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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

Nature of Centennial Global Climate Change from Observational Records

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American Journal of Climate Change 2015年第4期4卷 337-354页

作者： Mohammed anwer Department of Physical Sciences Independent University Bangladesh Dhaka Bangladesh

In order to provide a better benchmark for climate simulation programs, climate data at Global Historical Climatology Network (GHCN) and Global Summary of the Day (GSOD) archived by the National Climate Data Center (NCDC) are used to ascertain the nature of climate change over the last century. After data validation, about 6000 stations are considered globally to determine the change in mean temperature, and about 5000 stations to determine that change in maximum and minimum temperatures. Global nature of temperature and its change are presented separately for January and July. Both maximum and minimum daily temperatures are used in the analysis. Trend of global change in annual precipitation is also reported here. Least square linear regression is used to ascertain the nature of these changes. Global nature of temperatures in both January and July show bimodal distributions, with the geographical region between the tropics in one mode and the region outside the tropic in another mode. The individual distributions of temperatures of both these regions show separate and similar histograms. Results indicate that over the last century, temperature in January increased more than that during July. Furthermore the minimum temperature in each case increased more than the maximum temperature. Ten separate estimates of temperature change are obtained from the data presented here using different methods. Considering all these estimates, the mean rise in temperature during January was 2.19°C, and during July was 1.72°C. The geographical nature of the rise in temperature shows that though it rises in most locations, the temperature also reduces along eastern Asia, some parts of central Russia, along south-eastern Australia, and along the east coast of the United States. Predominant rise in temperature is mostly over Europe, and in the arctic. Change in precipitation shows that though there is significant reduction rainfall globally, rainfall increases along the equator, in area

关键词： Climate Change GHCN GSOD IPCC NCPC Temperature Precipitation

Machine Learning Based Depression,Anxiety,and Stress Predictive Model During COVID-19 Crisis

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Computers, Materials & Continua 2022年第3期70卷 5803-5820页

作者： Fahd N.Al-Wesabi Hadeel Alsolai anwer Mustafa Hilal Manar Ahmed Hamza Mesfer Al Duhayyim Noha Negm Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Computer Science Department King Khaled UniversityKSA Faculty of Science Mathematics and Computer Science DepartmentMenoufia UniversityEgypt

Corona Virus Disease-2019(COVID-19)was reported at first in Wuhan city,China by December *** Health Organization(WHO)declared COVID-19 as a pandemic i.e.,global health crisis onMarch 11,*** outbreak of COVID-19 pandemic and subsequent lockdowns to curb the spread,not only affected the economic status of a number of countries,but it also resulted in increased levels of Depression,Anxiety,and Stress(DAS)among ***,there is a need exists to comprehend the relationship among psycho-social factors in a country that is hypothetically affected by high levels of stress and fear;with tremendously-limitingmeasures of social distancing and lockdown in force;and with high rates of new cases and *** this motivation,the current study aims at investigating theDAS levels among college students during COVID-19 lockdown since they are identified as a highly-susceptible *** current study proposes to develop Intelligent Feature Subset Selection withMachine Learning-based DAS predictive(IFSSML-DAS)*** presented IFSSML-DAS model involves data preprocessing,Feature Subset Selection(FSS),classification,and parameter ***,IFSSML-DAS model uses Group Gray Wolf Optimization based FSS(GGWO-FSS)technique to reduce the curse of *** addition,Beetle Swarm Optimization based Least Square Support Vector Machine(BSO-LSSVM)model is also employed for classification in which the weight and bias parameters of the LSSVM model are optimally adjusted using BSO *** performance of the proposed IFSSML-DAS model was tested using a benchmark DASS-21 dataset and the results were investigated under different *** outcome of the study suggests the development of specialized programs to handleDAS among population so as to overcome COVID-19 crisis.

关键词： Psycho-social factors covid-19 crisis management predictive models decision making machine learning

An Optimal Text Watermarking Method for Sensitive Detecting of Illegal Tampering Attacks

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Computers, Materials & Continua 2022年第3期70卷 5583-5600页

作者： anwer Mustafa Hilal Fahd N.Al-Wesabi Mohammed Alamgeer Manar Ahmed Hamza Mohammad Mahzari Murad A.Almekhlafi Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Information Systems King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Department of English College of Science&HumanitiesPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Faculty of Engineering Department of Electrical EngineeringCommunication EngineeringSana’a UniversityYemen

