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Proposed Biometric Security System Based on Deep Learning and Chaos Algorithms

computers, Materials & Continua 2023年第2期74卷 3515-3537页

作者： Iman Almomani Walid El-Shafai Aala AlKhayer Albandari Alsumayt Sumayh S.Aljameel Khalid Alissa Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Computer Science Department King Abdullah II School of Information TechnologyThe University of Jordan11942Jordan Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Applied CollegeImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Computer Science Department College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia SAUDI ARAMCO Cybersecurity Chair Networks and Communications DepartmentCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia

Nowadays,there is tremendous growth in biometric authentication and cybersecurity ***,the efficient way of storing and securing personal biometric patterns is mandatory in most governmental and private ***,designing and implementing robust security algorithms for users’biometrics is still a hot research area to be *** work presents a powerful biometric security system(BSS)to protect different biometric modalities such as faces,iris,and *** proposed BSSmodel is based on hybridizing auto-encoder(AE)network and a chaos-based ciphering algorithm to cipher the details of the stored biometric patterns and ensures their *** employed AE network is unsupervised deep learning(DL)structure used in the proposed BSS model to extract main biometric *** obtained features are utilized to generate two random chaos *** first random chaos matrix is used to permute the pixels of biometric *** contrast,the second random matrix is used to further cipher and confuse the resulting permuted biometric pixels using a two-dimensional(2D)chaotic logisticmap(CLM)*** assess the efficiency of the proposed BSS,(1)different standardized color and grayscale images of the examined fingerprint,faces,and iris biometrics were used(2)comprehensive security and recognition evaluation metrics were *** assessment results have proven the authentication and robustness superiority of the proposed BSSmodel compared to other existing *** example,the proposed BSS succeeds in getting a high area under the receiver operating characteristic(AROC)value that reached 99.97%and low rates of 0.00137,0.00148,and 3516 CMC,2023,vol.74,no.20.00157 for equal error rate(EER),false reject rate(FRR),and a false accept rate(FAR),respectively.

关键词： Biometric security deep learning AE network 2D CLM cybersecurity and authentication applications feature extraction unsupervised learning

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Exploring the Role of Internet of Things in Wireless Sensor Networks for Industry 4.0 Applications 1

Exploring the Role of Internet of Things in Wireless Sensor ...

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1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Palarimath, Suresh Maran, Pyingkodi N.r, Wilfred Blessing Sujatha, T. Renuga, S. R.s, Chithra College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman Kongu Engineering College Department of Computer Application Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Panimalar Engineering College Department of Artificial Intelligence and Data Science Chennai India Ponjesly College of Engineering Department of Computer Science and Engineering Kanyakumari India

ISBN: (纸本)9798350365016

Recent developments in computer networking have made it possible to access information from a faraway location via either wireless or wired networks. The recent advancements in wireless infrastructure are directly responsible for the advent of wireless sensor networks, often known as WSNs. These networks make it easier to keep track of environmental happenings and activities, as well as document them and manage them. In WSNs, data relaying is accomplished through the use of a variety of routing strategies. Industry 4.0, also known as the fourth industrial revolution, is characterized by the incorporation of highly developed physical automation systems. These systems are made up of several pieces of machinery and gadgets that are linked together by sensors and are operated by software. The goal of the fourth industrial revolution is to improve the effectiveness and dependability of business processes. The Internet of Things (IoT) can be leveraged in manufacturing, which gives a means of linking engines, power grids, and sensors to the cloud in an industrial context. This is accomplished through the use of Industrial IoT. The purpose of this paper is to gain an understanding of how the Internet of Things functions in wireless sensor networks and its possible applications in a variety of settings. © 2024 IEEE.

关键词： Wireless sensor networks

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Single-Phase Direct PWM Bipolar Buck–Boost AC–AC Converter Without Commutation Problem

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2023年第6期11卷 5940-5953页

作者： Ahmed, Hafiz Furqan Ali Khan, Ashraf Abdoli, Iman Zaabi, Omar Al the Department of Electrical Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan the Department of Electrical and Computer Engineering Faculty of Engineering and Applied Science Memorial University of Newfoundland St. John’sNLA1B 3X7 Canada the Department of Electrical Engineering Shahid Beheshti University Tehran19839-69411 Iran Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates

