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1st International Conference on Computing, sciences and communications, ICCSC 2024

作者： Venkatesan, C. Thamaraimanalan, T. Mohammed Ibrahim, M. Vinoly, G. Ramkumar, M. Saranya, K. HKBK College of Engineering Department of Electronics and Communication Engineering Karnataka Bengaluru India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamilnadu Coimbatore India Sona College of Technology Department of Computer Science and Engineering Tamilnadu Salem India Tamilnadu Thiruchengode India Knowledge Institute of Technology Department of Computer Science and Business Systems Tamilnadu Salem India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353648

In Internet of Things (IoT) networks, long-range (LoRa) technology is well-known for its wide communication range, but using it with mobile devices presents unique difficulties. Frequently occurring channel attenuation, which causes packet loss, increased energy consumption, and longer transmission times, is one of these difficulties. An improved Adaptive Mobility-based IoT LoRa Clustering communication (AMILCC) strategy addressed to solve these problems. AMILCC partitions the network into optimal Spreading Factor (SF) areas by using a 2D random waypoint mobility model. The Hybrid Adaptive Data Rate (HADR) technique, which combines intra-and inter-SF region modifications to improve resource allocation for mobile IoT LoRa devices, is a key component of AMILCC. The simulation findings demonstrate the advantages of AMILCC over conventional stationary techniques, even under low mobility conditions (up to 5 m/s). In particular, AMILCC decreases energy usage by 56.5%, improves packet success ratio (PSR) by 72 %, and decreases end-to-end delay by 45.32%. These outcomes demonstrate how well AMILCC works to enhance efficiency and performance for mobile LoRa applications. © 2024 IEEE.

关键词： Resource allocation

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Ultrasound-based AI for COVID-19 detection: a comprehensive review of public and private lung ultrasound datasets and studies

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Multimedia Tools and Applications 2025年 1-55页

作者： Morshed, Abrar Al Shihab, Abdulla Jahin, Md Abrar Al Nahian, Md Jaber Hossain Sarker, Md Murad Ibne Wadud, Md Sharjis Uddin, Mohammad Istiaq Imtiaz Siraji, Muntequa Anjum, Nafisa Shristy, Sumiya Rajjab Rahman, Tanvin Khatun, Mahmuda Rubel Dewan, Md Hossain, Mosaddeq Sultana, Razia Chakma, Ripel Emon, Sonet Barua Islam, Towhidul Hussain, Mohammad Arafat Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka1208 Bangladesh Department of Electrical and Telecommunication Engineering Daffodil International University Dhaka1216 Bangladesh Khulna9203 Bangladesh Okinawa904-0412 Japan Department of Information and Communication Technology Bangladesh University of Professionals Dhaka1216 Bangladesh Department of Information and Communication Technology Comilla University Cumilla3506 Bangladesh Department of Biomedical Physics and Technology University of Dhaka Dhaka1000 Bangladesh Department of Computer Science and Engineering International Islamic University Chittagong Chittagong4318 Bangladesh Department of Electrical and Electronic Engineering Islamic University of Technology Gazipur1704 Bangladesh Department of Electronics and Telecommunication Engineering Chittagong University of Engineering and Technology Chittagong4349 Bangladesh Department of Computer Science and Engineering Notre Dame University Bangladesh Dhaka1000 Bangladesh Department of Electrical and Electronic Engineering Khulna University of Engineering & Technology Khulna9203 Bangladesh Department of Computer Science and Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Electronics and Communication Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Mathematics Jahangirnagar University Dhaka1342 Bangladesh Department of Electrical and Electronic Engineering Daffodil International University Dhaka1216 Bangladesh Department of Electrical and Electronic Engineering Dhaka University of Engineering and Technology Gazipur1707 Bangladesh Department of Computer Science and Telecommunication Engineering Noakhali Science and Technology University Noakhali3814 Bangladesh Department of Computer Science and Engineering Northern University Bangladesh Dhaka1230 Bangladesh Boston Children’s Hospital Harvard Medical School BostonMA02

