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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Banerjee, Suman Kuila, Pratyay Naik, Banavath Balaji National Institute of Technology Department of Computer Science and Engineering Sikkim737139 India National Institute of Technology Department of Computer Science and Engineering Patna800005 India

ISBN: (纸本)9798350370249

Vehicle-to-everything (V2X) communication is crucial in Intelligent Transportation Systems (ITSs) for enhancing road safety, traffic efficiency, and convenience. To address the need for trust in V2X communications, a trust management system for Vehicular Ad Hoc Networks (VANETs) using blockchain technology has been proposed. This system calculates dynamic trust scores for vehicles based on direct communication, recommendations from other vehicles, and proximity to roadside units (RSUs). The trust scores are stored in a transparent and tamperproof blockchain, ensuring the immutability and transparency of trust scores. Simulations demonstrate that this blockchain-based trust management system significantly improves the security and reliability of V2X communications. This system is essential for the widespread acceptance of VANETs by fostering a trusted communication environment. © 2024 IEEE.

关键词： and Edge computing Blockchain Trust V2X VANET

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Multi-factor Authentication and Data Integrity for WBAN Using Hash-Based Techniques 5th

Multi-factor Authentication and Data Integrity for WBAN Usin...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Pabitha, B. Vani, V. Sanshi, Shridhar Karthik, N. Department of Computer Science and Engineering National Institute of Technology Puducherry Karaikal India Department of Computer Science and Engineering National Institute of Technology Karnataka Mangalore India

ISBN: (纸本)9789819767250

In recent days, a wireless body area network (WBAN) has been developed as part of the Internet of Things (IoT) with sensors and actuators in three different modes, building its network, i.e., in-body sensors, wearable sensors, and on-body sensors. The doctor’s access the data recorded and monitored by the sensor embedded in the patient to treat critical situations immediately. Maintaining data integrity and guarding against threats is necessary to secure sensitive patient information. Several people have proposed schemes for authenticating data access through formal and informal verification. In this research work, we carry out multi-factor authentication extensively using zero-knowledge proofs. The anomaly detection of the sensors is detected using machine learning algorithms, which help tune the sensors to their correct working conditions. The work aims to concentrate on sensor working conditions promptly and to handle attacks like masquerade, forgery, and key escrow attacks. To assess whether performance metrics are superior in computing cost, storage overhead, and communication overhead, utilize the BAN logic tool. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Wearable sensors

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Dynamics analysis and cryptographic implementation of a fractional-order memristive cellular neural network model

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Chinese Physics B 2024年第4期33卷 418-433页

作者：周新卫蒋东华 Jean De Dieu Nkapkop Musheer Ahmad Jules Tagne Fossi Nestor Tsafack 吴建华 Department of Information Engineering Gongqing CollegeNanchang UniversityJiujiang 332020China School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 511400China Department of Electrical Engineering and Industrial Computing University Institute of TechnologyDoualaCameroon Department of Computer Engineering Jamia Millia IslamiaNew Delhi 110025India Department of Physics Faculty of ScienceUniversity of YaoundeCameroon Electrical Engineering Department and Industrial Computing of ISTAMA University of DoualaDoualaCameroon

Due to the fact that a memristor with memory properties is an ideal electronic component for implementation of the artificial neural synaptic function,a brand-new tristable locally active memristor model is first proposed in this ***,a novel four-dimensional fractional-order memristive cellular neural network(FO-MCNN)model with hidden attractors is constructed to enhance the engineering feasibility of the original CNN model and its ***,its hardware circuit implementation and complicated dynamic properties are investigated on multi-simulation ***,it is used toward secure communication application *** it as the pseudo-random number generator(PRNG),a new privacy image security scheme is designed based on the adaptive sampling rate compressive sensing(ASR-CS)***,the simulation analysis and comparative experiments manifest that the proposed data encryption scheme possesses strong immunity against various security attack models and satisfactory compression performance.

关键词： cellular neural network memristor hardware circuit compressive sensing privacy data protection

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Neural Network-Driven Base Station Selection in Next-Generation Cellular Networks 17

Neural Network-Driven Base Station Selection in Next-Generat...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Harrithha, B. Kotagi, Vijeth J. Thakur, Rahul Indian Institute of Technology Dharwad Department of Computer Science and Engineering Karnataka Dharwad India Indian Institute of Technology Roorkee Department of Computer Science and Engineering Uttarakhand Roorkee India

ISBN: (纸本)9798331531195

With the exponential increase in data consumption from activities like streaming, gaming, and IoT applications, existing network infrastructure is under significant strain. To address the growing demand for higher data rates and better coverage, increasing base station density and exploring data offloading in unlicensed spectrum have become essential. While higher base station density reduces the distance between devices and network nodes, enhancing signal strength and data throughput, it also complicates the process of base station selection for users, particularly for those at the cell edge. Thus, ever-increasing demand for high-speed, low-latency communication services in 5G and 5G advanced New Radio (NR) unlicensed (NR-U) networks necessitates intelligent and dynamic approaches for user admission control and resource *** paper presents a novel solution employing neural network technology to address these critical challenges. We first propose an efficient algorithm for admission control and resource allocation. Upon generation of training data using the above algorithm, we propose a neural network-based framework for cellular networks in unlicensed spectrum such as LTE/LTE-U and 5G/NR-U networks. © 2025 IEEE.