— The direct pulsewidth modulation (PWM) ac–ac converters are seeing rapid development due to their single-stage power conversion with reduced footprints, due to the elimination of intermediate dc-link capacitor. However, they only provide unipolar voltage gain and fixed frequency operation and use the bidirectional voltage-blocking devices without natural inductor current free-wheeling path, restricting their practical applications. This article proposes a direct PWM bipolar and step-variable frequency buck–boost ac–ac converter. The proposed converter is fabricated by connecting input–output LC filters and a small value film capacitor to six unidirectional voltage-blocking devices, which are arranged as a three-leg inverter module. It can provide noninverting and inverting buck–boost voltage operations with a symmetric bipolar voltage gain of ±d/(1 − d) and can also change the frequency of output voltage in discrete steps, same as that of single-phase matrix converters (MCs). It has no open-circuit risk of inductor due to the presence of natural current free-wheeling path during PWM dead time. Therefore, it is unaffected by the commutation problem of conventional direct ac–ac converters. The proposed converter can realize high-quality input and output due to the presence of second-order input–output filters with continuous input–output currents. A detailed analysis is provided based on proposed switch modulation strategies and confirmed through extensive experimental tests on a laboratory-fabricated hardware. © 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

关键词： Matrix converters

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Dual-Buck Bipolar Buck-Boost AC-AC Converter With Reduced Semiconductor Devices

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IEEE Transactions on Industrial Electronics 2024年第9期71卷 10495-10511页

作者： Ahmed, Hafiz Furqan Chien, Howard Khan, Ashraf Ali Islam, Shirazul Akbar, Fazal Alzaabi, Omar National Sun Yat-sen University Department of Electrical Engineering Kaohsiung80424 Taiwan Memorial University of Newfoundland Faculty of Engineering And Applied Science Department of Electrical And Computer Engineering St. John'sNLA1B 3X9 Canada Qatar University Department of Electrical Engineering Doha2713 Qatar University of Edinburgh EdinburghEH9 3JL United Kingdom Khalifa University Department of Electrical Engineering And Computer Science Advanced Power And Energy Center Abu Dhabi127788 United Arab Emirates

Dual-buck (DB) structured ac-ac converters are becoming advanced due to their inherent protection from open- and short-circuit risks, and elimination of commutation issue. However, the existing DB ac-ac converters provide restricted bipolar voltage operations with a large semiconductor device count and underutilized inductors. This article proposed a new reduced switch DB ac-ac converter without commutation issue. The propose converter provides multiple buck-boost voltage operations with noninverting and inverting outputs, making it a flexible topology for various ac voltage applications. The propose converter exhibits the continuous input and output currents irrespective of operation mode, and performs well with nonunity power factor loads. The positive and negative voltage gains of the proposed converter can address the grid voltage sag and swell issues, respectively, making it right candidate for application as dynamic voltage restorer. A detailed comparative evaluation is carried out with existing DB converters, which validated the effectiveness of the proposed converter, including;reduced semiconductor device count and their power ratings, reduced inductor sizes with improved magnetic utilization, enhanced voltage compensation ranges, and better efficiency. A laboratory scale hardware prototype of the proposed converter is built and thoroughly tested to validate its operation. © 1982-2012 IEEE.

关键词： Semiconductor devices

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An Integrated Deep Learning Framework for Fruits Diseases Classification

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computers, Materials & Continua 2022年第4期71卷 1387-1402页

作者： Abdul Majid Muhammad Attique Khan Majed Alhaisoni Muhammad Asfand Eyar Usman Tariq Nazar Hussain Yunyoung Nam Seifedine Kadry Department of Computer Science HITEC University TaxilaTaxilaPakistan College of Computer Science and Engineering University of Ha’ilHa’ilSaudi Arabia Department of Computer Science Bahria UniversityIslamabad College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway

:Agriculture has been an important research area in the field of image processing for the last five *** affect the quality and quantity of fruits,thereby disrupting the economy of a *** computerized techniques have been introduced for detecting and recognizing fruit ***,some issues remain to be addressed,such as irrelevant features and the dimensionality of feature vectors,which increase the computational time of the ***,we propose an integrated deep learning framework for classifying fruit *** consider seven types of fruits,i.e.,apple,cherry,blueberry,grapes,peach,citrus,and *** proposed method comprises several important ***,data increase is applied,and then two different types of features are *** the first feature type,texture and color features,i.e.,classical features,are *** the second type,deep learning characteristics are extracted using a pretrained *** pretrained model is reused through transfer ***,both types of features are merged using the maximum mean value of the serial ***,the resulting fused vector is optimized using a harmonic threshold-based genetic ***,the selected features are classified using multiple *** evaluation is performed on the PlantVillage dataset,and an accuracy of 99%is achieved.A comparison with recent techniques indicate the superiority of the proposed method.

关键词： Fruit diseases data augmentation deep learning classical features features fusion features selection

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An Empirical Study on Security Methods for Federated Learning Environments and Their Various Applications

An Empirical Study on Security Methods for Federated Learnin...

