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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Naveen, S. Dhamotharan, R. Sahebzathi, S. Krishnan, R. Santhana Kumar, V. Vinoth Sundaravadivel, P. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Mechanical Engineering Chennai India Kalasalingam Academy of Research and Education Department of Computer Science and Information Technology Krishnankoil India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331523923

The ElephantGuardNet system is an advanced technological framework designed to monitor and protect elephants while minimizing human-elephant conflict. The system leverages a combination of real-time data collection, AI-driven analysis, and IoT sensors to track elephant movements across vast territories. By integrating wildlife camera traps, drones, satellite imagery, and IoT sensors such as infrared motion detectors and seismic sensors, the system provides continuous monitoring of elephant activity, enabling proactive management of potential risks. A custom dataset, ElephantMovementTracker, compiled from diverse data sources, forms the foundation for the model's training. Preprocessing techniques such as data normalization, augmentation, and noise filtering enhance the dataset's quality and ensure its suitability for deep learning applications. EfficientNet B7 and YOLO are employed for image and real-time detection tasks, respectively, while 3D CNNs are used for video analytics, enabling accurate tracking of dynamic movement patterns. GRU-based models predict future elephant movements, helping assess the likelihood of conflict. XGBoost is utilized for classification tasks, evaluating the risk of human-elephant conflict. The system is deployed on AWS and incorporates NVIDIA Jetson Nano for real-time detection and automated alerting via SMS and email. IoT sensors and automated deterrent systems, including sound emitters and drones, further mitigate conflict risk. The system's effectiveness in reducing human-elephant conflicts and providing a scalable solution for elephant conservation demonstrates the potential of AI and IoT integration in wildlife protection efforts, offering a comprehensive approach to both conservation and human-elephant coexistence. © 2025 IEEE.

关键词： Drones

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Enhancing 6G-Satellite Network Integration with Artificial Intelligence: A Future communication Paradigm 22

Enhancing 6G-Satellite Network Integration with Artificial I...

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22nd International Learning and technology Conference, L and T 2025

作者： Salah, Ibrahim Hussein, Aziza I. Wageh Lotfy, Mostafa Mabrook, Mohamed Mourad Almaaqal University College of Engineering Control and Computer Department Basrah61001 Iraq Effat University College of Engineering Energy and Technology Research Center Saudi Arabia Faculty of Technology and Education Beni-Suef University Dept. of Process Control Technology Beni-Suef Egypt Beni-Suef University Faculty of Navigation Science & Space Technology Space Communication Dept. Beni-Suef Egypt

ISBN: (纸本)9798331532215

The integration of 6G networks and satellite communications is set to revolutionize global connectivity, offering seamless coverage across terrestrial and non-terrestrial environments. Artificial Intelligence (AI) is essential for improving this integration, addressing challenges such as dynamic resource management, latency reduction, and network optimization. AI techniques like machine learning, deep learning, and reinforcement learning offer innovative solutions to handle the complexities of 6G-satellite networks. These advancements promise to significantly improve network efficiency, enhance data transmission reliability, and ensure seamless connectivity across different areas. Potential use cases include smart cities, autonomous vehicles, and Internet of Things (IoT) applications, where AI-driven 6G-satellite integration will be crucial. The proposed AI-enhanced 6G-satellite framework not only addresses current challenges but also lays the groundwork for a resilient, scalable, and globally interconnected communication infrastructure, offering a promising future. © 2025 IEEE.

关键词： Satellite communication systems

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Temperature-insensitive micro-displacement sensor with spindle-shaped structure single mode fiber

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Optoelectronics Letters 2025年

作者： ZHANG Weihua MU Jinlin WANG Hao WANG Dong YU Haozheng TAO Yipeng Engineering Research Center of Optoelectronic Devices and Communication Technology，Ministry of Education， Tianjin Key Laboratory of Film Electronic and Communication Devices， School of Integrated Circuit Science and Engineering， Tianjin University of Technology Engineering Infrastructure Department，Autonomous Driving Department Xiaomi EV Company Ltd

A temperature-insensitive micro-displacement sensor based on a spindle-shaped single mode fiber (SMF) is presented and demonstrated. The SMF is bent into a balloon-shaped SMF and burned into a spindle-shaped SMF by a flame. Due to the bending of the fiber， part of the incident light leaks from the fiber core into the fiber cladding， which excites higher order cladding modes. Therefore， modal interference occurs. The experimental results show that the maximum sensitivity of the sensors is -203 pm/μm when micro-displacement varies from 0 to 80 μm. The sensors are insensitive to temperature in the range of 20-70℃. In addition， the sensors have the advantages of simple structure， low cost， small volume， high sensitivity and stability， which can be widely used in numerous high-precision industrial measurements and civil engineering structure monitoring fields.

