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Enhanced Artificial Intelligence-based Cybersecurity Intrusion Detection for Higher Education Institutions

Computers, Materials & Continua 2022年第8期72卷 2895-2907页

作者： Abdullah S.AL-Malaise AL-Ghamdi Mahmoud Ragab Maha Farouk S.Sabir Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt

As higher education institutions(HEIs)go online,several benefits are attained,and also it is vulnerable to several kinds of *** accomplish security,this paper presents artificial intelligence based cybersecurity intrusion detection models to accomplish *** incorporation of the strategies into business is a tendency among several distinct industries,comprising education,have recognized as game ***,the HEIs are highly related to the requirement and knowledge of the learner,making the education procedure highly ***,artificial intelligence(AI)and machine learning(ML)models have shown significant interest in *** study designs a novel Artificial Intelligence based Cybersecurity Intrusion Detection Model for Higher Education Institutions named AICIDHEI *** goal of the AICID-HEI technique is to determine the occurrence of distinct kinds of intrusions in higher education *** AICID-HEI technique encompassesmin-max normalization approach to preprocess the ***,the AICID-HEI technique involves the design of improved differential evolution algorithm based feature selection(IDEA-FS)technique is applied to choose the feature ***,the bidirectional long short-term memory(BiLSTM)model is utilized for the detection and classification of intrusions in the ***,the Adam optimizer is applied for hyperparameter tuning to properly adjust the hyperparameters in higher educational *** order to validate the experimental results of the proposed AICID-HEI technique,the simulation results of the AICIDHEI technique take place by the use of benchmark *** experimental results reported the betterment of the AICID-HEI technique over the other methods interms of different measures.

关键词： Higher education institutions deep learning machine learning cybersecurity intrusion detection

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Improved Harmony Search with Optimal Deep Learning Enabled Classification Model

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Computers, Materials & Continua 2022年第10期73卷 1783-1797页

作者： Mahmoud Ragab Adel A.Bahaddad Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre for Artificial Intelligence in Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

Due to drastic increase in the generation of data,it is tedious to examine and derive high level knowledge from the *** rising trends of high dimension data gathering and problem representation necessitates feature selection process in several machine learning *** feature selection procedure establishes a generally encountered issue of global combinatorial *** FS process can lessen the number of features by the removal of unwanted and repetitive *** this aspect,this article introduces an improved harmony search based global optimization for feature selection with optimal deep learning(IHSFS-ODL)enabled classification *** proposed IHSFS-ODL technique intends to reduce the curse of dimensionality and enhance classification *** addition,the IHSFSODL technique derives an IHSFS technique by the use of local search method with traditional harmony search algorithm(HSA)for global ***,ODL based classifier including quantum behaved particle swarm optimization(QPSO)with gated recurrent unit(GRU)is applied for data classification *** utilization of HSA for the choice of features and QPSO algorithm for hyper parameter tuning processes helps to accomplish maximum classification *** order to demonstrate the enhanced outcomes of the IHSFS-ODL technique,a series of simulations were carried out and the results reported the betterment over its recent state of art approaches.

关键词： Data classification feature selection global optimization deep learning metaheuristics

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A Drones Optimal Path Planning Based on Swarm Intelligence Algorithms

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Computers, Materials & Continua 2022年第7期72卷 365-380页

作者： Mahmoud Ragab Ali Altalbe Abdullah Saad Al-Malaise ALGhamdi SAbdel-khalek Rashid A.Saeed Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia Center of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah 21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City 11884CairoEgypt Department of Mathematics Faculty of ScienceTaif UniversityTaifSaudi Arabia Department of Mathematics Faculty of ScienceSohag UniversitySohagEgypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia

The smart city comprises various interlinked elements which communicate data and offers urban life to *** Aerial Vehicles(UAV)or drones were commonly employed in different application areas like agriculture,logistics,and *** improving the drone flying safety and quality of services,a significant solution is for designing the Internet of Drones(IoD)where the drones are utilized to gather data and people communicate to the drones of a specific flying region using the mobile devices is for constructing the Internet-of-Drones,where the drones were utilized for collecting the data,and communicate with *** addition,the SIRSS-CIoD technique derives a tuna swarm algorithm-based clustering(TSA-C)technique to choose cluster heads(CHs)and organize clusters in IoV ***,the SIRSS-CIoD technique involves the design of a biogeography-based optimization(BBO)technique to an optimum route selection(RS)*** design of clustering and routing techniques for IoD networks in smart cities shows the novelty of the study.A wide range of experimental analyses is carried out and the comparative study highlighted the improved performance of the SIRSS-CIoD technique over the other approaches.

