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17th IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip, MCSoC 2024

作者： Swiley, Richard Preston Alsulieman, Razan Sherif, Ahmed Elsersy, Mohamed Khalil, Kasem University of Southern Mississippi School of Computing Sciences and Computer Engineering MS United States Higher Colleges of Technology Computer and Information Systems Dept. AlAin Abu Dhabi United Arab Emirates University of Mississippi Electrical and Computer Engineering Department MS United States

ISBN: (纸本)9798331530471

Drunk driving continues to be a substantial public health issue, leading to a multitude of accidents and deaths on a global scale. Conventional techniques for identifying drunk driving, such as breathalyzers and field sobriety tests, tend to be reactive rather than proactive. This paper surveys the existing methods for detecting drunk driving to enhance traffic safety. The technologies studied can proactively detect impaired driving behaviors by combining machine learning (ML) approaches with real-time data processing. The study outlines various classifications based on measured metrics and driving behavior. © 2024 IEEE.

关键词： Artificial Intelligence Drunk Driving Detection Traffic Safety

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Developing Sustainable Solutions Using Technological Approaches for Disaster Management and Energy Access in Mountainous Ranges: A Case Study of Sera Village, India 4

Developing Sustainable Solutions Using Technological Approac...

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4th International Conference on Advances in electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Kumar, Y. Jagadish Surya Vamsi, D. Nishaanth, K.N. Mouria, Naren Raghul, K.R. Sampath Kumar, S. Cornet, Souresh Coimbatore Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering India Coimbatore Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electrical and Computer Engineering India Coimbatore Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electronics and Communication Engineering India Coimbatore Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electrical and Electronics Engineering India Amrita Vishwa Vidyapeetham Amritapuri Amrita School for Sustainable Futures India

ISBN: (纸本)9798350343670

This research looks at two essential aspects of sustainable development in India's mountainous regions: energy access and vulnerability to natural disasters such as landslides. The research is based on an inductive in-depth evaluation of Sera village in Uttarakhand using Participatory Rural Appraisal (PRA) tools and Human-Centered Design (HCD) techniques. This approach enables a thorough understanding of these regions' complex challenges in order to provide relevant technological alternatives to meet the identified challenges. An integrated solution for landslide prediction powered by a hydropower system is presented, incorporating image processing and deep learning. This research provides useful insights for future work to address the issues inhabitants face of mountainous settings, ultimately supporting sustainable development in rural areas. © 2024 IEEE.

关键词： Deep learning Image Processing Landslides Live-in-Labs® Participatory Rural Appraisal Rural Electrification

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A flexible capacitive photoreceptor for the biomimetic retina

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Light(Science & Applications) 2022年第1期11卷 38-49页

作者： Mani Teja Vijjapu Mohammed E.Fouda Agamyrat Agambayev Chun Hong Kang Chun-Ho Lin Boon S.Ooi Jr-Hau He Ahmed M.Eltawil Khaled N.Salama Sensors lab Advanced Membranes and Porous Materials CenterComputerElectrical and Mathematical Science and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia Communication and Computing Systems Lab ComputerElectrical and Mathematical Science and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia Department of Electrical Engineering and Computer Science University of California-IrvineIrvineCA 92612USA Department of Electrical Computer and Energy EngineeringArizona State UniversityTempeAZUSA Computer Electrical and Mathematical Science and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia Department of Materials Science and Engineering City University of Hong KongHong Kong SARChina

Neuromorphic vision sensors have been extremely beneficial in developing energy-efficient intelligent systems for robotics and privacy-preserving security *** is a dire need for devices to mimic the retina's photoreceptors that encode the light illumination into a sequence of spikes to develop such ***,we develop a hybrid perovskite-based flexible photoreceptor whose capacitance changes proportionally to the light intensity mimicking the retina's rod cells,paving the way for developing an efficient artificial retina *** proposed device constitutes a hybrid nanocomposite of perovskites(methyl-ammonium lead bromide)and the ferroelectric terpolymer(polyvinylidene fluoride trifluoroethylene-chlorofluoroethylene).A metal-insulator-metal type capacitor with the prepared composite exhibits the unique and photosensitive capacitive behavior at various light intensities in the visible light *** proposed photoreceptor mimics the spectral sensitivity curve of human photopic *** hybrid nanocomposite is stable in ambient air for 129 weeks,with no observable degradation of the composite due to the encapsulation of hybrid perovskites in the hydrophobic *** functionality of the proposed photoreceptor to recognize handwritten digits(MNIST)dataset using an unsupervised trained spiking neural network with 72.05%recognition accuracy is *** demonstration proves the potential of the proposed sensor for neuromorphic vision applications.