The COVID-19 pandemic has affected millions of people globally, with respiratory organs being strongly affected in individuals with comorbidities. Medical imaging-based diagnosis and prognosis have become increasingly popular in clinical settings for detecting COVID-19 lung infections. Among various medical imaging modalities, ultrasound stands out as a low-cost, mobile, and radiation-safe imaging technology. In this comprehensive review, we focus on AI-driven studies utilizing lung ultrasound (LUS) for COVID-19 detection and analysis. We provide a detailed overview of both publicly available and private LUS datasets and categorize the AI studies according to the dataset they used. Additionally, we systematically analyzed and tabulated the studies across various dimensions, including data preprocessing methods, AI models, cross-validation techniques, and evaluation metrics. In total, we reviewed 60 articles, 41 of which utilized public datasets, while the remaining employed private data. Our findings suggest that ultrasound-based AI studies for COVID-19 detection have great potential for clinical use, especially for children and pregnant women. Our review also provides a useful summary for future researchers and clinicians who may be interested in the field. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： COVID-19

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Data Mining with Privacy Protection Using Precise Elliptical Curve Cryptography

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Intelligent Automation & Soft Computing 2023年第1期35卷 839-851页

作者： B.Murugeshwari D.Selvaraj K.Sudharson S.Radhika Department of Computer Science and Engineering Velammal Engineering CollegeChennai600066India Department of Electronics and Communication Engineering Panimalar Engineering CollegeChennai600123India Department of Information Technology Velammal Insitutue of TechnologyChennai601204India Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai600124India

Protecting the privacy of data in the multi-cloud is a crucial *** mining is a technique that protects the privacy of individual data while mining those *** most significant task entails obtaining data from numerous remote *** algorithms can obtain sensitive information once the data is in the data *** traditional algorithms/techniques promise to provide safe data transfer,storing,and retrieving over the cloud *** strategies are primarily concerned with protecting the privacy of user *** study aims to present data mining with privacy protection(DMPP)using precise elliptic curve cryptography(PECC),which builds upon that algebraic elliptic curve *** approach enables safe data exchange by utilizing a reliable data consolidation approach entirely reliant on rewritable data concealing ***,it outperforms data mining in terms of solid privacy procedures while maintaining the quality of the *** approximation error,computational cost,anonymizing time,and data loss are considered performance *** suggested approach is practical and applicable in real-world situations according to the experimentalfindings.

关键词： Data mining cryptography privacy preserving elliptic curve information security

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Smart Water Management System: Automated Water Billing with Detection and Control of Water Leakage 15

Smart Water Management System: Automated Water Billing with ...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Ninawe, Swapnil S. Pavithra, G. Manjunath, T.C. Samyama Gunjal, G.H. Department of Electronics & Communication Engineering Dayananda Sagar College of Engineering Karnataka Bangalore India Department of Computer Science & Engineering University Visvesvaraya College of Engineering Bangalore University Bangalore India

ISBN: (纸本)9798331300579

Water leakage is an important component of water losses. In addition to raising consumer awareness of their water use, metering is also an important way to identify and locate water leakage. A leakage detection program can be highly proactive, helping water utilities automate water system, detecting problem areas earlier, giving customer tools to monitor water use, providing more accurate rates and reducing demand. The objective is to overcome the disadvantages of using meter technology and make the billing troubleshooting process faster along with reducing the wastage of water. Message and Wi-Fi(IOT) technology automatically collect consumption, diagnosis, and collect status data from energy meter and advancement save utility provides the expense of periodic trips to each physical location to read a meter. Another advantage is billing can be based on near real time consumption rather than one estimates based or previous or predicted consumption. © Grenze Scientific Society, 2024.

关键词： Information leakage

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Design & Development of an Automatic Defense System for Warfare Applications 15

Design & Development of an Automatic Defense System for Warf...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Tejas, R. Venkatesh, L. Shivamadu, D.N. Ravishankar, K. Ninawe, Swapnil S. Gunjal, Samyama G.H. Pavithra, G. Manjunath, T.C. Department of Electronics & Communication Engineering Dayananda Sagar College of Engineering Bangalore India Department of Computer Science & Engineering University Visvesvaraya College of Engineering Bangalore University Karnataka Bengaluru India