Due to the rapid increase in the exchange of text information via internet networks,the security and authenticity of digital content have become a major research *** main challenges faced by researchers are how to hide the information within the text to use it later for authentication and attacks tampering detection without effects on the meaning and size of the given digital *** this paper,an efficient text-based watermarking method has been proposed for detecting the illegal tampering attacks on theArabic text transmitted online via an Internet *** this purpose,the accuracy of tampering detection and watermark robustness has been improved of the proposed method as compared with the existing *** the proposed method,both embedding and extracting of the watermark are logically implemented,which causes no change in the digital *** is achieved by using the third level and alphanumeric strategy of the Markov model as a text analysis technique for analyzing the Arabic contents to obtain its features which are considered as the digital *** digital watermark will be used later to detecting any tampering of illegal attack on the received Arabic *** extensive set of experiments using four data sets of varying lengths proves the effectiveness of our approach in terms of detection accuracy,robustness,and effectiveness under multiple random locations of the common tampering attacks.

关键词： Text analysis text-watermarking tampering detection text authentication

Surface micro/nanostructure evolution of Au-Ag alloy nanoplates： Synthesis, simulation, plasmonic photothermal and surface-enhanced Raman scattering applications

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Nano Research 2016年第3期9卷 876-885页

作者： Hongmei Qian Meng Xu Xiaowei Li Muwei Ji Lei Cheng anwer Shoaib Jiajia Liu Lan Jiang Hesun Zhu Jiatao Zhang ing 100081 China Laser Micro/Nano-Fabrication Laboratory School of Mechanical Engineering Beijing Institute of Technology Beijing 100081 China

This study reports the controllable surface roughening of Au-Ag alloy nanoplates via the galvanic replacement reaction between single-crystalline triangular Ag nanoplates and HAuC14 in an aqueous medium. With a combination of experimental evidence and finite element method （FEM） simulations, improved electromagnetic field （E-field） enhancement around the surface-roughened Au- Ag nanoplates and tunable light absorption in the near-infrared （NIR） region （-800-1,400 nm） are achieved by the synergistic effects of the localized surface plasmon resonance （LSPR） from the maintained triangular shape, the controllable Au-Ag alloy composition, and the increased surface roughness. The NIR light extinction enables an active photothermal effect as well as a high photothermal conversion efficiency （78.5%）. The well-maintained triangular shape, surface- roughened evolutions of both micro- and nanostructures, and tunable NIR surface plasmon resonance effect enable potential applications of the Au-Ag alloy nanoplates in surface-enhanced Raman spectroscopic detection of biomolecules through 785-nm laser excitation.

关键词： Au-Ag alloy nanoplates surface roughening finite element method(FEM) simulation photothermal surface enhanced Ramanscattering (SERS)

Improved Archimedes Optimization Algorithm with Deep Learning Empowered Fall Detection System

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Computers, Materials & Continua 2022年第8期72卷 2713-2727页

作者： Ala Saleh Alluhaidan Masoud Alajmi Fahd N.Al-Wesabi anwer Mustafa Hilal Manar Ahmed Hamza Abdelwahed Motwakel Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science College of Science&Arts at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’a61101Yemen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

Human fall detection(FD)acts as an important part in creating sensor based alarm system,enabling physical therapists to minimize the effect of fall events and save human ***,elderly people suffer from several diseases,and fall action is a common situation which can occur at any *** this view,this paper presents an Improved Archimedes Optimization Algorithm with Deep Learning Empowered Fall Detection(IAOA-DLFD)model to identify the fall/non-fall *** proposed IAOA-DLFD technique comprises different levels of pre-processing to improve the input image ***,the IAOA with Capsule Network based feature extractor is derived to produce an optimal set of feature *** addition,the IAOA uses to significantly boost the overall FD performance by optimal choice of CapsNet ***,radial basis function(RBF)network is applied for determining the proper class labels of the test *** showcase the enhanced performance of the IAOA-DLFD technique,a wide range of experiments are executed and the outcomes stated the enhanced detection outcome of the IAOA-DLFD approach over the recent methods with the accuracy of 0.997.

