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Enhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologies

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Computers, Materials & Continua 2024年第5期79卷 2609-2623页

作者： Muhammad Ahmad Nawaz Ul Ghani Kun She Muhammad Arslan Rauf Shumaila Khan Javed Ali Khan Eman Abdullah Aldakheel Doaa Sami Khafaga School of information and Software Engineering University of Electronic Science and Technology of ChinaChengdu611731China department of Computer Science University of Science&TechnologyBannu28100Pakistan department of Computer Science University of HertfordshireHatfieldAL109ABUK department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The use of privacy-enhanced facial recognition has increased in response to growing concerns about data securityand privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a varietyof industries, including access control, law enforcement, surveillance, and internet communication. However,the growing usage of face recognition technology has created serious concerns about data monitoring and userprivacy preferences, especially in context-aware systems. In response to these problems, this study provides a novelframework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain,and distributed computing to solve privacy concerns while maintaining exact face recognition. The framework’spainstaking design and execution strive to strike a compromise between precise face recognition and protectingpersonal data integrity in an increasingly interconnected environment. Using cutting-edge tools like Dlib for faceanalysis,Ray Cluster for distributed computing, and Blockchain for decentralized identity verification, the proposedsystem provides scalable and secure facial analysis while protecting user privacy. The study’s contributions includethe creation of a sustainable and scalable solution for privacy-aware face recognition, the implementation of flexibleprivacy computing approaches based on Blockchain networks, and the demonstration of higher performanceover previous methods. Specifically, the proposed StyleGAN model has an outstanding accuracy rate of 93.84%while processing high-resolution images from the CelebA-HQ dataset, beating other evaluated models such asProgressive GAN 90.27%, CycleGAN 89.80%, and MGAN 80.80%. With improvements in accuracy, speed, andprivacy protection, the framework has great promise for practical use in a variety of fields that need face recognitiontechnology. This study paves the way for future research in privacy-enhanced face recognition systems, emphasizingt

关键词： Facial recognition privacy protection blockchain GAN distributed systems

Exciton-polaritons in a 2D hybrid organic-inorganic perovskite microcavity with the presence of optical Stark effect

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Chinese Physics B 2024年第3期33卷 551-555页

作者： Kenneth Coker 郑楚媛 Joseph Roger Arhin Kwame Opuni-Boachie Obour Agyekum 张伟利 School of information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China department of Electrical and Electronic Engineering Ho Technical UniversityHo 00233Ghana Telecommunication Engineering department Kwame Nkrumah University of Science and TechnologyKumasi 00233Ghana

This study investigates the properties of exciton-polaritons in a two-dimensional(2D)hybrid organic-inorganic perovskite microcavity in the presence of optical Stark *** both steady and dynamic state analyses,strong coupling between excitons of perovskite and cavity photons is revealed,indicating the formation of polaritons in the perovskite ***,it is found that an external optical Stark pulse can induce energy shifts of excitons proportional to the pulse intensity,which modifies the dispersion characteristics of the polaritons.

关键词： exciton-polaritons perovskite microcavity optical Stark effect

A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

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CAAI Transactions on Intelligence technology 2024年第4期9卷 790-804页

作者： Nechirvan Asaad Zebari Chira Nadheef Mohammed Dilovan Asaad Zebari Mazin Abed Mohammed Diyar Qader Zeebaree Haydar Abdulameer Marhoon Karrar Hameed Abdulkareem Seifedine Kadry Wattana Viriyasitavat Jan Nedoma Radek Martinek department of information technology Lebanese French UniversityErbilIraq department of Computer Science University of ZakhoZakhoKurdistan RegionIraq department of Computer Science College of ScienceNawroz UniversityDuhokKurdistan RegionIraq department of Artificial Intelligence College of Computer Science and Information TechnologyUniversity of AnbarRamadiIraq department of Telecommunications VSB‐Technical University of OstravaOstravaCzech Republic department of Cybernetics and Biomedical Engineering VSB‐Technical University of OstravaOstravaCzech Republic department of Computer Network and information Security Technical College of Informatics-AkreDuhok Polytechnic UniversityDuhokIraq information and Communication technology Research Group Scientific Research CenterAl‐Ayen UniversityThi‐QarIraq College of Computer Sciences and information technology University of KerbalaKarbalaIraq College of Agriculture Al‐Muthanna UniversitySamawahIraq department of Applied Data Science Noroff University CollegeKristiansandNorway Faculty of Commerce and Accountancy Chulalongkorn Business SchoolChulalongkorn UniversityBangkokThailand

