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An Integrated Blockchain Framework for Secure Data Sharing in IoT Fog Computing

清华大学学报自然科学版（英文版） 2025年第3期30卷 957-977页

作者： Peda Narayana Bathula M.Sreenivasulu Department of Computer Science Engineering Jawaharlal Nehru Technological UniversityAnanthapur 515002India Department of Computer Science Engineering Matrusri Engineering CollegeHyderabad 500059India

The importance of secure data sharing in fog computing is increasing due to the growing number of Internet of Things(IoT)*** article addresses the privacy and security issues brought up by data sharing in the context of IoT fog *** suggested framework,called"BlocFogSec",secures key management and data sharing through blockchain consensus and smart *** existing solutions,BlocFogSec utilizes two types of smart contracts for secure key exchange and data sharing,while employing a consensus protocol to validate transactions and maintain blockchain *** process and store data effectively at the network edge,the framework makes use of fog computing,notably reducing latency and raising *** successfully blocks unauthorized access and data breaches by restricting transactions to authorized *** addition,the framework uses a consensus protocol to validate and add transactions to the blockchain,guaranteeing data accuracy and *** compare BlocFogSec's performance to that of other models,a number of simulations are *** simulation results indicate that BlocFogSec consistently outperforms existing models,such as Security Services for Fog Computing(SSFC)and Blockchain-based Key Management Scheme(BKMS),in terms of throughput(up to 5135 bytes per second),latency(as low as 7 ms),and resource utilization(70％to 92％).The evaluation also takes into account attack defending accuracy(up to 100％),precision(up to 100％),and recall(up to 99.6％),demonstrating BlocFogSec's effectiveness in identifying and preventing potential attacks.

关键词： BlocFogSec Internet of Things(IoT) fog computing blockchain

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A Privacy-Preserving Graph Neural Network Framework with Attention Mechanism for Computational Offloading in the Internet of Vehicles

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computer Modeling in engineering & Sciences 2025年第4期143卷 225-254页

作者： Aishwarya Rajasekar Vetriselvi Vetrian Department of Computer Science and Engineering College of Engineering GuindyAnna UniversityChennai600025India

The integration of technologies like artificial intelligence,6G,and vehicular ad-hoc networks holds great potential to meet the communication demands of the Internet of Vehicles and drive the advancement of vehicle ***,these advancements also generate a surge in data processing requirements,necessitating the offloading of vehicular tasks to edge servers due to the limited computational capacity of *** recent advancements,the robustness and scalability of the existing approaches with respect to the number of vehicles and edge servers and their resources,as well as privacy,remain a *** this paper,a lightweight offloading strategy that leverages ubiquitous connectivity through the Space Air Ground Integrated Vehicular Network architecture while ensuring privacy preservation is *** Internet of Vehicles(IoV)environment is first modeled as a graph,with vehicles and base stations as nodes,and their communication links as ***,vehicular applications are offloaded to suitable servers based on latency using an attention-based heterogeneous graph neural network(HetGNN)***,a differential privacy stochastic gradient descent trainingmechanism is employed for privacypreserving of vehicles and offloading ***,the simulation results demonstrated that the proposedHetGNN method shows good performance with 0.321 s of inference time,which is 42.68%,63.93%,30.22%,and 76.04% less than baseline methods such as Deep Deterministic Policy Gradient,Deep Q Learning,Deep Neural Network,and Genetic Algorithm,respectively.

关键词： Internet of vehicles vehicular ad-hoc networks(VANET) multiaccess edge computing task offloading graph neural networks differential privacy

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Prediction and analysis of Covid-19 using the Deep Learning Models

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Research on Biomedical engineering 2024年第1期40卷 183-197页

作者： Indira, V. Geetha, R. Umarani, Srikanth Priyadarshini, D. Annal Department of Computer Science and Engineering S.A. Engineering College Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India

