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Showing 1 - 30 of 30 Journals sorted alphabetically
Advanced Technology for Learning     Full-text available via subscription   (Followers: 139)
Aprendo con NooJ     Open Access   (Followers: 1)
Australasian Journal of Educational Technology     Open Access   (Followers: 17)
Computer Assisted Language Learning     Hybrid Journal   (Followers: 28)
Computer Speech & Language     Hybrid Journal   (Followers: 13)
Education in the Knowledge Society     Open Access   (Followers: 2)
Educational Technology Research and Development     Partially Free   (Followers: 45)
eLearn Magazine     Full-text available via subscription   (Followers: 4)
European Journal of Open, Distance and E-Learning - EURODL     Open Access   (Followers: 11)
Human-centric Computing and Information Sciences     Open Access   (Followers: 6)
Interdisciplinary Journal of e-Skills and Lifelong Learning     Open Access   (Followers: 4)
Interdisciplinary Journal of Information, Knowledge, and Management     Open Access   (Followers: 12)
International Journal of Adult Education and Technology     Hybrid Journal   (Followers: 17)
International Journal of Ambient Computing and Intelligence     Full-text available via subscription   (Followers: 3)
International Journal of Computer-Assisted Language Learning and Teaching     Full-text available via subscription   (Followers: 14)
International Journal of Online Pedagogy and Course Design     Full-text available via subscription   (Followers: 7)
International Journal of Research Studies in Educational Technology     Open Access   (Followers: 10)
Journal of Assistive Technologies     Hybrid Journal   (Followers: 19)
Journal of Computers in Education     Hybrid Journal   (Followers: 9)
Journal of Machine Learning Research     Open Access   (Followers: 62)
Jurnal Inovasi Teknologi Pendidikan     Open Access  
Jurnal Komtika     Open Access  
Nordic Journal of Digital Literacy     Open Access  
Online Journal of Distance Learning Administration     Open Access   (Followers: 12)
Research and Practice in Technology Enhanced Learning     Open Access   (Followers: 8)
Research in Learning Technology     Open Access   (Followers: 72)
RIED. Revista Iberoamericana de Educación a Distancia     Open Access   (Followers: 1)
RU&SC. Revista de Universidad y Sociedad del Conocimiento     Open Access   (Followers: 1)
Tidsskriftet Læring og Medier (LOM)     Open Access   (Followers: 1)
UOC Papers. Revista sobre la sociedad del conocimiento     Open Access   (Followers: 1)
Similar Journals
Journal Cover
International Journal of Ambient Computing and Intelligence
Journal Prestige (SJR): 0.161
Citation Impact (citeScore): 2
Number of Followers: 3  
 
  Full-text available via subscription Subscription journal
ISSN (Print) 1941-6237 - ISSN (Online) 1941-6245
Published by IGI Global Homepage  [147 journals]
  • Continuous Attention Mechanism Embedded (CAME) Bi-Directional Long Short
           Term Memory Model for Fake News Detection

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      Abstract: The credible analysis of news on social media due to the fact of spreading unnecessary restlessness and reluctance in the community is a need of time. Numerous individual or social media marketing entities radiate unauthentic news through online social media. Henceforth, delineating these activities on social media and the apparent identification of delusive content is a challenging task. This work projected a continuous attention-driven memory-based deep learning model to predict the credibility of an article. To exhibit the importance of continuous attention, research work is presented in accretive exaggeration mode. Initially, Long short term memory (LSTM) based deep learning model has been applied which is extended by merging the concept of Bidirectional LSTM for fake news identification. This research work proposed a continuous attention mechanism embedded (CAME)-Bidirectional LSTM model for predicting the nature of news. Result shows the proposed CAME model outperforms the performance as compared to LSTM and the Bidirectional LSTM model.
      Keywords: Artificial Intelligence; Computer Science & IT; Artificial Intelligence
      Citation: International Journal of Ambient Computing and Intelligence (IJACI), Volume: 13, Issue: 1 (2022) Pages: 0-0
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJACI.309407
      Issue No: Vol. 13, No. 1 (2022)
       
  • Identification of Plant Diseases Using Multi-level Classification Deep
           Model

