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X-Ray Covid-19 Detection Based on Scatter Wavelet Transform and Dense Deep Neural Network

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Computer Systems Science & Engineering 2022年第6期41卷 1255-1271页

作者： ali sami al-Itbi ahmed Bahaaulddin A.alwahhab ali mohammed Sahan Informatics Department Technical College of ManagementMiddle Technical UniversityBaghdadIraq

Notwithstanding the discovery of vaccines for Covid-19, the virus'srapid spread continues due to the limited availability of vaccines, especially inpoor and emerging countries. Therefore, the key issues in the presentCOVID-19 pandemic are the early identification of COVID-19, the cautiousseparation of infected cases at the lowest cost and curing the disease in the earlystages. For that reason, the methodology adopted for this study is imaging tools,particularly computed tomography, which have been critical in diagnosing andtreating the disease. A new method for detecting Covid-19 in X-rays and CTimages has been presented based on the Scatter Wavelet Transform and DenseDeep Neural Network. The Scatter Wavelet Transform has been employed as afeature extractor, while the Dense Deep Neural Network is utilized as a binaryclassifier. An extensive experiment was carried out to evaluate the accuracy ofthe proposed method over three datasets: IEEE 80200, Kaggle, andCovid-19 X-ray image data Sets. The dataset used in the experimental part consists of 14142. The numbers of training and testing images are 8290 and 2810,respectively. The analysis of the result refers that the proposed methods achievedhigh accuracy of 98%. The proposed model results show an excellent outcomecompared to other methods in the same domain, such as (DeTraC) CNN, whichachieved only 93.1%, CNN, which achieved 94%, and stacked Multi-ResolutionCovXNet, which achieved 97.4%. The accuracy of CapsNet reached 97.24%.

关键词： Covid-19 detection scatter wavelet transform deep learning dense Deep Neural Network

Intelligent Firefly algorithm Deep Transfer Learning Based COVID-19 Monitoring System

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Computers, Materials & Continua 2023年第2期74卷 2889-2903页

作者： Mahmoud Ragab mohammed W.al-Rabia sami Saeed Binyamin ahmed A.aldarmahi Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia Centre for Artificial Intelligence in Precision Medicines King Abdulaziz UniversityJeddah 21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City 11884CairoEgypt Department of Medical Microbiology and Parasitology Faculty of MedicineKing Abdulaziz UniversityJeddah 21589Saudi Arabia Health Promotion Center King Abdulaziz UniversityJeddah 21589Saudi Arabia Computer and Information Technology Department The Applied CollegeKing Abdulaziz UniversityJeddah 21589Saudi Arabia Basic Science Department College of Science and Health ProfessionsKing Saud Bin Abdulaziz University for Health SciencesJeddah 21423Saudi Arabia King Abdullah International Medical Research Center Ministry of National Guard-Health AffairsJeddah 21423Saudi Arabia

With the increasing and rapid growth rate of COVID-19 cases,the healthcare scheme of several developed countries have reached the point of *** important and critical steps in fighting against COVID-19 is powerful screening of diseased patients,in such a way that positive patient can be treated and isolated.A chest radiology image-based diagnosis scheme might have several benefits over traditional *** accomplishment of artificial intelligence(AI)based techniques in automated diagnoses in the healthcare sector and rapid increase in COVID-19 cases have demanded the requirement of AI based automated diagnosis and recognition *** study develops an Intelligent Firefly algorithm Deep Transfer Learning Based COVID-19Monitoring System(IFFA-DTLMS).The proposed IFFADTLMSmodelmajorly aims at identifying and categorizing the occurrence of COVID19 on chest *** attain this,the presented IFFA-DTLMS model primarily applies densely connected networks(DenseNet121)model to generate a collection of feature *** addition,the firefly algorithm(FFA)is applied for the hyper parameter optimization of DenseNet121 ***,autoencoder-long short term memory(AE-LSTM)model is exploited for the classification and identification of *** ensuring the enhanced performance of the IFFA-DTLMS model,a wide-ranging experiments were performed and the results are reviewed under distinctive *** experimental value reports the betterment of IFFA-DTLMS model over recent approaches.

