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International Conference on Trends in Electronics and Informatics (ICEI)

作者： Kavinesh S Karunambikai Department of Computer Application Hindustan Institute of Technology and Science Chennai Tamilnadu India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

The highly established transport infrastructure, effective, precise, and timely vehicle-to-vehicle communication is a necessary process. Edge computing is transforming V2X communication by virtue of near-source real-time processing of data and thus accelerating decision-making and the system in turn. In this paper, an improved framework of Reinforcement Learning and Random Forest algorithms for improving data filtering, bandwidth utilization, and system scalability is presented. Compared to the classical cloud-based systems, the emerging edge computing paradigm allows for faster response times in mission-critical applications such as collision avoidance, intelligent traffic signaling, and real-time navigation. The system makes use of Reinforcement Learning for real-time decision-making tasks and Random Forest algorithms for optimal data clustering with ***. The approach also minimizes unnecessary data transfer to the cloud, thereby minimizing bandwidth usage and maximizing performance. Experimental results corroborate that the target model attains 20% reduced latency, 15% increased bandwidth efficiency, and 10% improved prediction accuracy over conventional V2X systems. In addition, network convergence with the 5G network infrastructure facilitates improved communication speed and quality and facilitates seamless end-to-end connectivity among and to connected and autonomous vehicles. While the system has positive impacts, current global issues such as hardware limitation and energy consumption are still present. Future research will further extend the hardware support, accelerate data processing mechanisms, and improve scalability to large-scale V2X environments. In conclusion, this paper presents a realistic and effective solution to improving V2X communication and making transportation systems safer, smarter, and more efficient.

关键词： Scalability Decision making Transportation Reinforcement learning Bandwidth Real-time systems Hardware Vehicle-to-everything Random forests Edge computing

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Identification of Key Genetic Drivers in Multidrug Resistance: A Co-Expression Network Analysis of Pseudomonas aeruginosa

Identification of Key Genetic Drivers in Multidrug Resistanc...

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2025 International Conference on Ambient Intelligence in Health Care, ICAIHC 2025

作者： Routray, Sweta Padma Mahapatra, Saswati Nayak, Debasish Swapnesh Kumar Subudhi, Enketeswara Swarnkar, Tripti University Centre for Biotechnology Bhubaneswar India University Department of Computer Application Bhubaneswar India Centurion University of Technology and Management Department of Computer Science and Engineering Bhubaneswar India National Institute of Technology Department of Computer Application Raipur India

ISBN: (纸本)9798331530259

Antimicrobial resistance (AMR) in Pseudomonas aeruginosa, a critical pathogen in healthcare-associated infections, demands new insights into underlying genetic mechanisms. This study aimed to identify key regulatory genes contributing to multidrug resistance (MDR) through a comprehensive analysis of gene expression in clinical isolates. RNA-Seq data from 246 clinical isolates of P. aeruginosa was analyzed using Weighted Gene Co-Expression Network Analysis (WGCNA) to construct co-expression networks. Modules of co-expressed genes were identified, and hub genes with high connectivity were selected, focusing on those within and across modules, including the largest module enriched with resistance-related genes. Pathway enrichment was performed using STRING and ShinyGO, highlighting key resistance-associated pathways. Pathway enrichment analysis of hub genes identified polA as a central player in DNA repair and replication processes, crucial for bacterial survival under antibiotic stress. In addition to polA, other highly connected hub genes, such as dnaB, tatC, and ppnK, were enriched in pathways including protein transport and mismatch repair. Within the turquoise module, hub genes like dnaA, uvrD, and gyrA were primarily involved in ATP-binding, ligase activity, and biosynthetic processes, essential for antibiotic resistance. Network analysis further emphasized the centrality of polA and its role in bridging multiple resistance pathways, highlighting its potential as a therapeutic target. This research highlights polA and other critical hub genes as key regulatory elements within the MDR networks of P. aeruginosa, playing essential roles in pathways vital for bacterial survival. The identification of these genes provides promising biomarkers and potential therapeutic targets to counteract MDR in clinical settings. © 2025 IEEE.

