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Machine Learning Enabled e-Learner Non-Verbal Behavior Detection in IoT Environment

computers, Materials & Continua 2022年第7期72卷 679-693页

作者： Abdelzahir Abdelmaboud Fahd N.Al-Wesabi Mesfer Al Duhayyim Taiseer Abdalla Elfadil Eisa Manar Ahmed Hamza Mohammed Rizwanullah Abu Serwar Zamani Radwa Marzouk Department of Information Systems College of Science and ArtsKing Khalid UniversityMahayil AsirSaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Science and Arts-Girls SectionKing Khalid UniversityMahayil AsirSaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia&Department of MathematicsFaculty of ScienceCairo UniversityGiza12613Egypt

Internet of Things(IoT)with e-learning is widely employed to collect data from various smart devices and share it with other ones for efficient e-learning *** the same time,machine learning(ML)and data mining approaches are presented for accomplishing prediction and classification *** this motivation,this study focuses on the design of intelligent machine learning enabled e-learner non-verbal behaviour detection(IML-ELNVBD)in IoT *** proposed IML-ELNVBD technique allows the IoT devices such as audio sensors,cameras,*** are then connected to the cloud server for further *** addition,the modelling and extraction of behaviour take ***,extreme learning machine sparse autoencoder(ELM-SAE)model is employed for the detection and classification of non-verbal ***,the Ant Colony Optimization(ACO)algorithm is utilized to properly tune the weight and bias parameters involved in the ELM-SAE *** order to ensure the improved performance of the IML-ELNVBD model,a comprehensive simulation analysis is carried out and the results highlighted the betterment compared to the recent models.

关键词： Internet of things e-learning machine learning non-verbal behavior extreme learning machine classification parameter tuning

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Knowledge Transfer Challenges: A Practical Study of Yemen Public Telecommunication Corporation 1

Knowledge Transfer Challenges: A Practical Study of Yemen Pu...

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Al-Taweel, Sadik Ali, Ghassan Ahmed Alhazbi, Saleh Yousef, Walid Al-Fuhaidi, Belal Albater, Thamer Hezzam, Essa Ghallab, Abdullatif Department of Information Systems University of Science and Technology Sana’a Yemen Faculty of Islamic Technology Universiti Islam Sultan Sharif Ali Brunei Darussalam Brunei Dept. of Computer Science & Eng. Qatar University Doha Qatar Dept. of Information Technology University of Science and Technology Sana’a Yemen Department of Computer Science University of Science and Technology Sana’a Yemen Faculty of Business Open University Malaysia Sana’a Yemen Department of Information Systems Taibah University Medina Saudi Arabia

ISBN: (纸本)9798331533557

The success of organizations today relies heavily on their intellectual capital, as knowledge is a key resource for value creation. Sharing and transferring knowledge enhances its value, embedding it within an organization’s processes and boosting competitiveness. This study focuses on the Yemen public telecommunication corporation (YPTC), which has adopted a knowledge-focused management approach. This study aims to explore challenges related to knowledge transfer in Yemen’s public sector, where such research is lacking. Using a descriptive and analytical method, data were collected via a questionnaire from 200 employees, yielding a response rate of 88%. The findings indicate a high level of knowledge transfer at YPTC, with individual, organizational, and technological factors identified as significant influences. The key barriers include trust, information technology support, motivation, and social networks. © 2024 IEEE.

关键词： Knowledge Transfer Yemen Public Telecom

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An Enhanced Particle Swarm Optimization for ITC2021 Sports Timetabling

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computers, Materials & Continua 2022年第7期72卷 1995-2014页

作者： Mutasem K.Alsmadi Ghaith M.Jaradat Malek Alzaqebah Ibrahim A.Lmarashdeh Fahad A.Alghamdi Rami Mustafa A.Mohammad Nahier Aldhafferi Abdullah Alqahtani Department of MIS College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia Department of Computer Science Faculty of Computer Science and InformaticsAmman Arab UniversityAmmanJordan Department of Mathematics College of ScienceImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia Basic and Applied Scientific Research CenterImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia Computer Information Systems Department College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia

