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Smart contract token-based privacy-preserving access control system for industrial Internet of Things

Digital Communications and Networks 2023年第2期9卷 337-346页

作者： Weizheng Wang Huakun Huang Zhimeng Yin Thippa Reddy Gadekallu Mamoun Alazab Chunhua Su Department of Computer Science City University of Hong KongHong Kong SAR999077China Division of Computer Science University of AizuFukushima965-8580Japan School of Information Technology and Engineering Vellore Institute of TechnologyTamil NaduIndia College of Engineering IT and EnvironmentCharles Darwin UniversityAustralia School of Computer Science and Cyber Engineering Guangzhou UniversityChina

Due to mobile Internet technology's rapid popularization,the Industrial Internet of Things(IIoT)can be seen everywhere in our daily *** IIoT brings us much convenience,a series of security and scalability issues related to permission operations rise to the surface during device ***,at present,a reliable and dynamic access control management system for IIoT is in urgent *** till now,numerous access control architectures have been proposed for ***,owing to centralized models and heterogeneous devices,security and scalability requirements still cannot be *** this paper,we offer a smart contract token-based solution for decentralized access control in IIoT ***,there are three smart contracts in our system,including the Token Issue Contract(TIC),User Register Contract(URC),and Manage Contract(MC).These three contracts collaboratively supervise and manage various events in IIoT *** also utilize the lightweight and post-quantum encryption algorithm-Nth-degree Truncated Polynomial Ring Units(NTRU)to preserve user privacy during the registration ***,to evaluate our proposed architecture's performance,we build a prototype platform that connects to the local ***,experiment results show that our scheme has achieved secure and dynamic access control for the IIoT system compared with related research.

关键词： Blockchain Privacy preservation Smart contract Industrial IoT

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Neuroadaptive Containment Control for Nonlinear Multiagent Systems With Input Saturation: An Event-Triggered Communication Approach

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3163-3173页

作者： Wang, Xin Zhang, Sen Li, Huaqing Zhang, Wei Li, Hongyi Huang, Tingwen Southwest University College of Electronic and Information Engineering Chongqing400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

In this article, a neuroadaptive event-triggered containment control strategy combined with the dynamic surface control (DSC) approach is proposed for nonlinear multiagent systems (MASs) with input saturation. Based on the event-triggered communication mechanisms, the updates of neural network weight and controllers are implemented solely under triggering conditions of violation, which markedly reduces unnecessary communication resources and minimizes inefficient control costs compared with the traditional control method. Radial basis function neural networks (RBF NNs) are employed to handle the nonlinear uncertainties of MASs. Simultaneously, an adaptive compensatory mechanism is incorporated within the backstepping process to address the nonlinear effect posed by input saturation. Additionally, we demonstrate the system stability by extending the Lyapunov theorem to jump and continuous scenarios while excluding Zeno behavior, realizing that all followers can enter the convex hull constructed by leaders. Finally, the effectiveness of the proposed methodology is verified through application simulations. © 2013 IEEE.

关键词： Radial basis function networks

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DPTP-LICD:A differential privacy trajectory protection method based on latent interest community detection

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High-Confidence Computing 2023年第2期3卷 60-70页

作者： Weiqi Zhang Guisheng Yin Yuxin Dong Fukun Chen Qasim Zia College of Computer Science and Technology Harbin Engineering UniversityHarbin 150001China Department of Computer Science Georgia State UniversityAtlanta 30303USA

With the rapid development of high-speed mobile network technology and high-precision positioning technology,the trajectory information of mobile users has received extensive attention from academia and industry in the field of Location-based Social *** can mine users’trajectories in Location-based Social Networks to obtain sensitive information,such as friendship groups,activity patterns,and consumption ***,mobile users’privacy and security issues have received growing attention in Location-based Social *** is crucial to strike a balance between privacy protection and data *** paper proposes a differential privacy trajectory protection method based on latent interest community detection(DPTP-LICD),ensuring strict privacy protection standards and user data ***,based on the historical trajectory information of users,spatiotemporal constraint information is extracted to construct a potential community strength model for mobile ***,the latent interest community obtained from the analysis is used to identify preferred hot spots on the user’s trajectory,and their priorities are assigned based on a popularity model.A reasonable privacy budget is allocated to prevent excessive noise from being added and rendering the protected trajectory data ***,to prevent privacy leakage,we add Laplace and exponential noise in generating preferred hot spots and recommending user interest *** and effectiveness analysis shows that our mechanism provides effective points of interest recommendations and protects users’privacy from disclosure.

