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Privacy-Aware and AI Techniques for Healthcare Based on K-Anonymity Model in Internet of Things

IEEE Transactions on engineering Management 2024年 71卷 12448-12462页

作者： Sangaiah, Arun Kumar Javadpour, Amir Ja'fari, Forough Pinto, Pedro Chuang, Huan-Ming National Yunlin University of Science and Technology International Graduate Institute of Artificial Intelligence Douliou64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102 2801 Lebanon Harbin Institute of Technology Department of Computer Science and Technology Shenzhen150001 China Instituto Politécnico de Viana Do Castelo ADiT-Lab Electrotechnics and Telecommunications Department Porto4900-347 Portugal Sharif University of Technology Department of Computer Engineering Tehran11155-9161 Iran National Yunlin University of Science and Technology Department of Information Management Yunlin640 Taiwan

The government and industry have given the recent development of the Internet of Things in the healthcare sector significant respect. Health service providers retain data gathered from many sources and are useful for patient diagnostics and research for pivotal analysis. However, sensitive personal information about a person is contained in healthcare data, which must be protected. Individual privacy protection is a crucial concern for both people and organizations, particularly when those firms must send user data to data centers due to data mining. This article investigated two general states of increasing entropy by changing the entropy of the class set of characteristics based on artificial intelligence and the k-anonymity model in privacy in context, and also three different strategies have been investigated, i.e., the strategy of 'selecting the feature with the lowest number of distinct values,' 'selecting the feature with the lowest entropy,' and 'selecting the feature with the highest entropy.' For future tasks, we can find an optimal strategy that can help us to achieve optimal entropy in the least possible repetition. The results of our work have been compared by lightweight and MH-Internet of Things, FRUIT methods and shown that the proposed method has high efficiency in entropy criteria. © 1988-2012 IEEE.

关键词： Data mining

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Instance-Level Panoramic Audio-Visual Saliency Detection and Ranking 24

Instance-Level Panoramic Audio-Visual Saliency Detection and...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Guo, Ruohao Niu, Dantong Qu, Liao Qi, Yanyu Shi, Ji Yue, Wenzhen Xing, Bowei Chen, Taiyan Ying, Xianghua National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University Beijing China Berkeley AI Research University of California Berkeley BerkeleyCA United States Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA United States College of Information and Electrical Engineering China Agricultural University Beijing China

ISBN: (纸本)9798400706868

Panoramic audio-visual saliency detection is to segment the most attention-attractive regions in 360° panoramic videos with sound. To meticulously delineate the detected salient regions and effectively model human attention shift, we extend this task to more fine-grained instance scenarios: identifying salient object instances and inferring their saliency ranks. In this paper, we propose the first instance-level framework that can simultaneously be applied to segmentation and ranking of multiple salient objects in panoramic videos. Specifically, it consists of a distortion-aware pixel decoder to overcome panoramic distortions, a sequential audio-visual fusion module to integrate audio-visual information, and a spatio-temporal object decoder to separate individual instances and predict their saliency scores. Moreover, owing to the absence of such annotations, we create the ground-truth saliency ranks for the PAVS10K benchmark. Extensive experiments demonstrate that our model is capable of achieving state-of-the-art performance on the PAVS10K for both saliency detection and ranking tasks. The code is available at https://***/ruohaoguo/pavsodr. © 2024 ACM.

关键词： Contrastive Learning

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Local Adaptive Gradient Variance Attack for Deep Fake Fingerprint Detection

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computers, Materials & Continua 2024年第1期78卷 899-914页

作者： Chengsheng Yuan Baojie Cui Zhili Zhou Xinting Li Qingming Jonathan Wu Engineering Research Center of Digital Forensics Ministry of EducationNanjing University of Information Science and TechnologyNanjing210044China School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Institute of Artificial Intelligence and Blockchain Guangzhou UniversityGuangzhou510006China School of International Relations National University of Defense TechnologyNanjing210039China Department of Electrical and Computer Engineering University of WindsorWindsorN9B 3P4Canada