Timetabling problem is among the most difficult operational tasks and is an important step in raising industrial productivity,capability,and *** tasks are usually tackled using metaheuristics techniques that provide an intelligent way of suggesting solutions or *** intelligence techniques including Particle Swarm Optimization(PSO)have proved to be effective *** recent experiments showed that the PSO algorithm is reliable for timetabling in many applications such as educational and personnel timetabling,machine scheduling,***,having an optimal solution is extremely challenging but having a sub-optimal solution using heuristics or metaheuristics is *** research paper seeks the enhancement of the PSO algorithm for an efficient timetabling *** algorithm aims at generating a feasible timetable within a reasonable *** enhanced version is a hybrid dynamic adaptive PSO algorithm that is tested on a round-robin tournament known as ITC2021 which is dedicated to sports *** competition includes several soft and hard constraints to be satisfied in order to build a feasible or sub-optimal *** consists of three categories of complexities,namely early,test,and middle *** showed that the proposed dynamic adaptive PSO has obtained feasible timetables for almost all of the *** feasibility is measured by minimizing the violation of hard constraints to *** performance of the dynamic adaptive PSO is evaluated by the consumed computational time to produce a solution of feasible timetable,consistency,and *** dynamic adaptive PSO showed a robust and consistent performance in producing a diversity of timetables in a reasonable computational time.

关键词： Sports timetabling particle swarm optimization ITC2021 roundrobin tournament dynamic adaptive

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IoT-Cloud Assisted Botnet Detection Using Rat Swarm Optimizer with Deep Learning

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computers, Materials & Continua 2023年第2期74卷 3085-3100页

作者： Saeed Masoud Alshahrani Fatma S.Alrayes Hamed Alqahtani Jaber S.Alzahrani Mohammed Maray Sana Alazwari Mohamed A.Shamseldin Mesfer Al Duhayyim Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial IntelligenceUnit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Mechanical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Nowadays,Internet of Things(IoT)has penetrated all facets of human life while on the other hand,IoT devices are heavily prone to *** has become important to develop an accurate system that can detect malicious attacks on IoT environments in order to mitigate security *** is one of the dreadfulmalicious entities that has affected many users for the past few *** is challenging to recognize Botnet since it has excellent carrying and hidden *** approaches have been employed to identify the source of Botnet at earlier *** Learning(ML)and Deep Learning(DL)techniques are developed based on heavy influence from Botnet detection *** spite of this,it is still a challenging task to detect Botnet at early stages due to low number of features accessible from Botnet *** current study devises IoT with Cloud Assisted Botnet Detection and Classification utilizingRat SwarmOptimizer with Deep Learning(BDC-RSODL)*** presented BDC-RSODL model includes a series of processes like pre-processing,feature subset selection,classification,and parameter ***,the network data is pre-processed to make it compatible for further ***,RSO algorithm is exploited for effective selection of subset of ***,Long Short TermMemory(LSTM)algorithm is utilized for both identification and classification of ***,Sine Cosine Algorithm(SCA)is executed for fine-tuning the hyperparameters related to LSTM *** order to validate the promising 3086 CMC,2023,vol.74,no.2 performance of BDC-RSODL system,a comprehensive comparison analysis was *** obtained results confirmed the supremacy of BDCRSODL model over recent approaches.

关键词： Internet of things cloud computing long short termmemory deep learning sine cosine algorithm feature selection

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AI-driven intrusion prevention system: leveraging mathematical foundations for advanced threats detection

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International Journal of information Technology (Singapore) 2025年 1-6页

作者： Vashistha, Avneesh Alghamdi, Tami Abdulrahman Almalki, Sultan Ahmed Ead, Waleed M. Kaur, Gaganpreet Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi-NCR Campus Modinagar Ghaziabad India Computer Science Department Faculty of Computing and Information Al-Baha University Al-Baha Saudi Arabia Computer Department Applied College Najran University Najran Saudi Arabia Faculty of Computing and Information Al-Baha University Al-Baha Saudi Arabia Faculty of Computers and Artificial Intelligence Beni-Suef University Beni-Suef Egypt Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

The given paper introduces a multi-staged AI-driven intrusion prevention system (IPS) that utilizes mathematical foundations like statistical analysis, probabilistic models, and optimization techniques to design strategic algorithms that recognize patterns as well as detect anomalies & potential attack vectors, which usually get missed out on by traditional security tools. The study focuses on the hybrid behavior that combines supervised and reinforcement learning (RL) methods to provide useful analysis of network traffic, which improves early detection of threats, whether they are known or unknown. This research demonstrates how the proposed AI-driven model can achieve a substantial decrease in the rate of false positives and improve threat detection accuracy. Thus, the given framework acts as a proactive component against today’s evolving cybersecurity threats and hence allows the creation of an efficient and responsive enterprise security framework. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Feedback loop Intrusion prevention Network traffic Reinforcement learning Threat detection

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Impact of Body Position on Imaging Ballistocardiographic Signals

Impact of Body Position on Imaging Ballistocardiographic Sig...

