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Fire Hawk Optimizer with Deep Learning Enabled Human Activity Recognition

Computer Systems Science & Engineering 2023年第6期45卷 3135-3150页

作者： mohammed Alonazi Mrim M.Alnfiai Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia

Human-Computer Interaction(HCI)is a sub-area within computer science focused on the study of the communication between people(users)and computers and the evaluation,implementation,and design of user interfaces for computer *** has accomplished effective incorporation of the human factors and software engineering of computing systems through the methods and concepts of cognitive *** is an aspect of HCI dedicated to guar-anteeing that human–computer communication is,amongst other things,efficient,effective,and sustaining for the ***,Human activity recognition(HAR)aim is to identify actions from a sequence of observations on the activities of subjects and the environmental *** vision-based HAR study is the basis of several applications involving health care,HCI,and video *** article develops a Fire Hawk Optimizer with Deep Learning Enabled Activ-ity Recognition(FHODL-AR)on HCI driven *** the presented FHODL-AR technique,the input images are investigated for the identification of different human *** feature extraction,a modified SqueezeNet model is intro-duced by the inclusion of few bypass connections to the SqueezeNet among Fire ***,the FHO algorithm is utilized as a hyperparameter optimization algorithm,which in turn boosts the classification *** detect and cate-gorize different kinds of activities,probabilistic neural network(PNN)classifier is *** experimental validation of the FHODL-AR technique is tested using benchmark datasets,and the outcomes reported the improvements of the FHODL-AR technique over other recent approaches.

关键词： Activity recognition fire hawks optimizer deep learning usability human computer interaction

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Causative bacteria of ventilator-associated pneumonia in intensive care unit in Bahrain:Prevalence and antibiotics susceptibility pattern

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World Journal of Critical Care Medicine 2023年第3期12卷 165-175页

作者： Mohamed Eliwa Hassan Safaa abdulaziz Al-Khawaja Nermin Kamal Saeed Sana abdulaziz Al-Khawaja Mahmood Al-Awainati Sara Salah Yusuf Radhi Mohamed Hameed Alsaffar mohammed Al-Beltagi Intensive Care Unit Department of Internal medicineSalmaniya Medical ComplexMinistry of HealthManamaKingdom of BahrainManama 12ManamaBahrain Infectious Disease Unit Department of Internal MedicineSalmaniya Medical ComplexMinistry of HealthKingdom of BahrainManama 12ManamaBahrain Medical Microbiology Section Department of PathologySalmaniya Medical ComplexMinistry of HealthKingdom of BahrainManama 12ManamaBahrain Medical Microbiology Section Department of PathologyIrish Royal College of SurgeonBahrainBusiateen 15503MuharraqBahrain Department of Family Medicine Ministry of HealthManamaKingdom of BahrainManama 12ManamaBahrain Department of Pediatrics Faculty of MedicineTanta UniversityTanta 31511AlghrabiaEgypt Department of Pediatrics University Medical CenterKing Abdulla Medical CityArabian Gulf UniversityDr.Sulaiman Al Habib Medical GroupManama 26671ManamaBahrain

BACKGROUND Ventilator-associated pneumonia(VAP)is defined as pneumonia that occurs two calendar days following endotracheal intubation or after *** is the most common infection encountered among intubated *** incidence showed wide variability between *** To define the VAP incidence in the intensive care unit(ICU)in the central gove-rnment hospital in Bahrain and review the risk factors and the predominant bacterial pathogens with their antimicrobial susceptibility *** The research was a prospective cross-sectional observational study over six months from November 2019 to June *** included adult and adolescent patients(>14 years old)admitted to the ICU and required intubation and mechanical *** was diagnosed when it occurred after 48 h after endotracheal intubation using the clinical pulmonary infection score,which considers the clinical,laboratory,microbiological,and radiographic *** The total number of adult patients admitted to the ICU who required intubation and mechanical ventilation during the study period was ***-six patients developed VAP during their ICU stay(29.7%).The calculated VAP rate was 22.14 events per 1000 ventilator days during the study period,with a mean age of 52 years±*** VAP cases had late-onset VAP with a mean number of ICU days before the development of VAP of 9.96±***-negative contributed to most VAP cases in our unit,with multidrug-resistant Acinetobacter being the most identified *** The reported VAP rate in our ICU was relatively high compared to the international benchmark,which should trigger a vital action plan for reinforcing the implementation of the VAP prevention bundle.