Purpose: The difficulty of diagnosing several lung disorders, including atelectasis, cardiomegaly, lung cancer, and COVID-19, is a challenging problem and needs to be addressed. These conditions exhibit some symptoms and demand advanced medical imaging process, thorough clinical assessments, and innovative procedures for accurate diagnosis. The shortage of qualified radiologists further makes the problem more complex to deal with. COVID-19 in particular has resulted in a remarkable number of fatalities around the world. Children below the age of 5 and individuals over 65 are more likely to be affected by lung disorders. It is very hard to diagnose and manage COVID-19 absolutely, but it can be identified earlier by employing computer-aided diagnosis (CAD) technologies to make timely diagnosis. Currently, radiologists adopt technologies, which are driven by artificial intelligence. By using them, medical imaging data, such as chest X-rays and CT scans, can be investigated to identify patterns to diagnose the severity of the virus. This expedites the diagnostic process and enhances accuracy, facilitating more timely and precise medical interventions. The efficiency of artificial intelligence in processing large datasets can directly help healthcare professionals in making diagnosis quicker and more accurate. The objective of the work in this paper is to design and implement deep learning model classifiers, which will effectively categorize the patterns found in the X-rays and CT scans. Methods: Three techniques for categorization are exploited to propose an entirely new hybrid convolutional neural network (CNN) model in this context. MRI and CT image categorization in the first classification method employ Fully Connected (FC) layers. The weights are calculated and tuned for training the algorithm over multiple periods to deliver the maximum precision for classification. The most accurate MRI and CT image characteristics are studied, and deep learning model classifiers

关键词： COVID-19

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Human Stress Detection in and Through Sleep Patterns Using Machine Learning Algorithms

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Journal of The Institution of Engineers (India): Series B 2024年第6期105卷 1691-1713页

作者： Geetha, R. Gunanandhini, S. Srikanth, G. Umarani Sujatha, V. Department of Computer Science and Engineering S.A. Engineering College Chennai600077 India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Master of Computer Applications S.A. Engineering College Chennai600077 India

Stress has a remarkable impact on various cognitive functions, demanding timely and effective detection using strategies deployed across interdisciplinary domains. It influences decision-making, attention, learning, and problem-solving abilities. As a result, stress detection and modeling have become important areas of study in both psychology and computer science. This study links the fields of psychology and machine learning to deal with the urgent requirement of accurate stress detection methodologies and highlights sleep patterns as a key indicator for stress detection, discussing a novel approach to understand and determine stress levels. Psychologists use affective states to measure stress, which refers to a sense of feeling an underlying emotional state. However, most stress classification work has been limited to user-dependent models, which new users cannot use without additional training. This can be a significant time burden for new users trying to predict their affective states. Therefore, it is critical to address basic mental health issues in children and adults to prevent them from developing more complex problems on account of undergoing stress. The medical field processes vast amounts of medical data;the machine learning algorithms sift through patterns that might escape the human eye. The machine learning algorithms act as detectives, able to spot correlations and bring out a sense of complex information. The machine learning algorithms reveal fine correlations and patterns, aiding in more precise and prompt diagnoses particularly to focus fundamental mental health issues in individuals of all ages. This research work deploys an enhanced Multilayer Perceptron (MLP), exhibiting an extensive feature analysis for processing medical datasets, resulting in improved effectiveness in predicting stress levels. This helps us to diagnose issues more accurately and swiftly which improves the patient outcomes. The proposed and enhanced MLP model undergoes stri

关键词： Decision trees

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AGBUNet: an enhanced CNN-UNET architecture for the prediction of above ground biomass using deep learning

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Neural Computing and Applications 2025年第5期37卷 3809-3826页

作者： Arumai Shiney, S. Geetha, R. Department of Computer Science and Engineering S.A. Engineering College Chennai India

Accurate prediction of above ground biomass (AGB) is critical for monitoring forest health and carbon cycling. It is crucial for understanding and managing forest ecosystems. In this paper, we propose an enhanced framework combining convolutional neural network (CNN) and UNet, termed AGBUNet, specifically designed for predicting AGB using remote sensing data. The framework consists of separate CNNs for processing each type of image, whose outputs are subsequently fused in a UNet architecture to enhance prediction accuracy. These modifications include customized convolutional layers, advanced preprocessing techniques, and a novel integration of data prompts from separate Sentinel-1 and Sentinel-2 image processing streams. The AGBUNet integrates Sentinel-1 and Sentinel-2 images to leverage complementary information from synthetic aperture radar (SAR) and optical sensors. This study underscores the potential of the AGBUNet model for enhancing biomass estimation from remote sensing data, contributing to better forest management and ecological monitoring. The performance measures obtained are compared with the other models, and the following results are obtained as follows for MSE value of 298.25, RMSE value of 15.27 and MAE value of 12.21, and the values are satisfying compared with earlier benchmarks. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Convolutional neural networks

