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Intelligent Machine Learning Based EEG Signal Classification Model

Computers, Materials & Continua 2022年第4期71卷 1821-1835页

作者： Mesfer al Duhayyim Haya Mesfer alshahrani Fahd N.al-Wesabi Mohammed abdullah al-Hagery Anwer Mustafa Hilal Abu Sarwar Zaman Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia&Faculty of Computer and ITSana’a UniversitySana’aYemen Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

In recent years,Brain-Computer Interface(BCI)system gained much popularity since it aims at establishing the communication between human brain and *** systems are applied in several research areas such as neuro-rehabilitation,robots,exoeskeletons,***(EEG)is a technique commonly applied in capturing brain *** is incorporated in BCI systems since it has attractive features such as noninvasive nature,high time-resolution output,mobility and *** classification process is highly essential in decision making process and it incorporates different processes namely,feature extraction,feature selection,and *** this motivation,the current research paper presents an Intelligent Optimal Fuzzy Support Vector Machine-based EEC recognition(IOFSVM-EEG)model for BCI *** Component Analysis(ICA)technique is applied onto the proposed IOFSVM-EEG model to remove the artefacts that exist in EEG signal and to retain the meaningful EEG ***,Common Spatial Pattern(CSP)-based feature extraction technique is utilized to derive a helpful set of feature vectors from the preprocessed EEG ***,OFSVM method is applied in the classification of EEG signals,in which the parameters involved in FSVM are optimally tuned using Grasshopper Optimization algorithm(GOA).In order to validate the enhanced EEG recognition outcomes of the proposed IOFSVM-EEG model,an extensive set of experiments was *** outcomes were examined under distinct *** experimental results highlighted the enhanced performance of the presented IOFSVM-EEG model over other state-of-the-art methods.

关键词： Brain computer interface EEG recognition human computer interface machine learning parameter tuning FSVM

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High-Bandwidth,Low-Power CMOS Transistor Based CAB for Field Programmable Analog Array

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Computers, Materials & Continua 2023年第3期74卷 5885-5900页

作者： Ameen Bin Obadi alaa El-Din Hussein Samir Salem al-Bawri Kabir Hossain abdullah Abdulhameed Muzammil Jusoh Thennarasan Sabapathy Ahmed Jamal abdullah al-Gburi Mahmoud A.albreem Advanced Communication Engineering(ACE) Centre of ExcellenceUniversiti Malaysia Perlis(UniMAP)Faculty of Electronic Engineering TechnologyKangar01000Malaysia Electrical Engineering King Fahd University of Petroleum and MineralsDhahranEastern31261Saudi Arabia Space Science Centre Climate Change InstituteUniversiti Kebangsaan MalaysiaBangi43600Malaysia Department of Electronics&Communication Engineering Faculty of Engineering&PetroleumHadhramout UniversityAl-MukallaHadhramout50512Yemen Faculty of Engineering Norwegian University of Science and Technology(NTNU)GjovikN-2815Norway Faculty of Electronic Engineering Technology Universiti Malaysia Perlis(UniMAP)Kampus Alam UniMAP Pauh PutraArau02600Malaysia Department of General Educational Development Faculty of Science and Information Technology(FSIT)Daffodil International UniversityDhaka1207Bangladesh Department of Electronics and Computer Engineering(FKEKK) Center for Telecommunication Research and Innovation(CeTRI)Universiti Teknikal Malaysia Melaka(UTeM)Durian Tungal76100Malaysia Department of Electrical Engineering University of SharjahSharjah27272United Arab Emirates

This article presents an integrated current mode configurable analog block(CAB)system for field-programmable analog array(FPAA).The proposed architecture is based on the complementary metal-oxide semiconductor(CMOS)transistor level design where MOSFET transistors operating in the saturation region are *** proposed CAB architecture is designed to implement six of thewidely used current mode operations in analog processing systems:addition,subtraction,integration,multiplication,division,and pass *** functionality of the proposed CAB is demonstrated through these six operations,where each operation is chosen based on the user’s selection in the CAB interface *** architecture of the CAB system proposes an optimized way of designing and integrating only three functional cells with the interface circuitry to achieve the six ***,optimized programming and digital tuning circuitry are implemented in the architecture to control and interface with the functional ***,these designed programming and tuning circuitries play an essential role in optimizing the performance of the proposed *** of the proposed CMOS Transistor Based CAB system is carried out using Tanner EDA Tools in 0.35μm standard CMOS *** design uses a±1.5 V power supply and results in maximum 3 dB bandwidth of 34.9 MHz and an approximate size of 0.0537 *** demonstrates the advantages of the design over the current state-of-the-art designs presented for comparison in this ***,the proposed design has a clear aspect of simplicity,low power consumption,and high bandwidth operation,which makes it a suitable candidate for mobile telecommunications applications.

