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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 communities.The recent developments of artificial intelligence(AI),natural language processing(NLP),and computational linguistics(CL)find useful in the analysis of regional low resource languages.Automatic lexical task participation might be elaborated to various applications in the NLP.It is apparent from the availability of effective machine recognition models and open access handwritten databases.Arabic language is a commonly spoken Semitic language,and it is written with the cursive Arabic alphabet from right to left.Arabic handwritten Character Recognition(HCR)is a crucial process in optical character recognition.In this view,this paper presents effective Computational linguistics with Deep Learning based Handwriting Recognition and Speech Synthesizer(CLDL-THRSS)for Indigenous Language.The presented CLDL-THRSS model involves two stages of operations namely automated handwriting recognition and speech recognition.Firstly,the automated handwriting recognition procedure involves preprocessing,segmentation,feature extraction,and classification.Also,the Capsule Network(CapsNet)based feature extractor is employed for the recognition of handwritten Arabic characters.For optimal hyperparameter tuning,the cuckoo search(CS)optimization technique was included to tune the parameters of the CapsNet method.Besides,deep neural network with hidden Markov model(DNN-HMM)model is employed for the automatic speech synthesizer.To validate the effective performance of the proposed CLDL-THRSS model,a detailed experimental validation process takes place and investigates the outcomes interms of different measures.The 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

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 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

Multi-scale fractures formation and distribution in tight sandstones—a case study of Triassic Chang 8 Member in the southwestern Ordos Basin

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Frontiers of Earth Science 2022年第2期16卷 483-498页

作者： Gaojian XIAO Ling HU Yang LUO Yujing MENG Ali bassam Taher AL-SALAFI Haoran LIU Key Laboratory of Tectonics and Petroleum Resources China University of GeosciencesWuhan 430074China Wenhua College Wuhan 430074China Faculty of Petroleum and Natural Resources Sana’a UniversitySana’aYemen Louisiana State University Baton Rouge LA 7080USA

Fracture system is an important factor controlling tight oil accumulation in the Triassic Chang 8 Member,southwestern Ordos Basin,China.A systematic characterization of the multi-scale natural fractures is a basis for the efficient tight oil production.Based on outcrops,seismic reflections,well cores,well logs(image and conventional logging),casting thin sections,and scanning electron microscope observation,the multi-scale fractures occurrences and their influences on Chang 8 tight sandstone reservoirs are revealed.The results show that three periods of strike-slip faults and four scales of natural fractures developed,namely mega-scale(length>7×10^(7) mm),macro-scale(3.5×10^(5)

关键词： natural fractures characteristics geological significance tight sandstone reservoir Upper Triassic Yanchang Formation

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

An Improved Optimized Model for Invisible Backdoor Attack Creation Using Steganography

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Computers, Materials & Continua 2022年第7期72卷 1173-1193页

作者： Daniyal M.Alghazzawi Osama bassam J.Rabie Surbhi Bhatia Syed Hamid Hasan Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversitySaudi Arabia

The Deep Neural Networks(DNN)training process is widely affected by backdoor attacks.The backdoor attack is excellent at concealing its identity in the DNN by performing well on regular samples and displaying malicious behavior with data poisoning triggers.The state-of-art backdoor attacks mainly follow a certain assumption that the trigger is sample-agnostic and different poisoned samples use the same trigger.To overcome this problem,in this work we are creating a backdoor attack to check their strength to withstand complex defense strategies,and in order to achieve this objective,we are developing an improved Convolutional Neural Network(ICNN)model optimized using a Gradient-based Optimization(GBO)(ICNN-GBO)algorithm.In the ICNN-GBO model,we are injecting the triggers via a steganography and regularization technique.We are generating triggers using a single-pixel,irregular shape,and different sizes.The performance of the proposed methodology is evaluated using different performance metrics such as Attack success rate,stealthiness,pollution index,anomaly index,entropy index,and functionality.When the CNN-GBO model is trained with the poisoned dataset,it will map the malicious code to the target label.The proposed scheme’s effectiveness is verified by the experiments conducted on both the benchmark datasets namely CIDAR-10 andMSCELEB 1M dataset.The results demonstrate that the proposed methodology offers significant defense against the conventional backdoor attack detection frameworks such as STRIP and Neutral cleanse.