ISBN: (纸本)9798331300579

This paper presents the design and implementation of an automated defence system interface leveraging Arduino *** an era of evolving security threats, the development of automatic defense systems has become increasingly *** abstract provides a concise overview of automatic defense systems, focusing on their technologies and *** defense systems encompass a wide range of technologies, including sensors, artificial intelligence, robotics, and communication *** systems are designed to detect, analyze, and respond to potential threats in real-time, minimizing human intervention and response *** components of automatic defense systems include sensor arrays for threat detection, data processing algorithms for threat analysis, and autonomous or semi-autonomous response *** core typically involves sensors (radar, lidar), a command-and-control system for threat evaluation, and countermeasures (missiles, lasers, guns).Applications of automatic defense systems span various domains, including military, homeland security, critical infrastructure protection, and public *** systems offer enhanced situational awareness, rapid threat response, and scalability to adapt to evolving security *** technology continues to advance, the integration of emerging technologies such as machine learning, drones, and IoT further enhances the capabilities of automatic defense *** project "Design & Development of an Automatic Defense System for Warfare Applications" focuses on creating a sophisticated, autonomous defense mechanism aimed at enhancing security and operational efficiency in military *** system integrates advanced sensor technologies, real-time data processing, and artificial intelligence to detect, identify, and neutralize potential threats with minimal human *** features include robust target recognition algorithms, adaptive threat response capabilities, and seamless in

关键词： Optical radar

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Design and Analysis of an Octagonal Chipless RFID System

Design and Analysis of an Octagonal Chipless RFID System

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Saranyaraj, D. Maheswari, A Uma Vellore Institute of Technology School of Computer Science and Engineering Chennai India Amrita School of Engineering Department of Electronics and Communication Engineering Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9798350361537

The proposed research introduces an approach to design an Octagonal chipless RFID tag to achieve tag identification to encode the information. These tags work by reflecting a unique electromagnetic signal when it is read by an RFID reader. The unique characteristics of the tag's structure or material cause modulation or reflection of the radio waves, which can be decoded by the reader to retrieve stored information. In this research, we propose an innovative approach for designing chipless RFID tags using electromagnetic resonators arranged in a 2x2 matrix configuration. While the design can be adapted to any nxn matrix, we specifically focus on the 2x2 arrangement in this study. The resonators are strategically positioned to achieve optimal performance, considering read range, robustness, and scalability factors. This paper provides a comprehensive review of Chipless RFID technology, covering its working principles, encoding techniques, applications, advantages, and challenges. © 2024 IEEE.

关键词： Scalability

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Integrated RFID Data Acquisition and Cloud-based Attendance Management System with Real-Time Google Sheets Synchronization 15

Integrated RFID Data Acquisition and Cloud-based Attendance ...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

ISBN: (纸本)9798331300579

This paper presents the development of an RFID-based attendance monitoring system utilizing NodeMCU ESP8266 microcontroller, MFRC522 RFID card module, and Google Sheets integration. The proposed system aims to automate the attendance process, offering a convenient and efficient solution for various educational and organizational settings. The system employs RFID technology for identification and authentication, and leverages the internet connectivity of NodeMCU ESP8266 to upload attendance data to Google Sheets in real-time. The integration with Google Sheets provides seamless data management and accessibility for administrators and stakeholders. The hardware components including NodeMCU ESP8266, MFRC522 RFID card module, 5V buzzer, breadboard, and jumper wires are utilized to build a compact and cost-effective attendance monitoring setup. The system architecture, hardware design, software implementation, and integration with Google Sheets are discussed in detail. Experimental results demonstrate the effectiveness and reliability of the proposed system in accurately recording attendance data and streamlining administrative tasks. © Grenze Scientific Society, 2024.

关键词： Information management

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Correntropy-Based Data Selective Adaptive Filtering

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IEEE Transactions on Circuits and Systems I: Regular Papers 2024年第2期71卷 754-766页

作者： Chien, Ying-Ren Wu, Sheng-Teng Tsao, Hen-Wai Diniz, Paulo S.R. Adaptive and Autonomous Communication Laboratory Department of Electrical Engineering National Ilan University Yilan City260007 Taiwan College of Electrical Engineering and Computer Science Graduate Institute of Communication Engineering National Taiwan University Taipei10617 Taiwan Program of Electrical Engineering Department of Electronics and Computer Engineering COPPE/Poli Universidade Federal do Rio de Janeiro Rio de Janeiro68504 Brazil