关键词： CMOS field programmable analog array configurable analog block current mode circuit

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Toxic tau: structural origins of tau aggregation in alzheimer’s disease

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Neural Regeneration Research 2020年第8期15卷 1417-1420页

作者： abdullah al Mamun Md.Sahab Uddin Bijo Mathew Ghulam Md Ashraf Department of Pharmacy Southeast UniversityDhakaBangladesh Pharmakon Neuroscience Research Network DhakaBangladesh Division of Drug Design and Medicinal Chemistry Research Lab Department of Pharmaceutical ChemistryAhalia School of PharmacyPalakkadIndia King Fahd Medical Research Center King Abdulaziz UniversityJeddahSaudi Arabia Department of Medical Laboratory Technology Faculty of Applied Medical SciencesKing Abdulaziz UniversityJeddahSaudi Arabia

alzheimer’s disease is characterized by the extracellular accumulation of the amyloidβin the form of amyloid plaques and the intracellular deposition of the microtubule-associated protein tau in the form of neurofibrillary *** of the alzheimer’s drugs targeting amyloidβhave been failed in clinical ***,tau pathology connects greatly in the pathogenesis of alzheimer’s *** protein enhances the stabilization of microtubules that leads to the appropriate function of the *** in the quantity or the conformation of tau protein could affect its function as a microtubules stabilizer and some of the processes wherein it is *** molecular mechanisms leading to the accumulation of tau are principally signified by numerous posttranslational modifications that change its conformation and structural ***,aberrant phosphorylation,as well as truncation of tau protein,has come into focus as significant mechanisms that make tau protein in a pathological ***,the shape-shifting nature of tau advocates to comprehend the progression of alzheimer’s disease *** this review,we emphasize the recent studies about the toxic and shape-shifting nature of tau in the pathogenesis of alzheimer’s disease.

关键词： alzheimer’s disease neurofibrillary tangles shape-shifting nature of tau tau aggregation toxic tau

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Optimal Wavelet Neural Network-Based Intrusion Detection in Internet of Things Environment

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Computers, Materials & Continua 2023年第5期75卷 4467-4483页

作者： Heba G.Mohamed Fadwa alrowais Mohammed abdullah al-Hagery Mesfer al Duhayyim Anwer Mustafa Hilal Abdelwahed Motwakel Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box84428Riyadh 11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box84428Riyadh 11671Saudi Arabia Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

As the Internet of Things(IoT)endures to develop,a huge count of data has been *** IoT platform is rather sensitive to security challenges as individual data can be leaked,or sensor data could be used to cause *** typical intrusion detection system(IDS)studies can be frequently designed for working well on databases,it can be unknown if they intend to work well in altering network *** learning(ML)techniques are depicted to have a higher capacity at assisting mitigate an attack on IoT device and another edge system with reasonable *** article introduces a new Bird Swarm algorithm with Wavelet Neural Network for Intrusion Detection(BSAWNN-ID)in the IoT *** main intention of the BSAWNN-ID algorithm lies in detecting and classifying intrusions in the IoT *** BSAWNN-ID technique primarily designs a feature subset selection using the coyote optimization algorithm(FSS-COA)to attain ***,to detect intrusions,the WNN model is *** last,theWNNparameters are optimally modified by the use of *** experiment is performed to depict the better performance of the BSAWNNID *** resultant values indicated the better performance of the BSAWNN-ID technique over other models,with an accuracy of 99.64%on the UNSW-NB15 dataset.

