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Chaotic Krill Herd with Deep Transfer Learning-Based Biometric Iris Recognition System

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Computers, Materials & Continua 2022年第12期73卷 5703-5715页

作者： Harbi Al-Mahafzah Tamer AbuKhalil bassam a.y.alqaralleh Department of Computer Science Faculty of Information TechnologyAl-Hussein Bin Talal University Ma’an71111Jordan MIS Department College of Business AdministrationUniversity of Business and TechnologyJeddah21448Saudi Arabia

Biometric verification has become essential to authenticate the individuals in public and private *** several biometrics,iris has peculiar features and its working mechanism is complex in *** recent developments in Machine Learning and Deep Learning approaches enable the development of effective iris recognition *** this motivation,the current study introduces a novel Chaotic Krill Herd with Deep Transfer Learning Based Biometric Iris Recognition System(CKHDTL-BIRS).The presented CKHDTL-BIRS model intends to recognize and classify iris images as a part of biometric *** achieve this,CKHDTL-BIRS model initially performs Median Filtering(MF)-based preprocessing and segmentation for iris *** addition,MobileNetmodel is also utilized to generate a set of useful feature ***,Stacked Sparse Autoencoder(SSAE)approach is applied for *** last,CKH algorithm is exploited for optimization of the parameters involved in SSAE *** proposed CKHDTL-BIRS model was experimentally validated using benchmark dataset and the outcomes were examined under several *** comparison study results established the enhanced performance of CKHDTL-BIRS technique over recent approaches.

关键词： Biometric verification iris recognition deep learning parameter tuning machine learning

Automated Handwriting Recognition and Speech Synthesizer for Indigenous Language Processing

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Computers, Materials & Continua 2022年第8期72卷 3913-3927页

作者： bassam a.y.alqaralleh Fahad Aldhaban Feras Mohammed A-Matarneh Esam A.alqaralleh MIS Department College of Business AdministrationUniversity of Business and TechnologyJeddah21448Saudi Arabia Department of Computer Science University College of DubaUniversity of Tabuk71491Saudi Arabia School of Engineering Princess Sumaya University for TechnologyAmman11941Jordan

In recent years,researchers in handwriting recognition analysis relating to indigenous languages have gained significant internet among research *** recent developments of artificial intelligence(AI),natural language processing(NLP),and computational linguistics(CL)find useful in the analysis of regional low resource *** lexical task participation might be elaborated to various applications in the *** is apparent from the availability of effective machine recognition models and open access handwritten *** language is a commonly spoken Semitic language,and it is written with the cursive Arabic alphabet from right to *** handwritten Character Recognition(HCR)is a crucial process in optical character *** this view,this paper presents effective Computational linguistics with Deep Learning based Handwriting Recognition and Speech Synthesizer(CLDL-THRSS)for Indigenous *** presented CLDL-THRSS model involves two stages of operations namely automated handwriting recognition and speech ***,the automated handwriting recognition procedure involves preprocessing,segmentation,feature extraction,and ***,the Capsule Network(CapsNet)based feature extractor is employed for the recognition of handwritten Arabic *** optimal hyperparameter tuning,the cuckoo search(CS)optimization technique was included to tune the parameters of the CapsNet ***,deep neural network with hidden Markov model(DNN-HMM)model is employed for the automatic speech *** validate the effective performance of the proposed CLDL-THRSS model,a detailed experimental validation process takes place and investigates the outcomes interms of different *** experimental outcomes denoted that the CLDL-THRSS technique has demonstrated the compared methods.