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16th International Joint Conference on Biomedical Engineering systems and Technologies, BIOSTEC 2023

作者： Woyczyk, Alexander Zaunseder, Sebastian Faculty of Information Technology University of Applied Sciences and Arts Dortmund Dortmund Germany Professorship for Diagnostic Sensing Faculty of Applied Computer Science University Augsburg Augsburg Germany

Current works direct at the unobtrusive acquisition of vital parameters from videos. The most common approach exploits subtle color variations. The analysis of cardiovascular induced motion from videos (imaging ballistocardiography, iBCG) is another approach that can supplement the analysis of color changes. The presented study systematically investigates the impact of body position (supine vs. upright) on iBCG. Our research directs at heart rate estimation by iBCG and on the possibility to analyse ballistocardiographic waveforms from iBCG. We use own data from 30 healthy volunteers, who went through repeated orthostatic maneuvers on a tilt table. Processing is done according to common procedures for iBCG processing including feature tracking, dimensionality reduction and bandpass filtering. Our results indicate that heart rate estimation works well in supine position (root mean square error of heart rate estimation 5.68 beats per minute). The performance drastically degrades in upright (standing) position (root mean square error of heart rate estimation 21.20 beats per minute). With respect to analysis of beat waveforms, we found large intra-subject and inter-subject variations. Only in few cases, the resulting waveform closely resembles the ideal ballistocardiographic waveform. Our investigation indicates that the actual position has a large effect on iBCG and should be considered in algorithmic developments and testing. © 2023 by SCITEPRESS science and Technology Publications, Lda.

关键词： Heart

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Heart Diseases Prediction Using Machine Learning Techniques: A Case Study from Jordan 25

Heart Diseases Prediction Using Machine Learning Techniques:...

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25th International Arab Conference on information Technology, ACIT 2024

作者： Alazaidah, Raed Safaqah, Mohammed Abu Alzoubi, Haneen Omar, Hani Al-Safarini, Maram Y. Elsoud, Esraa Abu Elrashidi, Ali Alluwaici, Mo'Ath Owaida, Suhaila Abu Zarqa University Faculty of It Department of Data Science and Ai Zarqa Jordan Department of Mechanical Engineering Muscat Oman Applied Science Private University Faculty of It Department of Computer Science Amman Jordan Zarqa University Faculty of It Department of Computer Science Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Al-al-Bayt University Faculty of Information Technology Department of Computer Science Mafraq Jordan

ISBN: (纸本)9798331540012

Heart disease is one of the most common diseases in Jordan. It is a major reason of death among Jordanian adult citizens. Worldwide, an average of 56,000 people dies each day or one death every 1.5 seconds. Hence, this research is interested in the early prediction of this disease among Jordanian people. To achieve this main objective, Machine Learning (ML) is utilized through a large number of classification models and considering five well-known evaluation metrics. These classification models have been trained on a primary dataset that has been collected for the purpose of the research. The results revealed that RandomForest and RandomCommittee are the best classification models to handle the task of the early prediction of Heart disease in Jordan. © 2024 IEEE.

关键词： Adversarial machine learning

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AFuNet: an attention-based fusion network to classify texts in a resource-constrained language

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Neural Computing and Applications 2025年第9期37卷 6725-6748页

作者： Hossain, Md. Rajib Hoque, Mohammed Moshiul Dewan, M. Ali Akber Hoque, Enamul Siddique, Nazmul Department of Computer Science & Engineering Chittagong University of Engineering & Technology Chittagong4349 Bangladesh School of Computing and Information Systems Faculty of Science and Technology Athabasca University AthabascaABT9S 3A3 Canada School of Information Technology York University TorontoONM3J 1P3 Canada School of Computing Engineering and Intelligent Systems Ulster University Coleraine United Kingdom

In the era of widespread Internet use and extensive social media interaction, the digital realm is accumulating vast amounts of unstructured text data. This unstructured data often contain undesirable information, necessitating time-consuming manual classification efforts. An intelligent text classification system capable of automatically categorizing digitized texts based on semantic meaning is crucial. However, this task is particularly challenging for low-resource languages like Bengali due to a shortage of annotated corpora, issues with out-of-vocabulary words, lack of domain-specific hyperparameter tuning, limited ability to extract generalized text features, and class imbalances within the corpus. AFuNet: an attention-based fusion network to classify texts in a resource-constrained language. AFuNet undergoes a comprehensive four-phase experimental process, including baseline model evaluation and hyperparameter tuning, late fusion and model selection, attention-based early fusion and model identification, and an ablation study with impact analysis. Fine-tuned based on five Bengali text classification corpora, AFuNet achieves impressive accuracies: 96.60 ± 0.2 (BTCC11), 85.37 ± 0.2 (OSBC), 97.35 ± 0.2 (BARD), 93.74 ± 0.2 (IndicNLP), and 96.51 ± 0.2 (ProthomAlo). In comparison with previous state-of-the-art models on these corpora, AFuNet demonstrates significant accuracy improvements ranging from 0.54% to 4.49%, showcasing its effectiveness in advancing text classification capabilities for the Bengali language. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Semantics