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

关键词： brain tumour deep learning feature fusion model MRI images multi‐classification

Grouping Decision Algorithm for Dynamic Terminals in Random Access Networks

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Journal of Communications and information Networks 2024年第2期9卷 176-183页

作者： Hongliang Sun Tongfei Chen Chuangye Zhao Mengxin Chen College of information and Control Engineering Jilin Institute of Chemical TechnologyJilin 132022China

This paper proposes a grouping decision algorithm for random access networks with the carrier sense multiple access (CSMA) mechanism, which can balance the traffic load and solve the hidden terminal issue. Considering the arrival characteristics of terminals and quality of service (QoS) requirements, the traffic load is evaluated based on the effective bandwidth theory. Additionally, a probability matrix of hidden terminals is constructed to take into account the dynamic nature of hidden terminal relations. In the grouping process, an income function is established with a view to the benefits of decreasing the probability of hidden terminal collisions and load balancing. Then, we introduce the grey wolf optimization (GWO) algorithm to implement the grouping decision. Simulation results demonstrate that the grouping algorithm can effectively alleviate the performance degradation and facilitate the management of network resources.

关键词： random access network hidden terminal collision probability effective bandwidth traffic load grey wolf optimization

Application of Stork Optimization Algorithm for Solving Sustainable Lot Size Optimization

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Computers, Materials & Continua 2024年第8期80卷 2005-2030页

作者： Tareq Hamadneh Khalid Kaabneh Omar Alssayed Gulnara Bektemyssova Galymzhan Shaikemelev Dauren Umutkulov Zoubida Benmamoun Zeinab Monrazeri Mohammad Dehghani department of Mathematics Al Zaytoonah University of JordanAmman11733Jordan Faculty of information technology Al-Ahliyya Amman UniversityAmman19328Jordan department of Mathematics Faculty of ScienceThe Hashemite UniversityP.O.Box 330127Zarqa13133Jordan department of Computer Engineering International Information Technology UniversityAlmaty050000Kazakhstan Faculty of Engineering Liwa CollegeAbu Dhabi41009United Arab Emirates department of Electrical and Electronics Engineering Shiraz University of TechnologyShiraz***Iran

The efficiency of businesses is often hindered by the challenges encountered in traditional Supply Chain Manage-ment(SCM),which is characterized by elevated risks due to inadequate accountability and *** address these challenges and improve operations in green manufacturing,optimization algorithms play a crucial role in supporting decision-making *** this study,we propose a solution to the green lot size optimization issue by leveraging bio-inspired algorithms,notably the Stork Optimization Algorithm(SOA).The SOA draws inspiration from the hunting and winter migration strategies employed by storks in *** theoretical framework of SOA is elaborated and mathematically modeled through two distinct phases:exploration,based on migration simulation,and exploitation,based on hunting strategy *** tackle the green lot size optimization issue,our methodology involved gathering real-world data,which was then transformed into a simplified function with multiple constraints aimed at optimizing total costs and minimizing CO_(2) *** function served as input for the SOA ***,the SOA model was applied to identify the optimal lot size that strikes a balance between cost-effectiveness and *** extensive experimentation,we compared the performance of SOA with twelve established metaheuristic algorithms,consistently demonstrating that SOA outperformed the *** study’s contribution lies in providing an effective solution to the sustainable lot-size optimization dilemma,thereby reducing environmental impact and enhancing supply chain *** simulation findings underscore that SOA consistently achieves superior outcomes compared to existing optimization methodologies,making it a promising approach for green manufacturing and sustainable supply chain management.