关键词： Drones smart city swarm intelligence route selection internet of drones networking

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Optimal Logistics Activities Based Deep Learning Enabled Traffic Flow Prediction Model

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Computers, Materials & Continua 2022年第12期73卷 5269-5282页

作者： Basim Aljabhan Mahmoud Ragab Sultanah M.Alshammari Abdullah S.Al-Malaise Al-Ghamdi Ports and Maritime Transportation Department Faculty of Maritime StudiesKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Center of Excellence in Smart Environment Research King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia S 7Information Systems Department HECI SchoolDar Alhekma UniversityJeddahSaudi Arabia

Traffic flow prediction becomes an essential process for intelligent transportation systems(ITS).Though traffic sensor devices are manually controllable,traffic flow data with distinct length,uneven sampling,and missing data finds challenging for effective *** traffic data has been considerably increased in recent times which cannot be handled by traditional mathematical *** recent developments of statistic and deep learning(DL)models pave a way for the effectual design of traffic flow prediction(TFP)*** this view,this study designs optimal attentionbased deep learning with statistical analysis for TFP(OADLSA-TFP)*** presentedOADLSA-TFP model intends to effectually forecast the level of traffic in the *** attain this,the OADLSA-TFP model employs attention-based bidirectional long short-term memory(ABLSTM)model for predicting traffic *** order to enhance the performance of the ABLSTM model,the hyperparameter optimization process is performed using artificial fish swarm algorithm(AFSA).A wide-ranging experimental analysis is carried out on benchmark dataset and the obtained values reported the enhancements of the OADLSA-TFP model over the recent approaches mean square error(MSE),root mean square error(RMSE),and mean absolute percentage error(MAPE)of 120.342%,10.970%,and 8.146%respectively.

关键词： Traffic flow prediction deep learning artificial fish swarm algorithm mass gatherings statistical analysis logistics

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Modified simulated annealing for university teacher course assignment considering socio-cultural constraints

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International Journal of information Technology (Singapore) 2025年第3期17卷 1533-1549页

作者： Brahimi, Samiha Sabba, Sara Elhussein, Mariam Alqahtani, Mohammed Department of Computer Information Systems College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Faculty of New Technologies of Information and Communication Laboratory of Data Science and Artificial Intelligence (LISIA) Abdelhamid Mahri Constantine-2 University Constantine 25016 Algeria Department of Mathematics and Computing Mount Royal University Calgary AB Canada

This paper aims to address the complex challenge of course assignment for faculty members within a Saudi university, taking into account the socio-cultural constraints imposed by gender-based segregation between students and faculty. To tackle this challenge, a combinatorial optimization model is proposed, which includes a binary representation of the problem, constraints reflecting gender segregation, and an objective function designed to maximize faculty satisfaction. The model employs a Simulated Annealing (SA) algorithm, supplemented with two neighborhood search heuristics (H1 and H2), to explore diverse combinations. Additionally, a greedy randomized adaptive search procedure Greedy Randomized Adaptive Search Procedure (GRASP) algorithm is implemented for comparative analysis, utilizing heuristic 1 for local search. Through the application of simulated annealing alongside both heuristics (SA_H1_H2), the optimal solution is achieved, resulting in a notable equilibrium of courses among faculty members. Objective 1 demonstrates the standardized deviation in workload distribution among faculty members, yielding a deviation value of 0.08. This study contributes to the field by proposing a novel approach to course assignment that explicitly addresses the gender-based segregation prevalent in Saudi universities. By integrating socio-cultural constraints into the optimization model and employing advanced algorithms, the study offers a pioneering solution to a previously unexplored problem domain. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： GRASP Mathematical model Operations Research Optimization Simulated annealing Teacher_Course assignment

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Efficient and Secure IoT Based Smart Home Automation Using Multi-Model Learning and Blockchain Technology

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Computer Modeling in Engineering & Sciences 2024年第6期139卷 3387-3415页