关键词： perovskite illumination preserving

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Differentially private locality sensitive hashing based federated recommender system

Differentially private locality sensitive hashing based fede...

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作者： Hu, Hongsheng Dobbie, Gillian Salcic, Zoran Liu, Meng Zhang, Jianbing Lyu, Lingjuan Zhang, Xuyun Department of Electrical Computer and Software Engineering The University of Auckland Auckland New Zealand School of Computer Science The University of Auckland Auckland New Zealand Department of Electrical and Computer Engineering The University of Auckland Auckland New Zealand School of Mechanical Electrical and Information Engineering Shandong University Weihai Shandong China Department of Computer Science and Technology Nanjing University Nanjing China Department of Computer Science National University of Singapore Singapore Department of Computing Macquarie University Sydney Australia

Recommender systems are important applications in big data analytics because accurate recommendation items or high-valued suggestions can bring high profit to both commercial companies and customers. To make precise recommendations, a recommender system often needs large and fine-grained data for training. In the current big data era, data often exists in the form of isolated islands, and it is difficult to integrate the data scattered due to privacy security concerns. Moreover, privacy laws and regulations make it harder to share data. Therefore, designing a privacy-preserving recommender system is of paramount importance. Existing privacy-preserving recommender system models mainly adapt cryptography approaches to achieve privacy preservation. However, cryptography approaches have heavy overhead when performing encryption and decryption operations and they lack a good level of flexibility. In this paper, we conduct privacy analysis on the existing locality sensitive hashing (LSH) approach based privacy-preserving recommender system and show how an attacker can retrieve user's information under such a recommender system. Given such privacy risks, we propose differentially private LSH approach to build recommender system that can offer differential privacy guarantees for users. Our proposed efficient and scalable federated recommender system can make full use of multiple source data from different data owners while guaranteeing privacy preservation of users' data in contributing parties. Extensive experiments on real-world benchmark datasets show that our approach can achieve both high time efficiency and accuracy under small privacy budgets. © 2021 John Wiley & Sons, Ltd.

关键词： Budget control

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Twitter sentiment analysis using conditional generative adversarial network

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International Journal of Cognitive computing in engineering 2024年第1期5卷 161-169页

作者： Mahalakshmi, V. Shenbagavalli, P. Raguvaran, S. Rajakumareswaran, V. Sivaraman, E. Department of Information Technology Sengunthar College of Engineering Tiruchengode Tiruchengode637205 India Assistant Professor Karunya Institute of Technology and Sciences Coimbatore India Assistant Professor Department of Computational Intelligence School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Kanchipuram Chennai Tamil Nadu 603203 India Assistant Professor Department of Computer Science and Design Erode Sengunthar Engineering College Tamil Nadu Thuduppathi India Department of Electrical and Computer Engineering Curtin University Miri Malaysia

Sentiment analysis, which aims to extract information from textual data indicating people's ideas or attitudes about a particular problem, has developed into one of the most exciting study issues in natural language processing (NLP) with the development of social media. Twitter is a social network with an extensive audience that expresses their thoughts and opinions clearly and readily. Due to the prevalence of slang phrases and incorrect spellings in short phrase styles, Twitter data analysis is more challenging than data analysis from other social networks. Automated feature selection still has several limitations, such as higher computing costs that rise with the number of characteristics. Deep learning, which is self-learned and more accurate at processing vast amounts of data, is utilized to overcome these challenges. This paper introduces a conditional generative adversarial network (GAN) for Twitter sentiment analysis, whereas a convolutional neural network (CNN) has been used to extract traits from Twitter data. Compared to existing works, the proposed work has outperformed in accuracy, recall, precision, and F1 score. The suggested method is the most accurate, with a classification accuracy of 93.33 %. © 2024

