and practical engineering applications, contributing to the advancement of understanding and management of frozen soils. PubDate: 2024-07-29
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Abstract: Abstract In recent times, there has been an increasing prevalence of surrogate models and metamodeling techniques in approximating the responses of complex systems. These surrogate models have proven to be effective in various engineering and scientific disciplines due to their ability to handle demanding computational requirements. The utilisation of surrogates can significantly reduce the time and resources required for calculations. However, practitioners and researchers in structural engineering face challenges in selecting the appropriate surrogate model due to the multitude of approaches available in surrogate modelling development. Despite the numerous advantages of surrogate models, their application in civil engineering has only been explored in the past few years. Consequently, there is a need for recommendations to guide practitioners in the proper utilisation of surrogate models. Additionally, comprehensive review studies are necessary to examine the current state-of-the-art in this area. Currently, there is a lack of research that investigates the implementation of surrogate models specifically in the context of structural engineering. Therefore, this article aims to address this gap by reviewing notable papers that have employed data-driven surrogate modelling in calculations within the field of structural engineering. To achieve this, a thorough analysis is conducted, encompassing a review of 91 journal articles published from 2003 onwards. The primary purpose of this analysis is to describe the various surrogate models employed, and to highlight the domains in which surrogates have been utilised so far. The study demonstrates that the utilisation of data-driven surrogate models in the field of structural engineering provides significant benefits owing to their flexible computational methods that produce accurate outcomes. However, there exist certain significant research gaps in the existing literature that need to be addressed in future studies. PubDate: 2024-07-13
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Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Artificial intelligence applications to enhance materials science have reduced the efforts and costs of developing new materials. Although it is still a recent research field, some promising results, and techniques have successfully been deployed for intelligent material discovery. This paper presents a systematic literature review considering applications of Artificial Intelligence (AI) approaches within the Materials Science context, presenting the literature and trends on intelligent materials through Artificial Intelligence. For this literature review, 527 articles and reviews were retrieved from Web of Science and Scopus databases from 1995 to 2022. The results showed that the number of AI applications in Materials Science has grown as well as the number of publications citing AI applications. Among the results, the most popular and relevant algorithms used in materials science are identified with a wide diversity of application possibilities with future directions. PubDate: 2024-07-11
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Abstract: Abstract Color space plays an important role in various aspects of imaging tasks. However, in deep learning-based computer vision, the RGB color model is predominantly employed. This research analyzes the impact of deep convolutional neural networks on cancer classification across different color spaces. The five most popular deep learning models undergo training and testing in eleven color spaces, revealing that YUV, LAB, and YIQ consistently outperform other color models in most cases. RGB images are frequently converted to alternative color spaces for enhanced representation in specific applications, like object detection and segmentation. This transformation induces alterations in the features of the color image due to variations in pixel intensity information across different color models. In this research, the aforementioned principle is applied to the classification of skin cancer using deep learning networks on images of skin lesions. The results exhibit diverse responses, with some networks achieving higher accuracy in alternative color spaces compared to RGB, while others do not. This study provides insights into the classification performance across RGB, HED, HSV, LAB, RGBCIE, XYZ, YCbCr, YDbDr, YIQ, YPbPr, and YUV color spaces. The research aims to illustrate how deep learning facilitates the analysis of skin cancer images in different color spaces. PubDate: 2024-07-09
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Abstract: Abstract Predicting the time rate of consolidation is one of the major aspects of structure design, founded on compressible fine-grained soil. The time to achieve the required advancement of the consolidation process is proportional to the coefficient of consolidation (cv). In practical applications, the settlement rate is directly related to the excess pore water pressure dissipation rate. A plethora of interpretation methods have been proposed for determining consolidation parameters from laboratory one-dimensional consolidation test in the past decades. This state-of-the-art review presents a comprehensive literature study of available approaches for establishing both coefficient of consolidation and end of primary (EOP) consolidation using compression and pore water pressure laboratory data. The classification of the methods has been made to set in order interpretation approaches for future selection and comparisons. The first part of the paper describes