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      Abstract: As plants are exposed to the various environmental elements, they are highly prone to many diseases which effect the crops yield and results in low productivity. Due to the lack of proper care and regular checking for any diseases in plants, severe consequences may be seen in a long term basis on plants and environments. Agricultural productivity is one of the important factors on which economy highly depends. Plant pathologists require a reliable and effective method to diagnose the disease effectively. Several physical methods and techniques have been applied to better predict and classify the plant disease. However, we need an automated method to identify and produce as accurate result as possible with minimum time. Previously, many Machine Learning models are developed, producing a limited accuracy. But, using Deep Learning, improved performance can be achieved for classification of plant diseases. We propose the multi-level classification model for plant diseases detection. The accuracy achieved by the proposed model is 96.70% which is higher than the other models.
      Keywords: Artificial Intelligence; Computer Science & IT; Artificial Intelligence
      Citation: International Journal of Ambient Computing and Intelligence (IJACI), Volume: 13, Issue: 1 (2022) Pages: 0-0
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJACI.309408
      Issue No: Vol. 13, No. 1 (2022)
       
  • AIFMS Autonomous Intelligent Fall Monitoring System for the Elderly
           Persons

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      Authors: Ramanujam; Elangovan, Perumal, Thinagaran
      Pages: 1 - 22
      Abstract: Falls are the major cause of injuries and death of elders who live alone at home. Various research works have provided the best solution to the fall detection approach during day vision. However, fall occurs more at the night due to many factors such as low or zero lighting conditions, intake of medication/ drugs, frequent urination due to nocturia disease, and slippery restroom. Based on the required factors, an autonomous monitoring system based on night condition has been proposed through retro-reflective stickers pasted on their upper cloth and infrared cameras installed in the living environment of elders. The developed system uses features such as changes in orientation angle and distance between the retro-reflective stickers to identify the human shape and its characteristics for fall identification. Experimental analysis has also been performed on various events of fall and non-fall activities during the night exclusively in the living environment of the elder, and the system achieves an accuracy of 96.2% and fall detection rate of 92.9%.
      Keywords: Artificial Intelligence; Computer Science & IT; Artificial Intelligence
      Citation: International Journal of Ambient Computing and Intelligence (IJACI), Volume: 13, Issue: 1 (2022) Pages: 1-22
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJACI.304727
      Issue No: Vol. 13, No. 1 (2022)
       
  • Multistage Transfer Learning for Stage Detection of Diabetic Retinopathy

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      Authors: Venkatesan, Varshini, K; Haripriya, M, Mounika, Gladston, Angelin
      Pages: 1 - 24
      Abstract: Diabetic retinopathy is one of the most threatening complications of diabetes that leads to permanent blindness if left untreated. Severity of the diabetic retinopathy disease is based on presence of microaneurysms, exudates, neovascularisation and haemorrhages. Convolutional neural networks have been successfully applied in many adjacent subjects, and for diagnosis of diabetic retinopathy itself. In this paper, an automatic deep-learning-based method for stage detection of diabetic retinopathy by single photography of the human fundus is proposed. Additionally, the multistage approach to transfer learning, which makes use of similar datasets with different labelling, is experimented. The proposed architecture gives high accuracy in classification through spatial analysis. Amongst other supervised algorithms involved, proposed solution is to find a better and optimized way to classifying the fundus image with little pre-processing techniques. The proposed architecture deployed with dropout layer techniques yields 78 percent accuracy.
      Keywords: Artificial Intelligence; Computer Science & IT; Artificial Intelligence
      Citation: International Journal of Ambient Computing and Intelligence (IJACI), Volume: 13, Issue: 1 (2022) Pages: 1-24
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJACI.304725
      Issue No: Vol. 13, No. 1 (2022)
       
  • Fuzzy Logic Inference-Based Automated Water Irrigation System

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      Authors: Patel; Usha, Oza, Parita Rajiv, Revdiwala, Riya, Haveliwala, Utsav Mukeshchandra, Agrawal, Smita, Kathiria, Preeti
      Pages: 1 - 15
      Abstract: To fulfill the food interest of consistently expanding populace of our planet, it is important to do essential in the field of agribusiness. Traditional techniques for water systems like trench, wells, and precipitation are tedious and occasional. With the help of an automated water irrigation system the water, energy, and time can be moderated. This paper presents fuzzy rule logic inference-based automated water system framework. The soil moisture, weather forecast, crop status, and water-tank level are taken as input parameters. Soil moisture and water tank level can be recorded by utilizing sensors. The fuzzy logic-based system uses eighty-one rules to identify the amount of time to irrigate the fields. The emphasis is to solve agricultural problems by employing symbolic logic and to develop a system using computer science and mathematical logic. The use of such an automated system will decline costs, water prerequisite, and give power streamlining, with expanded proficiency.
      Keywords: Artificial Intelligence; Computer Science & IT; Artificial Intelligence
      Citation: International Journal of Ambient Computing and Intelligence (IJACI), Volume: 13, Issue: 1 (2022) Pages: 1-15
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJACI.304726
      Issue No: Vol. 13, No. 1 (2022)
       
