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Designing blockchain based secure autonomous vehicular internet of things (IoT) architecture with efficient smart contracts

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International Journal of Information technology (Singapore) 2025年第2期17卷 1207-1223页

作者： Hemani Singh, Dayashankar Dwivedi, Rajendra Kumar Department of Information Technology and Computer Application Madan Mohan Malaviya University of Technology UP Gorakhpur India

The future of autonomous vehicles is exciting and holds significant potential for transforming transportation. The future is bright, but these vehicles require a high degree of connectivity complex software to function effectively. This connectivity, however, can also create a pathway for cyberattacks. If a hacker gains access to a connected vehicle, they may be able to control its systems, steal data, or cause it to malfunction. Additionally, the software systems might have flaws or vulnerabilities that intruders could take advantage of. The paper describe how blockchain technology can help to create a tamper-proof and transparent system that enables secure sharing of data between the nodes of autonomous vehicular (AV) network. A smart contract is utilized to establish predefined rules and conditions for data sharing and access. The paper proposes a efficient approach for securing the information of AVs and improving the safety and reliability of AV networks. In this proposed work, smart contracts are implemented using solidity language and deployed a blockchain using Ganache, and truffle development framework to create a test system while MATLAB is utilized for analysis. And the system configurations are Intel Core i5 CPU (2.30 GHz and 8 GB RAM). © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer applications and Management 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Autonomous vehicles Blockchain Fan-Vercauteren Smart contracts

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Exploration and Optimization of Lottery Ticket Hypothesis for Few-shot Image Classification Task 5

Exploration and Optimization of Lottery Ticket Hypothesis fo...

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5th Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2024

作者： Ma, Chenyu Jia, Jinfang Huang, Jianqiang Wang, Xiaoying University Xining Department of Computer Technology and Application Qinghai China

ISBN: (纸本)9798350374407

Few-Shot Learning (FSL) refers to the problem of learning the underlying pattern in the data just from a few training samples. However, when using transfer learning to solve few-shot image classification, deep neural networks have overfitting problems and low training efficiency. Lottery Ticket Hypothesis (LTH), a model pruning method, has gathered great focus since being proposed. This paper explore LTH of few-shot image classification task. Firstly, the search process is proposed to find the winning ticket, and the generalization ability and training efficiency of the model can be improved by training the sparse winning ticket. Secondly, in order to quickly find the winning ticket, an early pruning method was proposed, which included a novel Prune-aware(PA) strategy and Mixed-Precision(MP) training strategy. Experiments on the miniImageNet and $\mathrm{FC100}$ datasets verified the effectiveness of LTH in FSL, and winning tickets can be found faster with PA and MP strategies, which improves the efficiency of the search process. Early pruning method achieves a balance between accuracy and efficiency while mitigating the overfitting problem in few-shot learning. © 2024 IEEE.

关键词： Zero-shot learning

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application of adaptive guard channel reservation under hard handoff constraint in wireless cellular network

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International Journal of Wireless and Mobile Computing 2025年第2期28卷 170-185页

作者： Sahu, Promod Kumar Pati, Hemanta Kumar Pradha, Sateesh Kumar Department of Computer Science and Application Utkal University Vani Vihar Odisha Bhubaneswar India Department of Computer Science and Engineering International Institute of Information Technology Bhubaneswar Odisha Bhubaneswar India

Voice is the king of communication in wireless cellular network (WCN). Again, WCNs provide two types of calls, i.e., new call (NC) and handoff call (HC). Generally, HCs have higher priority than NCs because call dropping gives negative impact to users as compared to call blocking. So, to handle this problem some channels are reserved for HCs. If more channels are reserved for HCs, then handoff call dropping probability (HCDP) decreases at the same time as new call blocking probability (NCBP) increases;if fewer channels are reserved for HCs, HCDP increases and NCBP decreases. If channels are reserved for HCs by considering target HCDP then HCDP will be below the target and NCBP will be minimum for that target. So, keeping this in mind in this paper, we propose a mathematical model that estimates optimal number of guard channels (GCs) for HCs by considering target HCDP and keeping NCBP minimum. The mathematical model proposed assume static traffic parameters. However, in reality, the user traffic is dynamic and usually inherited through its testbed implementation. To obtain optimal performance we propose an adaptive GC reservation scheme by considering hard handoff constraint while implementing this model through testbed. Finally, we have verified testbed results with those of the model-based approach applying the parameters of different Global System for Mobile Communication (GSM) standards to evaluate its applicability to real systems. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Testbeds

