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Hunger Search Optimization with Hybrid Deep Learning Enabled Phishing Detection and Classification model

Computers, materials & Continua 2022年第12期73卷 6425-6441页

作者： Hadil Shaiba Jaber S.Alzahrani majdy m.eltahir Radwa marzouk Heba mohsen manar Ahmed Hamza Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Phishing is one of the simplest ways in cybercrime to hack the reliable data of users such as passwords,account identifiers,bank details,*** general,these kinds of cyberattacks are made at users through phone calls,emails,or instant *** anti-phishing techniques,currently under use,aremainly based on source code features that need to scrape the webpage *** third party services,these techniques check the classification procedure of phishing Uniform Resource Locators(URLs).Even thoughmachine Learning(mL)techniques have been lately utilized in the identification of phishing,they still need to undergo feature engineering since the techniques are not well-versed in identifying phishing *** tremendous growth and evolution of Deep Learning(DL)techniques paved the way for increasing the accuracy of classification *** this background,the current research article presents a Hunger Search Optimization with Hybrid Deep Learning enabled Phishing Detection and Classification(HSOHDL-PDC)*** presented HSOHDL-PDC model focuses on effective recognition and classification of phishing based on website *** addition,SOHDL-PDC model uses character-level embedding instead of word-level embedding since the URLs generally utilize words with no ***,a hybrid Convolutional Neural Network-Long Short Term memory(HCNN-LSTm)technique is also applied for identification and classification of *** hyperparameters involved in HCNN-LSTm model are optimized with the help of HSO algorithm which in turn produced improved *** performance of the proposed HSOHDL-PDC model was validated using different datasets and the outcomes confirmed the supremacy of the proposed model over other recent approaches.

关键词： Uniform resource locators phishing cyberattacks machine learning deep learning hyperparameter optimization

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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 ***-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 *** 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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Novel Image Encryption and Compression Scheme for IoT Environment

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Computers, materials & Continua 2022年第4期71卷 1443-1457页

作者： mesfer Al Duhayyim Fahd N.Al-Wesabi Radwa marzouk manar Ahmed Hamza Anwer mustafa Hilal majdy m.eltahir Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia 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

Latest advancements made in the processing abilities of smartdevices have resulted in the designing of Intelligent Internet of Things (IoT)environment. This advanced environment enables the nodes to connect, collect, perceive, and examine useful data from its surroundings. Wireless multimedia Surveillance Networks (WmSNs) form a vital part in IoT-assistedenvironment since it contains visual sensors that examine the surroundingsfrom a number of overlapping views by capturing the images *** IoT devices generate a massive quantity of digital media, it is thereforerequired to save the media, especially images, in a secure way. In order toachieve security, encryption techniques as well as compression techniques areemployed to reduce the amount of digital data, being communicated overthe network. Encryption Then Compression (ETC) techniques pave a wayfor secure and compact transmission of the available data to prevent unauthorized access. With this background, the current research paper presentsa new ETC technique to accomplish image security in IoT *** proposed model involves three major processes namely, IoT-based imageacquisition, encryption, and compression. The presented model involves optimal Signcryption Technique with Whale Optimization Algorithm (NmWOA)abbreviated as ST-NmWOA. The optimal key generation of signcryptiontechnique takes place with the help of NmWOA. Besides, the presented modelalso uses Discrete Fourier Transform (DFT) and matrix minimization (mm)algorithm-based compression technique. Extensive set of experimental analysis was conducted to validate the effective performance of the proposed *** obtained values infer that the presented model is superior in terms of bothcompression efficiency and data secrecy in resource-limited IoT environment.