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Improving Food Safety by IoT-based Climate Monitoring and Control Systems for Food Processing Plants

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 387-399页

作者： Kolikipogu, Ramakrishna Shivaputra Muniyandy, Elangovan Maroor, Jnaneshwar Pai Lakshmi, Gundala Venkata Rama Konduri, Bhagavan Naveenkumar, R. Department of Information Technology Chaitanya Bharathi Institute of Technology Hyderabad India Department of Electronics and Communication Engineering Dr. Ambedkar Institute of Technology Bengaluru India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Business Administration Nitte Institute of Professional Education Nitte (Deemed to Be University) Karnataka Mangaluru India Department of Computer Science and Engineering CVR College of Engineering Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Computer Science and Engineering Chandigarh Colleges of Engineering Chandigarh Group of Colleges Jhanjeri Punjab Mohali 140307 India

Web different IoT apps offer lot more options for every player in the meat supply chain to monitor, control, and optimize their manufacturing method. Beginning within late 2017, industrial IoF2020 program will concentrate toward massive deployments of IoT via three use cases in the meat supply chain: pig farm management, poultry chain tracking, and for livestock visibility with openness. This initiative will include eleven collaborators spanning 5 nations. Installing and integrating IoT sensors for environmental and livestock management would alleviate producers’ difficulty getting knowledge that data needed regarding continuously tracking how well they are doing. Alert detection is additionally being created, integrating multiple sources of data in order to give farmers helpful input along with communicating news with other partners. By carrying out this, preventative or remedial measures can be done for infections, swine taint, bird mortality, feed waste, the natural world, et cetera. Additionally, EPCIS-based traceability from farm to fork will be allowed to ensure customers may get precise data about the source and caliber of their livestock. Each of these three use scenarios’ existing stage in work along with future plans are expected to be discussed. IoF2020 seeks to support the digital revolution in smart farming by tackling various educational and commercial problems while also promoting its use throughout Europe. It maintains mechanical necessary degree and security against physical along with toxic contaminants during industrial butchering of chicken, a lot measures have been taken. Buyers’ worries about food safety and health are the result of eating chicken increasingly. Our authorities have begun many kinds of initiatives aimed at improving safeguards for food. Our goal is to create an apparatus that alerts the factory farms affecting potential threats for product hygiene of food. With such setup, the detector can be mounted on top of the poultry slaugh

关键词： Food safety Internet of Things Meat processing Monitoring Supply chain

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CNN-Based Pneumonia Detection and Severity Analysis in Chest X-Ray Images for Enhanced Diagnostic Support

CNN-Based Pneumonia Detection and Severity Analysis in Chest...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Muthukumar, D. Surendiran Chowdary, Bommineni Sreekar Kumar, D. Senthil Jagadeesan, P. Mythili, B. Swapna, B. Kalasalingam Academy of Research and Education Department of computer science and Engineering Tamilnadu Srivilliputhur India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Virudhunagar India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Vel tech Multi tech Dr.Rangarajan Dr. Sakunthala Engineering college Department of Computer Science and Engineering Tamil Nadu Chennai India Dr.M.G.R Educational and Research Institute Department of Electronics and communication engineering Chennai India

ISBN: (纸本)9798331509828

Pneumonia continues to pose a substantial global health challenge, necessitating prompt and precise diagnosis to enhance patient outcomes. In order to detect pneumonia and classify its severity into mild, moderate, and severe phases, the web-based program uses deep learning algorithms to analyse chest X-ray pictures. A Convolutional Neural Network (CNN) model that has been trained on publicly accessible datasets and achieved high accuracy is integrated into the application. Furthermore, it provides personalized health recommendation, downloadable PDF reports, and a local hospital database to facilitate users in locating nearby treatment facilities. The system utilizes Fast API for backend processing and user-friendly interfaces to enhance accessibility and efficiency. This solution underscores the potential of artificial intelligence in supporting early diagnosis and effective pneumonia management. © 2025 IEEE.

关键词： Convolutional neural networks

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Investigation of Topological Behavior and Glass Transition Temperature of Chalcogenide M5-Se60-Te20-Ge15 (M = Sn, Zn, Sb) Glassy Alloys 2nd

Investigation of Topological Behavior and Glass Transition T...

