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A Joint algorithm for Resource allocation in D2D 5G Wireless Networks

Computers, Materials & Continua 2021年第10期69卷 301-317页

作者： fahd N.al-Wesabi Imran Khan Mohammad alamgeer ali M.al-Sharafi Bong Jun Choi Abdallah aldosary Ehab Mahmood Mohammad Department of Computer Science King Khalid UniversityMuhayel AseerKSA Faculty of Computer and IT Sana’a UniversityYemen Department of Electrical Engineering University of Engineering and TechnologyPeshawarP.O.B 814Pakistan Department of Information Systems King Khalid UniversityMayahel AseerKSA Department of Computer Science College of Computers and Information TechnologyUniversity of BishaKSA School of Computer Science and Engineering Soongsil UniversitySouth Korea Department of Computer Science Prince Sattam bin Abdulaziz UniversityAs Sulayyil11991Saudi Arabia Electrical Engineering Department College of EngineeringPrince Sattam Bin Abdulaziz UniversityWadi Addwasir11991Saudi Arabia Electrical Engineering Department Faculty of EngineeringAswan UniversityAswan81542Egypt

With the rapid development of Internet technology,users have an increasing demand for *** continuous popularization of traffic-intensive applications such as high-definition video,3D visualization,and cloud computing has promoted the rapid evolution of the communications *** order to cope with the huge traffic demand of today’s users,5G networks must be fast,flexible,reliable and *** on these research backgrounds,the academic community has proposed D2D *** main feature of D2D communication is that it enables direct communication between devices,thereby effectively improve resource utilization and reduce the dependence on base stations,so it can effectively improve the throughput of multimedia *** of the most considerable factor which affects the performance of D2D communication is the co-channel interference which results due to the multiplexing of multiple D2D user using the same channel resource of the cellular *** solve this problem,this paper proposes a joint algorithm time scheduling and power *** main idea is to effectively maximize the number of allocated resources in each scheduling period with satisfied quality of service *** constraint problem is decomposed into time scheduling and power control *** power control subproblem has the characteristics of mixed-integer linear programming of ***,we proposed a gradual power control *** time scheduling subproblem belongs to the NP-hard problem having convex-cordinality,therefore,we proposed a heuristic scheme to optimize resource *** results show that the proposed algorithm effectively improved the resource allocation and overcome the co-channel interference as compared with existing algorithms.

关键词： 6G wireless networks mobile communication linear programming

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Intelligent Deep Learning Based Automated Fish Detection Model for UWSN

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Computers, Materials & Continua 2022年第3期70卷 5871-5887页

作者： Mesfer al Duhayyim Haya Mesfer alshahrani fahd Nal-Wesabi Mohammed alamgeer Anwer Mustafa Hilal Manar Ahmed Hamza 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 Information Systems King Khalid UniversityMuhayel AseerSaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

An exponential growth in advanced technologies has resulted in the exploration of Ocean *** has paved the way for new opportunities that can address questions relevant to diversity,uniqueness,and difficulty of marine *** Wireless Sensor Networks(UWSNs)are widely used to leverage such opportunities while these networks include a set of vehicles and sensors to monitor the environmental *** this scenario,it is fascinating to design an automated fish detection technique with the help of underwater videos and computer vision techniques so as to estimate and monitor fish biomass in water *** models have been developed earlier for fish ***,they lack robustness to accommodate considerable differences in scenes owing to poor luminosity,fish orientation,structure of seabed,aquatic plantmovement in the background and distinctive shapes and texture of fishes from different *** this motivation,the current research article introduces an Intelligent Deep Learning based Automated Fish Detection model for UWSN,named IDLAFD-UWSN *** presented IDLAFD-UWSN model aims at automatic detection of fishes from underwater videos,particularly in blurred and crowded ***-UWSN model makes use of Mask Region Convolutional Neural Network(Mask RCNN)with Capsule Network as a baseline model for fish ***,in order to train Mask RCNN,background subtraction process using GaussianMixtureModel(GMM)model is *** model makes use of motion details of fishes in video which consequently integrates the outcome with actual image for the generation of fish-dependent candidate ***,Wavelet Kernel Extreme Learning Machine(WKELM)model is utilized as a classifier *** performance of the proposed IDLAFD-UWSN model was tested against benchmark underwater video dataset and the experimental results achieved by IDLAFD-UWSN model were promising in comparison with other state-of-the-art methods under differe

关键词： Aquaculture background subtraction deep learning fish detection marine surveillance underwater sensor networks

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Sailfish Optimizer with EfficientNet Model for Apple Leaf Disease Detection

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Computers, Materials & Continua 2023年第1期74卷 217-233页

作者： Mazen Mushabab alqahtani Ashit Kumar Dutta Sultan almotairi M.Ilayaraja Amani Abdulrahman albraikan fahd N.al-Wesabi Mesfer al Duhayyim Physical Therapy Department Majmaah UniversityMajmaah11952Saudi Arabia Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd Diriyah13713RiyadhSaudi Arabia Department of Natural and Applied Sciences Faculty of Community CollegeMajmaah UniversityMajmaah11952Saudi Arabia School of Computing Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid University62529Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’a1247Yemen Department of Natural and Applied Sciences College of Community-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia

Recent developments in digital cameras and electronic gadgets coupled with Machine Learning(ML)and Deep Learning(DL)-based automated apple leaf disease detection models are commonly employed as reasonable alternatives to traditional visual inspection *** this background,the current paper devises an Effective Sailfish Optimizer with EfficientNet-based Apple Leaf disease detection(ESFO-EalD)*** goal of the proposed ESFO-EalD technique is to identify the occurrence of plant leaf diseases *** this scenario,Median Filtering(MF)approach is utilized to boost the quality of apple plant leaf ***,SFO with Kapur’s entropy-based segmentation technique is also utilized for the identification of the affected plant region from test ***,Adam optimizer with EfficientNet-based feature extraction and Spiking Neural Network(SNN)-based classification are employed to detect and classify the apple plant leaf images.A wide range of simulations was conducted to ensure the effective outcomes of ESFO-EalD technique on benchmark *** results reported the supremacy of the proposed ESFO-EalD approach than the existing approaches.

关键词： Agriculture computer vision image processing deep learning metaheuristics image segmentation

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A Reliable NLP Scheme for English Text Watermarking Based on Contents Interrelationship

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Computer Systems Science & Engineering 2021年第6期37卷 297-311页

作者： fahd N.al-Wesabi Saleh alzahrani Fuad alyarimi Mohammed Abdul Nadhem Nemri Mohammed M.almazah 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 Mathematics and computer in Ibb University Yemen and Department of MathematicsKing Khalid UniversityMuhayel AseerKingdom of Saudi Arabia

In this paper,a combined approach CAZWNLP(a combined approach of zero-watermarking and natural language processing)has been developed for the tampering detection of English text exchanged through the *** third gram of alphanumeric of the Markov model has been used with text-watermarking technologies to improve the performance and accuracy of tampering detection issues which are limited by the existing works reviewed in the literature of this *** third-grade level of the Markov model has been used in this method as natural language processing technology to analyze an English text and extract the textual characteristics of the given ***,the extracted features have been utilized as watermark information and then validated with the attacked English text to detect any suspected tampering occurred on *** embedding mechanism of CAZWNLP method will be achieved logically without effects or modifying the original text document to embed a watermark *** has been implemented using VS code IDE with *** experimental and simulation results using standard datasets of varying lengths show that the proposed approach can obtain high robustness and better detection accuracy of tampering common random insertion,reorder,and deletion attacks,e.g.,Comparison results with baseline approaches also show the advantages of the proposed approach.

关键词： NLP text analysis English text watermarking robustness tampering detection

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Artificial Intelligence Based Clustering with Routing Protocol for Internet of Vehicles

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Computers, Materials & Continua 2022年第3期70卷 5835-5853页

作者： Manar Ahmed Hamza Haya Mesfer alshahrani fahd Nal-Wesabi Mesfer al Duhayyim Anwer Mustafa Hilal Hany Mahgoub 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 IT Sana’a UniversitySana’aYemen Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

With recent advances made in Internet of Vehicles(IoV)and Cloud Computing(CC),the Intelligent Transportation Systems(ITS)find it advantageous in terms of improvement in quality and interactivity of urban transportation service,mitigation of costs incurred,reduction in resource utilization,and improvement in traffic management *** trafficrelated problems in future smart cities can be sorted out with the incorporation of IoV in *** communication enables the collection and distribution of real-time essential data regarding road network *** this scenario,energy-efficient and reliable intercommunication routes are essential among vehicular nodes in sustainable urban *** this motivation,the current research article presents a new Artificial Intelligence-based Energy Efficient Clustering with Routing(AI-EECR)Protocol for IoV in urban *** proposed AI-EECR protocol operates under three stages namely,network initialization,Cluster Head(CH)selection,and routing *** presented AI-EECR protocol determines the CHs from vehicles with the help of Quantum Chemical Reaction Optimization(QCRO)*** a fitness function with the help of vehicle speed,trust level,and energy level of the *** order to make appropriate routing decisions,a set of relay nodeswas selected usingGroup Teaching Optimization algorithm(GTOA).The performance of the presented AI-EECR model,in terms of energy efficiency,was validated against different aspects and a brief comparative analysis was *** experimental outcomes established that AI-EECR model outperformed the existing methods under different measures.