关键词： Computational linguistics handwriting character recognition natural language processing indigenous language

Optimal Deep Learning Based Inception Model for Cervical Cancer Diagnosis

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Computers, Materials & Continua 2022年第7期72卷 57-71页

作者： Tamer AbuKhalil bassam a.y.alqaralleh Ahmad H.Al-Omari Computer Science Department Faculty of Information TechnologyAl Hussein bin Talal UniversityMa’an71111Jordan MIS Department College of Business AdministrationUniversity of Business and TechnologyJeddah21448Saudi Arabia Faculty of Science Computer Science DepartmentNorthern Border UniversityArar91431Saudi Arabia

Prevention of cervical cancer becomes essential and is carried out by the use of Pap smear *** smear test analysis is laborious and tiresome work performed visually using a ***,automated cervical cancer diagnosis using automated methods are *** paper designs an optimal deep learning based Inception model for cervical cancer diagnosis(ODLIM-CCD)using pap smear *** proposed ODLIM-CCD technique incorporates median filtering(MF)based pre-processing to discard the noise and Otsu model based segmentation ***,deep convolutional neural network(DCNN)based Inception with Residual Network(ResNet)v2 model is utilized for deriving the feature ***,swallow swarm optimization(SSO)based hyperparameter tuning process is carried out for the optimal selection of ***,recurrent neural network(RNN)based classification process is done to determine the presence of cervical cancer or *** order to showcase the improved diagnostic performance of the ODLIM-CCD technique,a series of simulations occur on benchmark test images and the outcomes highlighted the improved performance over the recent approaches with a superior accuracy of 0.9661.

关键词： Median filtering convolutional neural network pap smear cervical cancer

Evolutionary Intelligence and Deep Learning Enabled Diabetic Retinopathy Classification Model

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Computers, Materials & Continua 2022年第10期73卷 87-101页

作者： bassam a.y.alqaralleh Fahad Aldhaban Anas Abukaraki Esam A.Al.Qaralleh MIS Department College of Business AdministrationUniversity of Business and TechnologyJeddah21448Saudi Arabia Department of Computer Science Faculty of Information TechnologyAl-Hussein Bin Talal UniversityMa’an71111Jordan School of Engineering Princess Sumaya University for TechnologyAmman11941Jordan

Diabetic Retinopathy(DR)has become a widespread illness among diabetics across the *** fundus images are generally used by physicians to detect and classify the stages of *** manual examination of DR images is a time-consuming process with the risks of biased results,automated tools using Artificial Intelligence(AI)to diagnose the disease have become *** this view,the current study develops an Optimal Deep Learning-enabled Fusion-based Diabetic Retinopathy Detection and Classification(ODL-FDRDC)*** intention of the proposed ODLFDRDC technique is to identify DR and categorize its different grades using retinal fundus *** addition,ODL-FDRDC technique involves region growing segmentation technique to determine the infected ***,the fusion of two DL models namely,CapsNet and MobileNet is used for feature ***,the hyperparameter tuning of these models is also performed via Coyote Optimization Algorithm(COA).Gated Recurrent Unit(GRU)is also utilized to identify *** experimental results of the analysis,accomplished by ODL-FDRDC technique against benchmark DR dataset,established the supremacy of the technique over existing methodologies under different measures.

关键词： Optimization algorithms medical images diabetic retinopathy deep learning fusion model

Optimal Machine Learning Enabled Intrusion Detection in Cyber-Physical System Environment

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Computers, Materials & Continua 2022年第9期72卷 4691-4707页

作者： bassam a.y.alqaralleh Fahad Aldhaban Esam A.alqarallehs Ahmad H.Al-Omari MIS Department College of Business AdministrationUniversity of Business and TechnologyJeddah21448Saudi Arabia School of Engineering Princess Sumaya University for TechnologyAmman11941Jordan Faculty of Science Computer Science DepartmentNorthern Border UniversityArar91431Saudi Arabia

