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Brain Cancer Tumor Classification from motion-Corrected mRI Images Using Convolutional Neural Network

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Computers, materials & Continua 2021年第8期68卷 1551-1563页

作者： hanan abdullah m.ngash hanan A.Hosni mahmoud Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer and Systems Engineering Faculty of EngineeringUniversity of AlexandriaAlexandriaEgypt

Detection of brain tumors in mRI images is the first step in brain cancer *** accuracy of the diagnosis depends highly on the expertise of ***,automated diagnosis of brain cancer from mRI is receiving a large amount of ***,mRI tumor detection is usually followed by a biopsy(an invasive procedure),which is a medical procedure for brain tumor *** is of high importance to devise automated methods to aid radiologists in brain cancer tumor diagnosis without resorting to invasive *** neural network(CNN)is deemed to be one of the best machine learning algorithms to achieve high-accuracy results in tumor identification and *** this paper,a CNN-based technique for brain tumor classification has been *** proposed CNN can distinguish between normal(no-cancer),astrocytoma tumors,gliomatosis cerebri tumors,and glioblastoma *** implemented CNN was tested on mRI images that underwent a motion-correction *** CNN was evaluated using two performance measurement *** first one is a k-fold cross-validation testing method,in which we tested the dataset using k=8,10,12,and *** best accuracy for this procedure was 96.26%when k=*** overcome the over-fitting problem that could be occurred in the k-fold testing method,we used a hold-out testing method as a second evaluation *** results of this procedure succeeded in attaining 97.8%accuracy,with a specificity of 99.2%and a sensitivity of 97.32%.With this high accuracy,the developed CNN architecture could be considered an effective automated diagnosis method for the classification of brain tumors from mRI images.

关键词： Classification convolutional neural network tumor classification mRI deep learning k-fold cross classification

Encephalitis Detection from EEG Fuzzy Density-Based Clustering model with multiple Centroid

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Intelligent Automation & Soft Computing 2023年第3期35卷 3129-3140页

作者： hanan abdullah m.ngash Alaaeldin m.Hafez hanan A.Hosni mahmoud Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia

Encephalitis is a brain inﬂammation *** can yield to seizures,motor disability,or some loss of vision or ***,encepha-litis can be a life-threatening and proper diagnosis in an early stage is very ***,in this paper,we are proposing a deep learning model for computerized detection of Encephalitis from the electroencephalogram data(EEG).Also,we propose a Density-Based Clustering model to classify the distinctive waves of *** clustering models usually employ a computed single centroid virtual point to deﬁne the cluster conﬁguration,but this single point does not contain adequate *** precisely extract accurate inner structural data,a multiple centroids approach is employed and deﬁned in this paper,which deﬁnes the cluster conﬁguration by allocating weights to each state in the *** multiple EEG view fuzzy learning approach incorporates data from every sin-gle view to enhance the model's clustering *** a fuzzy Density-Based Clustering model with multiple centroids(FDBC)is *** model employs multiple real state centroids to deﬁne clusters using Partitioning Around Centroids *** Experimental results validate the medical importance of the proposed clustering model.

关键词： Density clustering clustering structural data fuzzy set

multi-Attribute Selection Procedures Based on Regret and Rejoice for the Decision-maker

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Computers, materials & Continua 2022年第2期70卷 2777-2795页

作者： hanan abdullah m.ngash manel Farouk Ayadi Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Feelings influence human beings’decision-making;therefore,incorporation of feeling factors in decision-making is very *** and rejoice are very important emotional feelings that can have a great impact on decision-making if they are considered *** regret has received most of the attention in related research,rejoice has been less considered even though it can greatly influence people’s preferences in ***,systematically incorporating regret and rejoice in the multicriteria decision-making(mCDm)modeling frameworks for decision-making has received little research *** this paper,we introduce a new multiattribute selection procedure that incorporates both regret and rejoice to select the best *** utilize the positional advantage operator concept to develop regret and rejoice mathematical equations,and prove *** proposed mCDm procedure that incorporates these two emotional factors offers a decision-maker the flexibility to trade off some benefits in order to gain a state of psychological *** specifically,regret and rejoice are presentedmathematically to enable the decision-maker to determine the values of regret and rejoice,and then make the decision in which the rejoice value is higher than the regret *** test the performance of this new procedure,we apply it to three numerical examples proposed in previous *** results are matched with those obtained by other methods such as the regret model,VIKOR,PROmETHEE I,and PROmETHEE II,thereby proving the efficacy of the new procedure.

关键词： multi-criteria decision-making positional advantage operator regret rejoice

Intelligent machine Learning Enabled Retinal Blood Vessel Segmentation and Classification

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Computers, materials & Continua 2023年第1期74卷 399-414页

作者： Nora abdullah Alkhaldi hanan T.Halawani Department of Computer Science College of Computer Science and Information TechnologyKing Faisal UniversityP.O.Box 400AlAhsa31982Saudi Arabia College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia

Automated segmentation of blood vessels in retinal fundus images is essential for medical image *** segmentation of retinal vessels is assumed to be essential to the progress of the decision support system for initial analysis and treatment of retinal *** article develops a new Grasshopper Optimization with Fuzzy Edge Detection based Retinal Blood Vessel Segmentation and Classification(GOFED-RBVSC)*** proposed GOFED-RBVSC model initially employs contrast enhancement ***,GOAFED approach is employed to detect the edges in the retinal fundus images in which the use of GOA adjusts the membership *** ORB(Oriented FAST and Rotated BRIEF)feature extractor is exploited to generate feature ***,Improved Conditional Variational Auto Encoder(ICAVE)is utilized for retinal image classification,shows the novelty of the *** performance validation of the GOFEDRBVSC model is tested using benchmark dataset,and the comparative study highlighted the betterment of the GOFED-RBVSC model over the recent approaches.