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Enabling Security in Healthcare IoT System Using Blockchain with Efficient Consensus Protocols and Smart Contracts 6th

Enabling Security in Healthcare IoT System Using Blockchain ...

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6th International Conference on Recent Innovations in Computing, ICRIC 2023

作者： Shrivastava, Atul Lal Dwivedi, Rajendra Kumar Prakash, Shiva Department of Information Technology and Computer Application MMMUT Gorakhpur India

ISBN: (纸本)9789819778614

Electronic Medical Records (EMRs) in hospitals are generally handled by a centralized cloud provider. However, it creates one solitary area of vulnerability, and who are the real owners may lose track of them. To protect the sharing of EMRs among various entities involved in the smart healthcare system, this study seeks to build a smart contract-based access control system on top of a distributed ledger with blockchain. For this, we recommend using smart contracts in four different ways for user authentication. In the proposed system, the hashes of the EMRs are created and added to the Ethereum blockchain. The EMRs themselves are encrypted and saved in the cloud utilizing the cryptographic functions of Elliptic Curve Cryptography (ECC) and Edwards-Curve Digital Signature Algorithm (EdDSA), considering both the massive volume of patient data and the ledger's block size. Performance evaluation with a private Ethereum system is used to observe how well the recommended access control architecture works in the real-time smart healthcare system. Results show that the proposed model outperforms the existing schemes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electronic health record

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Construction and Evaluation of a Metabolic Syndrome Prediction Model Based on Classification Algorithms

Construction and Evaluation of a Metabolic Syndrome Predicti...

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2024 International Conference on Machine Learning and Intelligent Computing, MLIC 2024

作者： Zhao, Zhenghao Hu, Qiongqiong Department of Computer Technology and Application Qinghai University Xining China

Exploring the risk factors influencing Metabolic Syndrome (MS), constructing a risk prediction model based on multiple classification algorithms, comparing the predictive performance of different models for MS, and interpreting the models to derive specific MS prediction rules, providing scientific basis for MS prediction. A retrospective analysis was conducted on clinical data from 2,193 MS patients. Based on whether the patients developed MS, they were divided into a healthy group and an MS group. Statistical correlation tests were used to identify the risk factors associated with MS. Six classification algorithms, including decision trees, logistic regression, random forests, naive Bayes, K-nearest neighbors, and support vector machines, were employed to build an MS prediction model. The prediction model's performance was evaluated using R language by generating receiver operating characteristic (ROC) curves. Among the 2,193 MS patients, the incidence rate of MS was 34.66%. Significant differences (P¡0.05) were observed between the healthy group and the MS group in terms of age, marital status, income, ethnicity, waist circumference, body mass index (BMI), uric acid levels, blood glucose levels, high-density lipoprotein levels, and triglyceride levels. Blood glucose, waist circumference, BMI, and triglycerides showed a significant linear correlation with MS. The ROC curve results indicated that the random forest algorithm achieved an area under the curve (AUC) of 0.94 (95% CI: 0.914-0.957), logistic regression achieved an AUC of 0.90 (95% CI: 0.867-0.925), support vector machines achieved an AUC of 0.89 (95% CI: 0.859-0.920), decision trees achieved an AUC of 0.87 (95% CI: 0.831-0.905), K-nearest neighbors achieved an AUC of 0.81 (95% CI: 0.770-0.850), and naive Bayes achieved an AUC of 0.74 (95% CI: 0.694-0.785). The study results confirmed that factors such as age, marital status, waist circumference, BMI, blood glucose levels, and triglyceride levels are all r

关键词： classification algorithm Metabolic Syndrome Nomogram prediction model statistical analysis

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Hybrid Deep Learning Architecture for Accurate Classification of Alzheimer's Disease using MRI Images 8

Hybrid Deep Learning Architecture for Accurate Classificatio...

