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High-throughput computational screening and in vitro evaluation identifies 5-(4-oxo-4H-3,1-benzoxazin-2-yl)-2-[3-(4-oxo-4H-3,1-benzoxazin-2-yl)phenyl]-1H-isoindole-1,3(2H)-dione(C3),as a novel EGFR—HER2 dual inhibitor in gastric tumors

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Oncology Research 2024年第2期32卷 251-259页

作者： mesfer al SHAHRANI REEM GAHTANI MOHAMMAD ABOHASSAN MOHAMMAD alSHAHRANI YASSER alRAEY AYED DERA MOHAMMAD RAJEH ASIRI PRASANNA RAJAGOPalAN Department of Clinical Laboratory Sciences College of Applied Medical SciencesKing Khalid UniversityAbhaSaudi Arabia

Gastric cancers are caused primarily due to the activation and amplification of the EGFR or HER2 kinases resulting in cell proliferation,adhesion,angiogenesis,and *** therapies are ineffective due to the intra-tumoral heterogeneity and concomitant genetic ***,dual inhibition strategies are recommended to increase potency and reduce *** this study,we have conducted computational high-throughput screening of the ChemBridge library followed by in vitro assays and identified novel selective inhibitors that have a dual impediment of EGFR/HER2 kinase ***-based High-throughput Virtual Screening(D-HTVS)was used to screen the whole ChemBridge small molecular library against EGFR and *** atomistic molecular dynamic simulation was conducted to understand the dynamics and stability of the protein-ligand ***/HER2 kinase enzymes,KATOIII,and Snu-5 cells were used for in vitro *** atomistic Molecular Dynamics simulations followed by solvent-based Gibbs binding free energy calculation of top molecules,identified compound C3(5-(4-oxo-4H-3,1-benzoxazin-2-yl)-2-[3-(4-oxo-4H-3,1-benzoxazin-2-yl)phenyl]-1H-isoindole-1,3(2H)-dione)to have a good affinity for both EGFR and *** predicted compound,C3,was promising with better binding energy,good binding pose,and optimum interactions with the EGFR and HER2 residues.C3 inhibited EGFR and HER2 kinases with IC50 values of 37.24 and 45.83 nM,*** GI50 values of C3 to inhibit KATOIII and Snu-5 cells were 84.76 and 48.26 nM,*** on these findings,we conclude that the identified compound C3 showed a conceivable dual inhibitory activity on EGFR/HER2 kinase,and therefore can be considered as a plausible lead-like molecule for treating gastric cancers with minimal side effects,though testing in higher models with pharmacokinetic approach is required.

关键词： Dual inhibitor Drug discovery EGFR/HER2 kinase Gastric cancer High-throughput screening

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Digital Text Document Watermarking Based Tampering Attack Detection via Internet

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Computer Systems Science & Engineering 2024年第3期48卷 759-771页

作者： Manal Abdullah alohali Muna Elsadig Fahd N.al-Wesabi mesfer al duhayyi. Anwer Mustafa Hilal Abdelwahed Motwakel Department of Information Systems 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 UniversityAbha62217Saudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Aflaj16733Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Owing to the rapid increase in the interchange of text information through internet networks,the reliability and security of digital content are becoming a major research *** detection,Content authentication,and integrity verification of digital content interchanged through the Internet were utilized to solve a major concern in information and communication *** authors’difficulties were tampering detection,authentication,and integrity verification of the digital *** study develops an Automated Data Mining based Digital Text Document Watermarking for Tampering Attack Detection(ADMDTW-TAD)via the *** DM concept is exploited in the presented ADMDTW-TAD technique to identify the document’s appropriate characteristics to embed larger watermark *** presented secure watermarking scheme intends to transmit digital text documents over the Internet *** the watermark is embedded with no damage to the original document,it is then shared with the *** watermark extraction process is performed to get the original document *** experimental validation of the ADMDTW-TAD technique is carried out under varying levels of attack volumes,and the outcomes were inspected in terms of different *** simulation values indicated that the ADMDTW-TAD technique improved performance over other models.

