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An Initial Risk Assessment for Multimodal with LSTM-Based Trust Evaluation Framework for Autonomous Vehicle Security

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SN computer science 2025年第2期6卷 1-15页

作者： Renjith, P.N. Balasubramani, S. Ramesh, K. Patnala, Eswar School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India

This research aims to develop a new approach to increase the safety and reliability of Autonomous Vehicle (AV) through the proposed risk assessment framework, supported by the trust evaluation approach derived from a machine learning algorithm. This work proposes a comprehensive risk assessment model with real-time sensor data, Vehicle-to-Vehicle (V2V), key communication reliability, environmental cognition, and Improved Long Short-Term Memory (I-LSTM). This framework is multidimensional compared to most conventional models, focusing primarily on assessing individual threat factors. Here, the machine learning portion is designed to evaluate and predict trust by quickly processing vast amounts of information from sensors, signals, and prior operation history. Such an approach to trust assessment enables the system to continuously evaluate the dependability of automated vehicle operation and identify threats to security with improved accuracy. The effectiveness of the performance of this method is substantiated by a number of stand-alone and live scenarios, where the model's performance is higher in perceiving possible risks, avoiding threats, and enhancing safety than the conventional approaches. The findings suggest that the safety regulations of the AVs will be improved by using a multi-dimensional risk assessment model and integrating the I-LSTM trust rating with 95.5% accuracy, 96.7% risk identification, and 180 ms response time. This will cause AV and its surrounding environment to be accepted by the public and used on the roads. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Autonomous vehicles LSTM model Machine learning Risk assessment Trust evaluation

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MQTT and blockchain sharding:An approach to user-controlled data access with improved security and efficiency

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Blockchain(Research and Applications) 2023年第4期4卷 86-98页

作者： P.S.Akshatha S.M.Dilip Kumar Department of Computer Science and Engineering University of Visvesvaraya College of EngineeringBangalore UniversityBengaluruKarnataka 560056India

The rapid growth of the Internet of Things(IoT)has raised security concerns,including MQTT protocol-based applications that lack built-in security features and rely on resource-intensive Transport Layer Security(TLS)*** paper presents an approach that utilizes blockchain technology to enhance the security of MQTT communication while maintaining *** approach involves using blockchain sharding,which enables higher scalability,improved performance,and reduced computational overhead compared to traditional blockchain approaches,making it well-suited for resource-constrained IoT *** approach leverages Ethereum blockchain’s smart contract mechanism to ensure trust,accountability,and user ***,we introduce a shard-based consensus mechanism that enables improved security while minimizing computational *** also provide a user-controlled and secured algorithm using Proof-of-Access implementation to decentralize user access control to data stored in the blockchain *** proposed approach is analyzed for usability,including metrics such as bandwidth consumption,CPU usage,memory usage,delay,access time,storage time,and jitter,which are essential for IoT application *** analysis demonstrated that the approach reduces resource consumption,and the proposed system outperforms TLS and existing blockchain approaches in these metrics,regardless of the choice of the MQTT ***,thoroughly addressing future research directions,including issues and challenges,ensures careful consideration of potential advancements in this domain.

关键词： Blockchain Internet of Things MQTT Publish/Subscribe MQTT brokers HiveMQ Mosquitto EMQx Eclipse Sharding

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Feature Adaptive Developmental Mechanisms for Mobile Apps Recommendations System using the Nearest Centroid Classification Algorithm 7

Feature Adaptive Developmental Mechanisms for Mobile Apps Re...

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7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Twinkle Geojini, G. Ramnath, M. Rubavathi, C. Yesubai Thamirabharani Engineering College Department of Computer Science and Engineering India Department of Computer Science and Engineering Francis Xavier Engineering College India

ISBN: (纸本)9798350397284

Reviews and ratings play an important part in modern technology since they provide insights that may be used to enhance the functionality and performance of apps. Some customers give app reviews that don't provide developers with useful feedback for improving the app. Developers can't fix bugs or release new versions of their software if they don't catch them as soon as possible. With this method, we extract the most helpful user comments from existing app evaluations and compare them to the apps' overall star ratings. We present a Nearest Centroid Classification (NCC) approach based on supervised machine learning with the goal of spotting new app problems via review analysis. To successfully illustrate resilience to the extreme values that are beyond the range, the suggested framework contains mean vectors of k to evaluate both distance closeness and geographical extent of k-neighbors in each class. After a long time of waiting, developers may at last quickly correct bugs and fine-tune the app's features to enhance the user experience. © 2023 IEEE.

