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Delhi Section Conference (DELCON), IEEE

作者： Saravanan M. S Sivashankar S A. Rajesh Masrullizam Mat Ibrahim Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India Department of Computer Science Engineering Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Tamilnadu India GLA University Mathura India Faculty of Electronics and Computer Technology and Engineering Universiti Teknikal Malaysia Melaka (UTeM) Melaka Malaysia

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

The aim is to classify and predict floods in advance with rain data patterns of India using spatio-temporal logic. Two Classification algorithms are used to achieve the maximum accuracy namely K-Nearest Neighbour with a sample size=5 and Logistic Regression with a sample size=5 for continues iterations. The work focused towards comparison of K-Nearest Neighbour and logistic regression, which has confidential and forecast the standards from the rainfall statistics to produce estimated accuracy with K-nearest neighbour has higher accuracy by comparing with Logistic Regression accuracy. It has a high accuracy of 50.35%, in comparison with the Logistic Regression algorithm 45.96%. The significant values have been statistically defined with the value of (p< 0.001). Prediction in flood patterns, K-Nearest Neighbour consisting rainfall pattern expressively used to produce improved accuracy than the Logistic Regression.

关键词： Logistic regression Accuracy Rain Machine learning algorithms Machine learning Nearest neighbor methods Prediction algorithms Floods Logic Standards

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RIS-Driven Resource Allocation Strategies for Diverse Network Environments: A Comprehensive Review

arXiv

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arXiv 2025年

作者： Ahmed, Manzoor Xu, Fang Lyu, Yuanlin Soofi, Aized Amin Li, Yongxiao Khan, Feroz Khan, Wali Ullah Sheraz, Muhammad Chuah, Teong Chee Deng, Min The School of Computer and Information Science The Institute for AI Industrial Technology Research Hubei Engineering University Xiaogan432000 China The School of Computer Science and Information Engineering Hubei University Wuhan430062 China The College of Technology Hubei Engineering University Xiaogan432000 China The Department of Computer Science National University of Modern Languages Faisalabad38000 Pakistan The Electronics Engineering Department Beijing University of Posts and Telecommunications Beijing100876 China The Balochistan University of Information Technology Engineering & Management Sciences Quetta Pakistan University of Luxembourg Luxembourg City1855 Luxembourg The Centre for Wireless Technology Faculty of Engineering Multimedia University Selangor Cyberjaya63100 Malaysia

This comprehensive survey examines how Reconfigurable Intelligent Surfaces (RIS) revolutionize resource allocation in various network frameworks. It begins by establishing a theoretical foundation with an overview of RIS technologies, including passive RIS, active RIS, and Simultaneously Transmitting and Reflecting RIS (STAR-RIS). The core of the survey focuses on RIS's role in optimizing resource allocation within Single-Input Multiple-Output (SIMO), Multiple-Input Single-Output (MISO), and Multiple-Input Multiple-Output (MIMO) systems. It further explores RIS integration in complex network environments, such as Heterogeneous Wireless Networks (HetNets) and Non-Orthogonal Multiple Access (NOMA) frameworks. Additionally, the survey investigates RIS applications in advanced communication domains like Terahertz (THz) networks, Vehicular Communication (VC), and Unmanned Aerial Vehicle (UAV) communications, highlighting the synergy between RIS and Artificial Intelligence (AI) for enhanced network efficiency. Summary tables provide comparative insights into various schemes. The survey concludes with lessons learned, future research directions, and challenges, emphasizing critical open issues. Copyright © 2025, The Authors. All rights reserved.

关键词： Resource allocation

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SVM Classifier for Hygienic Food Handling with Cloud-Connected UV-C Sterilization Solutions

SVM Classifier for Hygienic Food Handling with Cloud-Connect...