关键词： Edge detection blood vessel segmentation retinal fundus images image classification deep learning

An authentication and plausibility model for big data analytic under LOS and NLOS conditions in 5G-VANET

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Science China(Information Sciences) 2020年第12期63卷 67-83页

作者： S.A.SOLEYmANI m.H.ANISI A.hanan abdullah m.Asri NGADI Sh.GOUDARZI m.Khurram KHAN m.Nazri KAmA School of Computing Faculty of Engineering Universiti Teknologi Malaysia (UTM) School of Computer Science and Electronic Engineering University of Essex Centre of Artificial Intelligence National University of Malaysia (UKM) Center of Excellence in Information Assurance (CoEIA) King Saud University Advanced Informatics School Menara Razak Universiti Teknologi Malaysia (UTM)

The exchange of correct and reliable data among legitimate nodes is one of the most important challenges in vehicular ad hoc networks(VANETs). malicious nodes and obstacles, by generating inaccurate information, have a negative impact on the security of 5G-VANET. The big data generated in the vehicular network is also an issue in the security of VANET. To this end, a security model based on authentication and plausibility is proposed to improve the safety of network named ‘AFPm’. In the first layer, an authentication mechanism using edge nodes along with 5G is proposed to deal with the illegitimate nodes who enter the network and broadcast wrong information. In the authentication mechanism, because of the growth of the connected vehicles to the edge nodes that lead to generating big data and hence the inappropriateness of the traditional data structures, cuckoo filter, as a space-efficient probabilistic data structure, is used. In the second layer, a plausibility model by performing fuzzy logic is presented to cope with inaccurate information. The plausibility model is based on detection of inconsistent data involved in the event message. The plausibility model not only tackles with inaccurate, incomplete, and inaccuracy data but also deals with misbehaviour nodes under both line-of-sight(LOS) and non-line-of-sight(NLOS) conditions. All obtained results are validated through well-known evaluation measures such as F-measure and communication overhead. The results presented in this paper demonstrate that the proposed security model possesses a better performance in comparison with the existing studies.

关键词： authentication plausibility fuzzy logic cuckoo filter 5G-VANET big data

Search and Rescue Optimization with machine Learning Enabled Cybersecurity model

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Computer Systems Science & Engineering 2023年第5期45卷 1393-1407页

作者： hanan abdullah m.ngash Jaber S.Alzahrani majdy m.Eltahir Fahd N.Al-Wesabi abdullah mohamed manar Ahmed Hamza Radwa marzouk 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 Science&Art at MahayilKing Khalid UniversitySaudi Arabia Research Centre Future University in EgyptNew Cairo11745Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of mathematics Faculty of ScienceCairo UniversityGiza12613Egypt

Presently,smart cities play a vital role to enhance the quality of living among human beings in several ways such as online shopping,e-learning,ehealthcare,*** the benefits of advanced technologies,issues are also existed from the transformation of the physical word into digital word,particularly in online social networks(OSN).Cyberbullying(CB)is a major problem in OSN which needs to be addressed by the use of automated natural language processing(NLP)and machine learning(mL)*** article devises a novel search and rescue optimization with machine learning enabled cybersecurity model for online social networks,named SRO-mLCOSN *** presented SRO-mLCOSN model focuses on the identification of CB that occurred in social networking *** SRO-mLCOSN model initially employs Glove technique for word embedding ***,a multiclass-weighted kernel extreme learning machine(m-WKELm)model is utilized for effectual identification and categorization of ***,Search and Rescue Optimization(SRO)algorithm is exploited to fine tune the parameters involved in the m-WKELm *** experimental validation of the SRO-mLCOSN model on the benchmark dataset reported significant outcomes over the other approaches with precision,recall,and F1-score of 96.24%,98.71%,and 97.46%respectively.

关键词： Cybersecurity cyberbullying social networking machine learning search and rescue optimization

Deep Consensus Network for Recycling Waste Detection in Smart Cities

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Computers, materials & Continua 2023年第5期75卷 4191-4205页

作者： manar Ahmed Hamza hanan abdullah m.ngash Noha Negm Radwa marzouk Abdelwahed motwakel Abu Sarwar Zamani Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz University16278AlKharjSaudi Arabia 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 and ArtsKing Khalid UniversityMahayil Asir62529Saudi Arabia

Recently,urbanization becomes a major concern for developing as well as developed *** to the increased urbanization,one of the important challenging issues in smart cities is waste ***,automated waste detection and classification model becomes necessary for the smart city and to accomplish better recyclable waste *** recycling of waste offers the chance of reducing the quantity of waste disposed to the land fill by minimizing the requirement of collecting raw *** study develops a novel Deep Consensus Network with Whale Optimization Algorithm for Recycling Waste Object Detection(DCNWORWOD)in Smart *** goal of the DCNWO-RWOD technique intends to properly identify and classify the objects into recyclable and non-recyclable *** proposed DCNWO-RWOD technique involves the design of deep consensus network(DCN)to detect waste objects in the input *** improving the overall object detection performance of the DCN model,the whale optimization algorithm(WOA)is ***,Na飗e Bayes(NB)classifier is used for the classification of detected waste objects into recyclable and non-recyclable *** performance validation of theDCNWO-RWOD technique takes place using the open access *** extensive comparative study reported the enhanced performance of the DCNWO-RWOD technique interms of several measures.

关键词： Smart city waste management object detection recycling deep consensus network

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 m.ngash 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

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 m.ngash 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