关键词： Ventilator-associated pneumonia Intensive care unit Antibiotics susceptibility pattern Kingdom of Bahrain Adults Bacterial resistance Acinetobacter

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IOT Based Smart Parking System Using Ensemble Learning

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Intelligent Automation & Soft Computing 2023年第6期36卷 3637-3656页

作者： Walaa H.Elashmawi Ahmad Akram mohammed Yasser Menna Hisham Manar mohammed Noha Ihab Ahmed Ali Department of Computer Science Faculty of Computers and InformaticsSuez Canal University4.5 Km the Ring RoadIsmailiaIsmailia41522Egypt Department of Computer Engineering and Software Systems Faculty of EngineeringAin Shams UniversityAl-Abbaseya4543070Egypt Department of Computer Science Faculty of Computer ScienceMisr International University28 KM Cairo–Ismailia RoadCairo44971Egypt College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Higher Future Institute for Specialized Technological Studies Cairo3044Egypt

Parking space is usually very limited in major cities,especially Cairo,leading to traffic congestion,air pollution,and driver *** car parking systems tend to tackle parking issues in a non-digitized *** systems require the drivers to search for an empty parking space with no guaran-tee of finding any wasting time,resources,and causing unnecessary *** address these issues,this paper proposes a digitized parking system with a proof-of-concept implementation that combines multiple technological concepts into one solution with the advantages of using IoT for real-time tracking of park-ing *** authentication and automated payments are handled using a quick response(QR)code on entry and *** experiments were done on real data collected for six different locations in Cairo via a live popular times *** machine learning models were investigated in order to estimate the occu-pancy rate of certain ***,a clear analysis of the differences in per-formance is illustrated with the final model deployed being *** has achieved the most efficient results with a R^(2) score of 85.7%.

关键词： IoT XGBoost linear regression random forest ensemble learning isolation forest

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Deep Learning with Image Classification Based Secure CPS for Healthcare Sector

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Computers, Materials & Continua 2022年第8期72卷 2633-2648页

作者： Ahmed S.Almasoud Abdelzahir Abdelmaboud Faisal S.Alsubaei Manar Ahmed Hamza Ishfaq Yaseen mohammed Abaker Abdelwahed Motwakel mohammed Rizwanullah Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversityRiyadh12435Saudi Arabia Department of Information Systems College of Science and ArtsKing Khalid UniversityMahayil Asir62529Saudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16278Saudi Arabia Department of Computer Science Community CollegeKing Khalid UniversityMahayil Asir62529Saudi Arabia

Cyber-Physical System(CPS)involves the combination of physical processes with computation and communication *** recent advancementsmade in cloud computing,Wireless Sensor Network(WSN),healthcare sensors,*** to develop CPS as a proficient model for healthcare applications especially,home patient *** several techniques have been proposed earlier related to CPS structures,only a handful of studies has focused on the design of CPS models for health care ***,the proposal for a dedicated CPS model for healthcare sector necessitates a significant interest to ensure data *** overcome the challenges,the current research paper designs a Deep Learning-based Intrusion Detection and Image Classification for Secure CPS(DLIDIC-SCPS)model for healthcare *** aim of the proposed DLIDIC-SCPS model is to achieve secure image transmission and image classification process for CPS in healthcare ***,data acquisition takes place with the help of sensors and detection of intrusions is performed using Fuzzy Deep Neural Network(FDNN)***,Multiple Share Creation(MSC)approach is used to create several shares of medical image so as to accomplish ***,blockchain is employed as a distributed data storage entity to create a ledger that provides access to the *** image classification,Inception v3 with Fuzzy Wavelet Neural Network(FWNN)is utilized that diagnose the disease from the applied medical ***,Salp Swarm Algorithm(SSA)is utilized to fine tune the parameters involved in WNN model,thereby boosting its classification performance.A wide range of simulations was carried out to highlight the superiority of the proposed DLIDIC-SCPS *** simulation outcomes confirm that DLIDIC-SCPS approach demonstrates promising results in terms of security,privacy,and image classification outcomes over recent state-of-the-art techniques.