关键词： COVID-19 artificial intelligence intelligent systems deep learning decision making

Sentiment Analysis and Classification Using Deep Semantic Information and Contextual Knowledge

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Computers, Materials & Continua 2023年第1期74卷 671-691页

作者： ahmed Abdulhakim al-Absi Dae-Ki Kang mohammed Abdulhakim al-Absi Department of Smart Computing Kyungdong University 464-gilBongpoGosungGangwon-do24764Korea Division of Computer Engineering Dongseo University47 Jurye-roSasang-guBusan47011Korea Department of Computer Engineering Dongseo University47 Jurye-roSasang-guBusan47011Korea

Sentiment analysis(AS)is one of the basic research directions in natural language processing(NLP),it is widely adopted for news,product review,and ***-based sentiment analysis(ABSA)aims at identifying the sentiment polarity of a given target context,previous existing model of sentiment analysis possesses the issue of the insufficient exaction of features which results in low *** this research work develops a deep-semantic and contextual knowledge networks(DSCNet).DSCNet tends to exploit the semantic and contextual knowledge to understand the context and enhance the accuracy based on given *** first temporal relationships are established then deep semantic knowledge and contextual knowledge are ***,a deep integration layer is introduced to measure the importance of features for efficient extraction of different *** of DSCNet model lies in introducing the deep *** is evaluated on three datasets i.e.,Restaurant,Laptop,and Twitter dataset considering different deep learning(DL)metrics like precision,recall,accuracy,and Macro-F1 ***,comparative analysis is carried out with different baselinemethods in terms of accuracy andMacro-F1 *** achieves 92.59%of accuracy on restaurant dataset,86.99%of accuracy on laptop dataset and 78.76%of accuracy on Twitter dataset.

关键词： Sentiment analysis aspect-sa deep learning DSCNet

Natural regeneration of Pinus brutia Ten. in a recreational public forest in Zawita-Kurdistan region, Iraq

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Journal of Forestry Research 2019年第5期30卷 1849-1857页

作者： Hassan Muhamed sami Youssef ahmed Mustafa Hishyer Suliman Aree Abdulqader Hishyer mohammed Richard Michalet Department of Forestry College of AgricultureUniversity of DuhokDuhokKurdistan RegionIraq AMAP(Botany and Modelling of Plant Architecture and Vegetation) University of Montpellier34398 Montpellier Cedex 5France Department of Ecotourism College of AgricultureUniversity of DuhokDuhokKurdistan RegionIraq Directorate of Forestry and Rangelands DuhokKurdistan RegionIraq UMR CNRS 5805 EPOC Universitede BordeauxAllee Geoffroy Saint-Hilaire CS 5002333615 Pessac CedexFrance

Zawita natural forest has recently has been subject to mass recreational activities during spring that have denuded large areas of the *** was thus essential to assess regeneration before designing optimizing *** this end,we studied the overstory canopy and microhabitat conditions for recruitment of Pinus brutia Ten in 10 plots(20×25 m)on the southern aspects where the Zawita natural forest is still *** total,1540 regenerating *** were recorded,854 seedlings,597 saplings,and 89 *** and saplings were more frequent beyond the canopy than under the canopy of the parent *** requirements differed between seedlings and *** probability of the occurrence of seedlings was negatively correlated with increasing litter depth and increasing soil *** density of saplings only showed a positive significant correlation with increasing *** nearest neighbor index showed a trend toward a positive spatial association between understory shrubs with their neighboring seedlings at a mean distance of 1.6 ***,the study highlighted the requirements for seedling regeneration as a relatively open canopy cover,a light understory litter layer,and noncompacted *** results are a step towards designing effective management and restoration programs.