关键词： Internet of things wavelet neural network security intrusion detection machine learning

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Towards Secure Fog: Establishing A Secure Authentication and Rogue Fog Node Detection in Fog Computing

Towards Secure Fog: Establishing A Secure Authentication and...

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作者： abdullah al-Noman Patwary 南京理工大学

学位级别：硕士

雾计算是一种新兴的计算技术,它将云计算服务扩展到网络边缘,例如网络、存储和计算。它具备了各种特性和功能,如降低服务延迟、大规模地理分布、异构性、实时性和移动性。而随着新的计算模式,雾计算面临了新的挑战,其中由于雾环境中分... 详细信息

雾计算是一种新兴的计算技术,它将云计算服务扩展到网络边缘,例如网络、存储和计算。它具备了各种特性和功能,如降低服务延迟、大规模地理分布、异构性、实时性和移动性。而随着新的计算模式,雾计算面临了新的挑战,其中由于雾环境中分布式的设备框架,安全性和隐私性成为了最大的关注点之一。在本文中,我们主要关注与雾有关的各种安全和隐私问题。从各种安全问题出发,以雾计算的角度,选择了两个不同的问题,提出不同的唯一解决方案。1.我们提出了一种与雾计算相关的各种安全和隐私问题的分类法。我们明确了各种安全与隐私问题,如信任管理,隐私保护,认证,访问控制,威胁和攻击,以及安全审计。我们对各种文献进行了讨论和分析,并提出了它们在雾计算架构方面的局限性和缺陷。2.由于大规模的分布式雾设备,在雾层之间提供安全的认证成为了一项关键的挑战。由于雾独特的结构和特点,传统的认证方法不能直接适用,而且传统的认证方法需要庞大的计算能力和较长的等待时间,无法满足雾的特性。为了填补这一空白,本文提出了一种设备到设备（D2D）的基于位置的安全分散认证模型。在这个模型中,雾设备可以通过使用区块链在雾层相互认证。我们利用了Ethereum Blockchain平台完成对雾设备的注册、认证、证明和数据存储。我们描述了系统组件、体系结构和设计,并且还讨论了与该方法的安全性分析、实现、功能性和性能测试等方面。通过与现有方法的比较,我们验证了所提出的认证机制的安全性和有效性。3.雾中最重要和最具挑战性的安全问题之一是检测恶意或流氓雾节点。如果没有合适的检测机制,分布式雾环境下的雾节点管理将变得十分困难。传统的云计算中已经提出了非常著名的对于流氓节点检测技术,然而,本文试图从不同的角度来解决这一问题。在本文中,我们引入Hidden Markov Model（隐藏马尔可夫模型,HMM）利用了一种基于统计的方法来检测流氓雾节点。我们训练的HMM能够在很短的计算时间内成功地检测到流氓雾节点的存在,并且具有很高的精度。我们使用MATLAB仿真环境验证了我们所提出的方法。此外,本文最后给出了一些与现有方法相比的评估结果,展示了该模型在性能方面的优点。研究结果表明,该认证机制和流氓雾节点检测技术是安全有效的。因此,该方法适合在雾环境下实现。最后,我们从雾的安全问题、认证和流氓雾节点检测方面提出并讨论了几个开放性问题。

关键词：雾计算安全隐私认证区块链数字身份管理流氓雾节点检测隐藏马尔可夫模型

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Intelligent Forensic Investigation Using Optimal Stacked Autoencoder for Critical Industrial Infrastructures

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Computers, Materials & Continua 2022年第8期72卷 2275-2289页

作者： abdullah S.al-Malaise al-Ghamdi Mahmoud Ragab F.J.alsolami Hani Choudhry Ibrahim Rizqallah alzahrani Information Systems Department Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Computer Science Department Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Biochemistry Department Faculty of ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia Computer Science and Engineering Department College of Computer Science and EngineeringUniversity of Hafr Al BatinAl JamiahHafar Al Batin39524Saudi Arabia