关键词： Cyber Physical Systems healthcare cybersecurity deep learning share creation image classification

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Improved Multileader Optimization with Shadow Encryption for Medical Images in IoT Environment

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Computers, Materials & Continua 2023年第2期74卷 3133-3149页

作者： Mesfer Al Duhayyim mohammed Maray Ayman Qahmash Fatma S.Alrayes Nuha Alshuqayran Jaber S.Alzahrani mohammed Alghamdi Abdullah Mohamed Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic UniversitySaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information and Technology Systems College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt

Nowadays,security plays an important role in Internet of Things(IoT)environment especially in medical services’domains like disease prediction and medical data *** healthcare sector,huge volumes of data are generated on a daily basis,owing to the involvement of advanced health care *** general terms,health care images are highly sensitive to alterations due to which any modifications in its content can result in faulty *** the same time,it is also significant to maintain the delicate contents of health care images during reconstruction ***,an encryption system is required in order to raise the privacy and security of healthcare data by not leaking any sensitive *** current study introduces Improved Multileader Optimization with Shadow Image Encryption for Medical Image Security(IMLOSIE-MIS)technique for IoT *** aim of the proposed IMLOSIE-MIS model is to accomplish security by generating shadows and encrypting them *** do so,the presented IMLOSIE-MIS model initially generates a set of shadows for every input medical ***,shadow image encryption process takes place with the help of Multileader Optimization(MLO)withHomomorphic Encryption(IMLO-HE)technique,where the optimal keys are generated with the help of MLO *** the receiver side,decryption process is initially carried out and shadow image reconstruction process is *** experimentation analysis was carried out on medical images and the results inferred that the proposed IMLOSIE-MIS model is an excellent performer compared to other *** comparison study outcomes demonstrate that IMLOSIE-MIS model is robust and offers high security in IoT-enabled healthcare environment.

关键词： Medical image security image encryption shadow images homomorphic encryption optimal key generation

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Coati Optimization-Based Energy Efficient Routing Protocol for Unmanned Aerial Vehicle Communication

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Computers, Materials & Continua 2023年第6期75卷 4805-4820页

作者： Hanan Abdullah Mengash Hamed Alqahtani mohammed Maray Mohamed K.Nour Radwa Marzouk mohammed Abdullah Al-Hagery Heba Mohsen Mesfer Al Duhayyim Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial Intelligence and Unit of CybersecurityKing Khalid UniversityAbha62529Saudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbha62529Saudi Arabia Department of Computer Sciences College of Computing and Information SystemsUmm Al-Qura UniversityMecca24382Saudi Arabia Department of Computer Science College of ComputerQassim UniversityBuraydah52571Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

With the flexible deployment and high mobility of Unmanned Aerial Vehicles(UAVs)in an open environment,they have generated con-siderable attention in military and civil applications intending to enable ubiquitous connectivity and foster agile *** difficulty stems from features other than mobile ad-hoc network(MANET),namely aerial mobility in three-dimensional space and often changing *** the UAV network,a single node serves as a forwarding,transmitting,and receiving node at the same ***,the communication path is multi-hop,and routing significantly affects the network’s performance.A lot of effort should be invested in performance analysis for selecting the optimum routing *** this motivation,this study modelled a new Coati Optimization Algorithm-based Energy-Efficient Routing Process for Unmanned Aerial Vehicle Communication(COAER-UAVC)*** presented COAER-UAVC technique establishes effective routes for communication between the *** is primarily based on the coati characteristics in nature:if attacking and hunting iguanas and escaping from ***,the presented COAER-UAVC technique concentrates on the design of fitness functions to minimize energy utilization and communication delay.A varied group of simulations was performed to depict the optimum performance of the COAER-UAVC *** experimental results verified that the COAER-UAVC technique had assured improved performance over other approaches.