approaches based on graphical curve-fitting. This part includes five approaches: square root of time fitting approach, Semi-logarithmic fitting approach, Differential methods, Hyperbolic approach, and approach based on excess pore water pressure dissipation. In addition, a method comparison study has been performed to evaluate the degree of agreement between selected methods statistically. For this purpose, simple regression and Bland & Altman differences analysis have been used. The second part refers to the computational-based approach, covering a wide range of methods centred on full-matching treated by least-squares, correlational equations linking cv with index properties and soft computing approaches. A thorough insight into recently published literature on machine learning and physics-informed deep learning incorporated to derive the representative value of cv has also been compiled. PubDate: 2024-07-04
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Abstract: Abstract Swarm Intelligence (SI) has proven to be useful in solving issues that are difficult to solve using traditional mathematical methodologies by using a collective behavior of a decentralized or self-organized system. SI-based optimization algorithms use a collaborative trial-and-error process to identify a solution. The development of various efficient swarm optimization methods is largely due to the peer-to-peer learning behavior of social colonies. SI is deeply engaged in the realm of IoT (Internet of Things) and IoT-based systems to control the operations logically. The mounting complexity of IoT devices’ infrastructure framework and continuous communication is lifting undesirable weaknesses with scalability, efficiency, safety, and real-time responses. These vulnerabilities give rise to privacy and security concerns, allowing attackers to potentially exploit them. Intrusion Detection System (IDS) has become a vital aspect of network security for implementing security in IoT devices. So, IDS with SI-supported decentralized algorithms are employed to overcome such difficulties. Since its conception, considerable research has been done to improve the SI-based optimization algorithm’s efficiency and adapt it to various issues. This paper provides an overview of SI advances for IoT-based IDS, applications, comparative performance, and research opportunities in the future for normalizing the IoT processes. The present study delves into the technical aspects of implementing feature selection and parameter tuning within the context of SI. Furthermore, it conducts a comprehensive analysis of SI approaches in the realm of IoT, particularly in conjunction with IDS. PubDate: 2024-07-01
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Abstract: Abstract This review explores the integration of sparse representation and compressed perception in optical image reconstruction. Beginning with an in-depth examination of sparse representation techniques, including dictionary learning and sparse coding, the study introduces a novel paradigm by incorporating compressed perception principles. The methodology aims to optimize efficiency, data storage, and reconstruction quality. The review delves into optimization strategies, adaptive techniques, multi-scale considerations, and real-time implementation, offering a comprehensive analysis of the current landscape. By synthesizing existing knowledge and proposing innovative approaches, this review contributes to advancing optical image reconstruction, promising future breakthroughs at the intersection of sparse representation and compressed perception. PubDate: 2024-07-01
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Abstract: Abstract Science and Engineering applications are typically associated with expensive optimization problem to identify optimal design solutions and states of the system of interest. Bayesian optimization and active learning compute surrogate models through efficient adaptive sampling schemes to assist and accelerate this search task toward a given optimization goal. Both those methodologies are driven by specific infill/learning criteria which quantify the utility with respect to the set goal of evaluating the objective function for unknown combinations of optimization variables. While the two fields have seen an exponential growth in popularity in the past decades, their dualism and synergy have received relatively little attention to date. This paper discusses and formalizes the synergy between Bayesian optimization and active learning as symbiotic adaptive sampling methodologies driven by common principles. In particular, we demonstrate this unified perspective through the formalization of the analogy between the Bayesian infill criteria and active learning criteria as driving principles of both the goal-driven procedures. To support our original perspective, we propose a general classification of adaptive sampling techniques to highlight similarities and differences between the vast families of adaptive sampling, active learning, and Bayesian optimization. Accordingly, the synergy is demonstrated mapping the Bayesian infill criteria with the active learning criteria, and is formalized for searches informed by both a single information source and multiple levels of fidelity. In addition, we provide guidelines to apply those learning criteria investigating the performance of different Bayesian schemes for a variety of benchmark problems to highlight benefits and limitations over mathematical properties that characterize real-world applications. PubDate: 2024-07-01