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2nd International Conference on Mechanical Engineering, INCOM 2024

作者： Patra, Debtanu Biswas, Dipankar Mondal, Rittwick Ghosh, Bidyut Kumar Modak, Nipu Department of Mechanical Engineering Jadavpur University Kolkata700032 India Department of Mechanical Engineering Regent Education and Research Foundation Barrackpore Kolkata700121 India Department of Electronics and Communication Engineering Institute of Engineering and Technology GLA University Uttar Pradesh Mathura281406 India Department of Science Chowhatta High School West Bengal Birbhum731201 India Department of Electrical Engineering Regent Education and Research Foundation Barrackpore Kolkata700121 India

ISBN: (纸本)9789819766666

Throughout this communication, it has been reported the theoretical outcomes of doping of various additive elements on the glass composition Se60-Te20-Ge15-M5 (M = Sn, Zn, Sb). The dependence on mean coordination number, mean bond energy, the transition temperature of the glass, and cohesive energy with various additive elements have been studied based on topological considerations. A comprehensive investigation of various parameters like mean coordination number, bond-stretching and bond-bending parameters, energy gap, cohesive energy, glass transition temperature, and mean bond energy has been carried out. By deploying basic ideas grounded in average coordination numbers and bond energies, the cohesive energies of the systems have been estimated by computing the number of each type of predicted bond. Band gap energies of the systems have been estimated theoretically and the Sb-doped system possesses the lowest energy band gap with a value of 1.39 eV. The chemically ordered network model and constraints theory have been used to discuss these obtained results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Glass transition

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Disaster-Based Geographical Region Analysis Using Climate Change Detection Using Deep Learning Algorithm

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 555-565页

作者： Swapna, P.K. Mapari, Rahul Ganpat Muniyandy, Elangovan Tham, Jacquline Sophia, M. Sandra Carmel Haldar, Ritwik Department of English Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur 522502 India Electronics and Telecommunication Engineering Pimpri Chinchwad College of Engineering and Research Haveli Pune 412101 India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India Applied Science Research Center Applied Science Private University Amman Jordan Management and Science University Shah Alam Selangor Malaysia Department of Electronics and Communication Engineering Haldia Institute of Technology West Bengal Haldia 721657 India

An increasingly important field of research has emerged in recent decades: the interaction between urban infrastructure, human lives, and natural disasters. Analysis and reaction to natural disasters have made extensive use of deep learning (DL) techniques using semantic segmentation networks. The aim of this research is to develop novel method in disaster-based geographical region changes and climate change detection utilising DL model. Here, input is collected as disaster-based geographical region and climate change dataset as well as processed for noise removal and smoothening. Then, this image feature has been extracted and classified using watershed U-net component analysis with Hopfield variational fuzzy encoder neural networks. The fundamental component of the suggested approach is a deep learning procedure that makes it possible to identify soccer fields and analyse helicopter landing sites. The technique uses satellite photos and provided geographic data to train a deep learning autoencoder. Experimental analysis has been carried out in terms of classification accuracy, specificity, sensitivity, ROC, and F-measure. The proposed technique obtained 96% of classification accuracy, 94% of sensitivity, 98% of specificity, 95% of ROC, and 97% of F-measure. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Climate change detection Deep learning model Fuzzy encoder Geographical region changes U-net component analysis

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GUARDTRACK: RFID and Wi-Fi based Smart Entry System

GUARDTRACK: RFID and Wi-Fi based Smart Entry System

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Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： P. Chinnasamy Subramanian A Nithish Selvam R Kabilash K N Syed Nasar Mohiadeen Ibrahim Yoga Swetha D Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India Department of Information Technology School of Computing Kalasalingam Academy of Research and Education Tamil Nadu India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Tamil Nadu India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

It is challenging to maintain effective and secure access control in modern workplaces due to unauthorized access, RFID tag cloning, and the vulnerabilities of single-layer authentication systems. Conventional methods, e.g., paper logs and independent RFID authentication, are vulnerable to security intrusions, identity spoofing, and employee tracking inefficiencies. To overcome these issues, this paper introduces GUARDTRACK, an IoT-based smart entry system that integrates Wi-Fi-based tracking and RFID-OTP authentication for enhanced security. The system consists of two prime parts. First, an ESP32 Wi-Fi module tracks the attendance of workers by recognizing static IP assignments when registered devices enter the network. Second, a Security Room authentication system requires workers to scan an RFID card, triggering an OTP (One-Time Password) delivered via a GSM module to be received at their registered telephone number. Entry is granted only after successful verification of OTP for providing multi-layered protection. Experimental testing confirms that GUARDTRACK securely blocks unauthorized access while permitting easy entry for authorized personnel. The system offers high tracking accuracy, real-time authentication, and high RFID cloning resistance, providing a cost-effective and scalable access control solution.