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International Conference on Advanced Communications and Machine Intelligence, MICA 2023

作者： Pamula, Narendra Babu Khan, Ajoy Kumar Sarkar, Arindam Department of Computer Engineering Mizoram Central University Aizawl796004 India Department of Computer Science and Electronics Ramakrishna Mission Vidyamandira Belur Math West Bengal Howrah711202 India

ISBN: (纸本)9789819762217

Federated learning (FL) is a networked gadget learning (ML) framework. FL allows numerous customers to collaborate so that it will deal with not unusual places allotted ML issues at the same time as maintaining their neighborhood privacy. A central server in charge of this manages the process. The common FL issues that need to be resolved are then described. Maintaining localized data while training statistical models over remote devices or siloed data centers, such as corporate offices or hospitals, is a requirement of federated learning. When training in diverse and possibly enormous networks, a considerable departure from the conventional approaches for large-scale system learning, allotted optimization, and privacy-maintaining statistics evaluation is needed. The study also examines the security of federated learning and its applications, as well as the many threats and assaults that the applications must deal with. The current level of the use of federated learning is reviewed, along with the introduction of some common privacy protection techniques. Last but not least, a summary and outlook on the use and security of federated learning. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Federated learning

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Exploring Diverse Methods in Visual Question Answering 5

Exploring Diverse Methods in Visual Question Answering

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5th International Conference on Electronic Communication and Artificial Intelligence, ICECAI 2024

作者： Li, Panfeng Yang, Qikai Geng, Xieming Zhou, Wenjing Ding, Zhicheng Nian, Yi University of Michigan Department of Electrical and Computer Engineering Ann Arbor United States University of Illinois Urbana-Champaign Department of Computer Science Urbana United States Chongqing University Department of Computer Science Chongqing China University of Michigan Department of Statistics Ann Arbor United States Fu Foundation School of Engineering and Applied Science Columbia University New York United States University of Chicago Department of Computer Science Chicago United States

ISBN: (纸本)9798350386943

This study explores innovative methods for improving Visual Question Answering (VQA) using Generative Adversarial Networks (GANs), autoencoders, and attention mechanisms. Leveraging a balanced VQA dataset, we investigate three distinct strategies. Firstly, GAN-based approaches aim to generate answer embeddings conditioned on image and question inputs, showing potential but struggling with more complex tasks. Secondly, autoencoder-based techniques focus on learning optimal embeddings for questions and images, achieving comparable results with GAN due to better ability on complex questions. Lastly, attention mechanisms, incorporating Multimodal Compact Bilinear pooling (MCB), address language priors and attention modeling, albeit with a complexity-performance trade-off. This study underscores the challenges and opportunities in VQA and suggests avenues for future research, including alternative GAN formulations and attentional mechanisms. © 2024 IEEE.

关键词： Generative adversarial networks

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Tunable and wideband high-performance rare earth-doped Ni-Mg-Cu-Zn nano ferrite-based meta-absorbers for C-band application

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Journal of Rare Earths 2025年第1期43卷 124-132,I0005页

作者： Majid Niaz Akhtar Magbool Alelyani Fatimah Mohammed AAlzahrani Abdullah Almohammedi Sabih Qamar MABaqir MIrfan Muhammad Azhar Khan Kheir SAlbarkaty ZAAlrowaili MSAl-Buriahi Institute of Physics The Islamia University of Bahawalpur63100BahawalpurPakistan Department of Radiological Sciences College of Applied Medical ScienceKing Khalid University61421bhaSaudi Arabia Department of Chemistry College of SciencePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabi Department of Physics Faculty of ScienceIslamic University of Madinah42352.Saudi Arabia Department of Chemical Engineering Muhammad Nawaz Sharif University of Engineering and Technology(MNSUET)60000MultanPakistan Department of Electrical and Computer Engineering COMSATS University IslamabadSahiwal Campus57000Pakistan Department of Physics Umm Al-Qura UniversityMakkah24382Saudi Arabia Department of Physics College of ScienceJouf UniversityP.O.Box:201474331SakakaSoudi Arabia Department of Physics Sakarya University6900SakaryaTurkey

