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25th International Arab Conference on Information Technology, ACIT 2024

作者： Nasir, Ola Alazaidah, Raed Al-Safarini, Maram Y. Alamleh, Amneh Zaid, Ali Abu Mansur, Hasan Elrashidi, Ali Alzoubi, Haneen Bani-Bakr, Alaa Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan American University of Madaba Faculty of Business Department of Accounting Madaba Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Private University Faculty of It Applied Science Department of Computer Science Amman Jordan

ISBN: (纸本)9798331540012

Several people suffer from irregular sleep patterns, a problem that negatively affects their daily lives. It presents a multitude of challenges, including daytime distress, disturbances in sleep-wake patterns, feelings of unease, and a variety of other complications. Diabetes, obesity, and psychological disorders are among the diseases that may be responsible for this sleep disorder. Therefore, the primary aim of this research is to employ the robust capabilities of machine learning in forecasting sleep disorders. This research classifies three diseases: diabetes, obesity, and psychological disorders, using thirty-two different classifiers belonging to five learning strategies. The research compares and evaluates their predictive performance on four datasets. The comparative analysis includes four evaluation metrics: Accuracy, Precision, ROC Area, and MCC. The research results indicate that the best classifier is SGD, followed by LMT, while NBM is the weakest classifier. In addition, the research results revealed that the best strategy is Functions, followed by Rules, with Bayes being the least effective strategy. While conducting the experiment and analyzing the data, it is observed that the best feature selection method that yielded the most favorable outcome is the CorrelationAttributeEval. Furthermore, it has been found that the classification algorithm known as SGD achieved the highest Accuracy and Precision in categorizing the data. This indicates that the combination of CorrelationAttributeEval as the feature selection approach and SGD as the classification algorithm proved to be the most successful and efficient strategy for this research © 2024 IEEE.

关键词： Sleep research

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Challenges of Reviews Scraped from Web Services Hosted on Edge Devices

Challenges of Reviews Scraped from Web Services Hosted on Ed...

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2022 IEEE International Conference on Current Development in engineering and Technology, CCET 2022

作者： Boparai, Rajdavinder Singh Bhatia, Rekha Punjabi University Department of Computer Science and Engineering Patiala India Punjabi University Department of Applied Management Patiala India

ISBN: (纸本)9781665454155

Machine learning, deep learning, artificial intelligence, the internet of things, big data analytics and cloud computing came together to evolve a new age of computing i.e., edge computing, which is an emerging paradigm that helps in resolving the challenges faced by traditional storage and computing techniques and ensuring the future internet of things. To optimize access anywhere at the any-time time anywhere in a fraction of a second, the migration of stored data is being proposed on edge networks virtually or physically and also an enormous amount of data which is produced from IoT devices need to be processed by edge devices virtually and physically to make intelligent decisions faster. The deployment and execution of intelligent applications over heterogeneous resources are complicated by the geographical dispersion of edge devices and their resource constraints. This article is presenting a study of edge computing methodologies and management of storage and processing resources in edge environments;the positives and negatives of adopting data analytics along with distributed intelligence in edge environments;characterization of a distributed learning architecture;future services webservices in edge computing;and the challenges for deployment of web services containing a huge amount of data and for analytical purposes. © 2022 IEEE.

关键词： Web services

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Graph Transformer for Communities Detection in Social Networks

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computers, Materials & Continua 2022年第3期70卷 5707-5720页

作者： G.Naga Chandrika Khalid Alnowibet K.Sandeep Kautish E.Sreenivasa Reddy Adel F.Alrasheedi Ali Wagdy Mohamed Department of Computer Science and Engineering ANU College of Engineering and TechnologyGuntur522510India Statistics and Operations Research Department College of ScienceKing Saud UniversityRiyadh11451Kingdom of Saudi Arabia LBEF Campus Kathmandu44600Nepal Department of Computer Science and Engineering ANUGuntur522510India Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGiza12588Egypt

Graphs are used in various disciplines such as telecommunication,biological networks,as well as social *** large-scale networks,it is challenging to detect the communities by learning the distinct properties of the *** deep learning hasmade contributions in a variety of domains,we try to use deep learning techniques to mine the knowledge from large-scale graph *** this paper,we aim to provide a strategy for detecting communities using deep autoencoders and obtain generic neural attention to *** advantages of neural attention are widely seen in the field of NLP and computer vision,which has low computational complexity for large-scale *** contributions of the paper are summarized as ***,a transformer is utilized to downsample the first-order proximities of the graph into a latent space,which can result in the structural properties and eventually assist in detecting the ***,the fine-tuning task is conducted by tuning variant hyperparameters cautiously,which is applied to multiple social networks(Facebook and Twitch).Furthermore,the objective function(crossentropy)is tuned by L0 ***,the reconstructed model forms communities that present the relationship between the *** proposed robust model provides good generalization and is applicable to obtaining not only the community structures in social networks but also the node *** proposed graph-transformer shows advanced performance on the social networks with the average NMIs of 0.67±0.04,0.198±0.02,0.228±0.02,and 0.68±0.03 on Wikipedia crocodiles,Github Developers,Twitch England,and Facebook Page-Page networks,respectively.