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International Conference on Communications and Signal Processing

作者： N. Arunfred Chitra Sabapathy Ranganathan P.S. Kumaresh B. Maruthukannan D. Gobinath Bhuvaneswari Arunagiri Faculty of Management Studies SRM Institute of Science and Technology Kattankulathur Chengalpattu Chennai Tamil Nadu India Corporation Chandler Arizona USA Department of Electronics and Communication Engineering K.L.N.College of Engineering Pottapalayam Sivagangai Tamil Nadu India Department of Computer Science and Engineering NPR College of Engineering & Technology Dindigul Tamil Nadu India Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai Tamil Nadu India Department of Technology Adhiparasakthi Engineering College Melmaruvathur Tamil Nadu India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

This research integrates a support vector machine (SVM) classifier with cloud-connected ultraviolet-C (UV-C) sterilization systems to improve food handling hygiene. To ensure food safety, this system uses machine learning to analyze ambient conditions, handling processes, and equipment cleanliness. The SVM classifier classifies hygiene levels based on input data for real-time monitoring and decision-making. Cloud-connected UV-C sterilization technologies automate and streamline food industry microorganism control. Remote monitoring and control via the cloud lets stakeholder’s access vital data and take immediate action. This increases food handling cleanliness, system speed, and responsiveness. The research examines the reduction of microbial contamination in various settings by using the SVM classifier and the cloud-connected UV-C sterilization solutions. Results show a considerable increase in hygiene level predictions and a significant decrease in the presence of bacteria in food processing. The SVM classifier and cloud-connected UV-C sterilization systems might improve food safety standards. Machine learning and cloud technologies provide a complete and adaptable solution to sanitary food handling issues, providing customers with safe and high-quality food items.

关键词： Support vector machines Visualization Machine learning Transforms Signal processing Safety Stakeholders

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Classification of Libya Floods and Morocco Earthquakes Using CNN-Based Deep Learning Models

Classification of Libya Floods and Morocco Earthquakes Using...

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electronics, Communications, and Computations (JAC-ECC), International Japan-Africa Conference on

作者： Kibitok Abraham Moataz Abdelwahab Mohammed Abo-Zahhad School of Electronics Communications & Computer Engineering Egypt-Japan University of Science & Technology (E-JUST) New Borg El Arab City Alexandria Egypt Electrical Engineering Department Faculty of Engineering Assiut University Assiut Egypt

Natural disaster's impact on human lives has been costly. The Libya floods and Morocco earthquakes affected more than two million people. Deep learning and computer vision techniques can mitigate the challenges brought by natural disasters. To enhance this field, a dataset from these two natural disasters, including aerial and lateral images, has been collected, augmented, and developed where 13 deep learning models, including AlexNet, VGG16, VGG19, GoogleNet, InceptionV3, ResNet-1S, ResNet-50, ResNet-101, DenseNet-201, NasNetLarge, EfficientNet-b0, MobileNetV2, and SqueezeNet have been tested. In addition, the newly developed YOLOvS deep learning models have been applied to the dataset. The YOLOvS-m model achieves the highest accuracy of 97.36 %. On the other hand, the SqueezeNet and YOLOvS-n model stand out for their lightweight nature, boasting a compact size of 2.658 MB and 3 MB, respectively. The AlexN et model exhibits a short training time of 367 seconds relative to other models that have been implemented. The YOLOvS-x model demonstrates the highest precision rate of 9S.4 %. Conversely, the YOLOvS-1 model exhibits the highest recall rate of 9S.51 % and the best F1-Score of 97.8 %.