Industrial Control Systems(ICS)can be employed on the industrial processes in order to reduce the manual labor and handle the complicated industrial system processes as well as communicate *** of Things(IoT)integrates numerous sets of sensors and devices via a data network enabling independent *** incorporation of the IoT in the industrial sector leads to the design of Industrial Internet of Things(IIoT),which find use in water distribution system,power plants,*** the IIoT is susceptible to different kinds of attacks due to the utilization of Internet connection,an effective forensic investigation process becomes *** study offers the design of an intelligent forensic investigation using optimal stacked autoencoder for critical industrial *** proposed strategy involves the design of manta ray foraging optimization(MRFO)based feature selection with optimal stacked autoencoder(OSAE)model,named MFROFS-OSAE *** primary objective of the MFROFS-OSAE technique is to determine the presence of abnormal events in critical industrial ***-OSAE approach involves several subprocesses namely data gathering,data handling,feature selection,classification,and parameter ***,the MRFO based feature selection approach is designed for the optimal selection of feature ***,the OSAE based classifier is derived to detect abnormal events and the parameter tuning process is carried out via the coyote optimization algorithm(COA).The performance validation of the MFROFS-OSAE technique takes place using the benchmark dataset and the experimental results reported the betterment of the MFROFS-OSAE technique over the recent approaches interms of different measures.

关键词： Industrial control systems internet of things artificial intelligence feature selection deep learning

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Stress intensity factors under combined bending and torsion moments

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2012年第1期13卷 1-8页

作者： al Emran ISMAIL Ahmad Kamal ARIFFIN Shahrum abdullah Mariyam Jameelah GHAZalI Mohammed ABDULRAZZAQ Ruslizam DAUD Faculty of Mechanical & Manufacturing Engineering Universiti Tun Hussein Onn Malaysia Faculty of Engineering & Built Environment Universiti Kebangsaan Malaysia School of Mechatronic Engineering Universiti Malaysia Perlis

This paper discusses stress intensity factor （SIF） calculations for surface cracks in round bars subjected to combined torsion and bending loadings. Different crack aspect ratios, a/b, ranging from 0.0 to 1.2 and relative crack depths, a/D, ranging from 0.1 to 0.6 were considered. Since the loading was non-symmetrical for torsion loadings, a whole finite element model was constructed. Then, the individual and combined bending and torsion loadings were remotely applied to the model. The equivalent SIF method, F＊EQ, was then used explicitly to combine the individual SIFs from the bending and torsion loadings. A comparison was then carried out with the combined SIF, F＊FE, obtained using the finite element analysis （FEA） under similar loadings. It was found that the equivalent SIF method successfully predicted the combined SIF for Mode I. However, discrepancies between the results determined from the different approaches occurred when Fm was involved. It was also noted that the predicted F＊ EE using FEA was higher than the F＊EQ predicted through the equivalent SIF method due to the difference in crack face interactions.

关键词： Stress intensity factor (SIF) Combined loadings Finite element analysis (FEA) Surface cracks Round solid bars

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Artificial Intelligence Based Optimal Functional Link Neural Network for Financial Data Science

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Computers, Materials & Continua 2022年第3期70卷 6289-6304页

作者： Anwer Mustafa Hilal Hadeel alsolai Fahd Nal-Wesabi Mohammed abdullah al-Hagery Manar Ahmed Hamza Mesfer al Duhayyim Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia&Faculty of Computer and ITSana’a UniversitySana’aYemen Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

In present digital era,data science techniques exploit artificial intelligence(AI)techniques who start and run small and medium-sized enterprises(SMEs)to have an impact and develop their *** science integrates the conventions of econometrics with the technological elements of data *** make use of machine learning(ML),predictive and prescriptive analytics to effectively understand financial data and solve related *** technologies for SMEs enable allows the firm to get smarter with their processes and offers efficient *** the same time,it is needed to develop an effective tool which can assist small to medium sized enterprises to forecast business failure as well as financial *** becomes a familiar tool for several businesses due to the fact that it concentrates on the design of intelligent decision making tools to solve particular real time *** this motivation,this paper presents a new AI based optimal functional link neural network(FLNN)based financial crisis prediction(FCP)model *** proposed model involves preprocessing,feature selection,classification,and parameter *** the initial stage,the financial data of the enterprises are collected and are preprocessed to enhance the quality of the ***,a novel chaotic grasshopper optimization algorithm(CGOA)based feature selection technique is applied for the optimal selection of ***,functional link neural network(FLNN)model is employed for the classification of the feature reduced ***,the efficiency of theFLNNmodel can be improvised by the use of cat swarm optimizer(CSO)algorithm.A detailed experimental validation process takes place on Polish dataset to ensure the performance of the presented *** experimental studies demonstrated that the CGOA-FLNN-CSO model has accomplished maximum prediction accuracy of 98.830%,92.100%,and 95.220%on the applied Polish dataset Year I-III respectively.