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Hybrid Deep Learning Approach for Sentiment Analysis on Twitter Data

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Multimedia Tools and Applications 2025年第15期84卷 15271-15292页

作者： Rakshit, Pranati Sarkar, Pronit Roy, Shubhankar Department of Computer Science and Engineering JIS College of Engineering Nadia Kalyani India

Social media is nowadays a vital platform where people can share their feelings about any incident, product, or any issue. Twitter is one of those platforms which are very popular. If we must make use of this to extract some information and to use that to make recommendation systems, to know the mental status of any person or to find the review, we have to perform the sentiment analysis of those tweets. Recently, deep learning approaches emerged as powerful computational models that discover intricate semantic representations of texts automatically from data without feature engineering. This present work discusses how to find the sentiments from a stream of tweets from the twitter, blogging site subjected to pre-processing and classification supported on emotion of the subject matter as "POSITIVE" or "NEGATIVE". We have used a twitter dataset of 1.6 million data consisting of general tweets on various domains with 800,000 POSITIVE and 800,000 NEGATIVE sentiments. We have developed 3 models with Recurrent Neural Network (RNN), Bi-directional RNN, Long Short-Term Memory (LSTM). Our work focuses on the way which will help us to understand the basic sentiment of people which they want to express by their tweets, comment, or blogs. We have obtained highest accuracy of 83.50%, F1 score of 84.60% from our model with LSTM architecture in analysing the sentiment. LSTMs are specifically designed to address the vanishing gradient problem that occurs in traditional RNNs when learning long-term dependencies. In sentiment analysis, understanding the sentiment of a tweet may require capturing dependencies between words that are spread across a potentially long sequence. LSTMs are better at retaining and utilizing information over extended sequences. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Long short-term memory

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WebFLex:A Framework for Web Browsers-Based Peer-to-Peer Federated Learning Systems Using WebRTC

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computers, Materials & Continua 2024年第3期78卷 4177-4204页

作者： Mai Alzamel Hamza Ali Rizvi Najwa Altwaijry Isra Al-Turaiki Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadhKingdom of Saudi Arabia Department of Computer Science and Engineering Punjab Engineering CollegeChandigarhIndia

Scalability and information personal privacy are vital for training and deploying large-scale deep learning *** learning trains models on exclusive information by aggregating weights from various devices and taking advantage of the device-agnostic environment of web ***,relying on a main central server for internet browser-based federated systems can prohibit scalability and interfere with the training process as a result of growing client ***,information relating to the training dataset can possibly be extracted from the distributed weights,potentially reducing the privacy of the local data used for *** this research paper,we aim to investigate the challenges of scalability and data privacy to increase the efficiency of distributed training *** a result,we propose a web-federated learning exchange(WebFLex)framework,which intends to improve the decentralization of the federated learning *** is additionally developed to secure distributed and scalable federated learning systems that operate in web browsers across heterogeneous ***,WebFLex utilizes peer-to-peer interactions and secure weight exchanges utilizing browser-to-browser web real-time communication(WebRTC),efficiently preventing the need for a main central *** has actually been measured in various setups using the MNIST *** results show WebFLex’s ability to improve the scalability of federated learning systems,allowing a smooth increase in the number of participating devices without central data *** addition,WebFLex can maintain a durable federated learning procedure even when faced with device disconnections and network ***,it improves data privacy by utilizing artificial noise,which accomplishes an appropriate balance between accuracy and privacy preservation.