关键词： Supervised learning

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Hybrid Metaheuristics Web Service Composition Model for QoS Aware Services

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computer Systems science & engineering 2022年第5期41卷 511-524页

作者： P.Rajeswari K.Jayashree Department of Computer Science and Engineering Rajalakshmi Engineering CollegeChennai602105India

Recent advancements in cloud computing(CC)technologies signified that several distinct web services are presently developed and exist at the cloud data ***,web service composition gains maximum attention among researchers due to its significance in real-time *** of Service(QoS)aware service composition concerned regarding the election of candidate services with the maximization of the whole *** these models have failed to handle the uncertainties of *** resulting QoS of composite service identified by the clients become unstable and subject to risks of failing composition by *** the other hand,trip planning is an essential technique in supporting digital map *** aims to determine a set of location based services(LBS)which cover all client intended activities quantified in the *** the available web service composition solutions do not consider the complicated spatio-temporal *** resolving this issue,this study develops a new hybridization of the firefly optimization algorithm with fuzzy logic based web service composition model(F3L-WSCM)in a cloud environment for location *** presented F3L-WSCM model involves a discovery module which enables the client to provide a query related to trip planning such as flight booking,hotels,car rentals,*** the next stage,the firefly algorithm is applied to generate composition plans to minimize the number of composition *** by,the fuzzy subtractive clustering(FSC)will select the best composition plan from the available composite ***,the presented F3L-WSCM model involves four input QoS parameters namely service cost,service availability,service response time,and user *** extensive experimental analysis takes place on CloudSim tool and exhibit the superior performance of the presented F3L-WSCM model in terms of accuracy,execution time,and efficiency.

关键词： Web service composition trip planning hybrid models firefly algorithm QoS aware services fuzzy logic

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Document Clustering Using Graph Based Fuzzy Association Rule Generation

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computer Systems science & engineering 2022年第10期43卷 203-218页

作者： P.Perumal Department of Computer Science and Engineering Sri Ramakrishna Engineering CollegeCoimbatoreIndia

With the wider growth of web-based documents,the necessity of automatic document clustering and text summarization is ***,document summarization that is extracting the essential task with appropriate information,removal of unnecessary data and providing the data in a cohesive and coherent manner is determined to be a most confronting *** this research,a novel intelligent model for document clustering is designed with graph model and Fuzzy based association rule generation(gFAR).Initially,the graph model is used to map the relationship among the data(multi-source)followed by the establishment of document clustering with the generation of association rule using the fuzzy *** method shows benefit in redundancy elimination by mapping the relevant document using graph model and reduces the time consumption and improves the accuracy using the association rule generation with *** framework is provided in an interpretable way for document *** iteratively reduces the error rate during relationship mapping among the data(clusters)with the assistance of weighted document ***,this model represents the significance of data features with class *** is also helpful in measuring the significance of the features during the data clustering *** simulation is done with MATLAB 2016b environment and evaluated with the empirical standards like Relative Risk Patterns(RRP),ROUGE score,and Discrimination Information Measure(DMI)***,DailyMail and DUC 2004 dataset is used to extract the empirical *** proposed gFAR model gives better trade-off while compared with various prevailing approaches.

关键词： Document clustering text summarization fuzzy model association rule generation graph model relevance mapping feature patterns

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Cost Effective Decentralized Key Management Framework for IoT

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computer Systems science & engineering 2022年第6期41卷 1057-1070页

作者： Raja Lavanya K.Sundarakantham S.Mercy Shalinie Department of Computer Science and Engineering Thiagarajar College of EngineeringTamil NaduIndia

Security is a primary concern in communication for reliable transfer ofinformation between the authenticated members, which becomes more complexin a network of Internet of Things (IoT). To provide security for group communication a key management scheme incorporating Bilinear pairing technique withMulticast and Unicast key management protocol (BMU-IOT) for decentralizednetworks has been proposed. The first part of the proposed work is to dividethe network into clusters where sensors are connected to and is administered bycluster head. Each sensor securely shares its secret keys with the cluster headusing unicast. Based on these decryption keys, the cluster head generates a common encryption key using bilinear pairing. Any sensor in the subgroup candecrypt the message, which is encrypted by the common encryption key. Theremaining part focuses to reduce communication, computation and storage costsof the proposed framework and the resilience against various attacks. The implementation is carried out and results are compared with the existing schemes thathave given considerably better results. Thus, the lightweight devices of IoT canprovide efficiency and security by reducing their overhead in terms of complexity.

关键词： Bilinear pairing common encryption key key distribution

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Climate Geoscience–Based Disaster Management in Healthcare Analysis by Markov Q-Transfer Adversarial with Cloud Computing