关键词： Content authentication tampering attacks detection model security digital watermarking

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

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

作者： mesfer al duhayyi. 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 duhayyi. 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 duhayyi. 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 duhayyi. 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 duhayyi. 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 duhayyi. 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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Modified Harris Hawks Optimization Based Test Case Prioritization for Software Testing

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Computers, Materials & Continua 2022年第7期72卷 1951-1965页

作者： Manar Ahmed Hamza Abdelzahir Abdelmaboud Souad Larabi-Marie-Sainte Haya mesfer alshahrani mesfer al duhayyi. Hamza Awad Ibrahim Mohammed Rizwanullah Ishfaq Yaseen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16278Saudi Arabia Department of Information Systems College of Science and ArtsKing Khalid UniversityMahayil Asir62529Saudi Arabia Department of Computer Science College of Computer and Information SciencesPrince Sultan UniversityP.O.Box No.66833Rafha StreetRiyadh11586Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz University16278Saudi Arabia College of Computer at al-Gunfudah Umm Al-Qura UniversityAl-Gunfudah24382Saudi Arabia

Generally,software testing is considered as a proficient technique to achieve improvement in quality and reliability of the ***,the quality of test cases has a considerable influence on fault revealing capability of software testing *** Case Prioritization(TCP)remains a challenging issue since prioritizing test cases is unsatisfactory in terms of Average Percentage of Faults Detected(APFD)and time spent upon execution *** ismainly intended to design a collection of test cases that can accomplish early optimization using preferred *** studies conducted earlier focused on prioritizing the available test cases in accelerating fault detection rate during software *** this aspect,the current study designs aModified Harris Hawks Optimization based TCP(MHHO-TCP)technique for software *** aim of the proposed MHHO-TCP technique is to maximize APFD and minimize the overall execution *** addition,MHHO algorithm is designed to boost the exploration and exploitation abilities of conventional HHO *** order to validate the enhanced efficiency of MHHO-TCP technique,a wide range of simulations was conducted on different benchmark programs and the results were examined under several *** experimental outcomes highlight the improved efficiency of MHHO-TCP technique over recent approaches under different measures.

关键词： Software testing harris hawks optimization test case prioritization apfd execution time metaheuristics

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Secured Framework for Assessment of Chronic Kidney Disease in Diabetic Patients

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Intelligent Automation & Soft Computing 2023年第6期36卷 3387-3404页

作者： Sultan mesfer aldossary Dean of the College of Arts and Sciences Prince Sattam Bin Abdulaziz UniversityWadi Al-Dawasir11990Saudi Arabia

With the emergence of cloud technologies,the services of healthcare systems have ***,machine learning systems have become important tools for developing matured and decision-making computer *** cloud computing and machine learning technologies have contributed significantly to the success of healthcare ***,in some areas,these technologies are needed to provide and decide the next course of action for patients suffering from diabetic kidney disease(DKD)while ensuring privacy preservation of the medical *** address the cloud data privacy problem,we proposed a DKD prediction module in a framework using cloud computing services and a data control *** framework can provide improved and early treatment before end-stage renal *** prediction purposes,we implemented the following machine learning algorithms:support vector machine(SVM),random forest(RF),decision tree(DT),naïve Bayes(NB),deep learning(DL),and k nearest neighbor(KNN).These classification techniques combined with the cloud computing services significantly improved the decision making in the progress of DKD *** applied these classifiers to the UCI Machine Learning Repository for chronic kidney disease using various clinical features,which are categorized as single,combination of selected features,and all *** single clinical feature experiments,machine learning classifiers SVM,RF,and KNN outperformed the remaining classification techniques,whereas in combined clinical feature experiments,the maximum accuracy was achieved for the combination of DL and *** the feature experiments presented increased accuracy and increased F-measure metrics from SVM,DL,and RF.

关键词： Cloud computing diabetic kidney disease machine learning prediction system privacy preservation integrity of data secured data transmission homomorphic authentication

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