  • Unstructured Road Detection Method Based on RGB Maximum Two-Dimensional
           Entropy and Fuzzy Entropy

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      Authors: Wu; Huayue, Xue, Tao, Zhao, Xiangmo, Wu, Kai
      Pages: 1 - 18
      Abstract: To solve the problem that lane keeping function for automatic driving and vehicle assisted driving will not work reliably on unstructured road without lane lines or other guide markings, this article uses the characteristics of information entropy to generate the RGB entropy image to pre-segment the road region on unstructured road image. At the same time, the maximum two-dimensional entropy algorithm is introduced to achieve the joint segmentation using gray and neighborhood gray to effectively reduce the impact of interference on segmentation. After that, the fuzzy entropy algorithm is used to judge and determine the actual road boundary by combining the results of RGB and maximum two-dimensional entropy image. Finally, using the improved least square fitting quadratic curve model to build the road boundary. Our method could well and rapidly extract the lane from unstructured road image and fit out the lane line, which helps to achieve visual based lane keeping on unstructured road for autopilot and driver assistance system.
      Keywords: Artificial Intelligence; Computer Science & IT; Artificial Intelligence
      Citation: International Journal of Ambient Computing and Intelligence (IJACI), Volume: 13, Issue: 1 (2022) Pages: 1-18
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJACI.300801
      Issue No: Vol. 13, No. 1 (2022)
       
  • Low-Cost Internet of Things Platform for Epilepsy Monitoring Using
           Real-Time Electroencephalogram

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      Authors: Sharma; Manoj Kumar, Kaiser, M. Shamim, Ray, Kanad
      Pages: 1 - 14
      Abstract: This work is focusing to develop a portable, low-cost remote diagnostic system for developing countries where the current state of health is not in the advanced stage. People with diseases like epilepsy, Alzheimer’s, an extreme turmeric state, or a disorder that makes it difficult to move have been observed. The authors propose a cost-effective remote neurology assessment health care system. To predict epilepsy form electroencephalogram (EEG) signals in real-time. The authors implemented the machine learning model that has been deployed in the raspberry pi micro-controller. The feature extraction stage was carried out in Matlab. The extracted features from the EEG signals were transferred wirelessly to the model deployed in pi raspberry to clearly predict epilepsy and normality cases. The results of the real-time prediction of the trained and deployed model were provided for the remote diagnosis system. The data visualizations can be done on Android/IOS and Matlab desktop.
      Keywords: Artificial Intelligence; Computer Science & IT; Artificial Intelligence
      Citation: International Journal of Ambient Computing and Intelligence (IJACI), Volume: 13, Issue: 1 (2022) Pages: 1-14
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJACI.300791
      Issue No: Vol. 13, No. 1 (2022)
       
  • Convolutional Neural Networks for Detection of COVID-19 From Chest X-Rays

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      Authors: Damania; Karishma, Pawar, Pranav M., Pramanik, Rahul
      Pages: 1 - 21
      Abstract: The Coronavirus (COVID-19) pandemic was rapid in its outbreak and the contagion of the virus led to an extensive loss of life globally. This study aims to propose an efficient and reliable means to differentiate between Chest X-Rays indicating COVID-19 and other lung conditions. The proposed methodology involved combining deep learning techniques such as data augmentation, CLAHE image normalization, and Transfer Learning with eight pre-trained networks. The highest performing networks for binary, 3-class (Normal vs COVID-19 vs Viral Pneumonia) and 4-class classifications (Normal vs COVID-19 vs Lung Opacity vs Viral Pneumonia) were MobileNetV2, InceptionResNetV2, and MobileNetV2, achieving accuracies of 97.5%, 96.69%, and 92.39%, respectively. These results outperformed many state-of-the-art methods conducted to address the challenges relating to the detection of COVID-19 from Chest X-Rays. The method proposed can serve as a basis for a computer-aided diagnosis (CAD) system to ensure that patients receive timely and necessary care for their respective illnesses.
      Keywords: Artificial Intelligence; Computer Science & IT; Artificial Intelligence
      Citation: International Journal of Ambient Computing and Intelligence (IJACI), Volume: 13, Issue: 1 (2022) Pages: 1-21
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJACI.300793
      Issue No: Vol. 13, No. 1 (2022)
       