Data selection can be used in conjunction with adaptive filtering algorithms to avoid unnecessary weight updating and thereby reduce computational overhead. This paper presents a novel correntropy-based data selection method as an alternative to conventional data selection mechanisms based on squared error values. We developed a variable correntropy sensing algorithm to maximize the instantaneous correntropy function for the Gaussian kernel function to mitigate the impact of impulse noise and other forms of noise that can be disregarded in data selection. The proposed data selection mechanism can be implemented with any adaptive filtering algorithm. In simulations, the proposed method (implemented with the least mean squared algorithm) outperformed comparable error-based data selection schemes in terms of hit rate and miss rate, and the resulting weight updating ratio was close to the expected weight updating ratio. © 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

关键词： Adaptive filters

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Hybrid CNN-LSTM Model for Enhanced Weather Forecasting: Leveraging Spatial and Temporal Dependencies 4

Hybrid CNN-LSTM Model for Enhanced Weather Forecasting: Leve...

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

作者： Vallileka, N. Rajkumar, G.Vinoth Krishnan, R. Santhana Shankar, S. Vijay Francis Raj, J. Relin Karthikeyan, M. Saravana Psna College of Engineering and Technology Department of Computer Applications Dindigul India J. P. College of Engineering Department of Electronics and Communication Engineering Tenkasi India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Sona College of Technology Department of Electrical and Electronics Engineering Salem India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India

ISBN: (纸本)9798331523923

Accurate weather forecasting is critical for decision-making in agriculture, disaster management, urban planning, and numerous other domains. Traditional forecasting methods often struggle to capture the intricate spatial and temporal dependencies in weather data. This work proposes a hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to address these challenges. The CNN component efficiently extracts spatial features from satellite images, such as cloud formations and precipitation zones, while the LSTM network models the temporal dependencies inherent in meteorological data, such as temperature, humidity, and wind speed. The model is trained on a dataset comprising historical weather records and satellite imagery, spanning several years. PyTorch, a dynamic deep learning framework, is used to implement and train the hybrid architecture. Data preprocessing involves normalization, image augmentation, and sequence structuring to prepare inputs for the model. The CNN layers, built with convolutional and pooling operations, extract spatial features, which are combined with temporal weather data and passed to the LSTM network. The LSTM layers capture long-term dependencies and trends in weather patterns. Fully connected layers map the learned features to final predictions, including temperature, rainfall, and other key weather parameters. The hybrid model is evaluated using metrics such as Root Mean Square Error (RMSE) for numerical predictions and accuracy for classification tasks. Results indicate that the proposed hybrid CNN-LSTM model outperforms standalone models and traditional statistical approaches, demonstrating its ability to leverage spatial and temporal dependencies for accurate weather forecasting. This work highlights the potential of hybrid deep learning models in advancing weather forecasting capabilities, offering a scalable and reliable solution for real-world applications such as e

关键词： Convolutional neural networks

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Optimized Mutual Authentication and AES Encryption Framework for Enhanced Security in Multi-Server Environments 2

Optimized Mutual Authentication and AES Encryption Framework...

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2nd International Conference on Computational Intelligence, communication Technology and Networking, CICTN 2025

作者： Kulkarni, Soham Sunil Kumar, Anant Gakhar, Suket Sharma, Vipin Kumar Arora, Sneha Department of Computer Science University of California Irvine Irvine United States Department of Computer Science and Engineering Manipal Institute of Technology Manipal India Department of Computer Science and Engineering Kurukshetra University Kurukshetra India Department of Electronics and Communication Engineering Lovely Professional University Phagwara India

ISBN: (纸本)9798331530389

This paper introduces a secure authentication framework by applying Mutual Authentication Coupled with Optimised AES (Advanced Encryption Standard) Encryption to improve the security of the multi-server environment. By addressing inefficiencies in traditional mutual authentication protocols without encryption, this framework significantly improves both performance and resilience. Encryption time was reduced by 25% and authentication time by 16.67% while CPU (Central Processing Unit) usage was reduced by 23.08%, with a corresponding 21.43% improvement in memory efficiency. The proposed system demonstrates a 70% improvement in attack resistance. Dynamic revocation mechanisms mitigate unauthorized access and malicious activity in real time. The protocol is designed for resource-constrained environments to ensure secure, scalable, and efficient communication in multi-server architectures, further increasing system reliability and user trust in cloud-based services. © 2025 IEEE.

关键词： Authentication Protocol

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