关键词： Social networks graph transformer community detection graph classification

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Prognostic Kalman Filter Based Bayesian Learning Model for Data Accuracy Prediction

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computers, Materials & Continua 2022年第7期72卷 243-259页

作者： S.Karthik Robin Singh Bhadoria Jeong Gon Lee Arun Kumar Sivaraman Sovan Samanta A.Balasundaram Brijesh Kumar Chaurasia S.Ashokkumar Department of ECE College of Engineering and TechnologySRM Institute of Science and TechnologyVadapalaniChennai600026India Department of CSE Birla Institute of Applied Sciences(BIAS)BhimtalUttarakhand263136India Division of Applied Mathematics Wonkwang University460Iksan-DaeroIksan-SiJeonbuk54538Korea School of Computer Science and Engineering Vellore Institute of Technology(VIT)Chennai600127India Department of Mathematics Tamralipta MahavidyalayaWest Bengal721636India School of Computer Science and Engineering Center for Cyber Physical SystemsVellore Institute of Technology(VIT)Chennai600127India Indian Institute of Information Technology(IIIT) LucknowUttar Pradesh226002India Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennai602105India

Data is always a crucial issue of concern especially during its prediction and computation in digital *** paper exactly helps in providing efficient learning mechanism for accurate predictability and reducing redundant data *** also discusses the Bayesian analysis that finds the conditional probability of at least two parametric based predictions for the *** paper presents a method for improving the performance of Bayesian classification using the combination of Kalman Filter and *** method is applied on a small dataset just for establishing the fact that the proposed algorithm can reduce the time for computing the clusters from *** proposed Bayesian learning probabilistic model is used to check the statistical noise and other inaccuracies using unknown *** scenario is being implemented using efficient machine learning algorithm to perpetuate the Bayesian probabilistic *** also demonstrates the generative function forKalman-filer based prediction model and its *** paper implements the algorithm using open source platform of Python and efficiently integrates all different modules to piece of code via Common Platform Enumeration(CPE)for Python.

关键词： Bayesian learning model kalman filter machine learning data accuracy prediction

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A Deep Neural Network for Image Classification Using Mixed Analog and Digital Infrastructure 3rd

A Deep Neural Network for Image Classification Using Mixed A...

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3rd International Conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023

作者： Kala, R. Punitha, M. Poomani Alias Banupriya, P.G. Veerasamy, B. Bharathi, B. Alzubi, Jafar Ahmad Abed Associate professor Department of Computer Science and Engineering Karpagam College of Engineering Tamil Nadu Coimbatore641032 India Department of Information Technology Sethu Institute of Technology Kariapatti Puloor India Department of Computer Science and Engineering Karpagam Institute of Technology Tamil Nadu Coimbatore641032 India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Tamil Nadu Krishnankoil626126 India Department of Information Technology Coimbatore Institute of Engineering and Technology Tamil Nadu Coimbatore641109 India School of Engineering Al-Balqa Applied University Salt Jordan

ISBN: (纸本)9789819986453

Recently, Deep Learning (DL) methods are a well-known solution for image and object detection in computer vision applications. The software-based implementation of DL algorithms requires huge resources and consumes more power. To overcome these challenges, a hardware implementation of Deep Neural Network (DNN) using the integrated analog–digital architecture was proposed in this article. The Very Large-Scale Integration (VLSI)-based DNN implementation utilizes Pulse-Width Modulation (PWM), and analog–digital circuits. The integration of analog and digital architecture integrates the benefits of both circuits, which reduces power consumption and enhances processing speed and classification accuracy. The fabrication of the VLSI chip includes the DNN neuron circuit, PWM circuit, digital adders-subtracters, and SRAM memory. The experimental result verifies that the presented DNN VLSI architecture reduces the power consumption of the neural system, and enhances the image classification performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep neural networks

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Transformative Dynamics: Unveiling the Influence of Artificial Intelligence, Cybersecurity, and Advanced Technologies in The Bitcoin 4

Transformative Dynamics: Unveiling the Influence of Artifici...