作者： Nazik Alturki Raed Alharthi Muhammad Umer Oumaima Saidani Amal Alshardan Reemah M.Alhebshi Shtwai Alsubai Ali Kashif Bashir Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Computer Science and Engineering University of Hafr Al-BatinHafar Al-BatinSaudi Arabia Department of Computer Science&Information Technology The Islamia University of BahawalpurBahawalpurPakistan Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterUK Woxsen School of Business Woxsen UniversityHyderabadIndia Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon

The concept of smart houses has grown in prominence in recent *** challenges linked to smart homes are identification theft,data safety,automated decision-making for IoT-based devices,and the security of the device *** home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical *** paper proposes a smart home system based on ensemble learning of random forest(RF)and convolutional neural networks(CNN)for programmed decision-making tasks,such as categorizing gadgets as“OFF”or“ON”based on their normal routine in *** have integrated emerging blockchain technology to provide secure,decentralized,and trustworthy authentication and recognition of IoT *** system consists of a 5V relay circuit,various sensors,and a Raspberry Pi server and database for managing *** have also developed an Android app that communicates with the server interface through an HTTP web interface and an Apache *** feasibility and efficacy of the proposed smart home automation system have been evaluated in both laboratory and real-time *** is essential to use inexpensive,scalable,and readily available components and technologies in smart home automation ***,we must incorporate a comprehensive security and privacy-centric design that emphasizes risk assessments,such as cyberattacks,hardware security,and other cyber *** trial results support the proposed system and demonstrate its potential for use in everyday life.

关键词： Blockchain Internet of Things(IoT) smart home automation cybersecurity

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Mathematical Modelling of Quantum Kernel Method for Biomedical Data Analysis

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Computers, Materials & Continua 2022年第6期71卷 5441-5457页

作者： Mahmoud Ragab Ehab Bahauden Ashary Maha Farouk S.Sabir Adel A.Bahaddad Romany F.Mansour Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

This study presents a novelmethod to detect themedical application based on Quantum computing(QC)and a few Machine Learning(ML)*** has a primary advantage i.e.,it uses the impact of quantum parallelism to provide the consequences of prime factorization issue in a matter of ***,this model is suggested for medical application only by recent researchers.A novel strategy i.e.,Quantum KernelMethod(QKM)is proposed in this paper for data *** this QKM process,Linear Tunicate Swarm Algorithm(LTSA),the optimization technique is used to calculate the loss function initially and is aimed at medical *** output of optimization is either 0 or 1 i.e.,odd or even in *** this output value,the data is identified according to the ***,the method also reduces time,saves cost and improves the efficiency by feature selection process i.e.,Filter *** the features are extracted,QKM is deployed as a classification model,while the loss function is minimized by *** motivation of the minimal objective is to remain ***,some computations can be performed more efficiently by the proposed *** testing,the test data was evaluated by minimal loss *** outcomes were assessed in terms of accuracy,computational time,and so *** this,databases like Lymphography,Dermatology,and Arrhythmia were used.

关键词： Medical data classification feature selection qkm classifier ltsa optimization

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An Optimization Approach for Convolutional Neural Network Using Non-Dominated Sorted Genetic Algorithm-Ⅱ

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Computers, Materials & Continua 2023年第3期74卷 5641-5661页

作者： Afia Zafar Muhammad Aamir Nazri Mohd Nawi Ali Arshad Saman Riaz Abdulrahman Alruban Ashit Kumar Dutta Badr Almutairi Sultan Almotairi Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaMalaysia School of Electronics Computing and MathematicsUniversity of DerbyUK Department of Computer Science National University of TechnologyIslamabadPakistan Department of Information Technology College of Computer and Information SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Kingdom of Saudi Arabia Department of Information Technology College of Computer Sciences and Information Technology CollegeMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Natural and Applied Sciences Faculty of Community CollegeMajmaah UniversityMajmaah11952Saudi Arabia Department of Information Systems Faculty of Computer and Information SciencesIslamic University of MadinahMadinah42351Saudi Arabia