  • Cross-Layer Distributed Attack Detection Model for the IoT

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      Authors: sayed Ahmed; Hassan Ibrahim, Nasr, Abdurrahman, Abdel-Mageid, Salah M., Aslan, Heba
      Pages: 1 - 17
      Abstract: The security of IoT that is based on layered approaches has shortcomings such as the redundancy, inflexibility and inefficiently of security solutions. There are many harmful attacks in IoT network such as DoS and DDoS attacks which can compromise the IoT architecture in all layers. Consequently, cross layer approach is proposed as an effective and practical security defending mechanism. Cross-Layer Distributed Attack Detection model (CLDAD) is proposed to enhance security solution for IoT environment. CLDAD presents a general detection method of DDoS in sensing layer, network layer and application layer. CLDAD is based on big data analytics techniques which enable the detection process to be performed in distributed way, so the model can detect DDoS attacks in any layer on-the-fly and the model support the scalability of the IoT environment. CLDAD is tested based on three datasets, namely, artificial jamming attack dataset, BoT-IoT dataset, and BoT-IoT based HTTP. The results showed that the proposed model is efficient in detecting attacks in the three layers of the IoT.
      Keywords: Artificial Intelligence; Computer Science & IT; Artificial Intelligence
      Citation: International Journal of Ambient Computing and Intelligence (IJACI), Volume: 13, Issue: 1 (2022) Pages: 1-17
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJACI.300794
      Issue No: Vol. 13, No. 1 (2022)
       
  • Evolutionary Algorithm With Self-Learning Strategy for Generation of
           Adversarial Samples

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      Authors: Pavate; Aruna Animish, Bansode, Rajesh
      Pages: 1 - 21
      Abstract: Knowledge engineering algorithms such as deep learning models have exhibited tremendous success in solving complex problems. However, the linear nature of the neural network is the primary reason for vulnerability to the perturbed samples. Adversarial attacks pose a severe threat to applying deep models, especially while designing safety-critical applications. This work proposes security attacks against neural architectures. In particular, we introduce a novel method to create adversarial samples. First, we propose a differential evolution population resizing scheme, which enlarges the generation of adversarial samples by allowing adversaries to speed the convergence process. The proposed system is a novel self-adaptive population resizing-based adversarial mechanism. The result shows the success rate for targeted attack LeNet(60.07%), Network_in_Network(97%), Wide_ResNet50(99%), Pure CNN (97%), DenseNet (54.11%),ResNet50(51%) and LeNet(85.13%), Network_in_Network(33.37%), WideResnet(24.40%), Pure_CNN(19.96%),DenseNet (63.67%), ResNet (68.00%) for non targeted attacks respectively.
      Keywords: Artificial Intelligence; Computer Science & IT; Artificial Intelligence
      Citation: International Journal of Ambient Computing and Intelligence (IJACI), Volume: 13, Issue: 1 (2022) Pages: 1-21
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJACI.300797
      Issue No: Vol. 13, No. 1 (2022)
       
  • Statistical Analysis on the Body Flexibility of the Laborer of the Indian
           Service Sector

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      Authors: Oraon; Manish, Mahto, Anulal
      Pages: 1 - 9
      Abstract: The ergonomics is one of the key factors in any service sector where the workers are involved in physical work. The physical strength of an individual is dependent on the human artefacts which may help the system to recruit the suitable person to right place. The present study comprises the body flexibility (Bf) of the servicemen associated to Indian railway. Four inputs i.e. age, height, weight, and waist of all recruited participants are measured and conduct the sit and reach test (S-R test) for the Bf. The statistical analysis is performed for investigating the significance of the inputs on the Bf. Statistically, the inputs Ag (P=0.002), Wg, (P=0.030), and Wa-g (P=0.001) are individually significant whereas the interactional relation of Hg with Ag (P=0.008) and Hg with Wa-g (P≤0.001) is reported. The Bf of Tall Hg is grown up gradually with Ag but it was 20.47% lesser than the maximum Bf. Simultaneously, with the increase in Wa-g of all Hg, the degradation in the Bf is reported.
      Keywords: Artificial Intelligence; Computer Science & IT; Artificial Intelligence
      Citation: International Journal of Ambient Computing and Intelligence (IJACI), Volume: 13, Issue: 1 (2022) Pages: 1-9
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJACI.300800
      Issue No: Vol. 13, No. 1 (2022)
       
  • A Reinforcement Learning Integrating Distributed Caches for Contextual
           Road Navigation