In recent years,deep learning has been the mainstream technology for fingerprint liveness detection(FLD)tasks because of its remarkable ***,recent studies have shown that these deep fake fingerprint detection(DFFD)models are not resistant to attacks by adversarial examples,which are generated by the introduction of subtle perturbations in the fingerprint image,allowing the model to make fake *** of the existing adversarial example generation methods are based on gradient optimization,which is easy to fall into local optimal,resulting in poor transferability of adversarial *** addition,the perturbation added to the blank area of the fingerprint image is easily perceived by the human eye,leading to poor visual *** response to the above challenges,this paper proposes a novel adversarial attack method based on local adaptive gradient variance for *** ridge texture area within the fingerprint image has been identified and designated as the region for perturbation ***,the images are fed into the targeted white-box model,and the gradient direction is optimized to compute gradient ***,an adaptive parameter search method is proposed using stochastic gradient ascent to explore the parameter values during adversarial example generation,aiming to maximize adversarial attack *** results on two publicly available fingerprint datasets show that ourmethod achieves higher attack transferability and robustness than existing methods,and the perturbation is harder to perceive.

关键词： FLD adversarial attacks adversarial examples gradient optimization transferability

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The Immense Impact of Reverse Edges on Large Hierarchical Networks

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engineering 2024年第5期36卷 240-249页

作者： Haosen Cao Bin-Bin Hu Xiaoyu Mo Duxin Chen Jianxi Gao Ye Yuan Guanrong Chen Tamás Vicsek Xiaohong Guan Hai-Tao Zhang MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Intelligent Manufacturing Equipment and TechnologySchool of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Jiangsu Key Laboratory of Networked Collective Intelligence School of MathematicsSoutheast UniversityNanjing 210096China Department of Computer Science Rensselaer Polytechnic InstituteTroyNY 12180USA Department of Electronic Engineering City University of Hong KongHong Kong 999077China Department of Biological Physics Eötvös UniversityBudapest 1117Hungary MOE Key Laboratory for Intelligent Networks and Network Security School of Automation Science and EngineeringXi’an Jiaotong UniversityXi’an 710049China

Hierarchical networks are frequently encountered in animal groups,gene networks,and artificial engineering systems such as multiple robots,unmanned vehicle systems,smart grids,wind farm networks,and so *** structure of a large directed hierarchical network is often strongly influenced by reverse edges from lower-to higher-level nodes,such as lagging birds’howl in a flock or the opinions of lowerlevel individuals feeding back to higher-level ones in a social *** study reveals that,for most large-scale real hierarchical networks,the majority of the reverse edges do not affect the synchronization process of the entire network;the synchronization process is influenced only by a small part of these reverse edges along specific *** surprisingly,a single effective reverse edge can slow down the synchronization of a huge hierarchical network by over 60%.The effect of such edges depends not on the network size but only on the average in-degree of the involved *** overwhelming majority of active reverse edges turn out to have some kind of“bunching”effect on the information flows of hierarchical networks,which slows down synchronization *** finding refines the current understanding of the role of reverse edges in many natural,social,and engineering hierarchical networks,which might be beneficial for precisely tuning the synchronization rhythms of these *** study also proposes an effective way to attack a hierarchical network by adding a malicious reverse edge to it and provides some guidance for protecting a network by screening out the specific small proportion of vulnerable nodes.

关键词： Synchronizability Large hierarchical networks Reverse edges Information flows Complex networks

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Machine Learning Approaches for Early Detection of Heart Diseases 25

Machine Learning Approaches for Early Detection of Heart Dis...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Al-Sheyab, Asalla M. Kousa, Msallam Hamad, Hanadi Bani Almomani, Adai Elrashidi, Ali Philadelphia Private University College of Information Technology Department of Computer Science Irbid25113 Jordan Isfahan University Faculty of Computer Engineering Department of Artificial Intelligence Isfahan Iran Zarqa Private University College of Information Technology Department of Computer Science Irbid25113 Jordan Zarqa University Department of Computer Science Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

The main objective of the study is to predict the probability of developing heart disease using a Decision Tree Classifier, K-nearest neighbors, and Support Vector Machine, Logistic Regression. Age, sexual pressure, quantity, and levels are among the variables included in the Kaggle dataset. A combination of accuracy, sensitivity, and specificity was used to train and evaluate the algorithms. Based on the accuracy of participants' predictions, the results suggest that learning algorithms could help treat heart *** "Heart Disease Prediction"project contains all project information and results. Contributing to the use of multiple algorithms improves patient outcomes to enhance the detection and treatment of heart diseases. Logistic Regression has achieved an accuracy of 86.41%. A module capable of predicting heart disease in a patient has been implemented. A variety of techniques are employed to assess models, including F-measure, ROC, precision, recall, and accuracy. The bagging set learning method using logistic regression provides the best performance, according to the data. © 2024 IEEE.