关键词： Fall detection intelligent model deep learning archimedes optimization algorithm capsule network

Metal-organic framework-derived CoP_(2)@N-doped carbon hollow spheres for efficient oxygen evolution reaction

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Journal of Materials Science & Technology 2023年第33期164卷 240-245页

作者： Wei Zhang Jiangshui Luo Xuan Zhang Wei Guo Ning Han Sijie Xie Zhenyu Zhou Zahid anwer Zhenhong Xue Jan Fransaer Department of Materials Engineering KU LeuvenLeuven 3001Belgium Lab of Electrolytes and Phase Change Materials College of Materials Science and EngineeringSichuan UniversityChengdu 610065China ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang UniversityHangzhou 311215China

*** is considered as a viable alternative to fossil fuels[1,2].Large-scale hydrogen production by electrochemical water splitting is regarded as an important *** consists of two half-reactions:hydrogen evolution reaction(HER)at the cathode and oxygen evolution reaction(OER)at the anode[3].Compared to the HER,the OER has sluggish kinetic and large overpotentials,re-sulting from the complex oxidation pathway[4,5].Although RuO_(2) and IrO_(2) have excellent catalytic OER performance[6],their large-scale deployment is unfeasible due to the scarcity and high cost of these noble metal ***,cheap OER electrocatalysts have been actively investigated[7].

关键词： spheres splitting noble

Metaheuristic Resource Allocation Strategy for Cluster Based 6G Industrial Applications

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Computers, Materials & Continua 2022年第4期71卷 667-681页

作者： anwer Mustafa Hilal Lamia Osman Widaa Fahd N.Al-Wesabi Mohammad Medani Manar Ahmed Hamza Mesfer Al Duhayyim Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

The emergence of Beyond 5G(B5G)and 6G networks translated personal and industrial operations highly effective,reliable,and gainful by speeding up the growth of next generation Internet of Things(IoT).Industrial equipment in 6G encompasses a huge number of wireless sensors,responsible for collecting massive quantities of *** the same time,6G network can take real-world intelligent decisions and implement automated equipment *** the inclusion of different technologies into the system increased its energy consumption for which appropriate measures need to be *** has become mandatory for optimal resource allocation in 6G-enabled industrial *** this scenario,the current research paper introduces a new metaheuristic resource allocation strategy for clusterbased 6G industrial applications,named MRAS-CBIA *** technique aims at accomplishing energy efficiency and optimal resource allocation in 6G-enabled industrial *** proposed MRAS-CBIR technique involves three major ***,Weighted Clustering Technique(WCT)is employed to elect the optimal Cluster Heads(CHs)or coordinating agents with the help of three parameters namely,residual energy,distance,and node ***,Decision Tree-based Location Prediction(DTLP)mechanism is applied to determine the exact location of Management Agent(MA).Finally,Fuzzy C-means with Tunicate Swarm Algorithm(FCMTSA)is used for optimal resource allocation in 6G industrial *** performance of the proposed MRAS-CBIA technique was validated and the results were examined under different *** resultant experimental values highlighted the superior performance of MRAS-CBIR technique over existing state-of-the-art methods.

关键词： 6G industrial internet of things energy efficiency resource allocation metaheuristics

Energy Aware Data Collection with Route Planning for 6G Enabled UAV Communication

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Computers, Materials & Continua 2022年第4期71卷 825-842页

作者： Mesfer Al Duhayyim Marwa Obayya Fahd N.Al-Wesabi anwer Mustafa Hilal Mohammed Rizwanullah Majdy M.Eltahir Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information Systems King Khalid UniversityMuhayel AseerSaudi Arabia

With technological advancements in 6G and Internet of Things(IoT), the incorporation of Unmanned Aerial Vehicles (UAVs) and cellularnetworks has become a hot research topic. At present, the proficient evolution of 6G networks allows the UAVs to offer cost-effective and timelysolutions for real-time applications such as medicine, tracking, surveillance,etc. Energy efficiency, data collection, and route planning are crucial processesto improve the network communication. These processes are highly difficultowing to high mobility, presence of non-stationary links, dynamic topology,and energy-restricted UAVs. With this motivation, the current research paperpresents a novel Energy Aware Data Collection with Routing Planning for6G-enabled UAV communication (EADCRP-6G) technique. The goal of theproposed EADCRP-6G technique is to conduct energy-efficient cluster-baseddata collection and optimal route planning for 6G-enabled UAV ***-6G technique deploys Improved Red Deer Algorithm-based Clustering (IRDAC) technique to elect an optimal set of Cluster Heads (CH) andorganize these clusters. Besides, Artificial Fish Swarm-based Route Planning(AFSRP) technique is applied to choose an optimum set of routes for UAVcommunication in 6G networks. In order to validated whether the proposedEADCRP-6G technique enhances the performance, a series of simulationswas performed and the outcomes were investigated under different *** experimental results showcase that the proposed model outperformed allother existing models under different evaluation parameters.

关键词： Unmanned aerial vehicle 6G networks artificial intelligence energy efficiency clustering route planning data collection metaheuristics