Cyber-attacks on cyber-physical systems(CPSs)resulted to sensing and actuation misbehavior,severe damage to physical object,and safety *** learning(ML)models have been presented to hinder cyberattacks on the CPS environment;however,the non-existence of labelled data from new attacks makes their detection quite *** Detection System(IDS)is a commonly utilized to detect and classify the existence of intrusions in the CPS environment,which acts as an important part in secure CPS *** developments in deep learning(DL)and explainable artificial intelligence(XAI)stimulate new IDSs to manage cyberattacks with minimum complexity and high *** this aspect,this paper presents an XAI based IDS using feature selection with Dirichlet Variational Autoencoder(XAIIDS-FSDVAE)model for *** proposed model encompasses the design of coyote optimization algorithm(COA)based feature selection(FS)model is derived to select an optimal subset of ***,an intelligent Dirichlet Variational Autoencoder(DVAE)technique is employed for the anomaly detection process in the CPS ***,the parameter optimization of the DVAE takes place using a manta ray foraging optimization(MRFO)model to tune the parameter of the *** order to determine the enhanced intrusion detection efficiency of the XAIIDS-FSDVAE technique,a wide range of simulations take place using the benchmark *** experimental results reported the better performance of the XAIIDSFSDVAE technique over the recent methods in terms of several evaluation parameters.

关键词： Cyber-physical systems explainable artificial intelligence deep learning security intrusion detection metaheuristics

Hybrid Metaheuristics Based License Plate Character Recognition in Smart City

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Computers, Materials & Continua 2022年第9期72卷 5727-5740页

作者： Esam A.Al.Qaralleh Fahad Aldhaban Halah Nasseif bassam a.y.alqaralleh Tamer AbuKhalil School of Engineering Princess Sumaya University for TechnologyAmman11941Jordan MIS Department College of Business AdministrationUniversity of Business and TechnologyJeddah21448Saudi Arabia Department of Computer Science Faculty of Information TechnologyAl-Hussein Bin Talal UniversityMa’an71111Jordan

Recent technological advancements have been used to improve the quality of living in smart *** the same time,automated detection of vehicles can be utilized to reduce crime rate and improve public *** the other hand,the automatic identification of vehicle license plate(LP)character becomes an essential process to recognize vehicles in real time scenarios,which can be achieved by the exploitation of optimal deep learning(DL)*** this article,a novel hybrid metaheuristic optimization based deep learning model for automated license plate character recognition(HMODL-ALPCR)technique has been presented for smart city *** major intention of the HMODL-ALPCR technique is to detect LPs and recognize the characters that exist in *** effective LP detection process,mask regional convolutional neural network(Mask-RCNN)model is applied and the Inception with Residual Network(ResNet)-v2 as the baseline *** addition,hybrid sunflower optimization with butterfly optimization algorithm(HSFO-BOA)is utilized for the hyperparameter tuning of the Inception-ResNetv2 ***,Tesseract based character recognition model is applied to effectively recognize the characters present in the *** experimental result analysis of the HMODL-ALPCR technique takes place against the benchmark dataset and the experimental outcomes pointed out the improved efficacy of the HMODL-ALPCR technique over the recent methods.

关键词： Smart city license plate recognition optimal deep learning metaheuristic algorithms parameter tuning

Smart Deep Learning Based Human Behaviour Classification for Video Surveillance

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Computers, Materials & Continua 2022年第9期72卷 5593-5605页

作者： Esam A.Al.Qaralleh Fahad Aldhaban Halah Nasseif Malek Z.Alksasbeh bassam a.y.alqaralleh School of Engineering Princess Sumaya University for TechnologyAmman11941Jordan MIS Department College of Business AdministrationUniversity of Business and TechnologyJeddah21448Saudi Arabia CIS Department Faculty of Information TechnologyAl Hussein bin Talal UniversityMa’an71111Jordan

Real-time video surveillance system is commonly employed to aid security professionals in preventing *** use of deep learning(DL)technologies has transformed real-time video surveillance into smart video surveillance systems that automate human behavior *** recognition of events in the surveillance videos is considered a hot research topic in the field of computer science and it is gaining significant *** action recognition(HAR)is treated as a crucial issue in several applications areas and smart video surveillance to improve the security *** advancements of the DL models help to accomplish improved recognition *** this view,this paper presents a smart deep-based human behavior classification(SDL-HBC)model for real-time video *** proposed SDL-HBC model majorly aims to employ an adaptive median filtering(AMF)based pre-processing to reduce the noise ***,the capsule network(CapsNet)model is utilized for the extraction of feature vectors and the hyperparameter tuning of the CapsNet model takes place utilizing the Adam ***,the differential evolution(DE)with stacked autoencoder(SAE)model is applied for the classification of human activities in the intelligent video surveillance *** performance validation of the SDL-HBC technique takes place using two benchmark datasets such as the KTH *** experimental outcomes reported the enhanced recognition performance of the SDL-HBC technique over the recent state of art approaches with maximum accuracy of 0.9922.

