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Parameter-Tuned Deep Learning-Enabled Activity Recognition for Disabled People

Computers, Materials & Continua 2023年第6期75卷 6287-6303页

作者： mesfer al duhayyim Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversityAl-Aflaj16273Saudi Arabia

Elderly or disabled people can be supported by a human activity recognition(HAR)system that monitors their activity intervenes and pat-terns in case of changes in their behaviors or critical events have *** automated HAR could assist these persons to have a more indepen-dent *** appropriate and accurate data regarding the activity is the most crucial computation task in the activity recognition *** the fast development of neural networks,computing,and machine learning algorithms,HAR system based on wearable sensors has gained popularity in several areas,such as medical services,smart homes,improving human communication with computers,security systems,healthcare for the elderly,mechanization in industry,robot monitoring system,monitoring athlete train-ing,and rehabilitation *** this view,this study develops an improved pelican optimization with deep transfer learning enabled HAR(IPODTL-HAR)system for disabled *** major goal of the IPODTL-HAR method was recognizing the human activities for disabled person and improve the quality of *** presented IPODTL-HAR model follows data pre-processing for improvising the quality of the ***,EfficientNet model is applied to derive a useful set of feature vectors and the hyperparameters are adjusted by the use of Nadam ***,the IPO with deep belief network(DBN)model is utilized for the recognition and classification of human *** utilization of Nadam optimizer and IPO algorithm helps in effectually tuning the hyperparameters related to the EfficientNet and DBN models *** experimental validation of the IPODTL-HAR method is tested using benchmark *** comparison study highlighted the betterment of the IPODTL-HAR model over recent state of art HAR approaches interms of different measures.

关键词： Human activity recognition disabled person artificial intelligence computer vision deep learning

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Automated Disabled People Fall Detection Using Cuckoo Search with Mobile Networks

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Intelligent Automation & Soft Computing 2023年第6期36卷 2473-2489页

作者： mesfer al duhayyim Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Falls are the most common concern among older adults or disabled peo-ple who use scooters and *** early detection of disabled persons’falls is required to increase the living rate of an individual or provide support to them whenever *** recent times,the arrival of the Internet of Things(IoT),smartphones,Artificial Intelligence(AI),wearables and so on make it easy to design fall detection mechanisms for smart *** current study devel-ops an Automated Disabled People Fall Detection using Cuckoo Search Optimi-zation with Mobile Networks(ADPFD-CSOMN)*** proposed model’s major aim is to detect and distinguish fall events from non-fall events *** attain this,the presented ADPFD-CSOMN technique incorporates the design of the MobileNet model for the feature extraction ***,the CSO-based hyperparameter tuning process is executed for the MobileNet model,which shows the paper’s ***,the Radial Basis Function(RBF)clas-sification model recognises and classifies the instances as either fall or *** order to validate the betterment of the proposed ADPFD-CSOMN model,a com-prehensive experimental analysis was *** results confirmed the enhanced fall classification outcomes of the ADPFD-CSOMN model over other approaches with an accuracy of 99.17%.

关键词： Disabled people human-computer interaction fall event detection deep learning computer vision

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Deep Learning Empowered Cybersecurity Spam Bot Detection for Online Social Networks

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Computers, Materials & Continua 2022年第3期70卷 6257-6270页

作者： mesfer al duhayyim Haya mesfer alshahrani Fahd Nal-Wesabi Mohammed alamgeer Anwer Mustafa Hilal Mohammed Rizwanullah 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

Cybersecurity encompasses various elements such as strategies,policies,processes,and techniques to accomplish availability,confidentiality,and integrity of resource processing,network,software,and data from *** this scenario,the rising popularity of Online Social Networks(OSN)is under threat from spammers for which effective spam bot detection approaches should be *** studies have developed different approaches for the detection of spam bots in *** those techniques primarily concentrated on hand-crafted features to capture the features of malicious users while the application of Deep Learning(DL)models needs to be *** this motivation,the current research article proposes a Spam Bot Detection technique using Hybrid DL model abbreviated as *** proposed SBD-HDL technique focuses on the detection of spam bots that exist in *** technique has different stages of operations such as pre-processing,classification,and parameter ***,SBD-HDL technique hybridizes Graph Convolutional Network(GCN)with Recurrent Neural Network(RNN)model for spam bot classification *** order to enhance the detection performance of GCN-RNN model,hyperparameters are tuned using Lion Optimization algorithm(LOA).Both hybridization of GCN-RNN and LOA-based hyperparameter tuning process make the current work,a first-of-its-kind in this *** experimental validation of the proposed SBD-HDL technique,conducted upon benchmark dataset,established the supremacy of the technique since it was validated under different measures.

关键词： Cybersecurity spam bot data classification social networks twitter deep learning

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Intelligent Machine Learning Based EEG Signal Classification Model

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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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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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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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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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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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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

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An Improved Evolutionary algorithm for Data Mining and Knowledge Discovery

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Computers, Materials & Continua 2022年第4期71卷 1233-1247页

作者： mesfer al duhayyim Radwa Marzouk Fahd N.al-Wesabi Maram alrajhi Manar Ahmed Hamza Abu Sarwar Zamani 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 Mathematics Faculty of ScienceCairo UniversityGiza 12613Egypt Department of Computer Science Faculty of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Information Systems Faculty of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

Recent advancements in computer technologies for data processing,collection,and storage have offered several chances to improve the abilities in production,services,communication,and *** mining(DM)is an interdisciplinary field commonly used to extract useful patterns from the *** the same time,educational data mining(EDM)is a kind of DM concept,which finds use in educational ***,artificial intelligence(AI)techniques can be used for mining a large amount of *** the same time,in DM,the feature selection process becomes necessary to generate subset of features and can be solved by the use of metaheuristic optimization *** this motivation,this paper presents an improved evolutionary algorithm based feature subsets election with neuro-fuzzy classification(IEAFSS-NFC)for data mining in the education *** presented IEAFSS-NFC model involves data pre-processing,feature selection,and ***,the Chaotic Whale Optimization algorithm(CWOA)is used for the selection of the highly related feature subsets to accomplish improved classification ***,Neuro-Fuzzy Classification(NFC)technique is employed for the classification of education *** IEAFSS-NFC model is tested against a benchmark Student Performance DataSet from the UCI *** simulation outcome has shown that the IEAFSS-NFC model is superior to other methods.

关键词： Educational data mining feature selection whale optimization classification

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