关键词： Data compression image security encryption signcryption optimal key generation

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Artificial Intelligence Enabled Apple Leaf Disease Classification for Precision Agriculture

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Computers, materials & Continua 2022年第3期70卷 6223-6238页

作者： Fahd N.Al-Wesabi Amani Abdulrahman Albraikan Anwer mustafa Hilal majdy m.eltahir manar Ahmed Hamza Abu Sarwar Zamani Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computer and IT Sana’a UniversityYemen 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

Precision agriculture enables the recent technological advancements in farming sector to observe,measure,and analyze the requirements of individual fields and *** recent developments of computer vision and artificial intelligence(AI)techniques find a way for effective detection of plants,diseases,weeds,pests,*** the other hand,the detection of plant diseases,particularly apple leaf diseases using AI techniques can improve productivity and reduce crop ***,earlier and precise apple leaf disease detection can minimize the spread of the *** works make use of traditional image processing techniques which cannot assure high detection rate on apple leaf *** this motivation,this paper introduces a novel AI enabled apple leaf disease classification(AIE-ALDC)technique for precision *** proposed AIE-ALDC technique involves orientation based data augmentation and Gaussian filtering based noise removal *** addition,the AIE-ALDC technique includes a Capsule Network(CapsNet)based feature extractor to generate a helpful set of feature ***,water wave optimization(WWO)technique is employed as a hyperparameter optimizer of the CapsNet ***,bidirectional long short term memory(BiLSTm)model is used as a classifier to determine the appropriate class labels of the apple leaf *** design of AIE-ALDC technique incorporating theWWO based CapsNetmodel with BiLSTm classifier shows the novelty of the *** range of experiments was performed to showcase the supremacy of the AIE-ALDC *** experimental results demonstrate the promising performance of the AIEALDC technique over the recent state of art methods.

关键词： Artificial intelligence apple leaf plant disease precision agriculture deep learning data augmentation

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Optimal Hybrid Feature Extraction with Deep Learning for COVID-19 Classifications

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Computers, materials & Continua 2022年第6期71卷 6257-6273页

作者： majdy m.eltahir Ibrahim Abunadi Fahd NAl-Wesabi Anwer mustafa Hilal Adil Yousif Abdelwahed motwakel mesfer Al Duhayyim manar Ahmed Hamza Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Information Systems Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Science King Khalid UniversityMuhayel Aseer62529Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’a61101Yemen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Faculty of Arts and Science Najran UniversitySharourahSaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Novel coronavirus 2019(COVID-19)has affected the people’s health,their lifestyle and economical status across the *** application of advanced Artificial Intelligence(AI)methods in combination with radiological imaging is useful in accurate detection of the *** also assists the physicians to take care of remote villages *** current research paper proposes a novel automatedCOVID-19 analysismethod with the help ofOptimal Hybrid Feature Extraction(OHFE)and Optimal Deep Neural Network(ODNN)called OHFE-ODNN from chest x-ray *** objective of the presented technique is for performing binary and multi-class classification of COVID-19 analysis from chest X-ray *** presented OHFE-ODNN method includes a sequence of procedures such as median Filtering(mF)-based pre-processed,feature extraction and finally,binary(COVID/Non-COVID)and multiclass(Normal,COVID,SARS)***,in OHFE-based feature extraction,Gray Level Co-occurrence matrix(GLCm)and Histogram of Gradients(HOG)are integrated *** presented OHFE-ODNN model includes Squirrel Search Algorithm(SSA)for finetuning the parameters of *** performance of the presented OHFEODNN technique is conducted using chest x-rays *** presented OHFE-ODNN method classified the binary classes effectively with a maximumprecision of 95.82%,accuracy of 94.01%and F-score of 96.61%.Besides,multiple classes were classified proficiently by OHFE-ODNN model with a precision of 95.63%,accuracy of 95.60%and an F-score of 95.73%.

关键词： COVID-19 classification deep learning radiological images

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Cuckoo Optimized Convolution Support Vector machine for Big Health Data Processing

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Computers, materials & Continua 2022年第11期73卷 3039-3055页

作者： Eatedal Alabdulkreem Jaber S.Alzahrani majdy m.eltahir Abdullah mohamed manar Ahmed Hamza Abdelwahed motwakel mohamed I.Eldesouki mohammed Rizwanullah Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMecca24382Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversityAbha 62529Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharj16278Saudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlKharj16278Saudi Arabia

