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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 occurred.An automated HAR could assist these persons to have a more indepen-dent life.Providing appropriate and accurate data regarding the activity is the most crucial computation task in the activity recognition system.With 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 systems.In this view,this study develops an improved pelican optimization with deep transfer learning enabled HAR(IPODTL-HAR)system for disabled persons.The major goal of the IPODTL-HAR method was recognizing the human activities for disabled person and improve the quality of living.The presented IPODTL-HAR model follows data pre-processing for improvising the quality of the data.Besides,EfficientNet model is applied to derive a useful set of feature vectors and the hyperparameters are adjusted by the use of Nadam optimizer.Finally,the IPO with deep belief network(DBN)model is utilized for the recognition and classification of human activities.The utilization of Nadam optimizer and IPO algorithm helps in effectually tuning the hyperparameters related to the EfficientNet and DBN models respectively.The experimental validation of the IPODTL-HAR method is tested using benchmark dataset.Extensive 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 wheelchairs.The early detection of disabled persons’falls is required to increase the living rate of an individual or provide support to them whenever required.In 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 homecare.The current study devel-ops an Automated Disabled People Fall Detection using Cuckoo Search Optimi-zation with Mobile Networks(ADPFD-CSOMN)model.The proposed model’s major aim is to detect and distinguish fall events from non-fall events automati-cally.To attain this,the presented ADPFD-CSOMN technique incorporates the design of the MobileNet model for the feature extraction process.Next,the CSO-based hyperparameter tuning process is executed for the MobileNet model,which shows the paper’s novelty.Finally,the Radial Basis Function(RBF)clas-sification model recognises and classifies the instances as either fall or non-fall.In order to validate the betterment of the proposed ADPFD-CSOMN model,a com-prehensive experimental analysis was conducted.The 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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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 spaces.It has paved the way for new opportunities that can address questions relevant to diversity,uniqueness,and difficulty of marine life.Underwater 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 conditions.In 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 bodies.Several models have been developed earlier for fish detection.However,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 genus.With this motivation,the current research article introduces an Intelligent Deep Learning based Automated Fish Detection model for UWSN,named IDLAFD-UWSN model.The presented IDLAFD-UWSN model aims at automatic detection of fishes from underwater videos,particularly in blurred and crowded environments.IDLAFD-UWSN model makes use of Mask Region Convolutional Neural Network(Mask RCNN)with Capsule Network as a baseline model for fish detection.Besides,in order to train Mask RCNN,background subtraction process using GaussianMixtureModel(GMM)model is applied.This 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 regions.Finally,Wavelet Kernel Extreme Learning Machine(WKELM)model is utilized as a classifier model.The 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 different aspects with the maximum accuracy of 98%and 9