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      Authors: Ilié; Jean-Michel, Chaouche, Ahmed-Chawki, Pêcheux, François
      Pages: 1 - 19
      Abstract: Due to contextual traffic conditions, the computation of optimized or shortest paths is a very complex problem for both drivers and autonomous vehicles. This paper introduces a reinforcement learning mechanism that is able to efficiently evaluate path durations based on an abstraction of the available traffic information. The authors demonstrate that a cache data structure allows a permanent access to the results whereas a lazy politics taking new data into account is used to increase the viability of those results. As a client of the proposed learning system, the authors consider a contextual path planning application and they show in addition the benefit of integrating a client cache at this level. Our measures highlight the performance of each mechanism, according to different learning and caching strategies.
      Keywords: Artificial Intelligence; Computer Science & IT; Artificial Intelligence
      Citation: International Journal of Ambient Computing and Intelligence (IJACI), Volume: 13, Issue: 1 (2022) Pages: 1-19
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJACI.300792
      Issue No: Vol. 13, No. 1 (2022)
       
  • Detection of Cardiovascular Disease Using Ensemble Feature Engineering
           With Decision Tree

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      Authors: Debasmita GhoshRoy; , Alvi, P. A., Tavares, João Manuel R. S.
      Pages: 1 - 16
      Abstract: Cardiovascular diseases are a cluster of heart-related issues, including many comorbidities, which are becoming a leading cause of human death across the globe. Hence, an essential framework is demanded for the early detection of CVDs which can help to prevent premature death. The application of Artificial Intelligence (AI) in healthcare has opted for this challenge and makes it easier to detect CVDs using a computational model. In this study, the authors built a reduced dataset using ensemble feature selection methods and got five features as per their weight values. Support Vector Machine, Logistic Regression, and Decision Tree classification techniques are utilized to check the effectiveness of newly designed datasets through different validation approaches. The authors also worked on data processing and visualization techniques, including Principal Component Analysis (PCA), and T-sne for understanding the data structure. From the findings, it was possible to conclude that DT has achieved an optimal accuracy and AUC of 98.9% and 0.99 ROC with leave one out Cross Validation (CV).
      Keywords: Artificial Intelligence; Computer Science & IT; Artificial Intelligence
      Citation: International Journal of Ambient Computing and Intelligence (IJACI), Volume: 13, Issue: 1 (2022) Pages: 1-16
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJACI.300795
      Issue No: Vol. 13, No. 1 (2022)
       
  • ECG Intervals and Segments Detection and Characterization for Analyzing
           Effects of Sahaja Yoga Meditation

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      Authors: Londhe; Aboli, Atulkar, Mithilesh
      Pages: 1 - 13
      Abstract: Meditation is expected to regularize autonomic nervous system and reduce metabolic movement, inciting physical and mental relaxation. A lot of research is being conducted to assess effects of different meditation techniques based on heart rate variability analysis or by observing characteristics of ECG. In this paper, effects of Sahaja Yoga meditation technique are analyzed based on ECG characteristics. For this, a new dataset from a total of 30 meditators and non-meditators recorded over a considerable period of 28 days, is used. The local ECG components like intervals and segments are detected using deep learning architecture. Furthermore, the detected fiducial points are localized and ECG characteristics are measured. Some ECG characteristics showed significant variations for meditators compared to non-meditators. From further results and analysis, it can be easily confirmed that sympathovagal balance is quickly attained and remains shifted to parasympathetic nervous system during meditation which helps not only to prevent stress, anxiety but also to cure cardiovascular diseases.
      Keywords: Artificial Intelligence; Computer Science & IT; Artificial Intelligence
      Citation: International Journal of Ambient Computing and Intelligence (IJACI), Volume: 13, Issue: 1 (2022) Pages: 1-13
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJACI.300796
      Issue No: Vol. 13, No. 1 (2022)
       
  • Factors Influencing Patient Adoption of the IoT for E-Health Management
           Systems (e-HMS) Using the UTAUT Model

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      Authors: Dadhich; Manish, Hiran, Kamal Kant, Rao, Shalendra Singh, Sharma, Renu
      Pages: 1 - 18
      Abstract: This study examines factors influencing patients' adoption of the IoT for e-Health Management System (e-HMS). A conceptual framework is built by applying the Unified Theory of Acceptance and Use of Technology (UTAUT) model to achieve the research objectives. The constructs viz. performance expectations, perceived healthcare threat, facilitation, and trust were employed to analyze the behaviour intention of healthcare IoT devices in the medical context. A questionnaire was prepared and circulated to 210 IoT healthcare users. Further, a synergy of Smart-PLS and ANN was used to determine factors influencing adopting the IoT for e-HMS. The study yields a novel insight that would render vital benefits to users and service providers. The outcomes of this research can assist IoT inventors, medicinal specialists, and vendors to enhance the optimization of e-HMS. The results make some new reporting facts to improve IoT 4.0 practices in the e-healthcare system and structural model assessment.
      Keywords: Artificial Intelligence; Computer Science & IT; Artificial Intelligence
      Citation: International Journal of Ambient Computing and Intelligence (IJACI), Volume: 13, Issue: 1 (2022) Pages: 1-18
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJACI.300798
      Issue No: Vol. 13, No. 1 (2022)
       
