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Blockchain-enabled IoT access control model for sharing electronic healthcare data

Multimedia Tools and Applications 2025年第10期84卷 8127-8148页

作者： Ilyas, Benkhaddra Kumar, Abhishek Ali, Setitra Mohamed Lei, Hang School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China Chandigarh University Gharuan Mohali Punjab140413 India School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China

Deploying the Internet of Things (IoT) in the transfer of enormous medical data often promotes challenges with the security, confidentiality, and privacy of the user’s sensitive data. In addition, the access control for prohibiting unauthorized access has been facing challenges with single-point-of-failure, collusion, and other security issues. To address these challenges associated with IoT access control, this research proposes a Blockchain-enabled IoT access control for sharing Electronic health records (EHR). The proposed authentic access control scheme transfers the data to the authenticated user without discrepancies. The proposed technique utilizes the hyperledger composer fabric blockchain that improves the transparency and traceability of the network, and the intelligent contract keeps track of the information, which reduces the possibility of unauthorized access. The proposed access control scheme validates the request effectively and transmits the data efficiently. The effectiveness of the proposed access control scheme is evaluated by analyzing the blocks in terms of metrics such as responsiveness of 185.47 secs, 95% Genuine User Detection (GUD), 96.50% Privacy, and 3.50% Information loss, for 100 blocks and proposed access control scheme works more efficiently in transmitting the data. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Smart contract

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Robust UAV Image Stitching via Multiscale Feature Matching and Local Homography Matrix Estimation

Robust UAV Image Stitching via Multiscale Feature Matching a...

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2024 International Conference Automatics and Informatics, ICAI 2024

作者： Li, Rou Xue, Wei School of Computer Science and Technology Anhui University of Technology Maanshan243032 China

ISBN: (纸本)9798350353907

Due to some factors such as optical aberrations and large parallax, a few stitching methods lead to artifacts and distortions in the panoramic image. To alleviate this issue, this paper proposes a robust unmanned aerial vehicle (UAV) image stitching algorithm based on multiscale feature matching and local homography matrix estimation. We firstly employ a multiscale feature matching method to establish a reliable correspondence between two sets of points, so as to suppress artifacts in overlapping areas better. Then, we estimate the local homography matrix to alleviate the distortion of target image by combining similarity transformation of matched feature points with projection transformation. Finally, we use a sigmoid metric to quantify the color differences in the overlapping region and achieve a smooth transition between the images by introducing a seam-cutting method. Extensive experiments demonstrate the effectiveness and robustness of our method for UAV image stitching. © 2024 IEEE.

关键词： Aberrations

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African Bison Optimization Algorithm:A New Bio-Inspired Optimizer with engineering Applications

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computers, Materials & Continua 2024年第10期81卷 603-623页

作者： Jian Zhao Kang Wang Jiacun Wang Xiwang Guo Liang Qi School of Science University of Science and Technology LiaoningAnshan114051China Team of Artificial Intelligence Theory and Application University of Science and Technology LiaoningAnshan114051China Department of Computer Science and Software Engineering Monmouth UniversityWest Long BranchNJ 07764USA Information and Control Engineering College Liaoning Petrochemical UniversityFushun113000China Department of Computer Science and Technology Shandong University of Science and TechnologyQingdao266590China

This paper introduces the African Bison Optimization(ABO)algorithm,which is based on biological *** is inspired by the survival behaviors of the African bison,including foraging,bathing,jousting,mating,and *** foraging behavior prompts the bison to seek a richer food source for *** bison find a food source,they stick around for a while by bathing *** jousting behavior makes bison stand out in the population,then the winner gets the chance to produce offspring in the mating *** eliminating behavior causes the old or injured bison to be weeded out from the herd,thus maintaining the excellent *** above behaviors are translated into ABO by mathematical *** assess the reliability and performance of ABO,it is evaluated on a diverse set of 23 benchmark functions and applied to solve five practical engineering problems with *** findings from the simulation demonstrate that ABO exhibits superior and more competitive performance by effectively managing the trade-off between exploration and exploitation when compared with the other nine popular metaheuristics algorithms.