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New Decision-Making Technique Based on Hurwicz Criteria for Fuzzy Ranking

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computers, Materials & Continua 2022年第12期73卷 4595-4609页

作者： Deepak Sukheja Javaid Ahmad Shah G.Madhu K.Sandeep Kautish Fahad A.Alghamdi Ibrahim.S.Yahia El-Sayed M.El-Kenawy Ali Wagdy Mohamed Department of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadIndia Department of Mathematics Dr.APJ Abdul Kalam University Indore(M.P)India Department of Information Technology VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadIndia LBEF Campus Kathmandu44600Nepal Department of Management Information Systems College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia Laboratory of Nano-Smart Materials for Science and Technology(LNSMST) Department of PhysicsFaculty of ScienceKing Khalid UniversityP.O.Box 9004Abha 61413Saudi Arabia Research Center for Advanced Materials Science(RCAMS) King Khalid UniversityP.O.Box 9004Abha 61413Saudi Arabia Nanoscience Laboratory for Environmental and Biomedical Applications(NLEBA) Semiconductor Lab.Department of PhysicsFaculty of EducationAin Shams UniversityRoxyCairo 11757Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Department of Mathematics and Actuarial Science School of Sciences Engineering The American University in CairoEgypt

Efficient decision-making remains an open challenge in the research community,and many researchers are working to improve accuracy through the use of various computational *** this case,the fuzzification and defuzzification processes can be very *** is an effective process to get a single number from the output of a fuzzy *** defuzzification as a center point of this research paper,to analyze and understand the effect of different types of vehicles according to their *** this paper,the multi-criteria decision-making(MCDM)process under uncertainty and defuzzification is discussed by using the center of the area(COA)or ***,to find the best solution,Hurwicz criteria are used on the defuzzified ***-making technique is proposed using Hurwicz criteria for triangular and trapezoidal fuzzy *** proposed technique considers all types of decision makers’perspectives such as optimistic,neutral,and pessimistic which is crucial in solving decisionmaking problems.A simple case study is used to demonstrate and discuss the Centroid Method and Hurwicz Criteria for measuring risk attitudes among *** significance of the proposed defuzzification method is demonstrated by comparing it to previous defuzzification procedures with its application.

关键词： Defuzzification decision-making fuzzy numbers Hurwicz multicriteria decision-making ranking order

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Stress Detection of Children With ASD Using Physiological Signals

Stress Detection of Children With ASD Using Physiological Si...

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IEEE Signal Processing and Communications Applications (SIU)

作者： Sevgi Nur Bilgin Aktaş Pı nar Uluer Buket Coş kun Elif Toprak Duygun Erol Barkana Hatice Kö se Tatjana Zorcec Ben Robins Agnieszka Landowska Department of AI and Data Engineering Faculty of Computer and Informatics Istanbul Technical University Istanbul Turkey Department of Computer Engineering Faculty of Technology and Engineering Galatasaray University Istanbul Turkey Department of Electrical and Electronics Engineering Faculty of Engineering Yeditepe University Istanbul Turkey University Children&#x2019 s Hospital Skopje North Macedonia School of Computer Science University of Hertfordshire Hatfield UK Department of Software Engineering Faculty of Electronics Telecommunications and Informatics Gdansk University of Technology Gdansk Poland

ISBN: (数字)9781665450928

ISBN: (纸本)9781665450935

This paper proposes a physiological signal-based stress detection approach for children with autism spectrum disorder (ASD) to be used in social and assistive robot intervention. Electrodermal activity (EDA) and blood volume pulse (BVP) signals are collected with an E4 smart wristband from children with ASD in different countries. The peak count and signal amplitude features are derived from EDA signal and used in order to detect the stress of children based on the previously provided reference baselines. Furthermore, a comparison has been made with the stress values determined using low frequency (LF) and high frequency (HF) values extracted from BVP signal.