关键词： Transcription

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SmartPark Visionaire: AI-Driven Advanced Parking Slot Detection

SmartPark Visionaire: AI-Driven Advanced Parking Slot Detect...

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International Conference on Inventive Computation Technologies (ICICT)

作者： N. Santosh M. Sathish Kumar Department of Computer Application Hindustan Institute of Technology & Science Padur Chennai Tamilnadu

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

The parking conflicts are now held most important among the urban problems leading to its own traffic congestion and wastage of fuel. To relieve the situation, SmartPark is hereby proposed, an intelligent car parking system implementing the YOLOv8 state-of-the-art object detection model capable of identifying parking slots in realtime and accurately. It is trained over a very large dataset of annotated parking lot images by applying different preprocessing and augmentation techniques to further improve the detection accuracy. Performance parameters of the model are determined via measuring mean Average Precision (mAP), confusion matrix analysis, and latency comparison, with the very commendable performance of accuracy and efficiency over the previous YOLO versions. The results strongly signify that SmartPark shows very high precision in detecting free and occupied parking slots, thus greatly reducing time in parking searches and contributing to traffic decongestion. The blend of deep learning and real-time detection supports SmartPark's proposition for smart city infrastructure paving way for automated parking governance and pleasant user experience. In summary, this paper underscores the real-world applicability of YOLOv8 to parking systems that subsequently optimize urban mobility and utilization of resources.

关键词： Deep learning YOLO Accuracy Smart cities Automated parking Real-time systems User experience Robustness Optimization Traffic congestion

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Secure Fingerprint Authentication in ATM Transaction using Blockchain technology

Secure Fingerprint Authentication in ATM Transaction using B...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： Dhivakar V. R Kalpana K Department of Computer Application Hindustan Institute of Technology and Science Chennai Tamilnadu India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Security of ATMs relies on a variety of authentication methods to verify user identity or ensure the integrity of transactions. The traditional PIN-based authentication faces mostly threats that jeopardize its very foundation, including card skimming, pin thefts, and software breaches, amongst others. The paper proposes a fingerprint authentication system for ATMs with integrated blockchain technology. This system employs tree-based classification algorithms to determine fingerprint features, such as ridge patterns and minutiae points, thus enabling accurate and reliable authentication of users. The ensemble classifiers profited boost in classification accuracy and reduced overfitting, thus increasing the robustness of the developed system. The successful authentication of fingerprints has given access to safer functionalities at ATMs-adverse to the fraud risks prevalent for all other methods. With the implementation of blockchain technology, it assures integrity of transactions data, along with transparency and protection from alteration owing to the immutable ledger of transactions. The accountability brought about by the decentralized nature of blockchain makes ATM transactions secured and reliable. This work intends to deliver a prompt, secured, and user-friendly authentication mechanism replacing PIN-based systems while making seamless financial transactions with higher fraud protection possible.

关键词： Accuracy Scalability Authentication Banking Fingerprint recognition Software Blockchains Fraud Security Protection

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AI-Powered Veterinary Chatbot for Automated Diagnosis

AI-Powered Veterinary Chatbot for Automated Diagnosis

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International Conference on Inventive Computation Technologies (ICICT)

作者： Prashanna J Vanita Jaitly Department Of Computer Application Hindustan Institute of Technology and Science Chennai Tamilnadu India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

The busy lifestyle today results in ignoring healthcare for want of time and inconvenience. Healthcare is essential for pets, but going to the veterinarian wastes plenty of time and money. This paper proposes the use of an AI-driven veterinary chatbot for disease diagnosis and providing preliminary information on health before a veterinarian consultation. The system operates based on machine learning (ML) and natural language processing (NLP) algorithms to emulate veterinary consultations in helping users suffering from health issues efficiently. The chatbot can be compared to a doctor's aide, saving on the costs of veterinary medicine and increasing the availability. The system is developed from a mammoth database in order to optimize diagnostic accuracy with a simple interface for real-time interaction. The system facilitates the purchase of medicines and integration of payments, thereby making healthcare more efficient. By employing AI, this chatbot enhances animal healthcare services to pet owners and vets effectively, at a reasonable price, and within affordable reach.