关键词： Optimization supply chain management sustainable lot size optimization bio-inspired metaheuristic stork

Deep Transfer Learning Models for Mobile-Based Ocular Disorder Identification on Retinal Images

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Computers, Materials & Continua 2024年第7期80卷 139-161页

作者： Roseline Oluwaseun Ogundokun Joseph Bamidele Awotunde Hakeem Babalola Akande Cheng-Chi Lee Agbotiname Lucky Imoize department of Multimedia Engineering Kaunas University of TechnologyKaunas44249Lithuania department of Computer Science Landmark UniversityOmu Aran251103Nigeria department of Computer Science Faculty of Information and Communication SciencesUniversity of IlorinIlorin240003Nigeria department of Telecommunication Science University of IlorinIlorin230003Nigeria department of Library and information Science Fu Jen Catholic UniversityNew Taipei City24205TaiwanChina department of Computer Science and information Engineering Fintech and Blockchain Research CenterAsia UniversityTaichung City41354TaiwanChina department of Electrical and Electronics Engineering Faculty of EngineeringUniversity of LagosAkokaLagos100213Nigeria department of Electrical Engineering and information technology Institute of Digital CommunicationRuhr UniversityBochum44801Germany

Mobile technology is developing *** phone technologies have been integrated into the healthcare industry to help medical ***,computer vision models focus on image detection and classification ***2 is a computer vision model that performs well on mobile devices,but it requires cloud services to process biometric image information and provide predictions to *** leads to increased *** biometrics image datasets on mobile devices will make the prediction faster,but mobiles are resource-restricted devices in terms of storage,power,and computational ***,a model that is small in size,efficient,and has good prediction quality for biometrics image classification problems is *** pre-trained CNN(PCNN)MobileNetV2 architecture combined with a Support Vector Machine(SVM)compacts the model representation and reduces the computational cost and memory *** proposed novel approach combines quantized pre-trained CNN(PCNN)MobileNetV2 architecture with a Support Vector Machine(SVM)to represent models efficiently with low computational cost and *** contributions include evaluating three CNN models for ocular disease identification in transfer learning and deep feature plus SVM approaches,showing the superiority of deep features from MobileNetV2 and SVM classification models,comparing traditional methods,exploring six ocular diseases and normal classification with 20,111 images postdata augmentation,and reducing the number of trainable *** model is trained on ocular disorder retinal fundus image datasets according to the severity of six age-related macular degeneration(AMD),one of the most common eye illnesses,Cataract,Diabetes,Glaucoma,Hypertension,andMyopia with one class *** the experiment outcomes,it is observed that the suggested MobileNetV2-SVM model size is *** testing accuracy for MobileNetV2-SVM,InceptionV3,and MobileNetV2 is 90.11%,86.88%,a

关键词： Retinal images ocular disorder deep transfer learning disease identification mobile device

An Evidence-Based CoCoSo Framework with Double Hierarchy Linguistic Data for Viable Selection of Hydrogen Storage Methods

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Computer Modeling in Engineering & Sciences 2024年第3期138卷 2845-2872页

作者： Raghunathan Krishankumar Dhruva Sundararajan K.S.Ravichandran Edmundas Kazimieras Zavadskas information technology Systems and Analytics Area Indian Institute of Management Bodh GayaBodh GayaBihar824234India department of Mathematics Amrita School of Physical SciencesAmrita Vishwa VidyapeethamCoimbatore641112India Institute of Sustainable Construction Vilnius Gediminas Technical UniversityVilnius10223Lithuania