关键词： Convolutional neural network gradient-based optimization steganography backdoor attack and regularization attack

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 risk.Machine 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 interesting.Intrusion 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 environment.Latest developments in deep learning(DL)and explainable artificial intelligence(XAI)stimulate new IDSs to manage cyberattacks with minimum complexity and high sophistication.In this aspect,this paper presents an XAI based IDS using feature selection with Dirichlet Variational Autoencoder(XAIIDS-FSDVAE)model for CPS.The proposed model encompasses the design of coyote optimization algorithm(COA)based feature selection(FS)model is derived to select an optimal subset of features.Next,an intelligent Dirichlet Variational Autoencoder(DVAE)technique is employed for the anomaly detection process in the CPS environment.Finally,the parameter optimization of the DVAE takes place using a manta ray foraging optimization(MRFO)model to tune the parameter of the DVAE.In order to determine the enhanced intrusion detection efficiency of the XAIIDS-FSDVAE technique,a wide range of simulations take place using the benchmark datasets.The 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

Evaluation of indoor environmental quality conditions in elementary schools＇ classrooms in the United Arab Emirates

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Frontiers of Architectural Research 2014年第2期3卷 166-177页

作者： Moshood Olawale Fadeyi Khawla Alkhaja Maryam bin Sulayem bassam Abu-Hijleh Faculty of Engineering and IT British University in Dubai PO Box 345015 Dubai United Arab Emirates School of Civil and Environmental Engineering Nanyang Technological University NI-01a-Z9 50 Nanyang AvenueSingapore 639798 Singapore Berkeley Education Alliance for Research in Singapore Create Way #11-01 Singapore 138602 Sinsapore

This study presents findings of indoor environmental quality （IEQ） investigations conducted in elementary schools＇ classrooms in the United Arab Emirates （UAE）. Average TVOC, CO2, O3, CO, and particle concentrations measured in the classrooms were 815 pg / m^3, 1605 ppm, 0.05 ppm, 1.16 ppm, and 1730 μg/m^3, respectively. Whereas, local authority known as Dubai Municipality recommended 300 pg/m^3, 800 ppm, 0.06 ppm, 9 ppm, and 150-300 μg/m^3 for TVOC, CO2, O3, CO, and particle, respectively. Dubai Municipality recommended temperature and retative humidity （RH） levels of 22.5 ℃ to 25.5 ℃ and 30%-60%, respectively. Average temperature and RH levels measured in the classrooms were 24.5 ℃ and 40.4%, respectively. Average sound level in the classrooms was 24 dB greater than recommended sound level limit of 35 dB. Six （6） classrooms had average lux levels in the range of 400-800 lux. Two （2） classrooms had average lux levels in the range of 100 200 lux. The remaining classrooms had lux levels around the recommended 300 lux. High occupancy density was observed in majority of the studied classrooms. Observations during walkthroush investigations could

关键词： Indoor environmentalquality （IEQ） Elementary school Health and comfort Building performanceand sustainability

Integrated Use of Organic and Bio-fertilizers to Improve Yield and Fruit Quality of Olives Grown in Low Fertility Sandy Soil in an Arid Environment

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Phyton-International Journal of Experimental Botany 2023年第6期92卷 1813-1829页

作者： bassam F.Alowaiesh M.M.Gad Mohamed Saleh M.Ali Biology Department College of ScienceJouf UniversitySakaka72341Saudi Arabia Horticulture Department Faculty of AgricultureZagazig UniversityZagazig44511Egypt Horticulture Department (pomology)Faculty of AgricultureSuez Canal UniversityIsmailia41522Egypt

Olive productivity should be improved through stimulating nutrition,particularly under poor fertility soils.Consequently,the objective of this study was to assess the efficacy of applying organic and bio-fertilizers on the physiological growth,yield and fruit quality of olive trees under newly reclaimed poor-fertility sandy soil in an arid environment.During a field experiment carried out at El-Qantara,North Sinai,Egypt over two consecutive seasons(2019-2020 and 2020-2021),olive Kalamata trees were evaluated under three organic fertilizer treatments alone or in combination with three bio-fertilizers treatments.Organic fertilizer was applied as goat manure(16.8 kg/tree/year),or olive pomace(8.5 kg/tree/year)in mid-December of each season vs.untreated trees.The bio-fertilizers were applied as N-fixing bacteria(150 g/tree)was inculated in early March of each season,or amino acid mixture(1.5%)was applied three times,at 70%of full bloom,21 days after full bloom,and a month later in comparison to a non-fertilized trees(control).The cultivar used was Kalamata,a dual-purpose cultivar for oil and table olives whose value increases when processed as table olives.The results indicated that the goat manure followed by olive pomace significantly enhanced photosynthetic pigments(chlorophyll a,b,and carotenoids),leaf mineral contents(N,P,K,Ca,Mg and Fe),tree canopy volume,number of flowers per inflorescence,number of inflorescences per shoot,initial fruit set,fruit retention.For fruit quality,fruit length and width,fruit weight,and total fruit yield was increased compared to the non-fertilized control.Likewise,The bio-fertilizer N-fixing bacteria followed by the amino acid mixture significantly improved all of the aforementioned parameters.Accordingly,it is recommended,both environmentally and economically to utilize organic and bio-fertizers,particularly goat manure combined with N-fixing bacteria,in low-fertility soil to sustain olive production as well as reducing mineral fertilization.

关键词： Organic and bio-fertilizers olives kalamata vegetative growth leaf mineral contents fruit quality

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