关键词： Base stations

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Multimodal Deep Learning Framework for Non-Intrusive Autism Spectrum Disorder Detection 3

Multimodal Deep Learning Framework for Non-Intrusive Autism ...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kavitha, G. Rajganesh, N. Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India Sri Venkateswara College of Engineering Department of Computer Science and Engineering Sriperumbudur Chennai India

ISBN: (纸本)9798331509675

The non-intrusive detection of Autism Spectrum Disorder (ASD) marks a significant advancement in early diagnosis and intervention. This approach allows users to upload videos to a web interface, where visual and auditory data are processed separately. Visual data is analyzed using a customized VGG19 Convolutional Neural Network (CNN) to identify facial expressions and movements indicative of autism, while auditory data is evaluated by a Recurrent Neural Network (RNN) to detect auditory cues like tone and rhythm. A voting mechanism across 10 frames determines the visual classification, labelling the subject autistic if most frames indicate autistic traits. Audio analysis provides an independent prediction. The subject is classified as autistic if either analysis indicates autistic features. By leveraging deep neural networks to process multimodal data, this method offers an accessible, early ASD detection tool for caregivers and professionals, promoting timely interventions and improved outcomes. © 2025 IEEE.

关键词： Convolutional neural networks

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Development of Blockchain-Enabled E-Healthcare Appointment System 5th

Development of Blockchain-Enabled E-Healthcare Appointment S...

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5th International Conference on Trends in Computational and Cognitive engineering, TCCE 2023

作者： Rawat, Abhishek Suryavanshi, Raghuraj Singh Nagar, Vishal Yadav, Divakar Department of Computer Science and Engineering Pranveer Singh Institute of Technology Uttar Pradesh Kanpur India Department of Computer Science and Engineering Institute of Engineering and Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819719228

The adoption of the technology of smart contracts to construct an e-appointment system has an array of advantages over conventional appointment systems. E-appointment system commitments can be instantly conducted and reported using blockchain, removing the requirement for mediators and the possibility of fraud. In this paper, a framework is implemented in Remix IDE by using the smart contract that manages agreements between the user and the healthcare service provider. The E-appointment system will be able to privately and openly manage end-users using Ethereum, with terms and conditions enforced automatically via the smart contract. This work has the potential to significantly disrupt the centralized appointment system because it provides a safer and more effective alternative to conventional appointment scheduling methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Smart contract

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Enhancing IoT Security and Efficiency: Advanced Public Key Cryptographic Solutions for the Modern Era 4

Enhancing IoT Security and Efficiency: Advanced Public Key C...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Sekaran, Ramesh Ramasamy, Dhanalakshmi Basha, John Basha Mohamed Maruthapillai, Kumaresan Annamalai, S. Parasuraman, Manikandan Department of Computer Science and Engineering Bangalore India Madanapalle Institute of Technology and Science Department of Computer Science and Engineering-AI Madanapalle India

ISBN: (纸本)9798350387490

The landscape of the Internet of Things is evolving rapidly. The security and efficiency of data handling and device management are multifaceted and complex problems, which are only exacerbated by the huge volumes of data and the sprawling growth of IoT deployments. The goal of this paper is to outline innovative solutions to the most pressing issues in the field through the use of advanced cryptographic protocols and scalable management systems. The integrity and maintainability of IoT ecosystems face threats not only from quantum-capable attackers but also from vendor lock-in, which necessitates special attention. First, the Secure Data Retrieval Encryption system will be presented, which is based on the Learning with Errors assumptions, Self-Managed Public Key Infrastructure, Next-generation Hashed Elliptic Curve Protocol, and Dynamic Unclonable Physical Properties. In a single round of queries, SDRE enables forward security and efficient searches defined by authorized clients. This work presents a distinctive innovation. Initially, the search process was accelerated greatly, and the data being transmitted and decrypted was implemented based on secure computation. In addition, the Self-Managed Public Key Infrastructure protocol is a simple and standard-acquiescent answer for changing control between IoT service providers. SMS-PKI seeks to diminish this necessity of manual effort, especially in light of the massive upsurge in IoT deployments. SM-PKI also mechanizes the IoT PKI credentials updating and trusted domains process, allowing multiple domains to be run on one client apparatus. Tamarin is used to prove security and efficiency, which demonstrates the necessity of using SM-PKI to maintain desired security properties with the minimal cost and device overhead. Finally, a universal authentication scheme grounded on edge computing and involving Next-generation Hashed Elliptic Curve Protocol and Dynamic Unclonable Physical Properties will be shown. This approach

关键词： Information management

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ResMHA-Net:Enhancing Glioma Segmentation and Survival Prediction Using a Novel Deep Learning Framework

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computers, Materials & Continua 2024年第10期81卷 885-909页