关键词： Artificial intelligence unmanned aerial vehicle data communication routing protocol energy efficiency

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Evolutionary Algorithsm with Machine Learning Based Epileptic Seizure Detection Model

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Computers, Materials & Continua 2022年第9期72卷 4541-4555页

作者： Manar Ahmed Hamza Noha Negm Shaha Al-Otaibi Amel AAlhussan Mesfer Al Duhayyim Fuad Ali mohammed Al-Yarimi mohammed Rizwanullah Ishfaq Yaseen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz University16278AlKharjSaudi Arabia Department of Computer Science College of Science and ArtsKing Khalid UniversityMahayilAsir62529Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz University16278Saudi Arabia

Machine learning (ML) becomes a familiar topic among decisionmakers in several domains, particularly healthcare. Effective design of MLmodels assists to detect and classify the occurrence of diseases using healthcaredata. Besides, the parameter tuning of the ML models is also essentialto accomplish effective classification results. This article develops a novelred colobuses monkey optimization with kernel extreme learning machine(RCMO-KELM) technique for epileptic seizure detection and *** proposed RCMO-KELM technique initially extracts the chaotic, time,and frequency domain features in the actual EEG signals. In addition, the minmax normalization approach is employed for the pre-processing of the EEGsignals. Moreover, KELM model is used for the detection and classificationof epileptic seizures utilizing EEG signal. Furthermore, the RCMO techniquewas utilized for the optimal parameter tuning of the KELM technique insuch a way that the overall detection outcomes can be considerably *** experimental result analysis of the RCMO-KELM technique has beenexamined using benchmark dataset and the results are inspected under severalaspects. The comparative result analysis reported the better outcomes of theRCMO-KELM technique over the recent approaches with the accuy of 0.956.

关键词： Epileptic seizures eeg signals machine learning kelm parameter tuning rcmo algorithm

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Automated Autism Spectral Disorder Classification Using Optimal Machine Learning Model

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Computers, Materials & Continua 2023年第3期74卷 5251-5265页

作者： Hanan Abdullah Mengash Hamed Alqahtani mohammed Maray Mohamed K.Nour Radwa Marzouk mohammed Abdullah Al-Hagery Heba Mohsen Mesfer Al Duhayyim Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial Intelligence and Unit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Autism Spectrum Disorder (ASD) refers to a neuro-disorder wherean individual has long-lasting effects on communication and interaction *** information technologywhich employs artificial intelligence(AI) model has assisted in early identify ASD by using pattern *** advances of AI models assist in the automated identification andclassification of ASD, which helps to reduce the severity of the *** study introduces an automated ASD classification using owl searchalgorithm with machine learning (ASDC-OSAML) model. The proposedASDC-OSAML model majorly focuses on the identification and classificationof ASD. To attain this, the presentedASDC-OSAML model follows minmaxnormalization approach as a pre-processing stage. Next, the owl searchalgorithm (OSA)-based feature selection (OSA-FS) model is used to derivefeature subsets. Then, beetle swarm antenna search (BSAS) algorithm withIterative Dichotomiser 3 (ID3) classification method was implied for ASDdetection and classification. The design of BSAS algorithm helps to determinethe parameter values of the ID3 classifier. The performance analysis of theASDC-OSAML model is performed using benchmark dataset. An extensivecomparison study highlighted the supremacy of the ASDC-OSAML modelover recent state of art approaches.