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New Media as a Tools to Improve Creative Thinking: A Systematic Literature Review 7

New Media as a Tools to Improve Creative Thinking: A Systema...

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7th International Conference on information Technology, information systems and Electrical Engineering, ICITISEE 2023

作者： Hermawan, Hellik Mahardika, Fajar Darmayanti, Irma Sumantri, R. Bagus Bambang Saputra, Dhanar Intan Surya Aminuddin, Afrig Universitas Amikom Purwokerto Faculty of Computer Science Department of Informatics Jawa Tengah Banyumas Indonesia Institut Teknologi Dan Sains Nahdlatul Computer Technology Study Program Faculty of Science and Technology Ulama Pekalongan Indonesia Universitas Amikom Purwokerto Faculty of Computer Science Department of Information Technology Jawa Tengah Banyumas Indonesia Universitas Harapan Information Systems Study Program Faculty of Science and Technology Bangsa Indonesia Universitas Amikom Yogyakarta Faculty of Computer Science Sleman Indonesia

ISBN: (纸本)9798350382266

This systematic literature review (SLR) study aims to analyze the role of New Media as a Tool to Improve Creative Thinking, with relevant articles from 2018 to 2023 taken from reputable international journals. It uses three research questions (RQ) to explore New Media's relevance in stimulating creativity. The results indicate that interactive platforms such as social media and online collaboration tools positively influence creative thinking skills. Interaction through New Media allows individuals to share ideas, discuss, and be exposed to various points of view, all of which stimulate creative thinking. In addition, New Media also facilitates the creative process of solving problems and generating original ideas. The immersive experiences offered by this technology can enhance the exploration of ideas and increase the ability to think out-of-the-box. While New Media offers great potential to enhance creativity, there are also potential risks, such as false information, media addiction, and privacy concerns. Therefore, awareness of the wise use of New Media needs to be increased, especially in education and everyday life. Overall, this literature review provides in-depth insight into the role of New Media in enhancing creative thinking skills and driving innovation. With the right approach, New Media can be an effective tool in stimulating creative thinking and encouraging the creation of original ideas beneficial for future social and technological developments. © 2023 IEEE.

关键词： Creative Thinking New Media Original Ideas Systematic Literature Review The Effect of Using New Media

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Application of Classification and Regression Tree and Spectral Clustering to Breast Cancer Prediction: Optimizing the Precision-Recall Trade-Off 3

Application of Classification and Regression Tree and Spectr...

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3rd IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2023

作者： Agaal, Asma Essgaer, Mansour Alshareef, Almahdi Alkhadafe, Hasan Benyahmed, Yahyia Sebha University Faculty of Sciences Department of Computer Science Sebha Libya Sebha University Faculty of Information Technology Department of Artificial Intelligence Sebha Libya Sebha University Faculty of Information Technology Department of Information Systems Sebha Libya University of Fezzan College of Education Traghen Department of Computer Science Murzuq Libya

ISBN: (纸本)9798350319897

The Receiver Operator Characteristic (ROC) test is often used to evaluate classification performance. However, it calls for special consideration when applied to the class-imbalanced data. The Precision-Recall Curve (PRC) is dependent on the class imbalance ratio. It summarizes the trade-off between the true positive rate and the positive predictive value for a predictive model using different probability thresholds. In this work, PRC is used to enhance the precision and recall of the Classification And Regression Tree (CART) algorithm to diagnose breast cancer;this enhanced the CART recall's capacity to predict wrongly classified samples by 10%, from 89% to 99%, when compared to the standard CART algorithm. On the other hand, when evaluating the performance of the improved CART on unseen data, it achieved a recall of 95%. Thus, the recall of the CART decreased by 4% from 99% to 95%. This drawback can be attributed to the small size of the training data, which has been labeled using one of the clustering methods, spectral clustering, which achieved the best Silhouette in separating data into two clustering with a score of 0.43. © 2023 IEEE.

关键词： Diseases

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