关键词： Optimization metaheuristics African bison optimization engineering problems

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Blockchain based federated learning for intrusion detection for Internet of Things

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Frontiers of computer science 2024年第5期18卷 109-119页

作者： Nan SUN Wei WANG Yongxin TONG Kexin LIU School of Cyber Science and Technology Beihang UniversityBeijing 100191China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China Zhongguancun Laboratory Beijing 100094China School of Computer Science and Engineering Beihang UniversityBeijing 100191China

In Internet of Things(loT),data sharing among different devices can improve manufacture efficiency and reduce workload,and yet make the network systems be more vulnerable to various intrusion *** has been realistic demand to develop an efficient intrusion detection algorithm for connected *** of existing intrusion detection methods are trained in a centralized manner and are incapable to identify new unlabeled attack *** this paper,a distributed federated intrusion detection method is proposed,utilizing the information contained in the labeled data as the prior knowledge to discover new unlabeled attack ***,the blockchain technique is introduced in the federated learning process for the consensus of the entire *** results are provided to show that our approach can identify the malicious entities,while outperforming the existing methods in discovering new intrusion attack types.

关键词： intrusion detection federated learning new attacks discovering blockchain

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Learning to Communicate Among Agents for Large-Scale Dynamic Path Planning With Genetic Programming Hyperheuristic

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1269-1283页

作者： Liao, Xiao-Cheng Hu, Xiao-Min Chen, Xiang-Ling Mei, Yi Jia, Ya-Hui Chen, Wei-Neng Victoria University of Wellington Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China Hanyang University Department of Electrical and Electronic Engineering Ansan15588 Korea Republic of South China University of Technology School of Future Technology Guangzhou510006 China Pazhou Lab Guangzhou510005 China South China University of Technology School of Computer Science and Engineering State Key Laboratory of Subtropical Building and Urban Science Guangzhou510006 China

Genetic programming hyperheuristic (GPHH) has recently become a promising methodology for large-scale dynamic path planning (LDPP) since it can produce reusable heuristics rather than disposable solutions. However, in this methodology, the extracted local and decentralized heuristic for agents that lack a global systemic view sometimes may be problematic. Therefore, a new challenge is to strike a balance between conciseness to improve generalization ability and incorporation of more global information to obtain better performance. In this work, we target the LDPP problem and propose a communication learning mechanism (ComLGP) for GPHH to address the above difficulties. In ComLGP, a communication function is introduced to serve as a communication protocol and exist in the form of an extra terminal in GPHH. Compared to the classic terminals which are fixed in genetic programing, this communication function undergoes optimization along with the evolutionary process of GPHH. In this way, the communication function can be learned which enables agents to communicate without a predefined communication protocol. Then, a caching and lazy updating mechanism for ComLGP is presented to accelerate the calculation of communication content. Last, we verified our method on 22 scenarios including two real world road networks. The experimental results demonstrate that the proposed ComLGP can successfully learn to communicate. Although in the absence of any manually designed communication features, ComLGP is capable of achieving performance competitive to the state-of-the-art method that employs a predefined communication protocol and outperforms the remaining compared methods in most scenarios. © 2020 IEEE.

关键词： Genetic programming

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Automated GUI widgets classification

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Frontiers of computer science 2023年第1期17卷 233-235页

作者： Kabir Sulaiman SAID Liming NIE Yuanchang LIN Yaowen ZHENG Zuohua DING School of Computer Science and Technology Zhejiang Sci-Tech UniversityHangzhou 310018China School of Computer Science and Engineering Nanyang Technological UniversitySingap

1 Introduction Graphical User Interface(GUI)widgets classification entails classifying widgets into their appropriate domain-specific types(e.g.,CheckBox and EditText)[1,2].The widgets classification is essential as it supports several software engineering tasks,such as GUI design and testing[1,3].The ability to obtain better widget classification performance has become one of the keys to the success of these *** in recent years have proposed many techniques for improving widget classification performance[1,2,4].For example,Moran et al.[1]proposed a deep learning technique to classify GUI widgets into their domain-specific *** authors used the deep learning algorithm,a Convolutional Neural Network(CNN)architecture,to classify the GUI *** et al.[2]proposed combining text-based and non-text-based models to improve the overall performance of GUI widget detection while classifying the widgets with the ResNet50 model.