关键词： Pediatrics Autism Social robots Signal processing Feature extraction Assistive robots Physiology

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Transformer-Based Deep Learning Detector for Dual-Mode Index Modulation 3D-OFDM

arXiv

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arXiv 2023年

作者： Gian, Toan Nguyen, Tien-Hoa Nguyen, Trung Tan Pham, Van-Cuong Van Luong, Thien The School of Electrical and Electronics Engineering Hanoi University of Science and Technology Hanoi Viet Nam The Faculty of Radio-Electronics Le Quy Don Technical University Hanoi11355 Viet Nam The Faculty of Computer Science Phenikaa University Hanoi12116 Viet Nam

In this paper, we propose a deep learning-based signal detector called TransD3D-IM, which employs the Transformer framework for signal detection in the Dual-mode index modulation-aided three-dimensional (3D) orthogonal frequency division multiplexing (DM-IM-3D-OFDM) system. In this system, the data bits are conveyed using dual-mode 3D constellation symbols and active subcarrier indices. As a result, this method exhibits significantly higher transmission reliability than current IM-based models with traditional maximum likelihood (ML) detection. Nevertheless, the ML detector suffers from high computational complexity, particularly when the parameters of the system are large. Even the complexity of the Log-Likelihood Ratio algorithm, known as a low-complexity detector for signal detection in the DM-IM-3D-OFDM system, is also not impressive enough. To overcome this limitation, our proposal applies a deep neural network at the receiver, utilizing the Transformer framework for signal detection of DM-IM-3D-OFDM system in Rayleigh fading channel. Simulation results demonstrate that our detector attains to approach performance compared to the model-based receiver. Furthermore, TransD3D-IM exhibits more robustness than the existing deep learning-based detector while considerably reducing runtime complexity in comparison with the benchmarks. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal detection

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Numerical solution of the problem of turbulent currents that occur in the case of edge hardening

Numerical solution of the problem of turbulent currents that...

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International Conference on electronics, computers and Artificial Intelligence (ECAI)

作者： Marius Codrean Mihaela Cornelia Novac Mihaela Codrean Cornelia Emilia Gordan Ovidiu Constantin Novac Gyongyi Bujdoso Francisc Slovac Department of Electrical Engineering Electrical Engineering and Information Technology Faculty University of Oradea Oradea Romania National Geothermal Research Center Technology Transfer Center NGRC-TTC University of Oradea Oradea Romania Department of Electronics and Telecommunications Electrical Engineering and Information Technology Faculty University of Oradea Oradea Romania Department of Computers and Information Technology Electrical Engineering and Information Technology Faculty University of Oradea Oradea Romania Department of Computer Science and Library and Information Science Faculty of Informatics University of Debrecen Debrecen Hungary

The aim of this work is to develop numerical procedures for analyzing edge hardening using induction heating by turbulent currents. The section through the steel bar, whose lower edge needs to be hardened, is presented. For the inductor adapted to the piece configuration, the 2D model is assumed. The surface treatment of the piece must be differentiated: in the tip area, the heating must be greater than in the rest of the surface. Only the tip area needs to be subjected to hardening, while the rest of the piece must remain elastic to ensure the desired mechanical strength. The main purpose of this work is to propose inductor models for achieving differentiated heating of the blade.

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Enhancing Video Captioning: A Bayesian Normalized Attention-based Multi-Dimensional Graph Network with Moss Growth Optimization

Enhancing Video Captioning: A Bayesian Normalized Attention-...

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： Khallikkunaisa H. S. Niranjana Murthy Shirish Kulkarni Syed Mohd Uzair Iqbal Umang Pancha Natrayan L Department of Computer Science and Engineering HKBK College of Engineering Bangalore Karnataka India Department of Electronics and Instrumentation Engineering Ramaiah Institute of Technology Bangalore Karnataka India Symbiosis Law School Symbiosis Centre for Advanced Legal Studies and Research Symbiosis International (Deemed University) Pune India Faculty of Law Symbiosis Centre for Advanced Legal Studies and Research Symbiosis Law School Symbiosis International (Deemed University) Pune India Department of Computer Science and Engineering Faculty of Engineering and Technology Parul Institute of Technology Parul University Post Limda Gujarat India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