关键词： Human action recognition video surveillance intelligent systems deep learning security image classification

Nasal flap preservation in endoscopic dacryocystorhinostomy for nasolacrimal duct obstruction

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International Journal of Ophthalmology(English edition) 2024年第9期17卷 1633-1638页

作者： Marianne Romanos Charbel Medawar Souheil Hallit bassam Romanos School of Medicine and Medical Sciences Holy Spirit University of KaslikP.O.Box 446JouniehLebanon Department of Ophtalmology Eye and Ear Hospital InternationalNaccacheLebanon Applied Science Research Center Applied Science Private UniversityAmman 11937Jordan

AIM:To compare surgical outcomes between the conventional endoscopic dacryocystorhinostomy(DCR)and a modified endoscopic DCR for the treatment of nasolacrimal duct obstruction(NLDO),and evaluate factors associated with the surgical success ***:Medical records of patients who underwent primary DCR surgery between January 2016 and July 2020 at the Otorhinolaryngology Department of Eye and Ear International Hospital,Lebanon were ***:The study group consisted of 50 consecutive modified endoscopic DCR and the control group consisted of 138 consecutive conventional endoscopic *** success rates at 1y were 98.0%(49 out of 50)for modified DCR,significantly higher compared to 84.8%(117/138)for the conventional DCR;there was no significant difference in the success rate throughout the years in terms of both surgical *** modified surgery vs traditional[adjusted odds ratio(aOR)=14.96]and having an adjunctive septoplasty surgery vs not(aOR=3.99)were significantly associated with higher odds of ***:Mucosal flap preservation and apposition shows significant improvement in the surgical success ***,there is no statistically significant difference found in terms of complication rate and mean operative time between the conventional and the modified techniques.

关键词： nasolacrimal duct obstruction endoscopic dacryocystorhinostomy mucosal flap

Management of Left Ventricular Aneurysm: A Study from Iraq

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International Journal of Clinical Medicine 2014年第4期5卷 127-132页

作者： Abdulsalam y. Taha bassam A. Mahmoud Department of Cardiac Surgery Ibn-Albitar Center for Cardiac Surgery (IBCCS) Baghdad Iraq Department of Thoracic and Cardiovascular Surgery School of Medicine and Sulaimania Teaching Hospital Sulaimania Iraq

Background: The most appropriate surgical approach for post-myocardial infarction left ventricular aneurysm (LVA) is controversial. This study aims to display the results of surgical treatment of LVA in a major Iraqi cardiac surgical center. Methods: The surgical management of LVAs over the period 2001 to 2011 was retrospectively reviewed. The presenting signs and symptoms, results of investigations, operative findings, and outcomes of patients were determined. Results: Twenty-seven true LVAs associated with 4 ventricular septal defects (VSDs) were treated surgically. During the same period, 1136 coronary artery bypass graft (CABG) operations were done, thus LVA represented 2.4%. Males constituted the majority (74.1%). The mean age was 54.6 years old. The typical ECG changes were seen in 42.1%. Apical and antero-apical locations predominated. The majority of patients (84.2%) had subnormal values of ejection fraction (EF). Most patients had multi-vessel coronary artery disease (CAD). The most frequent was the left anterior descending artery (LAD). All patients had CABG except 3. Linear repair and Dor technique were used equally. The commonest postoperative complication was bleeding (38.4%). The overall hospital mortality was 18.5%. Conclusion: Concomitant CABG improves early postoperative course and must be added when significant lesions in coronary arteries particularly the LAD are present.

关键词： Left Ventricular Aneurysm Post-Ischemic VSD Linear Repair and Dor Technique