关键词： Federated learning web browser privacy deep learning

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Adversarial attacks and defenses for digital communication signals identification

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Digital Communications and Networks 2024年第3期10卷 756-764页

作者： Qiao Tian Sicheng Zhang Shiwen Mao Yun Lin College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China College of Information and Communication Engineering Harbin Engineering UniversityHarbin150000China Department of Electrical and Computer Engineering Auburn UniversityAuburnAL36849USA

As modern communication technology advances apace,the digital communication signals identification plays an important role in cognitive radio networks,the communication monitoring and management *** has become a promising solution to this problem due to its powerful modeling capability,which has become a consensus in academia and ***,because of the data-dependence and inexplicability of AI models and the openness of electromagnetic space,the physical layer digital communication signals identification model is threatened by adversarial *** examples pose a common threat to AI models,where well-designed and slight perturbations added to input data can cause wrong ***,the security of AI models for the digital communication signals identification is the premise of its efficient and credible *** this paper,we first launch adversarial attacks on the end-to-end AI model for automatic modulation classifi-cation,and then we explain and present three defense mechanisms based on the adversarial *** we present more detailed adversarial indicators to evaluate attack and defense ***,a demonstration verification system is developed to show that the adversarial attack is a real threat to the digital communication signals identification model,which should be paid more attention in future research.

关键词： Digital communication signals identification AI model Adversarial attacks Adversarial defenses Adversarial indicators

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Evaluating the adversarial robustness of Arabic spam classifiers

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Neural Computing and Applications 2025年第6期37卷 4323-4343页

作者： Alajmi, Anwar Ahmad, Imtiaz Mohammed, Ameer Department of Computer Engineering College of Engineering and Petroleum Kuwait University Kuwait City Kuwait

Several studies have exposed the vulnerability of Natural Language Processing (NLP) models to adversarial attacks, which are inputs crafted by attackers to deceive NLP models. Adversarial robustness measures the performance of these systems under such attacks. In the Arabic literature, very limited contributions exist in the field of NLP adversarial robustness. Hence, this study focuses on examining the adversarial robustness of an Arabic NLP model, especially in a classic black-box spam evasion scenario. This work introduces eight diverse adversarial attacks (character, word, sentence, and multi-level) against Arabic NLP models. Moreover, we employ local post-hoc explanations such as SHapely Additive exPlanations (SHAP) to optimize the attacks strategies. Despite the excellent unfortified model’s accuracy of 99.4%, three of the attacks reduced the accuracy by more than 90%. This work also employs local post-hoc explanations such as SHapely Additive exPlanations (SHAP) Nevertheless, the proposed adversarial attacks were effective in terms of maintaining high semantic similarity and low perturbation distance. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Adversarial machine learning

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MLRT-UNet:An Efficient Multi-Level Relation Transformer Based U-Net for Thyroid Nodule Segmentation

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computer Modeling in engineering & Sciences 2025年第4期143卷 413-448页

作者： Kaku Haribabu Prasath R Praveen Joe IR Department of Computer Science and Engineering RMK College of Engineering and TechnologyTiruvallur601206India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India

Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and ***,achieving precise segmentation remains a challenge due to various factors,including scattering noise,low contrast,and limited resolution in ultrasound *** existing segmentation models have made progress,they still suffer from several limitations,such as high error rates,low generalizability,overfitting,limited feature learning capability,*** address these challenges,this paper proposes a Multi-level Relation Transformer-based U-Net(MLRT-UNet)to improve thyroid nodule *** MLRTUNet leverages a novel Relation Transformer,which processes images at multiple scales,overcoming the limitations of traditional encoding *** transformer integrates both local and global features effectively through selfattention and cross-attention units,capturing intricate relationships within the *** approach also introduces a Co-operative Transformer Fusion(CTF)module to combine multi-scale features from different encoding layers,enhancing the model’s ability to capture complex patterns in the ***,the Relation Transformer block enhances long-distance dependencies during the decoding process,improving segmentation *** results showthat the MLRT-UNet achieves high segmentation accuracy,reaching 98.2% on the Digital Database Thyroid Image(DDT)dataset,97.8% on the Thyroid Nodule 3493(TG3K)dataset,and 98.2% on the Thyroid Nodule3K(TN3K)*** findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation,addressing the limitations of existing models.

关键词： Thyroid nodules endocrine system multi-level relation transformer U-Net self-attention external attention co-operative transformer fusion thyroid nodules segmentation

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