关键词： GUI User classify

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Deep-BERT:Transfer Learning for Classifying Multilingual Offensive Texts on Social Media

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computer Systems science & engineering 2023年第2期44卷 1775-1791页

作者： Md.Anwar Hussen Wadud M.F.Mridha Jungpil Shin Kamruddin Nur Aloke Kumar Saha Department of Computer Science and Engineering Bangladesh University of Business and TechnologyDhakaBangladesh School of Computer Science and Engineering University of AizuAizuwakamatsuJapan Department of Computer Science American International University-BangladeshDhakaBangladesh Department of Computer Science and Engineering University of Asia PacificDhakaBangladesh

Offensive messages on social media,have recently been frequently used to harass and criticize *** recent studies,many promising algorithms have been developed to identify offensive *** algorithms analyze text in a unidirectional manner,where a bidirectional method can maximize performance results and capture semantic and contextual information in *** addition,there are many separate models for identifying offensive texts based on monolin-gual and multilingual,but there are a few models that can detect both monolingual and multilingual-based offensive *** this study,a detection system has been developed for both monolingual and multilingual offensive texts by combining deep convolutional neural network and bidirectional encoder representations from transformers(Deep-BERT)to identify offensive posts on social media that are used to harass *** paper explores a variety of ways to deal with multilin-gualism,including collaborative multilingual and translation-based ***,the Deep-BERT is tested on the Bengali and English datasets,including the different bidirectional encoder representations from transformers(BERT)pre-trained word-embedding techniques,and found that the proposed Deep-BERT’s efﬁcacy outperformed all existing offensive text classiﬁcation algorithms reaching an accuracy of 91.83%.The proposed model is a state-of-the-art model that can classify both monolingual-based and multilingual-based offensive texts.

关键词： Offensive text classiﬁcation deep convolutional neural network(DCNN) bidirectional encoder representations from transformers(BERT) natural language processing(NLP)

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Facial Depression Estimation via Multi-Cue Contrastive Learning

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 6007-6020页

作者： Wang, Xinke Xu, Jingyuan Sun, Xiao Li, Mingzheng Hu, Bin Qian, Wei Guo, Dan Wang, Meng Anhui University School of Artificial Intelligence Hefei230601 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230031 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China University of Science and Technology of China Institute of Advanced Technology Hefei230094 China Beijing Institute of Technology School of Medical Technology Beijing100081 China

Vision-based depression estimation is an emerging yet impactful task, whose challenge lies in predicting the severity of depression from facial videos lasting at least several minutes. Existing methods primarily focus on fusing frame-level features to create comprehensive representations. However, they often overlook two crucial aspects: 1) inter- and intra-cue correlations, and 2) variations among samples. Hence, simply characterizing sample embeddings while ignoring to mine the relation among multiple cues leads to limitations. To address this problem, we propose a novel Multi-Cue Contrastive Learning (MCCL) framework to mine the relation among multiple cues for discriminative representation. Specifically, we first introduce a novel cross-characteristic attentive interaction module to model the relationship among multiple cues from four facial features (e.g., 3D landmarks, head poses, gazes, FAUs). Then, we propose a temporal segment attentive interaction module to capture the temporal relationships within each facial feature over time intervals. Moreover, we integrate contrastive learning to leverage the variations among samples by regarding the embeddings of inter-cue and intra-cue as positive pairs while considering embeddings from other samples as negative. In this way, the proposed MCCL framework leverages the relationships among the facial features and the variations among samples to enhance the process of multi-cue mining, thereby achieving more accurate facial depression estimation. Extensive experiments on public datasets, DAIC-WOZ, CMDC, and E-DAIC, demonstrate that our model not only outperforms the advanced depression methods but that the discriminative representations of facial behaviors provide potential insights about depression. Our code is available at:https://***/xkwangcn/***. © 1991-2012 IEEE.