  • Machine Learning Approaches to Predict Crop Yield Using Integrated
           Satellite and Climate Data

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      Authors: Jhajharia; Kavita, Mathur, Pratistha
      Pages: 1 - 17
      Abstract: India is the second-largest producer of wheat crop. Timely and appropriate prediction of wheat crop yield is essential for global and local food security. This research work has integrated multiple source data to predict crop yield across the Rajasthan state of India using lasso regression, support vector machine, random forest regression, and linear regression for crop yield prediction. This study used multiple vegetation indices (enhanced vegetation index, normalized vegetation index, soil adjusted vegetation index, chlorophyll vegetation index, and normalized difference water index). The results indicated that integrating multiple source data improves the model performance for all the machine learning models. Satellite data contributed additional information to the crop yield prediction than other data variables, and SAVI achieved better performance than other vegetation indices. We found that the support vector machine outperformed all the other approaches. The present study is a significant effort to integrate the multiple source data for the considerable area yield prediction.
      Keywords: Artificial Intelligence; Computer Science & IT; Artificial Intelligence
      Citation: International Journal of Ambient Computing and Intelligence (IJACI), Volume: 13, Issue: 1 (2022) Pages: 1-17
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJACI.300799
      Issue No: Vol. 13, No. 1 (2022)
       
  • Hybrid Approach Using Deep Autoencoder and Machine Learning Techniques for
           Cyber-Attack Detection

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      Authors: Kumar; Vikash, Sinha, Ditipriya
      Pages: 1 - 21
      Abstract: The feature reduction from the vast amount of data collected from the Internet is challenging and labor-intensive. Data imbalance is another problem in decision-making analysis that leads to a biased model favoring classes with larger samples. This paper proposes a hybrid model using autoencoder and machine learning models. It deals with feature reduction and handles imbalance attack classes using SMOTE method to balance the dataset, and then AE is trained. The bottleneck code of AE is stacked with different classifiers on datasets such as NSL-KDD, UNSW-NB15 and BoT-IoT to evaluate the proposed method. The performance of the proposed approach shows improvement over attack detection without AE. The most noticeable change occurred for SVM on the NSL-KDD dataset that shows doubled improvement of accuracy. In the case of UNSW-NB15, the results vary and see an improvement for the LR model. The BoT-IoT dataset sees the lowest performance variation, i.e., 0%-6%.
      Keywords: Artificial Intelligence; Computer Science & IT; Artificial Intelligence
      Citation: International Journal of Ambient Computing and Intelligence (IJACI), Volume: 13, Issue: 1 (2022) Pages: 1-21
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJACI.293098
      Issue No: Vol. 13, No. 1 (2022)
       
  • Topic Modeling Techniques for Text Mining Over a Large-Scale Scientific
           and Biomedical Text Corpus

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      Authors: Avasthi; Sandhya, Chauhan, Ritu, Acharjya, Debi Prasanna
      Pages: 1 - 18
      Abstract: Topic models are efficient in extracting central themes from large-scale document collection and it is an active research area. The state-of-the-art techniques like Latent Dirichlet Allocation, Correlated Topic Model (CTM), Hierarchical Dirichlet Process (HDP), Dirichlet Multinomial Regression (DMR) and Hierarchical Pachinko Allocation (HPA) model is considered for comparison. . The abstracts of articles were collected between different periods from PUBMED library by keywords adolescence substance use and depression. A lot of research has happened in this area and thousands of articles are available on PubMed in this area. This collection is huge and so extracting information is very time-consuming. To fit the topic models this extracted text data is used and fitted models were evaluated using both likelihood and non-likelihood measures. The topic models are compared using the evaluation parameters like log-likelihood and perplexity. To evaluate the quality of topics topic coherence measures has been used.
      Keywords: Artificial Intelligence; Computer Science & IT; Artificial Intelligence
      Citation: International Journal of Ambient Computing and Intelligence (IJACI), Volume: 13, Issue: 1 (2022) Pages: 1-18
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJACI.293137
      Issue No: Vol. 13, No. 1 (2022)
       
  • Money Transaction Fraud Detection Using Harris Grey Wolf-Based Deep
           Stacked Auto Encoder