Hydrogen is the new age alternative energy source to combat energy demand and climate *** of hydrogen is vital for a nation’s *** of literature provide different methods for storing the produced hydrogen,and the rational selection of a viable method is crucial for promoting sustainability and green ***,hydrogen storage is associated with diverse sustainable and circular economy(SCE)*** a result,the authors consider the situation a multi-criteria decision-making(MCDM)*** infer that previous models for hydrogen storage method(HSM)selection(i)do not consider preferences in the natural language form;(ii)weights of experts are not methodically determined;(iii)hesitation of experts during criteria weight assessment is not effectively explored;and(iv)three-stage solution of a suitable selection of HSM is *** by these gaps,in this paper,authors put forward a new integrated framework,which considers double hierarchy linguistic information for rating,criteria importance through inter-criteria correlation(CRITIC)for expert weight calculation,evidence-based Bayesian method for criteria weight estimation,and combined compromise solution(CoCoSo)for ranking *** applicability of the developed framework is testified by using a case example of HSM selection in *** and comparative analysis reveal the merits and limitations of the developed framework.

关键词： Hydrogen storage methods double hierarchy hesitant fuzzy linguistic term set evidence theory CoCoSo method sustainability circular economy

Fortifying healthcare Data Security in the Cloud:A Comprehensive Examination of the EPM-KEA Encryption Protocol

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Computers, Materials & Continua 2024年第5期79卷 3397-3416页

作者： Umi Salma Basha Shashi Kant Gupta Wedad Alawad SeongKi Kim Salil Bharany Computer Science and information technology Jazan UniversityJazanSaudi Arabia CSE Eudoxia Research UniversityNew CastleUSA department of information technology College of Computer ScienceQassim UniversityBuraydahSaudi Arabia National Center of Excellence in Software Sangmyung UniversitySeoulKorea department of Computer Science and Engineering Lovely Professional UniversityPhagwaraPunjabIndia

A new era of data access and management has begun with the use of cloud computing in the healthcare *** the efficiency and scalability that the cloud provides, the security of private patient data is still a majorconcern. Encryption, network security, and adherence to data protection laws are key to ensuring the confidentialityand integrity of healthcare data in the cloud. The computational overhead of encryption technologies could leadto delays in data access and processing rates. To address these challenges, we introduced the Enhanced ParallelMulti-Key Encryption Algorithm (EPM-KEA), aiming to bolster healthcare data security and facilitate the securestorage of critical patient records in the cloud. The data was gathered from two categories Authorization forHospital Admission (AIH) and Authorization for High Complexity *** use Z-score normalization forpreprocessing. The primary goal of implementing encryption techniques is to secure and store massive amountsof data on the cloud. It is feasible that cloud storage alternatives for protecting healthcare data will become morewidely available if security issues can be successfully fixed. As a result of our analysis using specific parametersincluding Execution time (42%), Encryption time (45%), Decryption time (40%), Security level (97%), and Energyconsumption (53%), the system demonstrated favorable performance when compared to the traditional *** suggests that by addressing these security concerns, there is the potential for broader accessibility to cloudstorage solutions for safeguarding healthcare data.

关键词： Cloud computing healthcare data security enhanced parallel multi-key encryption algorithm(EPM-KEA)

NeurDB: an AI-powered autonomous data system

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Science China(information Sciences) 2024年第10期67卷 129-150页

作者： Beng Chin OOI Shaofeng CAI Gang CHEN Yanyan SHEN Kian-Lee TAN Yuncheng WU Xiaokui XIAO Naili XING Cong YUE Lingze ZENG Meihui ZHANG Zhanhao ZHAO School of Computing National University of Singapore College of Computer Science and technology Zhejiang University department of Computer Science and Engineering Shanghai Jiao Tong University School of information Renmin University of China School of Computer Science and technology Beijing Institute of Technology

In the wake of rapid advancements in artificial intelligence(AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB(AI×DB) promises a new generation of data systems, which will relieve the burden on end-users across all industry sectors by featuring AI-enhanced functionalities, such as personalized and automated in-database AI-powered analytics, and selfdriving capabilities for improved system performance. In this paper, we explore the evolution of data systems with a focus on deepening the fusion of AI and DB. We present NeurDB, an AI-powered autonomous data system designed to fully embrace AI design in each major system component and provide in-database AI-powered analytics. We outline the conceptual and architectural overview of NeurDB, discuss its design choices and key components, and report its current development and future plan.

关键词： AI$\times$DB in-database AI intelligent data system