In computer vision,convolutional neural networks have a wide range of *** representmost of today’s data,so it’s important to know how to handle these large amounts of data *** neural networks have been shown to solve image processing problems ***,when designing the network structure for a particular problem,you need to adjust the hyperparameters for higher *** technique is time consuming and requires a lot of work and domain *** a convolutional neural network architecture is a classic NP-hard optimization *** the other hand,different datasets require different combinations of models or hyperparameters,which can be time consuming and *** approaches have been proposed to overcome this problem,such as grid search limited to low-dimensional space and queuing by random *** address this issue,we propose an evolutionary algorithm-based approach that dynamically enhances the structure of Convolution Neural Networks(CNNs)using optimized *** study proposes a method using Non-dominated sorted genetic algorithms(NSGA)to improve the hyperparameters of the CNN *** addition,different types and parameter ranges of existing genetic algorithms are *** study was conducted with various state-of-the-art methodologies and *** have shown that our proposed approach is superior to previous methods in terms of classification accuracy,and the results are published in modern computing literature.

关键词： Non-dominated sorted genetic algorithm convolutional neural network hyper-parameter optimization

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Optimized Deep Learning Model for Colorectal Cancer Detection and Classification Model

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Computers, Materials & Continua 2022年第6期71卷 5751-5764页

作者： Mahmoud Ragab Khalid Eljaaly Maha Farouk SSabir Ehab Bahaudien Ashary S.M.Abo-Dahab E.M.Khalil Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Center of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Department of Pharmacy Practice Faculty of PharmacyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia Computer Science Department Faculty of Computers and InformationLuxor University85951Egypt Mathematics Department Faculty of ScienceSouth Valley UniversityQena83523Egypt Department of Mathematics Faculty of ScienceTaif UniversityTaif21944Saudi Arabia

The recent developments in biological and information technologies have resulted in the generation of massive quantities of data it speeds up the process of knowledge discovery from biological *** to the advancements of medical imaging in healthcare decision making,significant attention has been paid by the computer vision and deep learning(DL)*** the same time,the detection and classification of colorectal cancer(CC)become essential to reduce the severity of the disease at an earlier *** existing methods are commonly based on the combination of textual features to examine the classifier results or machine learning(ML)to recognize the existence of *** this aspect,this study focuses on the design of intelligent DL based CC detection and classification(IDL-CCDC)model for bioinformatics *** proposed IDL-CCDC technique aims to detect and classify different classes of *** addition,the IDLCCDC technique involves fuzzy filtering technique for noise removal ***,water wave optimization(WWO)based EfficientNet model is employed for feature extraction ***,chaotic glowworm swarm optimization(CGSO)based variational auto encoder(VAE)is applied for the classification of CC into benign or *** design of WWO and CGSO algorithms helps to increase the overall classification *** performance validation of the IDL-CCDC technique takes place using benchmark Warwick-QU dataset and the results portrayed the supremacy of the IDL-CCDC technique over the recent approaches with the maximum accuracy of 0.969.

关键词： Colorectal cancer deep learning medical imaging bioinformatics metaheuristics parameter tuning

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Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images

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Computers, Materials & Continua 2023年第6期75卷 5255-5270页

作者： JoséEscorcia-Gutierrez Margarita Gamarra Roosvel Soto-Diaz Safa Alsafari Ayman Yafoz Romany F.Mansour Departament of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080002Colombia Biomedical Engineering Program Universidad Simón BolívarBarranquilla080002Colombia Department of Computer Science and AI Faculty of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the *** X-ray(CXR)gained much interest after the COVID-19 outbreak thanks to its rapid imaging time,widespread availability,low cost,and *** radiological investigations,computer-aided diagnostic tools are implemented to reduce intra-and inter-observer *** lately industrialized Artificial Intelligence(AI)algorithms and radiological techniques to diagnose and classify disease is *** current study develops an automatic identification and classification model for CXR pictures using Gaussian Fil-tering based Optimized Synergic Deep Learning using Remora Optimization Algorithm(GF-OSDL-ROA).This method is inclusive of preprocessing and classification based on *** data is preprocessed using Gaussian filtering(GF)to remove any extraneous noise from the image’s ***,the OSDL model is applied to classify the CXRs under different severity levels based on CXR *** learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the *** model,applied in this study,was validated using the COVID-19 *** experiments were conducted upon the proposed OSDL model,which achieved a classification accuracy of 99.83%,while the current Convolutional Neural Network achieved less classification accuracy,i.e.,98.14%.

关键词： Artificial intelligence chest X-ray COVID-19 optimized synergic deep learning preprocessing public health

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