Big health data collection and storing for further analysis is a challenging task because this knowledge is big and has many *** cloud-based IoT health providers have been described in the literature ***,there are a number of issues related to time consumed and overall network performance when it comes to big data *** the existing method,less performed optimization algorithms were used for optimizing the *** the proposed method,the Chaotic Cuckoo Optimization algorithm was used for feature selection,and Convolutional Support Vector machine(CSVm)was *** research presents a method for analyzing healthcare information that uses in future *** major goal is to take a variety of data while improving efficiency and minimizing process *** suggested method employs a hybrid method that is divided into two *** the first stage,it reduces the features by using the Chaotic Cuckoo Optimization algorithm with Levy flight,opposition-based learning,and distributor *** the second stage,CSVm is used which combines the benefits of convolutional neural network(CNN)and *** CSVm modifies CNN’s convolution product to learn hidden deep inside data *** improved economic flexibility,greater protection,greater analytics with confidentiality,and lower operating cost,the suggested approach is built on fog *** results of the experiments show that the suggested method can minimize the number of features in the datasets,enhances the accuracy by 82%,and decrease the time of the process.

关键词： Healthcare convolutional support vector machine feature selection chaotic cuckoo optimization accuracy processing time convolutional neural network

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Resource Assessment of Wind Energy Potential of mokha in Yemen with Weibull Speed

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Computers, materials & Continua 2021年第10期69卷 1123-1140页

作者： Abdulbaset El-Bshah Fahd N.Al-Wesabi Ameen m.Al-Kustoban mohammad Alamgeer Nadhem Nemri majdy m.eltahir Hany mahgoub Noha Negm Department of Electronic Engineering Faculty of EngineeringUniversity of Science and Technology UST-Sana’aYemen Department of Computer Science King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Electronic Faculty of EngineeringUSTSana’aYemen Department of Information Systems King Khalid UniversityMayahel AseerKSA Department of Computer Science King Khaled UniversityKSA&Faculty of Computers and InformationMenoufia UniversityEgypt Department of Computer Science King Khaled UniversityKSA&Faculty of ScienceDepartment of Mathematics and Computer ScienceMenoufia UniversityEgypt

The increasing use of fossil fuels has a significant impact on the environment and ecosystem,which increases the rate of *** the high potential of renewable energy sources in Yemen and other Arabic countries,and the absence of similar studies in the *** study aims to examine the potential of wind energy in mokha *** was done by analyzing and evaluating wind properties,determining available energy density,calculating wind energy extracted at different altitudes,and then computing the capacity factor for a few wind turbines and determining the *** speed was verified as the closest to the average actual wind speed using the cube root,as this was verified using 3 criteria for performance analysis methods(R^(2)=0.9984,RmSE=0.0632,COE=1.028).The wind rose scheme was used to determine the appropriate direction for directing the wind turbines,the southerly direction was appropriate,as the winds blow from this direction for 227 days per year,and the average southerly wind velocity is 5.27 m/s at an altitude of 3 *** turbine selected in this study has a tower height of 100m and a rated power of 3.45 *** capacitance factor was calculated for the three classes of wind turbines classified by the International Electrotechnical Commission(IEC)and compared,and the turbine of the first class was approved,and it is suitable for the study site,as it resists storms more than *** daily and annual capacity of a single,first-class turbine has been assessed to meet the needs of 1,447 housing units in mokha *** amount of energy that could be supplied to each dwelling was around 19 kWh per day,which was adequate to power the basic loads in the home.

关键词： Wind energy system probability density wind speed Weibull velocity Rayleigh velocity wind rose

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Feature Selection with Stacked Autoencoder Based Intrusion Detection in Drones Environment

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Computers, materials & Continua 2022年第12期73卷 5441-5458页

作者： Heba G.mohamed Saud S.Alotaibi majdy m.eltahir Heba mohsen manar Ahmed Hamza Abu Sarwar Zamani Ishfaq Yaseen Abdelwahed motwakel Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