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8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Balakrishnan, Nair Bini Pillai, Anitha S Panackal, Jisha Jose Hindustan Institute of Technology and Science Department of Computer Application Tamilnadu Chennai India Sacred Heart College Department of Computer Application Kerala India

ISBN: (纸本)9798350367904

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and memory loss. Precise and timely identification of this condition is significant for proper treatment. Magnetic Resonance Imaging (MRI) is a widely employed technology for analyzing brain structures and identifying abnormalities interconnected with AD. The recent advancements in medical fields, including the introduction of Deep Learning (DL) approaches, have automated the AD diagnosis process. However, achieving high accuracy remains a challenge due to the complexity and heterogeneity of AD manifestations. In this proposed work, we introduce a novel hybrid deep learning architecture designed to improve the accuracy of AD classification. The proposed architecture combines the strengths of convolutional neural networks (CNNs) for feature extraction and Autoencoder for capturing temporal dependencies in sequential MRI scans. Utilizing the Dragonfly optimization technique for fine-tuning the parameters and hyperparameters of deep learning models utilized in AD classification. The developed strategy was trained and validated using the publicly available AD and Munich databases. The proposed method achieved an overall accuracy of 98.34 %, precision of 99.06 %, recall of 99.65 %, and f-measure of 99.90 %. We anticipate that the proposed hybrid architecture will improve AD classification models' discriminative power and robustness, thereby offering early and accurate diagnosis of this debilitating disease. © 2024 IEEE.

关键词： Convolutional neural networks

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Optimized graph transformer with molecule attention network based multi class attack detection framework for enhancing privacy and security in WSN

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Multimedia Tools and applications 2025年第15期84卷 14273-14304页

作者： Anoop, M. Mary, L. William Wilson, Allan J. Kiran, W.S. Department of Computer Science and Application SRM Institute of Science and Technology Ramapuram campus Chennai600089 India Department of Electronics and Communication Engineering Amrita College of Engineering and Technology Nagercoil India

In today's rapidly evolving network landscape, cybersecurity has become increasingly crucial. However, wireless sensor networks face unique challenges due to their limited resources and diverse composition, high computational demands, diminished accuracy rates, making them susceptible to a wide array of cyber threats. To tackle these challenges, this research introduced a novel method known as Optimized Graph Transformer with Molecule Attention Network (OGTMAN) on the fusion of Secure Multi-party Computation (SMC) and differential privacy aimed at enhancing privacy and security. This approach starts with data preprocessing, employing min–max normalization to address dataset imbalances. N-Tuple Contrastive Learning with Generative transformer(N-TCLG)feature extractor distinguish between different classes of data instances and effectively learns representations that capture relevant information about network traffic patterns Further, feature selection by enhanced chi-square statistics with kepler algorithm, is utilized to assess the significance of each feature in characteristic between normal network behavior and malicious activities further refines the feature selection method by optimizing the selection criteria based on the statistical properties of the dataset. Finally, the proposed OGTMAN classifies various attacks based on their behavioural patterns with the help of enhanced ant aphid optimization with adaptive step factor. Ultimately, this fine-tuning yield superior model training outcomes, resulting more accurate attack classification outcomes. To evaluate the effectiveness of this approach, datasets encompassing with KDD Cup 99 datasets are employed. The efficiency of the introduced method is demonstrated when compared with prevailing attack detection methods in terms of accuracy, recall, precision and F-measure of 99.98%, 99.5%.,99.9% and 99.5% respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer N

关键词： Molecules

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Classification and Feature Selection of Alzheimer’s Disease for MRI Data Utilizing Convolutional Neural Network and Support Vector Machine

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SN computer Science 2024年第6期5卷 707页

作者： Yashodhar, Archana Kini, Shashidhar Department of Computer Science and Application St Aloysius College (Autonomous) Srinivas Institute of Technology