关键词： Support vector regression

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BDGOA:A bot detection approach for GitHub OAuth Apps

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Intelligent and Converged Networks 2023年第3期4卷 181-197页

作者： Zhifang Liao Xuechun Huang Bolin Zhang Jinsong Wu Yu Cheng the School of Computer Science and Engineering Central South UniversityChangsha 410083China the School of Artificial Intelligence Guilin University of Electronic TechnologyGuilin 541004China the Department of Electrical Engineering University of ChileSantiago 8320000 Hunan Glozeal Science and Technology Co. Ltd.Changsha 410083China

As various software bots are widely used in open source software repositories,some drawbacks are coming to light,such as giving newcomers non-positive feedback and misleading empirical studies of software engineering *** techniques have been proposed by researchers to perform bot detection,but most of them are limited to identifying bots performing specific activities,let alone distinguishing between GitHub App and OAuth *** this paper,we propose a bot detection technique for OAuth App,named BDGOA.24 features are used in BDGOA,which can be divided into three dimensions:account information,account activity,and text *** better explore the behavioral features,we define a fine-grained classification of behavioral events and introduce self-similarity to quantify the repeatability of behavioral *** leverage five machine learning classifiers on the benchmark dataset to conduct bot detection,and finally choose random forest as the classifier,which achieves the highest F1-score of 95.83%.The experimental results comparing with the state-of-the-art approaches also demonstrate the superiority of BDGOA.

关键词： Github DevBots machine learning text similarity

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Heart Disease Prediction Using Novel Ensemble and Blending Based Cardiovascular Disease Detection Networks: EnsCVDD-Net and BlCVDD-Net

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IEEE Access 2024年 12卷 109230-109254页

作者： Khan, Hira Javaid, Nadeem Bashir, Tariq Akbar, Mariam Alrajeh, Nabil Aslam, Sheraz Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad44000 Pakistan Department of Computer Science COMSATS University Islamabad Islamabad44000 Pakistan International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Yunlin Douliu64002 Taiwan Department of Biomedical Technology College of Applied Medical Sciences King Saud University Riyadh11633 Saudi Arabia Cyprus University of Technology Limassol3036 Cyprus

Cardiovascular Diseases (CVDs) have emerged as a significant physiological condition, being a primary contributor to mortality. Timely and precise diagnosis of heart disease is crucial to safeguard patients from additional harm. Recent studies show that the usage of data driven approaches, such as Deep Learning (DL) and Machine Learning (ML) techniques, in the field of medical science is highly useful in accurately diagnosing heart disease in less time. However, statistical learning and traditional ML approaches require feature engineering to generate robust and effective features from data, which are then used in the prediction models. In the case of large complex data, both processes pose many challenges. Whereas, DL techniques are capable of learning features automatically from the data and are effective at handling large and intricate datasets while outperforming the ML models. This study focuses on the accurate prediction of CVDs, considering the patient’s health and socio-economic conditions while mitigating the challenges presented by imbalanced data. The Adaptive Synthetic Sampling Technique is used for data balancing, while the Point Biserial Correlation Coefficient is used as a feature selection technique. In this study, two DL models, Ensemble based Cardiovascular Disease Detection Network (EnsCVDD-Net) and Blending based Cardiovascular Disease Detection Network (BlCVDD-Net), are proposed for accurate prediction and classification of CVDs. EnsCVDD-Net is made by applying an ensemble technique to LeNet and Gated Recurrent Unit (GRU), and BlCVDD-Net is made by blending LeNet, GRU and Multilayer Perceptron. SHapley Additive exPlanations is used to provide a clear understanding of the influence different factors have on CVD diagnosis. The network’s performance is evaluated on the basis of various performance metrics. The results indicate that the EnsCVDD-Net outperforms all base models with 88% accuracy, 88% F1-score, 91% precision, 85% recall, and 777s execu