关键词： Resistance Accuracy Simulation Employment Authentication Cloning Telephone sets Real-time systems Wireless fidelity Testing

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research trends in science education from 2018 to 2022: a systematic content analysis of publications in selected journals

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International Journal of science education 2025年第4期47卷 510-538页

作者： Lin, Tzung-Jin Lin, Tzu-Chiang Potvin, Patrice Tsai, Chin-Chung Program of Learning Sciences School of Learning Informatics National Taiwan Normal University Taipei Taiwan Institute for Research Excellence in Learning Sciences National Taiwan Normal University Taipei Taiwan Center for Liberal Arts National Kaohsiung University of Science and Technology Kaohsiung Taiwan Center for Teacher Education National Kaohsiung University of Science and Technology Kaohsiung Taiwan Département de didactique Université du Québec à Montréal Montréal Canada

This study uncovers research trends by analysing 1,142 papers published in science education, Journal of research in science Teaching, and International Journal of science education: Part A between 2018 and 2022, followed by a series of systematic reviews dating back to 1998. The main findings indicate that, during the period of 2018–2022, the three most studied research topics were associated with learner characteristics and classroom contexts (Learning-Context), teacher thinking/cognition and pedagogical issues (Teaching), and preservice education/in-service professional development (Teacher education). An emerging interest in investigating the influence of cultural, social, and gender factors on science education (Culture, Social, and Gender) was observed. The analysis of the top 10 most-cited papers unveiled a notable focus on pertinent theoretical discussions and empirical research within the context of STEM/STEAM education. Besides, issues regarding engagement in and out of school settings, learners’ epistemologies, or sensemaking and science as practice were also highly cited. It is worth noting that there has been a rapid surge and new trend in research concerning science identity, garnering substantial attention by researchers as a meaningful lens for exploring relevant issues associated with participation and pipeline/career paths in STEM-related fields. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Literature review research trend science education Systematic content analysis

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Cognitive Congestion Alleviation Framework in IoT-Enabled WSN for Next-Gen Intelligent Transport Systems via Optimized Capsule Attention Network 3

Cognitive Congestion Alleviation Framework in IoT-Enabled WS...

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3rd International Conference on Intelligent Data communication Technologies and Internet of Things, IDCIoT 2025

作者： Mohandas, R. Sudha, Y. Narmatha, P. Venkata Prasad, P. Manikandan, N. Department of Electronics & Communication Engineering Chennai Institute of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Presidency University Karnataka Bangalore India Department of Computer Science and Engineering Sri Sairam College of Engineering Karnataka Bangalore India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Telangana Hyderabad India Department of Business Administration Kalasalingam Business School Kalasalingam Academy of Research and Education Viruthunagar Krishnankoil India

ISBN: (纸本)9798331527549

The rapid advancement of Intelligent Transportation Systems (ITS) has heightened the importance of reliable, real-time data transmission in Wireless Sensor Networks (WSNs). However, congestion in data-heavy Internet of Things (IoT)-enabled networks remains a critical challenge, impacting communication efficiency and decision-making in ITS. In high-density IoT-ITS applications, traditional congestion control methods are insufficient to address the complexity and dynamics of data flows. Existing solutions often fail to adapt in real time to varying traffic loads, leading to delays and data loss. There is a strong need for a congestion alleviation framework capable of intelligently prioritizing data while dynamically managing network resources to ensure seamless information exchange in large-scale deployments. This study presents a Cognitive Congestion Alleviation Framework in IoT-Enabled WSN for Next-Gen Intelligent Transport Systems via Optimized Capsule Attention Network (V-CapMiAN-Parr), which combines the Vectorized Adaptive Capsule Neural Network (V-AdCapNet) and Multi-instance Attention Network (MAN), fine-tuned by the Parrot Optimizer (ParrOpt). This integration aims to effectively detect, mitigate, and prevent congestion through an advanced capsule-based attention mechanism with adaptive optimization. The suggested V-CapMiAN-Parr framework demonstrated significant improvements in congestion control, achieving data throughput efficiencies above 99.7%, packet delivery rates above 99.5%, and network reliability reaching 99.2% under high traffic loads. The model’s adaptive weighting mechanism ensures real-time responsiveness and reliability, crucial for next-gen ITS applications. The V-CapMiAN-Parr framework effectively addresses congestion issues in IoT-enabled WSNs, providing a robust, scalable solution for ITS applications. This cognitive framework’s advanced data prioritization and adaptive optimization capabilities are well-suited for enhancing the performanc

关键词： Traffic congestion

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