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Abstract: Abstract The United Nations launched sustainable development goals in 2015 that include goals for sustainable energy. From global energy consumption, households consume 20–30% of energy in Europe, North America and Asia; furthermore, the overall global energy consumption has steadily increased in the recent decades. Consequently, to meet the increased energy demand and to promote efficient energy consumption, there is a persistent need to develop applications enhancing utilization of energy in buildings. However, despite the potential significance of AI in this area, few surveys have systematically categorized these applications. Therefore, this paper presents a systematic review of the literature, and then creates a novel taxonomy for applications of smart building energy utilization. The contributions of this paper are (a) a systematic review of applications and machine learning methods for smart building energy utilization, (b) a novel taxonomy for the applications, (c) detailed analysis of these solutions and techniques used for the applications (electric grid, smart building energy management and control, maintenance and security, and personalization), and, finally, (d) a discussion on open issues and developments in the field. PubDate: 2024-07-01
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Abstract: Abstract Task scheduling and resource utilization have always been among the most critical issues for high performance in heterogeneous computing. The heterogeneity of computation costs on a given set of computing elements and the communication costs among computing elements increase the complexity of the scheduling problem. Extensive research proves that the list-based task scheduling algorithms generate the most efficient schedules for complex workflow applications in the heterogeneous computing environment. The workflow applications comprise thousands of interconnected tasks with dependencies. In the last decades, various list-based scheduling algorithms have been proposed to achieve some kinds of performance objectives such as minimization of makespan and energy consumption and maximization of resource utilization and reliability. In this article, various list-based workflow scheduling algorithms have been reviewed from the last two decades with the assumption of heterogeneous computing systems being used as the underlying computing infrastructure. This review process categorizes the algorithms based on scheduling objectives. For a better analysis of the algorithms, each algorithm is compared with other algorithms based on its objectives, merits, comparison metrics, workload type, experimental scale, experimental environment, and results compared. Finally, experimental analysis of seven state-of-art algorithms has been conducted on randomly generated workflow to understand the working of list-scheduling algorithms. The main purpose of this article is to give proper direction to new researchers who are willing to work in workflow scheduling in heterogeneous computing environments. PubDate: 2024-07-01
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Abstract: Abstract Diabetes type 2 remains a pressing worldwide health subject, highlighting the need for advanced early detection methods. In this study, we performed a comprehensive analysis of current literature presented at conferences and journals, focusing on the effectiveness of machine learning techniques for the early detection of diabetes type 2. Our review included thorough examination of various papers, examining the methodologies, and assessing the accuracy of these methods. We diagnosed developments and patterns within the application of machine-learning algorithms for diabetes detection. Our study synthesizes these findings and proposes a complete framework utilizing present-day system-getting-to-know algorithms. Via rigorous comparative evaluation, we encouraged precise algorithms with demonstrated efficacy. We also delved into the combination of novel technologies, enhancing the accuracy and reliability of diabetes prediction methods. The proposed framework no longer only showcases promising accuracy quotes but also addresses the realistic elements of implementation, ensuring actual global effectiveness. Additionally, we explored the socio-financial effect of early diabetes detection and underscored the significance of timely interventions in reducing healthcare expenses and enhancing affected person outcomes. This review serves as a treasured resource for researchers, practitioners, and policymakers, offering insights into the ultra-modern advancements in device learning applications for diabetes type 2 early detection. By amalgamating cutting-edge technology with insightful analysis, our studies contribute to the continued efforts to combat diabetes and improve public fitness on a global scale. PubDate: 2024-07-01
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Abstract: Abstract The efficiency of Three-Dimensional Convolutional Neural Networks (3-D-CNNs) in precisely delineating the complex architecture of the kidney has been well-established. Volumetric segmentation technologies, such as deformable registration, have shown promise in addressing the issue of variability in renal imaging. This variability can arise from differences across subjects as well as within the same subject. By offering an accurate template for image segmentation, these technologies have the potential to mitigate this obstacle effectively. The integration of deep learning frameworks and volumetric segmentation algorithms offers a robust and effective solution for the automated segmentation of kidneys. These methods can improve the accuracy of diagnosing kidney-related illnesses, specifically renal cysts, and offer the potential to better the monitoring of clinical development in individuals with such conditions. PubDate: 2024-07-01