关键词： Differential privacy Community detection Trajectory data Community of interest Recommendation

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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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Cardiomegaly Prediction Using computer Vision 2

Cardiomegaly Prediction Using Computer Vision

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Arunasakthi, K. Dhaneshvarraj, A.K.B. Koushikram, N.S. Deepalakshmi, R. Chakkaravarthy, G. Vinoth Velammal College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798350328202

Cardiomegaly, the enlargement of the heart, poses a significant challenge in the field of healthcare. Outdated diagnostic methods often lead to delayed detection and intervention. This project presents an innovative solution employing artificial intelligence (AI) to predict cardiomegaly. By leveraging computer vision techniques on medical imaging data, specifically chest X-rays, this system aims to automate the identification and prediction of cardiomegaly. Through AI-driven analysis and feature extraction, the project seeks to provide accurate and timely diagnoses, facilitating early intervention and treatment. Key factors such as image-based indicators, patient demographics, and clinical history are considered in the prediction process. By enhancing the accuracy of cardiomegaly prediction, this initiative contributes to improved patient outcomes and healthcare efficiency. The project also offers a user-friendly interface for healthcare professionals, making it accessible and practical in clinical settings. © 2024 IEEE.

关键词： Forecasting

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SUDM-SP:A method for discovering trajectory similar users based on semantic privacy

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High-Confidence Computing 2023年第3期3卷 68-80页

作者： Weiqi Zhang Guisheng Yin Bingyi Xie College of Computer Science and Technology Harbin Engineering UniversityHarbin 150009China Department of Computer Science Georgia State UniversityGA 30303USA

With intelligent terminal devices’widespread adoption and global positioning systems’advancement,Location-based Social Networking Services(LbSNs)have gained considerable *** recommendation mechanism,which revolves around identifying similar users,holds significant importance in *** order to enhance user experience,LbSNs heavily rely on accurate *** mining and analyzing users who exhibit similar behavioral patterns to the target user,LbSNs can offer personalized services that cater to individual ***,trajectory data,a form encompassing various sensitive attributes,pose privacy *** disclosure of users’precise trajectory information can have severe consequences,potentially impacting their daily ***,this paper proposes the Similar User Discovery Method based on Semantic Privacy(SUDM-SP)for trajectory *** approach involves employing a model that generates noise trajectories,maximizing expected noise to preserve the privacy of the original *** users are then identified based on the published noise trajectory ***-SP consists of two key ***,a puppet noise location,exhibiting the highest semantic expectation with the original location,is generated to derive noisesuppressed trajectory ***,a mechanism based on semantic and geographical distance is employed to cluster highly similar users into communities,facilitating the discovery of noise trajectory similarity among *** trials conducted using real datasets,the effectiveness of SUDM-SP,as a recommendation service ensuring user privacy protection is substantiated.

关键词： Differential privacy LbSNs Semantic trajectory Recommendation

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Breast cancer diagnosis using machine learning model 4th

Breast cancer diagnosis using machine learning model

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4th International Conference on Computational Methods in science and technology, ICCMST 2024

作者： Arthi, T.S. Partheeban, N. Singh, Bhoomika Singh, Aparajitha Tripathi, Nikhil Department of Computer Science and Engineering Galgotias College of Engineering & Technology Greater Noida India Department of Computer Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9781032911571

Early and accurate detection of breast cancer, particularly Invasive Ductal Carcinoma (IDC), is critical for improving patient outcomes. Traditional diagnostic methods like histopathology and mammography have limitations in terms of time, cost, and potential subjectivity. This study proposes a lightweight Machine Learning (ML) model for IDC classification using digital whole-mount breast cancer slide images. The model aims to achieve high accuracy while being suitable for deployment in resource-constrained environments. We evaluate our model on a publicly available breast histopathology dataset and compare its performance with other Deep Learning (DL) models, focusing on both accuracy and computational efficiency. The results demonstrate that our model achieves a high classification accuracy (around 89.3 percent) with minimal processing power and memory requirements. Furthermore, successful deployment on a Raspberry Pi embedded system validates its potential for real-world application in resource-limited settings, potentially leading to improved breast cancer diagnosis accessibility. Index Terms—Bi-directional Long Short-Term Memory (BiLSTM), Convolutional Neural Network (CNN), Deep Learning (DL), Invasive Ductal Carcinoma (IDC), Multilayer Perceptron (MLP), Raspberry Pi, Tiny Machine Learning (TinyML). © 2025 the Author(s).