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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Jamalpur, Bhavana Kant, Gur Sharan Singla, Atul Veena, Ch Raj, Vijilius Helena Majeed, Mohammed Rasheed Sr University School of Computer Science and Artificial Intelligence Telangana Warangal India Sir Chhotu Ram Institute of Engg. & Technology Department of Computer Science India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Applied Sciences Bangalore India National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350307757

The tremendous implications that new technology has had on the Bitcoin industry are investigated in full throughout this examination. This research uses a rigorous approach to analyse how artificial intelligence (AI), cybersecurity, and other cutting-edge technological breakthroughs like as quantum computing and the Internet of Things (IoT) are affecting the workings of Bitcoin's operational structure and market dynamics. By using a wide variety of statistical methods, the study evaluates the influence of these technologies on several aspects of Bitcoin, including as the transaction volume, market capitalization, and the efficiency of predictive analytics. The findings indicate that artificial intelligence (AI) has a significant beneficial influence on the research of market trends and the development of investment plans;cybersecurity plays a major role in improving the security of transactional processes;and modern technologies have the potential to broaden and diversify the applications of bitcoin. © 2024 IEEE.

关键词： Investments

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Semi-analytical finite element method applied for characterizing micropolar fibrous composites

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applied mathematics and Mechanics(English Edition) 2024年第12期45卷 2147-2164页

作者： J.A.OTERO Y.ESPINOSA-ALMEYDA R.RODRIGUEZ-RAMOS J.MERODIO School of Engineering and Sciences Monterrey Institute of TechnologyEstado de Mexico 52926Mexico Institute of Engineering and Technology Autonomous University of Ciudad JuarezCiudad JuarezChihuahua 32310Mexico Postgraduate Program in Computational Modeling in Science and Technology Federal Fluminense UniversityRıo de Janeiro 27255-125Brazil Faculty of Mathematics and Computer Science University of HavanaLa Habana 10400Cuba Department of Mathematics Applied to ICT School of Informatics Systems EngineeringTechnical University of MadridMadrid 28031Spain

A semi-analytical finite element method(SAFEM),based on the two-scale asymptotic homogenization method(AHM)and the finite element method(FEM),is implemented to obtain the effective properties of two-phase fiber-reinforced composites(FRCs).The fibers are periodically distributed and unidirectionally aligned in a homogeneous *** framework addresses the static linear elastic micropolar problem through partial differential equations,subject to boundary conditions and perfect interface contact *** mathematical formulation of the local problems and the effective coefficients are presented by the *** local problems obtained from the AHM are solved by the FEM,which is denoted as the *** numerical results are provided,and the accuracy of the solutions is analyzed,indicating that the formulas and results obtained with the SAFEM may serve as the reference points for validating the outcomes of experimental and numerical computations.

关键词： semi-analytical approach fiber-reinforced composite(FRC) effective property finite element method(FEM) asymptotic homogenization method(AHM) micropolar elasticity

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Grassroots Systems: Concept, Examples, Implementation and Applications

arXiv

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arXiv 2023年

作者： Shapiro, Ehud Department of Computer Science and Applied Math Weizmann Institute of Science Israel

Informally, a grassroots system is a distributed system that can have multiple instances, independent of each other and of any global resources, that can interoperate once interconnected. More formally, in a grassroots system the set of all correct behaviors of a set of agents P is strictly included in the set of the correct behaviors of P when embedded within a larger set of agents P′ ⊃ P: Included, meaning that members of P are correct to ignore outside agents indefinitely;strictly included, meaning that the interaction of members of P with outside agents is also a possible and correct behavior. Client-server/cloud computing systems are not grassroots, and neither are systems designed to have a single global instance (Bitcoin/Ethereum with hardwired seed miners/bootnodes), and systems that rely on a single global data structure (IPFS, DHTs). An example grassroots system would be a serverless smartphone-based social network supporting multiple independently-budding communities that can merge when a member of one community becomes also a member of another. Grassroots applications are potentially important as they may allow people to conduct their social, economic, civic, and political lives in the digital realm solely using the networked computing devices they own and operate (e.g., smartphones), free of third-party control, surveillance, manipulation, coercion, or rent seeking (e.g., by global digital platforms such as Facebook or Bitcoin). Here, we formalize the notion of grassroots systems and grassroots implementations;specify an abstract grassroots dissemination protocol;describe and prove an implementation of grassroots dissemination for the model of asynchrony;extend the implementation to mobile (address-changing) devices that communicate via an unreliable network (e.g. smartphones using UDP);and discuss how grassroots dissemination can realize applications that support digital sovereignty – grassroots social networking and grassroots currencies. The mathemat