Due to advanced technology,electromagnet interference and dissipation problems in the electronic and portable devices at GHz range are increasing *** absorbing materials with outstanding electromagnetic properties,wide bandwidth,and strong absorption are highly *** present investigation deals with the preparation of Ni-Mg-Cu-Zn(NMCZ)substituted nano ferrites with composition of Ni_(0.3)Mg_(0.2)Cu_(0.3)Zn_(0.2)X_(0.02)Fe_(1.98)O_(4)(X=Nd,Ho,Pr,Gd,Yb).X-ray diffraction(XRD),Fourier transform infrared spectroscopy(FTIR),field emission scanning electron microscopy(FESEM),transmission electron microscopy(TEM),a vibratory sample magnetometer(VSM),and a Vector network analyzer(VNA)were used to investigate these rare earth-doped nanocrystalline ferrites'*** reveals the single spinel phase structure in all Ni-Mg-Cu-Zn *** spectroscopy shows the presence of tetrahedral and octahedral bands of spinel *** images reveal the lowest agglomeration for the Ho-doped NMCZ nano-spinel ferrites *** images show the hexagon shapes of the Yb-and Nd-doped NMCZ ***-doped NMCZ ferrites show more coercivity than other rare earth metals substituted NMCZ nanocrystalline *** analysis was used to calculate the magnetic features like initial permeability,magnetic anisotropy constant,remanence,coercivity,and magnetic ***-frequency switching field distributions(SFD)analyses were also *** characteristics such as losses,permittivity,modulus,Q,ac conductivity,and impedance of the Nd^(-),Ho^(-),Pr^(-),Gd^(-),Yb-doped Ni-Mg-Cu-Zn ferrites were *** minimum reflection loss(-57.3 dB)is found at 1.4 GHz for Pr-doped Ni-Mg-Cu-Zn ferrite ***,the reflection loss(RL)of-53.9 dB at 2.9 GHz is observed for Ho-doped Ni-Mg-Cu-Zn ferrite *** magnetization,low coercivity,outstanding magnetodielectric,and absorption properties of theNd^(-),Ho^(-),Pr^(-),Gd^(-)and Yb^(-)doped Ni-Mg

关键词： Rare earths Reflection loss Cubic phase Magnetodielectric Ni-Mg-Cu-Zn(NMCZ)meta-absorber

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Explainable-AI for DoS Attacks Detection in 5G Network Using Deep Learning Models 5

Explainable-AI for DoS Attacks Detection in 5G Network Using...

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5th International Conference on Intelligent Computing, Communication, Networking and Services, ICCNS 2024

作者： Albashayreh, Amjad Tashtoush, Yahya Aldosary, Abdallah Darwish, Omar Albalas, Firas The University of Jordan Department of Computer Science Amman Jordan Jordan University of Science and Technology Department of Computer Science Irbid Jordan Prince Sattam bin Abdulaziz University The Department of Computer Engineering Ar Riyadh Saudi Arabia Eastern Michigan University Information Security and Applied Computing Department YpsilantiMI48197 United States

ISBN: (纸本)9798350354690

With the emergence of the fifth-generation (5G) net-work, numerous revolutionary applications are enabled, including low-latency and machine-type communications. This great in-crease creates a broader security threat concerns, such as denial-of-service (DoS) attacks, which can disrupt network functionality. The complexity and decentralization of 5G networks create new vulnerabilities for adversaries, necessitating comprehensive security procedures to identify, mitigate, and prevent DoS attacks in 5G networks. This paper introduces a novel approach for DoS detection in 5G networks, utilizing deep learning and machine learning models, along with Local Interpretable Model-Agnostic Explanations (LIME), to interpret model predictions and identify the significant role of data features in detecting DoS attacks. The results revealed that the random forest model demonstrated superior recall of 99.98, while BiLSTM demonstrated exceptional performance with a recall of 98.02. © 2024 IEEE.

关键词： 5G mobile communication systems

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IOT and Machine Learning Based Real-Time Protection Against Cyber-Physical Threat Mitigation 4

IOT and Machine Learning Based Real-Time Protection Against ...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Saini, Harnit Mohammad, Faz Singh, Manoj Kumar Sharma, Kapil Kumar Shukla, Meenu Singh, Ramendra Ajay Kumar Garg Engineering College Department of Computer Science Ghaziabad India Gl Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering Greater Noida India Ims Engineering College Department of Computer Science & Engineering Ghaziabad India Ims Engineering College Department of Mca Ghaziabad India Maharishi University of Information Technology School of Engineering and Technology Noida India Ghaziabad India

ISBN: (纸本)9798350360165

An investigation is carried out into the dynamic relationship between machine learning algorithms used in IoT security, with detailed scrutiny of performance indicators to give a fuller view of what happens. The study pays particular attention to metrics like accuracy, precision, recall, and F1 score when ten varied samples are being examined. As a result, all the algorithms exhibit excellent performance with 95.5 %-98.2 % accuracy values, inspiring high reliability under diverse scenarios. Precision and recall metrics also depict the models' capabilities. Balancing precision and recall can be observed in F1 scores that are between 95.2% and 98.0%, and these numbers show that the approach based on this understanding of trade-offs is characterized by its operationalization. At the other end, results obtained from performance metrics go beyond numerical benchmarks to give a holistic appreciation of the algorithms' strength and areas for possible enhancement. The paper also does not provide only a theoretical approach to machine learning related to security in the IoT environment but also provides information about security considerations for the successful deployment of powerful, adaptive models into real-life scenarios. In this case, findings of the research help in understanding how to make sure that smart and effective measures are implemented during the process of developing security solutions considering all related issues in connection with the IoT technology landscape. © 2024 IEEE.

关键词： Adversarial machine learning

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