The explosive increase of video data requires the creation of advanced automated video captioning systems that can produce effective textual descriptions. Current methods, such as traditional deep learning (DL) models, are plagued with drawbacks such as high computational expense, overfitting, and inefficacy in contextual modeling, resulting in poor caption generation. For resolution of these difficulties, this contribution introduces the Bayesian Normalized Attention-based Multi-Dimensional Graph Network with Moss Growth Optimization (BN-AMGN-MGO) model, engineered to improve video captioning automatization on the MSVD set. The proposed framework starts off with pre-processing, in which unnecessary frames are removed via template matching to get only the representative frames. The feature encoding process utilizes a Bayesian Normalized Neural Network (BNNN) for dynamically manipulating probabilistic weight distributions to counter overfitting and increase robustness. During the decoder process, a Hierarchical Attention-based Multi-Dimensional Edge Graph Neural Network (HAM-GNN) combines temporal context modeling, graph neural networks, and hierarchical attention mechanisms to create structured textual descriptions. Classification stage employs a temporal attention mechanism to project dialogue act labels into attention-weighted representations, utilizing speaker dependencies for better accuracy. To further optimize performance, MGO is employed to refine HAM-GNN’s hyperparameters through wind direction determination, spore dispersal search, dual propagation search, and cryptobiosis mechanisms, ensuring effective exploration and exploitation of the search space. The proposed BN-AMGN-MGO model achieves the lowest MSE (0.021), highest PSNR (34.89), and SSIM (0.905), ensuring high-quality caption generation. Additionally, it outperforms existing models with an accuracy of 94.5% while maintaining the shortest computational time (5.6 sec), making it a robust and efficient

关键词： Attention mechanisms Accuracy Computational modeling Streaming media Graph neural networks Encoding Bayes methods Computational efficiency Context modeling Overfitting

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Talk to your data: Enhancing Business Intelligence and Inventory Management with LLM-Driven Semantic Parsing and Text-to-SQL for Database Querying

Talk to your data: Enhancing Business Intelligence and Inven...

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Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Jerry Zhu Saad Ahmed Bazaz Srimonti Dutta Bhavaraju Anuraag Imran Haider Srijita Bandopadhyay Faculty of Mathematics University of Waterloo Ontario Canada Dept. of Computer Science National University of Computer and Emerging Sciences Islamabad Pakistan School of Agro and Rural Technology Indian Institute of Technology Guwahati Guwahati India Dept. of Electronics and Communication Engineering Vellore Institute of Technology India Dept. of Industrial and Systems Engineering Indian Institute of Technology Kharagpur Kharagpur India Dept. of Electronics and Communication Engineering University of Engineering and Management Kolkata India

ISBN: (数字)9798350369786

ISBN: (纸本)9798350369793

This paper delves into the potential of Large Language Models (LLMs) in revolutionizing business intelligence and inventory management through semantic parsing and text-to-SQL methodologies. It assesses various LLM models, such as DIN-SQL, DSP, NSQL, GPT, CoPilot, and LLaMa, elucidating their capabilities and contributions. Two critical analyses are presented here. The first compares cutting-edge LLM models using cosine similarity and cost efficiency metrics. The second analysis enhances GPT’s precision through prompt engineerings, like few-shot techniques, and explores frameworks like DIN-SQL, NSQL, and DSP. DIN-SQL substantially boosts accuracy, and NSQL demonstrates potential in specific scenarios. This research underscores the transformative potential of LLM-driven models in business intelligence and inventory management. DIN-SQL, in particular, emerges as a game-changer with the potential to reshape inventory management practices. GPT showcases its versatility through fine-tuning for tasks beyond conventional programming, while CoPilot offers a cost-effective alternative. This study emphasizes the importance of cost-effectiveness in real-world applications, with LLaMa and CoPilot being practical choices. NSQL, with its budget-friendly and semi-accurate solution, holds promise for semantic parsing in growing companies. These insights are a foundation for further innovation, promising unmatched efficiency and competitiveness across industries in the evolving Artificial intelligence landscape.

关键词： Industries Structured Query Language Technological innovation Costs Large language models Semantics Inventory management

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