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Evolutionary Intelligence and Deep Learning Enabled Diabetic Retinopathy Classification Model

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 globe.Retinal fundus images are generally used by physicians to detect and classify the stages of DR.Since 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 essential.In this view,the current study develops an Optimal Deep Learning-enabled Fusion-based Diabetic Retinopathy Detection and Classification(ODL-FDRDC)technique.The intention of the proposed ODLFDRDC technique is to identify DR and categorize its different grades using retinal fundus images.In addition,ODL-FDRDC technique involves region growing segmentation technique to determine the infected regions.Moreover,the fusion of two DL models namely,CapsNet and MobileNet is used for feature extraction.Further,the hyperparameter tuning of these models is also performed via Coyote Optimization algorithm(COA).Gated Recurrent Unit(GRU)is also utilized to identify DR.The 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

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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 cities.At the same time,automated detection of vehicles can be utilized to reduce crime rate and improve public security.On 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)approaches.In 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 environments.The major intention of the HMODL-alPCR technique is to detect LPs and recognize the characters that exist in them.For 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 network.In addition,hybrid sunflower optimization with butterfly optimization algorithm(HSFO-BOA)is utilized for the hyperparameter tuning of the Inception-ResNetv2 model.Finally,Tesseract based character recognition model is applied to effectively recognize the characters present in the LPs.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

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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 crimes.The use of deep learning(DL)technologies has transformed real-time video surveillance into smart video surveillance systems that automate human behavior classification.The recognition of events in the surveillance videos is considered a hot research topic in the field of computer science and it is gaining significant attention.Human action recognition(HAR)is treated as a crucial issue in several applications areas and smart video surveillance to improve the security level.The advancements of the DL models help to accomplish improved recognition performance.In this view,this paper presents a smart deep-based human behavior classification(SDL-HBC)model for real-time video surveillance.The proposed SDL-HBC model majorly aims to employ an adaptive median filtering(AMF)based pre-processing to reduce the noise content.also,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 optimizer.Finally,the differential evolution(DE)with stacked autoencoder(SAE)model is applied for the classification of human activities in the intelligent video surveillance system.The performance validation of the SDL-HBC technique takes place using two benchmark datasets such as the KTH dataset.The 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

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Tuning Recurrent Neural Networks for Recognizing Handwritten Arabic Words

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Journal of Software Engineering and Applications 2013年第10期6卷 533-542页

作者： esam qaralleh Gheith Abandah Fuad Jamour Computer Engineering Depart- ment The University of Jordan Amman Jordan Computer Engineering Department Princess Sumaya University for Technology Amman Jordan Graduate Student King Abdullah University of Science and Technology Thuwal Saudi Arabia

Artificial neural networks have the abilities to learn by example and are capable of solving problems that are hard to solve using ordinary rule-based programming. They have many design parameters that affect their performance such as the number and sizes of the hidden layers. Large sizes are slow and small sizes are generally not accurate. Tuning the neural network size is a hard task because the design space is often large and training is often a long process. We use design of experiments techniques to tune the recurrent neural network used in an Arabic handwriting recognition system. We show that best results are achieved with three hidden layers and two subsampling layers. To tune the sizes of these five layers, we use fractional factorial experiment design to limit the number of experiments to a feasible number. Moreover, we replicate the experiment configuration multiple times to overcome the randomness in the training process. The accuracy and time measurements are analyzed and modeled. The two models are then used to locate network sizes that are on the Pareto optimal frontier. The approach described in this paper reduces the label error from 26.2% to 19.8%.

关键词： Optical Character Recognition Handwritten Arabic Words Recurrent Neural Networks Design of Experiments

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