关键词： Convolutional neural networks

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GEMD-UNet: Graph Structure Enhanced Multi-dimensional Learning Unet for Cloud Detection

GEMD-UNet: Graph Structure Enhanced Multi-dimensional Learni...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Chen, Jianing Chen, Chuhao Yang, Junze Li, Wei Yadav, Rahul Zheng, Wenqi College of Computer Science and Technology Harbin Engineering University China

ISBN: (纸本)9798350368741

Cloud detection (CD) in remote sensing images is commonly used in satellite imaging and laser communication. UNet-based methods with multi-level feature caching and interaction learning, are popular for superior CD performance. However, most current CD methods focus on spatial feature enhancement through global feature learning, which often overlooks the detailed perception of color, shape, and boundaries due to insufficient channel interactions. Additionally, these methods tend to increase computational requirements. To ensure flexible feature attention, enhancing both structural and detailed understanding, while maintaining low computational complexity. In this paper, we propose Graph Structure Knowledge Enhanced Multi-Dimensional Learning U-shaped Network (GEMD-UNet) for CD. Specifically, we utilize tensor dimension transformations to cleverly employ two fundamental learnable parameters alternately for spatial/channel relationship learning. Furthermore, to enhance feature knowledge capacity and focus more on local detail, we employ local window partitioning to divide hidden features into multiple independent learning groups and inject two different physically meaningful graph structures knowledge in a non-Euclidean space into features. Experimental results demonstrate that GEMD-UNet outperforms previous SOTA methods on both HRC-WHU and L8SPARCS datasets. Code can be available at: https://***/mxx123321/cloud_model. © 2025 IEEE.

关键词： Cloud detection (CD) Deep Learning Graph Neural Network UNet

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A combinatorial algorithm for finding straight skeleton of a digital object

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Multimedia Tools and Applications 2025年 1-21页

作者： Maity, Anukul Dutt, Mousumi Biswas, Arindam Department of Computer Science and Engineering Narula Institute of Technology 81 Nilgunj Road Agarpara West Bengal Kolkata700109 India Department of Computer Science and Engineering St. Thomas’ College of Engineering and Technology 4 Diamond Harbour Road Kidderpore West Bengal Kolkata700023 India Department of Information Technology Indian Institute of Engineering Science and Technology Botanical Garden Road Shibpur West Bengal Howrah711103 India

The paper presents a combinatorial algorithm to find the straight skeleton of the inner isothetic cover of a digital object imposed on a uniform background grid. The isothetic polygon (orthogonal polygon) tightly inscribes the given digital object. The algorithm stated here finds the straight skeleton of any isothetic polygon (simple or non-simple) in O(nglogng) time in a single traversal by applying combinatorial rules, where n is the number of pixels on the boundary of the digital object and g is the grid size on which the digital object is imposed. To find the straight skeleton of non-simple orthogonal polygon, the polygon is divided into sub-parts which are a set of simple orthogonal polygons. The straight skeleton is generated for each of the sub-parts and the corresponding results are merged. The orthogonal polygon containing holes is cut at each holes and the polygon becomes hole-free. The algorithm to obtain the straight skeleton is applied on it. The disconnected hole parts are rejoined and the result is merged. The straight skeleton is useful shape descriptor of digital object. This algorithm stated here has applications in shape analysis. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Combinatorial optimization

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Multi-Objective Equilibrium Optimizer for Feature Selection in High-Dimensional English Speech Emotion Recognition

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computers, Materials & Continua 2024年第2期78卷 1957-1975页

作者： Liya Yue Pei Hu Shu-Chuan Chu Jeng-Shyang Pan Fanli Business School Nanyang Institute of TechnologyNanyang473004China School of Computer and Software Nanyang Institute of TechnologyNanyang473004China College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Information Management Chaoyang University of TechnologyTaichung413310Taiwan

Speech emotion recognition(SER)uses acoustic analysis to find features for emotion recognition and examines variations in voice that are caused by *** number of features acquired with acoustic analysis is extremely high,so we introduce a hybrid filter-wrapper feature selection algorithm based on an improved equilibrium optimizer for constructing an emotion recognition *** proposed algorithm implements multi-objective emotion recognition with the minimum number of selected features and maximum ***,we use the information gain and Fisher Score to sort the features extracted from ***,we employ a multi-objective ranking method to evaluate these features and assign different importance to *** with high rankings have a large probability of being ***,we propose a repair strategy to address the problem of duplicate solutions in multi-objective feature selection,which can improve the diversity of solutions and avoid falling into local *** random forest and K-nearest neighbor classifiers,four English speech emotion datasets are employed to test the proposed algorithm(MBEO)as well as other multi-objective emotion identification *** results illustrate that it performs well in inverted generational distance,hypervolume,Pareto solutions,and execution time,and MBEO is appropriate for high-dimensional English SER.

关键词： Speech emotion recognition filter-wrapper high-dimensional feature selection equilibrium optimizer multi-objective

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