关键词： Technological innovation Machine learning algorithms Supervised learning Medical services Machine learning Chatbots Real-time systems Data mining Medical diagnosis Medical diagnostic imaging

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Blockchain technology for digital twin security in smart grids using interpretable generalized additive neural networks

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Peer-to-Peer Networking and applications 2025年第4期18卷 1-17页

作者： Suggala, Ravi Kumar Kumar, Jaydip Jain, Prateek Kumar, Boddepalli Kiran Department of Information Technology Shri Vishnu Engineering College for Women Bhimavaram India Department of Computer Science and Application COER University 7th km on Roorkee – Haridwar Road Vardhmanpuram Uttarakhand Roorkee247 667 India Department of Computer Science School of Engineering and Sciences GD Goenka University Sohna Gurugram India Department of Computer Science and Engineering Aditya University Surampalem Kakinada533437 India

The Smart Grid (SG), as the next generation of efficient and intelligent electric power transmission networks, is driven by the Internet of Things (IoT). However, Smart Meters (SMs) within the SG environment often rely on unsecured public channels to exchange data and services, posing significant security risks. This manuscript proposes Blockchain technology for Digital Twin Security in Smart Grids using Interpretable Generalized Additive Neural Networks (BDTS-SG-IGANN) to address these challenges. The methodology begins with data collection from the N-BaIoT dataset, followed by pre-processing using the Constrained Normalized Subband Adaptive Filter (CNSAF) to enhance data quality and normalization. The pre-processed data is then secured through blockchain technology utilizing the Proof-of-Monitoring (PoM) consensus mechanism for authenticating data sources. Subsequently, secured data is analyzed using an Interpretable Generalized Additive Neural Network (IGANN) for intrusion detection. To optimize IGANN weight parameters, the Leaf-in-Wind Optimization (LWO) algorithm is employed. The proposed BDTS-SG-IGANN technique is put into practice using Python and evaluated using performance metrics like Precision, Recall, Accuracy, F1 Score, Mean Square Error (MSE), and Specificity. Experimental results demonstrate that BDTS-SG-IGANN outperforms existing methods, achieving 29.36%, 26.42%, and 23.27% higher accuracy and 18.36%, 14.42% and 12.27% lower compared to digital twin-based reinforcement learning for extracting network structures and load patterns in planning and operation of distribution systems (DTRL-ENS-CNN), Digital twin-driven SDN for smart grid: A deep learning integrated blockchain for cyber security (DTD-IBCS-SGN) and Blockchain-Based Cyber-Physical Security for Electrical Vehicle Aided Smart Grid Ecosystem (BCPS-EVSGE-SDN). This study establishes BDTS-SG-IGANN as an innovative and effective framework for securing digital twins in SG environments. © The Author

关键词： Smart power grids

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Enhancing Weather Prediction Accuracy with CNN-Based Machine Learning

Enhancing Weather Prediction Accuracy with CNN-Based Machine...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Brionel Justin Raj J Sherin Eliyas Sathish Kumar Department Of Computer Application Hindustan Institute of Technology and Science Chennai Tamilnadu India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

The accurate as well as efficient prediction of the weathers is considered a big scientific and technological challenge due to the highly dynamic and complex nature of the atmosphere under which such severe developments occur. This project thus focuses on the utilization of datamining techniques, mostly clustering and classification, to improve the accuracy of prediction-enabled weather forecasting systems. Historical and real-time weather data, including satellite images, radar scans, etc., are utilized to extract patterns and relationships, which lend towards predictive modeling. A Multilayer Perceptron (MLP) is used for mapping temperature, humidity, and rainfall as different weather variables' classes. The pre-processing of raw data into useful and relevant features plays a very important role in improving model performance. In this context, effective data mining and machine learning methods put forward by this study will contribute toward more reliable weather prediction against the universe of concern regarding accuracy and efficiency on meteorological systems.