The Internet of Drones(IoD)offers synchronized access to organized airspace for Unmanned Aerial Vehicles(known as drones).The availability of inexpensive sensors,processors,and wireless communication makes it possible in real time *** several applications comprise IoD in real time environment,significant interest has been received by research *** IoD operates in wireless environment,it is needed to design effective intrusion detection system(IDS)to resolve security issues in the IoD *** article introduces ametaheuristics feature selection with optimal stacked autoencoder based intrusion detection(mFSOSAEID)in the IoD *** major intention of the mFSOSAE-ID technique is to identify the occurrence of intrusions in the IoD *** do so,the proposed mFSOSAE-ID technique firstly pre-processes the input data into a compatible *** addition,the presented mFSOSAEID technique designs a moth flame optimization based feature selection(mFOFS)technique to elect appropriate ***,firefly algorithm(FFA)with stacked autoencoder(SAE)model is employed for the recognition and classification of intrusions in which the SAE parameters are optimally tuned with utilize of *** performance validation of the mFSOSAE-ID model was tested utilizing benchmark dataset and the outcomes implied the promising performance of the mFSOSAE-ID model over other techniques with maximum accuracy of 99.72%.

关键词： Internet of drones unmanned aerial vehicles security intrusion detection machine learning

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manta Ray Foraging Optimization with machine Learning Based Biomedical Data Classification

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Computers, materials & Continua 2022年第11期73卷 3275-3290页

作者： Amal Al-Rasheed Jaber S.Alzahrani majdy m.eltahir Abdullah mohamed Anwer mustafa Hilal Abdelwahed motwakel Abu Sarwar Zamani mohamed I.Eldesouki Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMecca24382Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The biomedical data classification process has received significant attention in recent times due to a massive increase in the generation of healthcare data from various *** developments of artificial intelligence(AI)and machine learning(mL)models assist in the effectual design of medical data classification ***,this article concentrates on the development of optimal Stacked Long Short Term memory Sequence-toSequence Autoencoder(OSAE-LSTm)model for biomedical data *** presented OSAE-LSTm model intends to classify the biomedical data for the existence of ***,the OSAE-LSTm model involves min-max normalization based pre-processing to scale the data into uniform *** by,the SAE-LSTm model is utilized for the detection and classification of diseases in biomedical *** last,manta ray foraging optimization(mRFO)algorithm has been employed for hyperparameter optimization *** utilization of mRFO algorithm assists in optimal selection of hypermeters involved in the SAE-LSTm *** simulation analysis of the OSAE-LSTm model has been tested using a set of benchmark medical datasets and the results reported the improvements of the OSAELSTm model over the other approaches under several dimensions.

关键词： Biomedical data classification deep learning manta ray foraging optimization healthcare machine learning artificial intelligence

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Competitive multi-Verse Optimization with Deep Learning Based Sleep Stage Classification

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Computer Systems Science & Engineering 2023年第5期45卷 1249-1263页

作者： Anwer mustafa Hilal Amal Al-Rasheed Jaber SAlzahrani majdy m.eltahir mesfer Al Duhayyim Nermin m.Salem Ishfaq Yaseen Abdelwahed motwakel 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 UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt

Sleep plays a vital role in optimum working of the brain and the *** people suffer from sleep-oriented illnesses like apnea,insomnia,*** stage classification is a primary process in the quantitative examination of polysomnographic *** stage scoring is mainly based on experts’knowledge which is laborious and time ***,it can be essential to design automated sleep stage classification model using machine learning(mL)and deep learning(DL)*** this view,this study focuses on the design of Competitive multi-verse Optimization with Deep Learning Based Sleep Stage Classification(CmVODL-SSC)model using Electroencephalogram(EEG)*** proposed CmVODL-SSC model intends to effectively categorize different sleep stages on EEG ***,data pre-processing is performed to convert the actual data into useful ***,a cascaded long short term memory(CLSTm)model is employed to perform classification *** last,the CmVO algorithm is utilized for optimally tuning the hyperparameters involved in the CLSTm *** order to report the enhancements of the CmVODL-SSC model,a wide range of simulations was carried out and the results ensured the better performance of the CmVODL-SSC model with average accuracy of 96.90%.

关键词： Signal processing EEG signals sleep stage classification clstm model deep learning cmvo algorithm

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