关键词： Vehicular communication internet of vehicles energy efficient smart transportation smart city urban computing

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Optimal Resource allocation Method for Device-to-Device Communication in 5G Networks

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Computers, Materials & Continua 2022年第4期71卷 1-15页

作者： fahd N.al-Wesabi Imran Khan Saleem Latteef Mohammed Huda Farooq Jameel Mohammad alamgeer ali M.al-Sharafi Byung Seo Kim Department of Computer Science King Khalid UniversityMuhayel AseerKSA Faculty of Computer and IT Sana’a UniversityYemen Department of Electrical Engineering University of Engineering and TechnologyPeshawarPakistan Department of Medical Instrumentation Techniques Engineering Electrical Engineering Technical CollegeMiddle Technical UniversityBaghdad10013Iraq Department of Information Systems King Khalid UniversityMayahel AseerKSA Department of Computer Science College of Computers and Information TechnologyUniversity of BishaKSA Department of Software and Communications Engineering Hongik UniversityKorea

With the rapid development of the next-generation mobile network,the number of terminal devices and applications is growing ***,how to obtain a higher data rate,wider network coverage and higher resource utilization in the limited spectrum resources has become the common research goal of ***-to-Device(D2D)communication technology and other frontier communication technologies have ***-to-Device communication technology is the technology that devices in proximity can communicate directly in cellular *** has become one of the key technologies of the fifth-generation mobile communications system(5G).D2D communication technology which is introduced into cellular networks can effectively improve spectrum utilization,enhance network coverage,reduce transmission delay and improve system throughput,but it would also bring complicated and various interferences due to reusing cellular resources at the same *** resource management is one of the most challenging and importing issues to give full play to the advantages of D2D *** resource allocation is an important factor that needs to be addressed in D2D ***,this paper proposes an optimization method based on the game-matching *** main idea is to model the optimization problem of the quality-of-experience based on user fairness and solve it through game-matching *** results show that the proposed algorithm effectively improved the resource allocation and utilization as compared with existing algorithms.

关键词： D2D communication resource allocation optimization networks

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Visualizing risk factors of dementia from scholarly literature using knowledge maps and next-generation data models

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Visual Computing for Industry,Biomedicine,and Art 2021年第1期4卷 165-182页

作者： Kiran fahd Sitalakshmi Venkatraman Department of Information Technology Melbourne Polytechnic3181 PrahranVICAustralia

Scholarly communication of knowledge is predominantly document-based in digital repositories,and researchers find it tedious to automatically capture and process the semantics among related *** the present digital era of big data,there is a lack of visual representations of the knowledge present in scholarly articles,and a time-saving approach for a literature search and visual navigation is *** majority of knowledge display tools cannot cope with current big data trends and pose limitations in meeting the requirements of automatic knowledge representation,storage,and dynamic *** address this limitation,the main aim of this paper is to model the visualization of unstructured data and explore the feasibility of achieving visual navigation for researchers to gain insight into the knowledge hidden in scientific articles of digital *** topics of research and practice,including modifiable risk factors leading to a dramatic increase in alzheimer’s disease and other forms of dementia,warrant deeper insight into the evidence-based knowledge available in the *** goal is to provide researchers with a visual-based easy traversal through a digital repository of research *** paper takes the first step in proposing a novel integrated model using knowledge maps and next-generation graph datastores to achieve a semantic visualization with domain-specific knowledge,such as dementia risk *** model facilitates a deep conceptual understanding of the literature by automatically establishing visual relationships among the extracted knowledge from the big data resources of research *** also serves as an automated tool for a visual navigation through the knowledge repository for faster identification of dementia risk factors reported in scholarly ***,it facilitates a semantic visualization and domain-specific knowledge discovery from a large digital repository and their associations.

关键词： Big data Data visualization Knowledge maps Dementia Non-relational database Graph database Neo4j Semantic visualization

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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

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Machine Learning Enabled e-Learner Non-Verbal Behavior Detection in IoT Environment

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Computers, Materials & Continua 2022年第7期72卷 679-693页

作者： Abdelzahir Abdelmaboud fahd N.al-Wesabi Mesfer al Duhayyim Taiseer Abdalla Elfadil Eisa Manar Ahmed Hamza Mohammed Rizwanullah Abu Serwar Zamani Radwa Marzouk Department of Information Systems College of Science and ArtsKing Khalid UniversityMahayil AsirSaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Science and Arts-Girls SectionKing Khalid UniversityMahayil AsirSaudi Arabia 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 MathematicsFaculty of ScienceCairo UniversityGiza12613Egypt

Internet of Things(IoT)with e-learning is widely employed to collect data from various smart devices and share it with other ones for efficient e-learning *** the same time,machine learning(ML)and data mining approaches are presented for accomplishing prediction and classification *** this motivation,this study focuses on the design of intelligent machine learning enabled e-learner non-verbal behaviour detection(IML-ELNVBD)in IoT *** proposed IML-ELNVBD technique allows the IoT devices such as audio sensors,cameras,*** are then connected to the cloud server for further *** addition,the modelling and extraction of behaviour take ***,extreme learning machine sparse autoencoder(ELM-SAE)model is employed for the detection and classification of non-verbal ***,the Ant Colony Optimization(ACO)algorithm is utilized to properly tune the weight and bias parameters involved in the ELM-SAE *** order to ensure the improved performance of the IML-ELNVBD model,a comprehensive simulation analysis is carried out and the results highlighted the betterment compared to the recent models.

关键词： Internet of things e-learning machine learning non-verbal behavior extreme learning machine classification parameter tuning

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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

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