关键词： Accuracy Machine learning algorithms Weather forecasting Machine learning Predictive models Prediction algorithms Mathematical models Data models Data mining Reliability

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A Bayesian Optimization Framework for Adaptive Node Deployment in Wireless Sensor Networks 14

A Bayesian Optimization Framework for Adaptive Node Deployme...

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14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Priyadarshi, Rahul Rout, Jayant Kumar Vishwakarma, Anish Kumar Dhengre, Nikhil Ranjan, Rakesh Verma, Amit Faculty of Engineering and Technology ITER SOA Bhubaneswar India School of Computer Application KIIT Bhubaneswar India School of Computer Science UPES Uttarakhand Dehradun India Department of ECE IIIT Maharashtra Nagpur India

ISBN: (纸本)9798331531935

Wireless Sensor Networks (WSNs) require efficient node deployment strategies to optimize network longevity, energy consumption, and coverage. This paper introduces a novel approach for strategic node deployment in WSNs using Bayesian Optimization (BO). A BO-based framework that iteratively modifies node positions to maximize network performance metrics, such as energy efficiency and coverage ratio, while minimizing execution time is proposed. Through extensive simulations across diverse deployment scenarios, the proposed method is evaluated and demonstrates superior performance compared to both traditional and machine learning (ML)-based techniques. The results indicate significant improvements in coverage and energy efficiency, alongside competitive execution times. These findings highlight the potential of BO to enhance WSN deployment strategies, offering a scalable and adaptive solution for real-world applications. Future research will focus on integrating BO with other optimization techniques and extending the approach to dynamic environments and varied network configurations. © 2025 IEEE.

关键词： Bayesian Optimization Coverage Ratio Energy Efficiency Node Deployment Wireless Sensor Networks

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Reliable Communication in Opportunistic Network based on Trust: A Review

Reliable Communication in Opportunistic Network based on Tru...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Mala Ahmed Amrita Bose Paul Jyoti Prokash Goswami Department of Computer Application AECAssam Science and Technology University Assam India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Communication plays a significant role in a network where the nodes get connected opportunistically. To establish reliable communication in such a network is very much challenging due to the intermittent node connectivity. Recent research work focuses on establishing trust relationship between the nodes for reliable communication in wireless networks. Generally trust of a node is calculated based on historical information or feedback given by other nodes that has previous communication with that particular node. This paper provides an overview of the trust models that are available in the literature and connote their issues and challenges for opportunistic network. The authors propose a trust based framework for reliable communication in opportunistic network. The framework signifies the importance of belief-trust for establishing reliable communication over the network.

关键词： Reviews Wireless networks Computational modeling Reliability Intelligent systems Recommender systems

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Domain Transfer Learning for Medical Image Segmentation Tasks 5th

Domain Transfer Learning for Medical Image Segmentation Task...

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5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Kharbas, Vikas Kumar Srivastava, Pulkit Bargavi, Manju Pandeya, Megha Department of Computer Science and Application Vivekananda Global University Jaipur India Department of Mechanical Engineering Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Domain transfer learning is a powerful device for clinical image Segmentation duties. It involves transferring information from a source domain to a goal area so that related tasks on those domains can be advantageous from every difference. This era allows improving and robust clinical photo segmentation models by exploiting the prevailing area information and facts from a source area. This approach has been used to enhance the performance of sure segmentation responsibilities in medical imaging together with the segmentation of pathologies from imaging, segmentation of anatomic structures, and system getting to know-based total segmentation. The most critical domain version strategies encompass domain-adversarial neural networks, generative fashions, and meta-getting to know. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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