关键词： Autism spectral disorder machine learning owl search algorithm feature selection id3 classifier

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Reliability Analysis of HEE Parameters via Progressive Type-II Censoring with Applications

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Computer Modeling in Engineering & Sciences 2023年第12期137卷 2761-2793页

作者： Heba S.mohammed Mazen Nassar Refah Alotaibi Ahmed Elshahhat Department of Mathematical Sciences College of SciencePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Statistics Faculty of ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Statistics Faculty of CommerceZagazig UniversityZagazigEgypt Faculty of Technology and Development Zagazig UniversityZagazig44519Egypt

A new extended exponential lifetime model called Harris extended-exponential(HEE)distribution for data modelling with increasing and decreasing hazard rate shapes has been *** the reliability context,researchers prefer to use censoring plans to collect data in order to achieve a compromise between total test time and/or test sample ***,this study considers both maximum likelihood and Bayesian estimates of the Harris extended-exponential distribution parameters and some of its reliability indices using a progressive Type-II censoring *** the premise of independent gamma priors,the Bayesian estimation is created using the squared-error and general entropy loss *** to the challenging form of the joint posterior distribution,to evaluate the Bayes estimates,samples from the full conditional distributions are generated using Markov Chain Monte Carlo *** each unknown parameter,the highest posterior density credible intervals and asymptotic confidence intervals are also *** a simulated study,the usefulness of the various suggested strategies is *** optimal progressive censoring plans are also shown,and various optimality criteria are *** actual data sets,taken from engineering and veterinary medicine areas,are analyzed to show how the offered point and interval estimators can be used in practice and to verify that the proposed model furnishes a good fit than other lifetimemodels:alpha power exponential,generalized-exponential,Nadarajah-Haghighi,Weibull,Lomax,gamma and exponential *** evaluations revealed that in the presence of progressively Type-II censored data,the Bayes estimation method against the squared-error(symmetric)loss is advised for getting the point and interval estimates of the HEE distribution.

关键词： Harris extended-exponential model progressive Type-II censoring reliability maximum likelihood MCMC techniques Monte Carlo experiments

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Optimal Fuzzy Logic Enabled Intrusion Detection for Secure IoT-Cloud Environment

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Computers, Materials & Continua 2023年第3期74卷 6737-6753页

作者： Fatma S.Alrayes Nuha Alshuqayran Mohamed K Nour Mesfer Al Duhayyim Abdullah Mohamed Amgad Atta Abdelmageed mohammed Gouse Pasha mohammed Ishfaq Yaseen Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic UniversitySaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Recently,Internet of Things(IoT)devices have developed at a faster rate and utilization of devices gets considerably increased in day to day *** the benefits of IoT devices,security issues remain challenging owing to the fact that most devices do not include memory and computing resources essential for satisfactory security ***,IoT devices are vulnerable to different kinds of attacks.A single attack on networking system/device could result in considerable data to data security and *** the emergence of artificial intelligence(AI)techniques can be exploited for attack detection and classification in the IoT *** this view,this paper presents novel metaheuristics feature selection with fuzzy logic enabled intrusion detection system(MFSFL-IDS)in the IoT *** presented MFSFL-IDS approach purposes for recognizing the existence of intrusions and accomplish security in the IoT *** achieve this,the MFSFL-IDS model employs data pre-processing to transform the data into useful ***,henry gas solubility optimization(HGSO)algorithm is applied as a feature selection approach to derive useful feature ***,adaptive neuro fuzzy inference system(ANFIS)technique was utilized for the recognition and classification of intrusions in the ***,binary bat algorithm(BBA)is exploited for adjusting parameters involved in the ANFIS model.A comprehensive experimental validation of the MFSFL-IDS model is carried out using benchmark dataset and the outcomes are assessed under distinct *** experimentation outcomes highlighted the superior performance of the MFSFL-IDS model over recentapproaches with maximum accuracy of 99.80%.

关键词： Cloud computing security fuzzy logic intrusion detection internet of things metaheuristics

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