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      Authors: Kolli; Chandra Sekhar, Tatavarthi, Uma Devi
      Pages: 1 - 21
      Abstract: Due to the intrinsic properties of transactional data, like concept drift, noise, data imbalance, and borderline entities, the fraud detection poses a challenging issue in bank transaction. A number of solutions are developed for detecting the fraud, but these solutions reveal ineffective performance. Therefore, an effective fraud detection framework named Harris Grey Wolf (HGW)-based Deep stacked auto encoder is proposed to perform the fraud detection mechanism in bank transaction by solving the data imbalance issues. The HGW-based deep stacked auto encoder is developed using the characteristic features of the standard Harris Hawks Optimizer (HHO), and Grey Wolf Optimizer (GWO). The proposed HGW-based Deep stacked auto encoder provides an effective and optimal solution in detecting the frauds using the fitness function, which considers the minimal error value and evaluate the best solution based on the iterations. The useful and the appropriate features are effectively selected from the transactional data, as these features enhanced the accuracy of detection rate.
      Keywords: Artificial Intelligence; Computer Science & IT; Artificial Intelligence
      Citation: International Journal of Ambient Computing and Intelligence (IJACI), Volume: 13, Issue: 1 (2022) Pages: 1-21
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJACI.293157
      Issue No: Vol. 13, No. 1 (2022)
       
  • Cloud Intrusion Detection Model Based on Deep Belief Network and
           Grasshopper Optimization

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      Authors: Parganiha; Vivek, Shukla, Soorya Prakash, Sharma, Lokesh Kumar
      Pages: 1 - 24
      Abstract: Cloud computing is a vast area which uses the resources cost-effectively. The performance aspects and security are the main issues in cloud computing. Besides, the selection of optimal features and high false alarm rate to maintain the highest accuracy of the testing are also the foremost challenges focused. To solve these issues and to increase the accuracy, an effective cloud IDS using Grasshopper optimization Algorithm (GOA) and Deep belief network (DBN) is proposed in this paper. GOA is used to choose the ideal features from the set of features. Finally, DBN is developed for classification according to their selected feasible features. The introduced IDS is simulated on the Python platform and the performance of the suggested model of deep learning is assessed based on statistical measures named as Precision, detection accuracy, f-measure and Recall. The NSL_KDD, and UNSW_NB15 are the two datasets used for the simulation, and the results showed that the proposed scheme achieved maximum classification accuracy and detection rate.
      Keywords: Artificial Intelligence; Computer Science & IT; Artificial Intelligence
      Citation: International Journal of Ambient Computing and Intelligence (IJACI), Volume: 13, Issue: 1 (2022) Pages: 1-24
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJACI.293123
      Issue No: Vol. 13, No. 1 (2022)
       
  • An Approach to Ensure Secure Inter-Cloud Data and Application Migration
           Using End-to-End Encryption and Content Verification

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      Authors: Koushik S; , Patil, Annapurna P.
      Pages: 1 - 21
      Abstract: Cloud Computing is one of the most popular platforms in recent times and has many services to offer. The resources are deployed on the Cloud and are made available to cloud users over high-speed internet connectivity. Many enterprises think of migrating the data or application hosted from one Cloud to another based on the requirements. Migration from one Cloud to another Cloud requires security as it is vital for any data. This article presents a novel secure framework called ‘InterCloudFramework,’ considering well-established criteria to migrate various services across clouds with minimal supervision and interruption. Security is the primary concern to migrate the data among inter-clouds. The study incorporates the Elliptical-Curve Diffie-Hellman algorithm to encrypt the data and Merkle Hash Trees to check the integrity of the data. In addition to security during migration, the framework reduces the migration time of data or applications.
      Keywords: Artificial Intelligence; Computer Science & IT; Artificial Intelligence
      Citation: International Journal of Ambient Computing and Intelligence (IJACI), Volume: 13, Issue: 1 (2022) Pages: 1-21
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJACI.293148
      Issue No: Vol. 13, No. 1 (2022)
       
  • Automatic Brain Tumor Detection From MRI Using Curvelet Transform and
           Neural Features

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      Authors: Mostafiz; Rafid, Uddin, Mohammad Shorif, Jabin, Iffat, Hossain, Muhammad Minoar, Rahman, Mohammad Motiur
      Pages: 1 - 18
      Abstract: The brain tumor is one of the most health hazard diseases across the world in recent time. The development of the intelligent system has extended its applications in the automated medical diagnosis domains. However, image-based medical diagnosis result strongly depends on the selection of relevant features. This research focuses on the automatic detection of brain tumors based on the concatenation of curvelet transform and convolutional neural network (CNN) features extracted from the preprocessed MRI sequence of the brain. Relevant features are selected from the feature vector using mutual information based on the minimum redundancy maximum relevance (mRMR) method. The detection is done using the ensemble classifier of the bagging method. The experiment is performed using two standard datasets of BraTS 2018 and BraTS 2019. After five-fold cross-validation, we have obtained an outperforming accuracy of 98.96%.
      Keywords: Artificial Intelligence; Computer Science & IT; Artificial Intelligence
      Citation: International Journal of Ambient Computing and Intelligence (IJACI), Volume: 13, Issue: 1 (2022) Pages: 1-18
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJACI.293163
      Issue No: Vol. 13, No. 1 (2022)
       