Alzheimer's disease (AD) is a neurological disease that affect numerous people. According to the literature, forecasting this type of disease can be an extremely difficult process. Machine Learning and Artificial Intelligence play a significant part in healthcare and other relevant fields. Deep Learning prediction model can be used by taking into consideration the basic principles of Machine Learning and Deep Learning algorithm. There are wide range of feature selection and medical image preprocessing approaches, and applied multiple classification algorithms to the datasets to determine which methods produced the best results. This paper proposes by considering Convolutional Neural network and Support vector machine as a base for classification and evaluated the model using a variety of methodologies to obtain the best possible outcome. That is considerably more effective than prior work in the same field. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Alzheimer’s disease (AD) Convolutional neural network (CNN) Mild cognitive impairments (MCI) Multi-layer perceptron (MLP) Support vector machine (SVM)

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Zoom Classroom Engagement and Attention Detection System

Zoom Classroom Engagement and Attention Detection System

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Kumar, Annuri Praveen Kumar, N. Siva Hindustan Institute of Technology and Science Department of Computer Application Chennai India

ISBN: (纸本)9798350375237

This research presents a Zoom Classroom engagement and attention detection system, designed to enhance remote teaching and learning experiences. In the wake of the COVID-19 Pandemic, online Education has become the norm, and ensuring student engagement and attention in virtual classrooms has become a significant challenge. The proposed system aims to address these challenges by leveraging computer vision and YOLO V8. YOLO V8 is a deep learning model that falls under Convolutional Neural Networks (CNNs) and it is trained on large dataset to learn detecting and classifying objects in images. By analyzing video and audio streams, the system can detect students' engagement levels and attention spans in real time. To achieve this, various features and cues are extracted from students' facial expressions, body language, and speech patterns. These features are then fed into a YOLO v8 model, which predicts the level of engagement and attention for each student. Key features of the system include the ability to track students' eye movements, head orientations, and facial expressions to assess focus and attention. In addition, speech analysis algorithms are employed to detect instances of disengagement or distraction based on tone and content. The system can provide visualizations and summaries of students' engagement levels for educators, allowing them to adapt their teaching strategies accordingly. Through this Zoom classroom engagement and attention detection system, educators can identify students who may be struggling or disengaged, enabling proactive interventions and personalized support. © 2024 IEEE.

关键词： computer vision

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A Hybrid Recommendation Model for Tourist Using Evolutionary Algorithm Combined with Local Search Algorithm for Trip Planning

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SN computer Science 2024年第6期5卷 743页

作者： Lakshmi, J.V.N. Pallavi, M.O. School of Computer Science and Application Reva University Department of MCA Acharya Institute of Technology

Recommender systems play a crucial role in assisting tourists with travel recommendations considering the customized demands is a necessary practice for improving the tourism, business, and aids significantly in decision-making process. The study proposes using a evolutionary genetic algorithm (GA) to efficiently determine the shortest tourist route within a constrained area, allowing for quick visits to multiple destinations. The goal is to find the most efficient route with minimal physical exertion. By exploring all possible routes, the GA can identify the quickest path from the starting point to the destination. The study first provides a brief overview of the enhanced GA, followed by a detailed analysis of its construction and solution. The paper introduces a novel approach to recommendation systems in the tourism industry, combining the Genetic algorithm with 2-Opt algorithm for local search adaptation. The hybrid model is used to optimize the system by considering multiple criteria. Data for this study were collected through online google map resources defining a positive ideal distance matrix for 11 locations with 121 edges. Finally, the GA is further optimized by pipeline the 2-opt algorithm, and a simulation is conducted to examine the average path convergence for each individual route by customizing based on the preferences. Finally proposed model is compared against the traditional algorithms such as Heuristic and Graph network algorithms. Subsequently, the hybrid algorithm searches among destinations to recommend the best tourist trip plan to users. Selecting the shortest trip route plan will reduce the travel cost and time. This model enhances the practical value for researchers and for practical application. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： 2-opt algorithm Evolutionary algorithm Genetic algorithm Hybrid recommendation Optimization Travel planning

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