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

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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 computer.BCI systems are applied in several research areas such as neuro-rehabilitation,robots,exoeskeletons,etc.Electroencephalography(EEG)is a technique commonly applied in capturing brain signals.It is incorporated in BCI systems since it has attractive features such as noninvasive nature,high time-resolution output,mobility and cost-effective.EEG classification process is highly essential in decision making process and it incorporates different processes namely,feature extraction,feature selection,and classification.With this motivation,the current research paper presents an Intelligent Optimal Fuzzy Support Vector Machine-based EEC recognition(IOFSVM-EEG)model for BCI system.Independent 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 information.Besides,Common Spatial Pattern(CSP)-based feature extraction technique is utilized to derive a helpful set of feature vectors from the preprocessed EEG signals.Moreover,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 conducted.The outcomes were examined under distinct aspects.The 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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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 2019.World Health Organization(WHO)declared COVID-19 as a pandemic i.e.,global health crisis onMarch 11,2020.The 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 people.Therefore,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 mortalities.With 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 population.The current study proposes to develop Intelligent Feature Subset Selection withMachine Learning-based DAS predictive(IFSSML-DAS)model.The presented IFSSML-DAS model involves data preprocessing,Feature Subset Selection(FSS),classification,and parameter tuning.Besides,IFSSML-DAS model uses Group Gray Wolf Optimization based FSS(GGWO-FSS)technique to reduce the curse of dimensionality.In 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 algorithm.The performance of the proposed IFSSML-DAS model was tested using a benchmark DASS-21 dataset and the results were investigated under different measures.The 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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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 businesses.Data science integrates the conventions of econometrics with the technological elements of data science.It make use of machine learning(ML),predictive and prescriptive analytics to effectively understand financial data and solve related problems.Smart technologies for SMEs enable allows the firm to get smarter with their processes and offers efficient operations.At 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 crisis.AI 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 problems.With this motivation,this paper presents a new AI based optimal functional link neural network(FLNN)based financial crisis prediction(FCP)model forSMEs.The proposed model involves preprocessing,feature selection,classification,and parameter tuning.At the initial stage,the financial data of the enterprises are collected and are preprocessed to enhance the quality of the data.Besides,a novel chaotic grasshopper optimization algorithm(CGOA)based feature selection technique is applied for the optimal selection of features.Moreover,functional link neural network(FLNN)model is employed for the classification of the feature reduced data.Finally,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 model.The 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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Securing Arabic Contents algorithm for Smart Detecting of Illegal Tampering Attacks

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Computers, Materials & Continua 2022年第2期70卷 2879-2894页

作者： Mesfer Al Duhayyim Manal Abdullah alohali Fahd N.al-Wesabi Anwer Mustafa Hilal Mohammad Medani 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 IT Sana’a UniversitySana’aYemen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Themost common digital media exchanged via the Internet is in text form.The Arabic language is considered one of themost sensitive languages of content modification due to the presence of diacritics that can cause a change in the meaning.In this paper,an intelligent scheme is proposed for improving the reliability and security of the text exchanged via the Internet.The core mechanism of the proposed scheme depends on integrating the hidden Markov model and zero text watermarking techniques.The watermark key will be generated by utilizing the extracted features of the text analysis process using the third order and word level of the Markov model.The Embedding and detection processes of the proposed scheme will be performed logically without the effect of the original text.The proposed scheme is implemented using PHP with VS code IDE.The simulation results,using varying sizes of standard datasets,show that the proposed scheme can obtain high reliability and provide better accuracy of the common illegal tampering attacks.Comparison results with other baseline techniques show the added value of the proposed scheme.

关键词： Tampering detection zero-watermarking soft computing text analysis hidden Markov model

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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 networks.EADCRP-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 dimensions.The 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

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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 researches.Data mining(DM)is an interdisciplinary field commonly used to extract useful patterns from the data.At the same time,educational data mining(EDM)is a kind of DM concept,which finds use in educational sector.Recently,artificial intelligence(AI)techniques can be used for mining a large amount of data.At 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 algorithms.With 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 sector.The presented IEAFSS-NFC model involves data pre-processing,feature selection,and classification.Besides,the Chaotic Whale Optimization algorithm(CWOA)is used for the selection of the highly related feature subsets to accomplish improved classification results.Then,Neuro-Fuzzy Classification(NFC)technique is employed for the classification of education data.The IEAFSS-NFC model is tested against a benchmark Student Performance DataSet from the UCI repository.The 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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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 data.At the same time,6G network can take real-world intelligent decisions and implement automated equipment operations.But the inclusion of different technologies into the system increased its energy consumption for which appropriate measures need to be taken.This has become mandatory for optimal resource allocation in 6G-enabled industrial applications.In this scenario,the current research paper introduces a new metaheuristic resource allocation strategy for clusterbased 6G industrial applications,named MRAS-CBIA technique.MRASCBIA technique aims at accomplishing energy efficiency and optimal resource allocation in 6G-enabled industrial applications.The proposed MRAS-CBIR technique involves three major processes.Firstly,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 degree.Secondly,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 applications.The performance of the proposed MRAS-CBIA technique was validated and the results were examined under different dimensions.The 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

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