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Abstract: Abstract This article reported an extensive review of computational modelling and analysis on damping and vibrational behaviors of viscoelastic structures, including experimental techniques. viscoelastic materials have emerged as an effective technology for enhancing damping characteristics in composite structures because of their ability to damp vibration and the ability to conserve the geometry of the material after deformation. In structural design viscoelastic material is never employed alone, it is always adhered to the thin-walled structure to form a damping layer of different structural configurations such as sandwich structure configurations and reinforced configurations. It is worth mentioning that viscoelastic materials are quite frequency-dependent meaning that the vibrational properties keep varying with the excitation frequency, accordingly, yielding complex natural frequencies of vibration. This paper provides a primary discussion on the basic forming principles of linear viscoelastic material providing comparisons of different viscoelastic material mathematical models, and applications of viscoelastic material including their effects on composite structure damping and vibrational behaviors. Moreover, different computational methods for dynamic behaviors of viscoelastic sandwich structures including analytical method, finite element method, and mixed method were assessed to provide insights into the limitations of each approach. The drawbacks of each calculation theory for various traditional and recent configurations are systematically presented in engineering structures containing viscoelastic followed by a detailed description and comparison of different modeling methods, their applicability, and their respective comparisons. PubDate: 2024-07-01
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Abstract: Abstract Cancer remains a substantial worldwide health issue that requires careful and exact classification to plan treatment in its early stages. Classical methods of cancer diagnosis involve lab-based testing using biopsy, and imaging tests. Modern technologies may contribute effectively to speed up the diagnosis of cancer. Machine learning-based algorithms have been more prominent in cancer classification in recent years. These algorithms hold great promise in interpreting complex datasets and applying the learned knowledge to categorize unseen samples for cancer classification. In addition, many computer vision-based algorithms play a vital role in image pre-processing, segmentation, and feature extraction. This review article discusses nine major cancer types: carcinoma, sarcoma, neuroendocrine tumor, melanoma, lymphoma, germ cell tumor, leukemia, brain tumor, and multiple myeloma. We conducted a detailed survey of recent literature. We focused on systems that utilize clinical imaging modalities as input and preprocessing, segmentation, and feature extraction as intermediate stages with machine learning classifier as their concluding stage. We have examined the works that classify cancer as mentioned above types using machine learning algorithms. We have analyzed six prominent machine learning-based algorithms: Support vector machines, decision trees, random forest, Naïve Bayes, logistic regression, and K-nearest neighbors. This work also gives insights into various imaging modalities, such as Computed Tomography scan, histopathological images, dermoscopic images, and their utility in diagnosing cancer. In addition, the paper discusses the performance measures used for evaluating the efficiency of machine learning-based models, including accuracy, sensitivity, specificity, F1-score. We have reviewed various pre-processing and segmentation techniques suitable for clinical image-based cancer classification. This survey also discusses some significant challenges researchers face during cancer classification studies. The main objective of this systematic review is to provide researchers and medical experts with extensive knowledge of the present status of cancer classification with the aid of computer vision and machine learning-based systems. We intend to provide a foundation for enhanced cancer detection and therapy precision using these techniques. This effort eventually contributes to the progression of the field of cancer and the enhancement of patient predictions. In addition, we have recognized a few possible directions for research in this domain. PubDate: 2024-07-01
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Abstract: Abstract Electronic fetal monitoring is used to evaluate fetal well-being by assessing fetal heart activity. The signals produced by the fetal heart carry valuable information about fetal health, but due to non-stationarity and present interference, their processing, analysis and interpretation is considered to be very challenging. Therefore, medical technologies equipped with Artificial Intelligence algorithms are rapidly evolving into clinical practice and provide solutions in the key application areas: noise suppression, feature detection and fetal state classification. The use of artificial intelligence and machine learning in the field of electronic fetal monitoring has demonstrated the efficiency and superiority of such techniques compared to conventional algorithms, especially due to their ability to predict, learn and efficiently handle dynamic Big data. Combining multiple algorithms and optimizing them for given purpose enables timely and accurate diagnosis of fetal health state. This review summarizes the currently used algorithms based on artificial intelligence and machine learning in the field of electronic fetal monitoring, outlines its advantages and limitations, as well as future challenges which remain to be solved. PubDate: 2024-07-01
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