关键词： Smartphones

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Heart Disease Risk Prediction Expending of Classification Algorithms

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computers, Materials & Continua 2022年第12期73卷 6595-6616页

作者： Nisha Mary Bilal Khan Abdullah A.Asiri Fazal Muhammad Salman Khan Samar Alqhtani Khlood M.Mehdar Hanan Talal Halwani Muhammad Irfan Khalaf A.Alshamrani City University of Science and Information Technology Peshawar25000Pakistan Radiology Science Department College of Applied Medical Science Najran University Kingdom of Saudi Arabia Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajranSaudi Arabia Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia

Heart disease prognosis(HDP)is a difficult undertaking that requires knowledge and expertise to predict early *** failure is on the rise as a result of today’s *** healthcare business generates a vast volume of patient records,which are challenging to manage *** it comes to data mining and machine learning,having a huge volume of data is crucial for getting meaningful *** methods for predictingHDhave been used by researchers over the last few decades,but the fundamental concern remains the uncertainty factor in the output data,aswell as the need to decrease the error rate and enhance the accuracy of HDP assessment ***,in order to discover the optimal HDP solution,this study compares multiple classification algorithms utilizing two separate heart disease datasets from the Kaggle repository and the University of California,Irvine(UCI)machine learning *** a comparative analysis,Mean Absolute Error(MAE),Relative Absolute Error(RAE),precision,recall,fmeasure,and accuracy are used to evaluate Linear Regression(LR),Decision Tree(J48),Naive Bayes(NB),Artificial Neural Network(ANN),Simple Cart(SC),Bagging,Decision Stump(DS),AdaBoost,Rep Tree(REPT),and Support Vector Machine(SVM).Overall,the SVM classifier surpasses other classifiers in terms of increasing accuracy and decreasing error rate,with RAE of 33.2631 andMAEof 0.165,the precision of 0.841,recall of 0.835,f-measure of 0.833,and accuracy of 83.49 percent for the dataset gathered from *** SC improves accuracy and reduces the error rate for the Kaggle dataset,which is 3.30%for RAE,0.016 percent for MAE,0.984%for precision,0.984 percent for recall,0.984 percent for f-measure,and 98.44%for accuracy.

关键词： Heart disease heart disease datasets evaluation measures classification techniques

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Advancing Brain Image Segmentation: A Comprehensive Exploration of Enhanced VGG16 Architecture for Precise Neuroanatomical Mapping 2

Advancing Brain Image Segmentation: A Comprehensive Explorat...

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2nd International Conference on Computing and Data Analytics, ICCDA 2024

作者： Sheeja Kumari, V. Wilfred Blessing, N.R. Palarimath, Suresh Gunasekaran, Hemalatha Poorana Senthilkumar, S. Sutherlin Subitha, G. Saveetha School of Engineering Simats University Department of Computational Intelligence Chennai600 025 India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman Dr. N. G. P Arts and Science College Department of Computer Applications Tamil Nadu Coimbatore India Stella Mary's College of Engineering Department of Computer Science and Engineering Tamil Nadu Kanyakumari India

ISBN: (纸本)9798331542573

This study introduces a novel approach to brain image segmentation by enhancing the VGG16 architecture. Neuroanatomical mapping is a critical aspect of understanding brain structures, and our research focuses on refining the VGG16 model to achieve more accurate segmentation results. We address the intricacies of brain image analysis, optimizing the architecture for the nuanced structures inherent in neuroimaging data. Our enhanced VGG16 model is thoroughly evaluated across diverse datasets, demonstrating superior performance compared to state-of-the-art segmentation methods. The framework proves robust and efficient in accurately delineating complex brain regions. We also delve into the interpretability of the segmentation results, shedding light on the learned features that contribute to the model's efficacy. The significance of our work extends beyond improved segmentation, offering valuable insights into the interpretability of deep learning models in neuroimaging. This research contributes to the broader field of biomedical image analysis and holds promise for applications in neuroscience and medical diagnostics. The proposed enhanced VGG16 framework emerges as a potent tool for advancing our understanding of brain structures and enhancing the capabilities of neuroimaging technologies. © 2024 IEEE.

关键词： Brain mapping

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