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 474-483页

作者： Srinivas, Jhade Rebecca, B. Bhargavi, Kovvuri N Swetha, Velma Reddy Spandana, S. Degala, Divya Priya Department of Computer Science and Engineering-Data Science KG Reddy College of Engineering and Technology Moinabad Telangana Hyderabad India Computer Science and Engineering Marri Laxman Reddy Institute of Technology and Management Telangana Dundigal 500043 India Department of CSE Aditya University AP Surampalem India Department of Computer Science and Engineering (AI-ML) Malla Reddy Engineering College Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Bowrampet Telangana Hyderabad India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Analysis and reaction to natural disasters have made extensive use of deep learning methods using semantic segmentation networks. These implementations’ foundation is based on convolutional neural networks (CNNs), which are capable of precisely identifying and locating the relevant areas of interest within satellite imagery or other types of remote sensing data. This helps with rescue planning, restoration efforts, and disaster evaluation. Difficulties in the provision of healthcare services, due to natural disasters, are made worse by the existing problems prevalent in most healthcare systems. The aim of this research is to propose a novel technique in climate change–based disaster management with their public healthcare analysis using cloud computing with deep learning techniques. In this research, the cloud network has been used in LiDAR image collection for climate change–based disaster-struck regions and their public healthcare analysis. This collected image has been classified and optimized using a deep Markov Q-transfer adversarial neural network with binary grey whale swarm optimization. Experimental analysis has been carried out for various disaster management–based public healthcare datasets in terms of classification accuracy, specificity, NSE, sensitivity, and F-1 score. The proposed technique attained a classification accuracy of 97%, F-1 score of 94%, specificity of 96%, sensitivity of 95%, and NSE of 57%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Adversarial neural network Cloud computing Disaster management LiDAR image Whale swarm optimization

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A proactive load balanced handoff scheme for vehicular ad-hoc networks

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International Journal of Vehicle Information and Communication Systems 2023年第4期8卷 345-362页

作者： Madli, Rajeshwari Varaprasad, G. Department of Computer Science and Engineering B.M.S. College of Engineering Bengaluru Karnataka Bangalore India

The embracement of the wireless technology by the automobile industry has led to novel research interests in the field of Vehicular Ad-hoc Networks (VANETs). In addition to ad-hoc mode, a VANET supports infrastructure mode of communication, which provides access to internet services such as Hyper Text Transfer Protocol (HTTP), Voice over Internet Protocol (VoIP) and File Transfer Protocol (FTP), through access points known as Road Side Units (RSU). Vehicles can benefit from these services when associated with an RSU. However, because of uneven distribution and high mobility of vehicles, some RSUs are overloaded, resulting in network congestion and affecting the packet delivery ratio and throughput. This paper proposes a proactive load-balanced handoff scheme, in which vehicles evade overloaded RSUs and choose an RSU based on signal strength, network load and service reliability. Simulation results indicate that vehicles using the load-balanced handoff method, experience a higher packet delivery ratio and throughput. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Vehicular ad hoc networks

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IOT-Enabled Smart Crop Recommendation System Using Machine Learning 5

IOT-Enabled Smart Crop Recommendation System Using Machine L...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Saranya, S.S. Dharani, A.S. Kavitha, M.N. Selya Dharsnee, M. Pragatheeswari, E. Selya Varsnee, M. Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India Kongu Engineering College Department of Computer Technology - Ug Perundurai India Kongu Engineering College Department of Computer Science and Engineering Perundurai India Kongu Engineering College Department of Computer Technology Perundurai India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379945

Agriculture, a foundation of India's economy, faces challenges in maximizing yield and resource efficiency. This research presents a machine learning-based system to recommend optimal crops and fertilizers based on soil quality and geographic location. By providing data-driven insights, the proposed system aims to enhance crop productivity, promote sustainable farming practices, and empower farmers to make informed decisions. © 2024 IEEE.

关键词： Smart agriculture

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Metaheuristic Based Clustering with Deep Learning Model for Big Data Classification

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computer Systems science & engineering 2023年第1期44卷 391-406页

作者： R.Krishnaswamy Kamalraj Subramaniam V.Nandini K.Vijayalakshmi Seifedine Kadry Yunyoung Nam Department of Electronics and Communication Engineering University College of Engineering AriyalurAriyalur621704India Department of Biomedical Engineering Faculty of EngineeringKarpagam Academy of Higher EducationCoimbatore641021India Department of Computer Science and Engineering Sona College of TechnologySalem636005India Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai600077India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Recently,a massive quantity of data is being produced from a distinct number of sources and the size of the daily created on the Internet has crossed two *** the same time,clustering is one of the efficient techniques for mining big data to extract the useful and hidden patterns that exist in ***-based clustering techniques have gained significant attention owing to the fact that it helps to effectively recognize complex patterns in spatial *** data clustering is a trivial process owing to the increasing quantity of data which can be solved by the use of Map Reduce *** this motivation,this paper presents an efficient Map Reduce based hybrid density based clustering and classification algorithm for big data analytics(MR-HDBCC).The proposed MR-HDBCC technique is executed on Map Reduce tool for handling the big *** addition,the MR-HDBCC technique involves three distinct processes namely pre-processing,clustering,and *** proposed model utilizes the Density-Based Spatial Clustering of Applications with Noise(DBSCAN)techni-que which is capable of detecting random shapes and diverse clusters with noisy *** improving the performance of the DBSCAN technique,a hybrid model using cockroach swarm optimization(CSO)algorithm is developed for the exploration of the search space and determine the optimal parameters for density based ***,bidirectional gated recurrent neural network(BGRNN)is employed for the classification of big *** experimental validation of the proposed MR-HDBCC technique takes place using the benchmark dataset and the simulation outcomes demonstrate the promising performance of the proposed model interms of different measures.

关键词： Big data data classification clustering mapreduce dbscan algorithm

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