作者： Novsheena Rasool Javaid Iqbal Bhat Najib Ben Aoun Abdullah Alharthi Niyaz Ahmad Wani Vikram Chopra Muhammad Shahid Anwar Department of Computer Science School of Engineering and TechnologyIslamic University of Science and TechnologyAwantipora192122India Faculty of Computing and Technology Al-Baha UniversityAlaqiq65779-7738Saudi Arabia Research Groups in Intelligent Machines National School of Engineers of SfaxUniversity of SfaxSfax3038Tunisia Department of Electrical Engineering College of EngineeringKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyPatiala147004Punjab Department of Electrical and Instrumentation Engineering Thapar Institute of Engineering and TechnologyPatiala147004Punjab Department of AI and Software Gachon UniversitySeongnam-si13120Republic of Korea

Gliomas are aggressive brain tumors known for their heterogeneity,unclear borders,and diverse locations on Magnetic Resonance Imaging(MRI)*** factors present significant challenges for MRI-based segmentation,a crucial step for effective treatment planning and monitoring of glioma *** study proposes a novel deep learning framework,ResNet Multi-Head Attention U-Net(ResMHA-Net),to address these challenges and enhance glioma segmentation ***-Net leverages the strengths of both residual blocks from the ResNet architecture and multi-head attention *** powerful combination empowers the network to prioritize informative regions within the 3D MRI data and capture long-range *** doing so,ResMHANet effectively segments intricate glioma sub-regions and reduces the impact of uncertain tumor *** rigorously trained and validated ResMHA-Net on the BraTS 2018,2019,2020 and 2021 ***,ResMHA-Net achieved superior segmentation accuracy on the BraTS 2021 dataset compared to the previous years,demonstrating its remarkable adaptability and robustness across diverse ***,we collected the predicted masks obtained from three datasets to enhance survival prediction,effectively augmenting the dataset *** features were then extracted from these predicted masks and,along with clinical data,were used to train a novel ensemble learning-based machine learning model for survival *** model employs a voting mechanism aggregating predictions from multiple models,leading to significant improvements over existing *** ensemble approach capitalizes on the strengths of various models,resulting in more accurate and reliable predictions for patient ***,we achieved an impressive accuracy of 73%for overall survival(OS)prediction.

关键词： Glioma MRI segmentation multihead attention survival prediction deep learning

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Disaster Management Based on Biodiversity Conservation Using Remote Sensing Data Analysis Using Machine Learning Model

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Remote Sensing in Earth Systems sciences 2025年第1期8卷 191-199页

作者： Pokkuluri, Kiran Sree Mounika, Talla Devi, N. Durga Kishore, D. Ratna Balakiruthiga, B. Krishna, B. Murali Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Telangana Bhimavaram India Department of CSE-DS Mallareddy College of Engineering Hyderabad India Department of Computer Science and Engineering Aditya University Andhra Pradesh Surampalem India Dept of Information Technology Lakireddy Balireddy College of Engineering Mylavram Vijayawada India Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Artificial intelligence (AI) applications in forestry as well as wildlife domains have become more feasible due to the advancements in data science and digital and satellite technologies. However, there is a serious global threat to biodiversity due to the rapid expansion of urban, agricultural, and development initiatives. Therefore, the effective monitoring, management, and preservation of biodiversity as well as forest resources may be facilitated by integration of cutting-edge methods like AI in disciplines of forests as well as biodiversity. This study suggests a novel approach to disaster management based on biodiversity protection that makes use of machine learning models and remote sensing image analysis. In this case, the data for disaster management has been gathered and processed for normalisation and smoothing. The processed image features have been segmented using active contour Gaussian Q- histogram equalisation model and feature extracted using probabilistic Bayesian fuzzy transfer NN. Experimental analysis is carried out based on parameters like random accuracy, average precision, sensitivity, AUC, and normalized co-efficient. The proposed technique attained average precision was 95%, sensitivity was 97%, random accuracy was 98%, and normalized coefficient was 94%, AUC of 96%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Biodiversity conservation Disaster management Machine learning model Q- histogram equalisation Remote sensing

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SISRR: Semantically Inclined Strategic Learning Model for Software Requirement Recommendation Using Artificial Intelligence 1

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Shyamalan, Raj Deepak, Gerard Santhanavijayan Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli India Department of Computer Science and Engineering BMS Institute of Technology and Management Yelahanka Bangalore India

ISBN: (数字)9783031647796

ISBN: (纸本)9783031647789

The following paper proposes the SISRR framework for knowledge centric, semantically inclined, framework for software requirement recommendations. This framework is intelligent driven by integrating semantically inclined techniques with knowledge model. The SISRR model is a metadata driven framework, where the initial requirements are preprocessed, and metadata is generated to intensify the auxiliary knowledge. This is handled by deep learning GRU classifier. Subsequently ontologies are generated from the SVN repository, which is applied along with Bag of Words to enrich the ontologies. The semantic similarity is computed using CoSimRank and Tang Index. Shannons entropy is also computed to measure the information content. The context map is generated using the DBSCAN algorithm in the model. Thus, the best precision, recall and lowest FDR has been achieved by the SISRR model compared to other existing models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Ontology

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