  • Generation of Adversarial Mechanisms in Deep Neural Networks

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      Authors: Pavate; Aruna Animish, Bansode, Rajesh
      Pages: 1 - 18
      Abstract: Deep learning is a subspace of intelligence system learning that experienced prominent results in almost all the application domains. However, Deep Neural Network found to be susceptible to perturbed inputs such that the model generates output other than the expected one. By including insignificant perturbation to the input effectuate computer vision models to make an erroneous prediction. Though, it is still a dilemma whether humans are prone to comparable errors. In this paper, we focus on this issue by leveraging the latest practices that help to generate adversarial examples in computer vision applications by considering diverse identified parameters, unidentified parameters, and architectures. The analysis of the distinct techniques has been done by considering different common parameters. Adversarial examples are easily transferable while designing computer vision applications that control the condition of the classifications of labels. The finding highlights that some methods like Zoo and Deepfool achieved 100% success for the nontargeted attack but are application-specific.
      Keywords: Artificial Intelligence; Computer Science & IT; Artificial Intelligence
      Citation: International Journal of Ambient Computing and Intelligence (IJACI), Volume: 13, Issue: 1 (2022) Pages: 1-18
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJACI.293111
      Issue No: Vol. 13, No. 1 (2022)
       
  • Neuro-Immune Model Based on Bio-Inspired Methods for Medical Diagnosis

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      Authors: Djahafi; Fatiha, Gafour, Abdelkader
      Pages: 1 - 18
      Abstract: In this article, a hybrid bio-inspired algorithm called neuro-immune is proposed based on Multi-Layer Perceptron Neural Network (MLPNN) and the Clonal Selection Classification (CSC) principle of the Artificial Immune System (AIS) for the classifying and diagnosing of medical disease. The proposed approach consists in the first phase to code the weights and biases of MLPNN concatenation vector of the input samples into an antigen vector and to decompose it into new weights to generate population memory cells which will be applied by the processes of the CSC algorithm clone and mutate in the second phase, to optimize the accuracy class of data and updating the MLPNN weights to minimize the mean squared error. Experimental results show that the proposed hybrid neuro-immune model allows obtaining a high diagnosis performance on a set of medical data problems from the UCI repository with an improved classification accuracy compared to existing works in the literature.
      Keywords: Artificial Intelligence; Computer Science & IT; Artificial Intelligence
      Citation: International Journal of Ambient Computing and Intelligence (IJACI), Volume: 13, Issue: 1 (2022) Pages: 1-18
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJACI.293176
      Issue No: Vol. 13, No. 1 (2022)
       
  • Design and Deployment of E-Health System Using Machine Learning in the
           Perspective of Developing Countries

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      Authors: Zishan; Md. Saniat Rahman, Zishan, Md. Saniat Rahman, Mohamed, Mohamad Afendee, Mohamed, Mohamad Afendee, Hossain, Chowdhury Akram, Hossain, Chowdhury Akram, Ahasan, Rabiul, Ahasan, Rabiul, Sharun, Siti Maryam, Sharun, Siti Maryam
      Pages: 1 - 20
      Abstract: Machine learning is tightening its grasp on many sectors of modern life and medical sector is not an exception. In developing countries like Bangladesh, disease classification process mostly remains manual, time consuming and sometimes erroneous. Designing an E-health system comprised of disease identification model would be a great aid in such circumstances. The automation of identifying the diseases with the help of machine learning will be more accurate and time-saving. In this paper, Decision Tree, Gaussian Naive-Bayes, Random Forest, Logistic Regression, k-NN, MLP, and SVM machine learning techniques are applied for three diseases: Dengue, Diabetes, and Thyroid. MLP for Dengue, Logistic Regression for Diabetes, and Random Forest for Thyroid performed the best with accuracies of 88.3%, 82.5%, and 98.5% respectively. Additionally, a medical specialist recommendation model and a medicine suggestion model are also integrated in the proposed E-Health system.
      Keywords: Artificial Intelligence; Computer Science & IT; Artificial Intelligence
      Citation: International Journal of Ambient Computing and Intelligence (IJACI), Volume: 13, Issue: 1 (2022) Pages: 1-20
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJACI.293186
      Issue No: Vol. 13, No. 1 (2022)
       
 
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