关键词： Sentiment analysis

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Modelling and Performance Analysis of Visible Light Communication System in Industrial Implementations

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computers, Materials & Continua 2023年第11期77卷 2189-2204页

作者： Mohammed S.M.Gismalla Asrul I.Azmi Mohd R.Salim Farabi Iqbal Mohammad F.L.Abdullah Mosab Hamdan Muzaffar Hamzah Abu Sahmah M.Supa’at Faculty of Electrical Engineering Faculty of EngineeringUniversiti TeknologiMalaysiaSkudai81310Malaysia Lightwave Communications Research Group(LCRG) Faculty of Electrical EngineeringUniversiti TeknologiSkudaiJohorMalaysia Department of Communication Engineering Faculty of Electrical and Electronic EngineeringUniversiti Tun Hussein Onn Malaysia(UTHM)Parit RajaBatu PahatJohor86400Malaysia Faculty of Computing and Informatics Universiti Malaysia SabahKota Kinabalu88400Malaysia Department of Computer Science University of São PauloSão PauloBrazil

Visible light communication(VLC)has a paramount role in industrial implementations,especially for better energy efficiency,high speed-data rates,and low susceptibility to ***,since studies on VLC for industrial implementations are in scarcity,areas concerning illumination optimisation and communication performances demand further *** such,this paper presents a new modelling of light fixture distribution for a warehouse model to provide acceptable illumination and communication *** proposed model was evaluated based on various semi-angles at half power(SAAHP)and different height levels for several parameters,including received power,signal to noise ratio(SNR),and bit error rate(BER).The results revealed improvement in terms of received power and SNR with 30 Mbps data *** modulations were studied to improve the link quality,whereby better average BER values of 5.55×10^(−15) and 1.06×10^(−10) had been achieved with 4 PAM and 8 PPM,*** simulation outcomes are indeed viable for the practical warehouse model.

关键词： Visible light communication(VLC) industrial applications warehouse model light fixtures bit error rate(BER)

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A Comparative Evaluation of 3D Geometries of Scenes Estimated using Factor Graph Based Disparity Estimation Algorithms

A Comparative Evaluation of 3D Geometries of Scenes Estimate...

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IS and T International Symposium on Electronic Imaging: 3D Imaging and Applications, 3DIA 2023

作者： Shabanian, Hanieh Balasubramanian, Madhusudhanan Computer Science Department School of Computing and Analytics Northern Kentucky University Highland HeightsKY United States Electrical and Computer Engineering Department University of Memphis MemphisTN United States

Passive stereo vision systems are useful for estimating 3D geometries from digital images similar to the human biological system. In general, two cameras are situated at a known distance from each other and simultaneously capture images of the same scene from different views. This paper presents a comparative evaluation of 3D geometries of scenes estimated by three disparity estimation algorithms, namely the hybrid stereo matching algorithm (HCS), factor graph-based stereo matching algorithm (FGS), and a multi-resolution FGS algorithm (MR-FGS). Comparative studies were conducted using our stereo imaging system as well as hand-held, consumer-market digital cameras and camera phones of a variety of makes/models. Based on our experimental results, the factor graph algorithm (FGS) and multi-resolution factor graph algorithm (MR-FGS) result in a higher level of 3D reconstruction accuracy than the HCS algorithm. When compared with the FGS algorithm, MR-FGS provides a significant improvement in the disparity contrast along the depth boundaries and minimal depth discontinuities. © 2023, Society for Imaging Science and Technology.

关键词： Stereo image processing

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Explainable AI Model for Automatic Irrigation System using IOT 2

Explainable AI Model for Automatic Irrigation System using I...

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2nd International Conference on IT Innovations and Knowledge Discovery, ITIKD 2024

作者： Rajagopal, R. Sumathi, D. Sungheetha, Akey Rajesh Sharma, R. Mahapatra, Sheila Sivakumar, N. Alliance College of Advanced Computing Department of Computer Science and Engineering Alliance University Bangalore India Alliance University Department of Computer Science and Engineering Bengaluru India Alliance University Department of Electrical and Electronics Engineering Karnataka Bengaluru India Marwadi University Department of Computer Engineering Gujarat Rajkot India