关键词： Face recognition

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JPEG Reversible Data Hiding via Block Sorting Optimization and Dynamic Iterative Histogram Modification

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IEEE Transactions on Multimedia 2025年 27卷 3729-3743页

作者： Li, Fengyong Wang, Qiankuan Cheng, Hang Zhang, Xinpeng Qin, Chuan Shanghai University of Electric Power College of Computer Science and Technology Shanghai201306 China Fuzhou University School of Mathematics and Statistics Fuzhou Fujian350108 China Fudan University School of Computer Science Shanghai200433 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China

JPEG reversible data hiding (RDH) refers to covert communication technology to accurately extract secret data while also perfectly recovering the original JPEG image. With the development of cloud services, a large number of private JPEG images can be efficiently managed in cloud platforms by embedding user ID or authentication labels. Nevertheless, data embedding operations may inadvertently disrupt the encoding sequence of the original JPEG image, resulting in severe distortion of the host image when it is re-compressed to JPEG format. To address this problem, this paper proposes a new JPEG RDH scheme based on block sorting optimization and dynamic iterative histogram modification. We firstly design a block ordering optimization strategy by combining the number of zero coefficients and the quantization table values of non-zero coefficients in a DCT block. Subsequently, a dynamic iterative histogram modification scheme is proposed by considering the local features and embedding capability of histograms generated from different texture images. According to the given payloads, we introduce different parameters to control the iterations of two-dimensional histogram and then adaptively generate the optimal histogram modification mapping, which can realize low JPEG file size increments by guaranteeing most of the AC coefficients unchanged as much as possible. Numerous experiments have shown that our scheme can achieve an effective balance among embedding capacity, visual quality, file size increment, computational complexity, and outperforms the state-of-the-arts in terms of the above metrics. © 1999-2012 IEEE.

关键词： Embeddings

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Integrating machine learning and extreme value theory for estimating crash frequency-by-severity via AI-based video analytics

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Communications in Transportation Research 2024年第1期4卷 382-392页

作者： Fizza Hussain Yuefeng Li Md Mazharul Haque School of Civil&Environment Engineering Queensland University of TechnologyBrisbane4000Australia School of Computer ScienceQueensland University of TechnologyBrisbane4000Australia

Traffic conflict techniques rely heavily on the proper identification of conflict extremes,which directly affects the prediction performance of extreme value *** sampling techniques,namely,block maxima and peak over threshold,form the core of these *** studies have demonstrated the inefficacy of extreme value models based on these sampling approaches,as their crash estimates are too imprecise,hindering their widespread practical ***,anomaly detection techniques for sampling conflict extremes have been used,but their application has been limited to estimating crash frequency without considering the crash severity *** address this research gap,this study proposes a hybrid model of machine learning and extreme value theory within a bivariate framework of traffic conflict measures to estimate crash frequency by severity *** particular,modified time-to-collision(MTTC)and expected post-collision change in velocity(Delta-V orΔV)have been proposed in the hybrid modeling framework to estimate rear-end crash frequency by severity ***-end conflicts were identified through artificial intelligence-based video analytics for three four-legged signalized intersections in Brisbane,Australia,using four days of ***-stationary bivariate hybrid generalized extreme value models with different anomaly detection/sampling techniques(isolation forest and minimum covariance determinant)were *** non-stationarity of traffic conflict extremes was handled by parameterizing model parameters,including location,scale,and both location and scale parameters *** results indicate that the bivariate hybrid models can estimate severe and non-severe crashes when compared with historical crash records,thereby demonstrating the viability of the proposed approach.A comparative analysis of two anomaly techniques reveals that the isolation forest model marginally outperforms the minimum covariance determinant ***,the modeling f

关键词： Bivariate models Traffic conflicts Crash severity Crash frequency Generalized extreme value Anomaly detection techniques

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