关键词： Data science small and medium-sized enterprises business sectors financial crisis prediction intelligent systems artificial intelligence decision making machine learning

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Optimized Artificial Neural Network Techniques to Improve Cybersecurity of Higher Education Institution

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Computers, Materials & Continua 2022年第8期72卷 3385-3399页

作者： abdullah Saad al-Malaise al-Ghamdi Mahmoud Ragab Maha Farouk S.Sabir Ahmed Elhassanein Ashraf A.Gouda Information Systems Department Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Mathematics Department College of ScienceUniversity of BishaBishaSaudi Arabia Mathematics Department Faculty of ScienceDamanhour UniversityDamanhourEgypt

Education acts as an important part of economic growth and improvement in human *** educational sectors have transformed a lot in recent days,and Information and Communication Technology(ICT)is an effective part of the education *** every action in university and college,right from the process fromcounselling to admissions and fee deposits has been *** records,quiz,evaluation,mark,and grade submissions involved the utilization of the ***,security is essential to accomplish cybersecurity in higher security institutions(HEIs).In this view,this study develops an Automated Outlier Detection for CyberSecurity in Higher Education Institutions(AOD-CSHEI)*** AOD-CSHEI technique intends to determine the presence of intrusions or attacks in the *** AOD-CSHEI technique initially performs data pre-processing in two stages namely data conversion and class *** addition,the Adaptive Synthetic(ADASYN)technique is exploited for the removal of outliers in the ***,the sparrow search algorithm(SSA)with deep neural network(DNN)model is used for the classification of data into the existence or absence of intrusions in the HEIs ***,the SSA is utilized to effectually adjust the hyper parameters of the DNN *** order to showcase the enhanced performance of the AOD-CSHEI technique,a set of simulations take place on three benchmark datasets and the results reported the enhanced efficiency of the AOD-CSHEI technique over its compared methods with higher accuracy of 0.9997.

关键词： Higher security institutions intrusion detection system artificial intelligence deep neural network hyperparameter tuning deep learning

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High Thermoelectric Figure of Merit of Ag8SnS6 Component Prepared by Electrodeposition Technique

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Chinese Physics Letters 2015年第12期32卷 126-130页

作者： TAHER Ghrib AMal Lafy al-Otaibi MUNIRAH abdullah almessiere IBTISSEM Ben Assaker RADHOUANE Chtourou Laboratory of Physical alloys Science Faculty of Dammam University of Dammam Saudi Arabia Laboratoire Photovoltaique Centre de Recherches et des Technologies de l'Energie Technopole bozj cedria Bp 95 hammam lif 2050 Tunisie

A new thermoelectric material Ag8SnS6, with ultra-low thermal conductivity in thin film shape, is prepared on indium tin oxide coated g/ass （ITO） substrates using a chemical process via the electrodeposition technique. The structural, thermal and electrical properties are studied and presented in detail, which demonstrate that the material is of semiconductor type, orthorhombic structure, with a band gap in the order of 1.56eV and a free carrier concentration of 1.46 × 10^17 cm-3. The thermal conductivity, thermal diffusivity, thermal conduction mode, Seebeck coefficient and electrical conductivity are determined using the photo-thermal deflection technique combined with the Boltzmann transport theory and Cahill＇s model, showing that the AgsSnS6 material has a low thermal conductivity of 3.8 Wm - 1K- 1, high electrical conductivity of 2.4 × 10^5 Sm- 1, Seebeck coefficient of -180μVK-1 and a power factor of 6.9mWK-2m-1, implying that it is more efficient than those obtained in recently experimental investigations for thermoelectric devices.

关键词： Figure High Thermoelectric Figure of Merit of Ag8SnS6 Component Prepared by Electrodeposition Technique Ag SnS

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