ISBN: (纸本)9798350355468

This paper presents an explainable artificial intelli-gence (XAI) model for automatic irrigation systems using Inter-net of Things (IoT) technology. The proposed system integrates soil moisture sensors, temperature sensors, and humidity sensors with a microcontroller to make data-driven irrigation decisions. A Random Forest classifier i s employed top redict irrigation requirements based on environmental parameters, with SHAP (SHapley Additive exPlanations) values providing transparency and interpretability of model decisions. The system demonstrates a 35 % reduction in water usage and a 20 % increase in crop yield compared to traditional irrigation methods. The integration of explainability enhances user trust and system adoption, making advanced agricultural technology accessible to farmers with varying technical expertise. Experimental results show that the Random Forest model outperforms XGBoost and SVM in accuracy (75%), precision (100% for non-irrigation decisions), and robustness for irrigation management. © 2025 IEEE.

关键词： Agricultural robots

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Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning

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computers, Materials & Continua 2023年第1期74卷 607-621页

作者： Anwer Mustafa Hilal Aisha Hassan Abdalla Hashim Heba G.Mohamed Mohamed K.Nour Mashael M.Asiri Ali M.Al-Sharafi Mahmoud Othman Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University Malaysia 53100 Kuala LumpurMalaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyUniversity of BishaSaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

Cybersecurity-related solutions have become familiar since it ensures security and privacy against cyberattacks in this digital *** Uniform Resource Locators(URLs)can be embedded in email or Twitter and used to lure vulnerable internet users to implement malicious data in their *** may result in compromised security of the systems,scams,and other such *** attacks hijack huge quantities of the available data,incurring heavy financial *** the same time,Machine Learning(ML)and Deep Learning(DL)models paved the way for designing models that can detect malicious URLs accurately and classify *** this motivation,the current article develops an Artificial Fish Swarm Algorithm(AFSA)with Deep Learning Enabled Malicious URL Detection and Classification(AFSADL-MURLC)*** presented AFSADL-MURLC model intends to differentiate the malicious URLs from genuine *** attain this,AFSADL-MURLC model initially carries out data preprocessing and makes use of glove-based word embedding *** addition,the created vector model is then passed onto Gated Recurrent Unit(GRU)classification to recognize the malicious ***,AFSA is applied to the proposed model to enhance the efficiency of GRU *** proposed AFSADL-MURLC technique was experimentally validated using benchmark dataset sourced from Kaggle *** simulation results confirmed the supremacy of the proposed AFSADL-MURLC model over recent approaches under distinct measures.

关键词： Malicious URL cybersecurity deep learning machine learning metaheuristics gated recurrent unit

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pFedES: Generalized Proxy Feature Extractor Sharing for Model Heterogeneous Personalized Federated Learning 39

pFedES: Generalized Proxy Feature Extractor Sharing for Mode...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yi, Liping Yu, Han Ren, Chao Wang, Gang Liu, Xiaoguang Li, Xiaoxiao College of Computer Science TMCC SysNet DISSec GTIISC Nankai University China College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering The University of British Columbia Canada Vector Institute Canada

ISBN: (纸本)157735897X

Federated learning (FL), as a privacy-preserving collaborative machine learning paradigm, has attracted significant interest from industry and academia. To allow each data owner (FL client) to train a heterogeneous and personalized local model based on its local data distribution, system resources and requirements on model structure, the field of model-heterogeneous personalized federated learning (MHPFL) has emerged. Existing MHPFL approaches either rely on the availability of a public dataset with special characteristics to facilitate knowledge transfer, incur high computational and communication costs, or face potential model leakage risks. To address these limitations, we propose a model-heterogeneous personalized Federated learning approach based on generalized proxy feature Extractor Sharing (pFedES) for supervised image classification tasks. (1) We devise a shared small proxy homogeneous feature extractor before each client’s heterogeneous local model. (2) Clients train them via the proposed iterative learning to enable the exchange of global generalized knowledge and local personalized knowledge. (3) The small proxy local homogeneous extractors produced after local training are uploaded to the server for aggregation to facilitate knowledge fusion across clients. We theoretically prove pFedES converges with a non-convex convergence rate O(1/T). Experiments on 3 benchmark datasets against 9 baselines demonstrate that pFedES performs state-of-the-art model accuracy while maintaining efficient communication and computation. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

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