关键词： Heart

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A genetic programming approach with adaptive region detection to skin cancer image classification

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Journal of Automation and intelligence 2024年第4期3卷 240-249页

作者： Kunjie Yu Jintao Lian Ying Bi Jing Liang Bing Xue Mengjie Zhang School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou450001China State Key Laboratory of Intelligent Agricultural Power Equipment Luoyang471000China Longmen Laboratory Luoyang471000China School of Electrical Engineering and Automation Henan Institute of TechnologyXinxiang453000China School of Engineering and Computer Science&Centre for Data Science and Artificial Intelligence Victoria University of WellingtonWellington6201New Zealand

Dermatologists typically require extensive experience to accurately classify skin *** recent years,the development of computer vision and machine learning has provided new methods for assisted *** skin cancer image classification methods have certain limitations,such as poor interpretability,the requirement of domain knowledge for feature extraction,and the neglect of lesion area information in skin *** paper proposes a new genetic programming(GP)approach to automatically learn global and/or local features from skin images for *** achieve this,a new function set and a new terminal set have been *** proposed GP method can automatically and flexibly extract effective local/global features from different types of input images,thus providing a comprehensive description of skin images.A new region detection function has been developed to select the lesion areas from skin images for feature *** performance of this approach is evaluated on three skin cancer image classification tasks,and compared with three GP methods and six non-GP *** experimental results show that the new approach achieves significantly better or similar performance in most *** analysis validates the effectiveness of our parameter settings,visualizes the multiple region detection functions used in the individual evolved by the proposed approach,and demonstrates its good convergence ability.

关键词： Genetic programming Skin cancer image classification Region detection Feature extraction

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Guest Editorial Enabling Technologies and Systems for Industry 5.0: From Foundation Models to Foundation intelligence

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2024年第11期54卷 6496-6499页

作者： Tang, Ying Tian, Yonglin Lin, Yilun Lv, Chen Fanti, Maria Pia Rowan University Department of Electrical and Computer Engineering GlassboroNJ08028 United States Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing100190 China Institute of Automation Chinese Academy of Sciences Beijing100190 China Shanghai AI Laboratory Urban Computing Laboratory Shanghai200232 China Nanyang Technological University School of Mechanical and Aerospace Engineering Jurong West639798 Singapore Polytechnic University of Bari Department of Electrical and Information Engineering Bari70126 Italy

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Post-Stroke Lesion Segmentation using 2D and 3D Dense U-net 7

Post-Stroke Lesion Segmentation using 2D and 3D Dense U-net

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7th International Conference on Advances in Biomedical engineering, ICABME 2023

作者： Mansour, Rena Khreiss, Zeinab Sbeity, Fatima Ali, Mohamad Abou Kassem, Abdallah Hamawy, Lara Lebanese International University Department of Biomedical Engineering Lebanon Lebanese University Department of Physics & Electronics Lebanon University of the Basque Country Department of Computer Science & Artificial Intelligence Spain Notre Dame University Department of Electrical &computer Engineering Lebanon

ISBN: (纸本)9798350325850

Strokes are brain attacks that cause death. Survivors suffer from damage to their brain (known as lesions). Detecting and segmenting these lesions using imaging techniques is crucial for medical diagnosis and treatment. However, manual post-stroke lesion segmentation is challenging and not yet fully automated. This research aims to assess the effectiveness of the Dimensional Dense U-Net architecture in this task. The architecture has shown promising results in other segmentation tasks, and we aim to evaluate its performance on the Anatomical Tracing of Lesion after Stroke (ATLAS) dataset. Initially, we employed a 3D version of the model and later switched to a 2D version when the 3D results were unsatisfactory. Among numerous trials, the 3D dense architecture achieved its highest dice score of 13% on patients with a single lesion in the left hemisphere, using 110 images. The 2D dense architecture achieved the highest dice score among all using 1000 random images. Each set was split into 70% for training, 20% for validation and 10% for testing. The training dice score was 87%, with a validation dice score of 9.8%. © 2023 IEEE.

关键词： Architecture

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