关键词： Forest structure Litter depth Regeneration niche Spatial pattern Soil compaction

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Chloride ingress and macro-cell corrosion of steel in concrete made with recycled brick aggregate

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Frontiers of Structural and Civil Engineering 2021年第6期15卷 1358-1371页

作者： Tarek Uddin mohammed Mahfuzur RAHMAN ahmed SABBIR Mohammad Mehedi HASAN Abdullah al MAMUN Department of Civil and Environmental Engineering Islamic University of TechnologyBoard BazarGazipur 1704Bangladesh Department of Civil Engineering University of Information Technology and SciencesBaridharaDhaka 1202Bangladesh Bangladesh Road Research Laboratory Roads and Highways DepartmentMirpurDhaka 1216Bangladesh

An investigation on chloride ingress and macro-cell corrosion of steel bars in concrete made with recycled brick aggregate(RBA)was carried *** control cases,virgin brick aggregate(BA)and stone aggregate(SA)were also *** cylindrical and cracked prism specimens were studied for 16 different *** prism specimens were made with a segmented steel bar providing electrical connection from outside of the specimens to measure macro-cell corrosion current continuously under seawater splash exposure for a period of 30 d using a data *** specimens were submerged in 3%NaCl solution at a temperature of 40℃ to investigate chloride ingress in concrete made with RBA,BA,and SA after 120 and 180 ***-cell potential,corrosion area,and depths of corrosion were also *** chloride ingress as well as corrosion of steel bars in concrete made with the different types of aggregate is ordered as RBA>BA>SA.

关键词： brick aggregate chloride ingress macro-cell corrosion recycled brick aggregate

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Prevalence and co-infections of schistosomiasis/hepatitis B and C viruses among school children in an endemic areas in Taiz,Yemen

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Asian Pacific Journal of Tropical Medicine 2011年第5期4卷 404-408页

作者： Adam H al-Shamiri mohammed A al-Taj Ameera S ahmed Department of Microbiology Faculty of Applied ScienceTaiz UniversityYemen Department of Parasitology and Immunology Faculty of Applied ScienceTaiz UniversityYemen

Objective:To determine the disease prevalence and its relationship with hepatitis B and C viruses among school children in five endemic areas by ***:During June 2007 and March 2009,1 484 school children aged between 5-16 years participated in the current study from 32 basic schools in five districts(al-Dhabab,Hedran,Warazan,al-Barh and al-Shmaytin) in Taiz Governorate,Republic of *** of school children who participated in the study;1 406 stool samples,1 484 urine samples and 214 blood samples were collected and ***:Schistosoma mansoni(***) was found in all the studied areas except ***,Schistosoma haematobium(***) was recorded only in al-Shmaytin and *** *** and *** were observed in al-Shmaytin *** overall prevalence was 20.76%for *** and 7.41%for ***. The prevalence rate of infection among males was higher than females,shwing no significant *** of light,moderate and heavy infections in the case of *** were 41.78%, 25.34%and 32.87%***,for *** it was 50.90%for light infection and 49.09%for heavy *** to the prevalence of viral hepatitis among infected school children with schistosomiasis,it could be noticed that hepatitis B virus was higher than the prevalence of hepatitis C ***,the presence of HBsAg and anti-HCV was not associated with Schistosoma ***:Schistosomiasis infection is an important public health problem in Taiz Governorate,Republic of *** was a correlation between S. haematobium and hepatitis B,but no association between *** infections and hepatitis B and C viruses.

关键词： Schistosomosis Intensity Hepatitis B and C virus School children Yemen

Facilities Provided by ISDN for Telemedicine

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Journal of Systems Engineering and Electronics 1999年第3期10卷 7-14页

作者： al-Kassab, mohammed Hmeed ahmed Lu, Dongming Pan, Yunhe National Key Lab. of CAD & CG Dept. of Computer Science Zhejiang University Hangzhou P. R. China

In this paper, we discuss the advantages of integrated services digital network (ISDN) as an infrastructure for telemedicine applications. The facilities provided by ISDN, which are necessary to telemedicine, are listed and some of the ISDN solutions to telemedicine are discussed. Some of the current popular telemedicine systems that based on ISDN are mentioned, with some discussion of the future ISDN telemedicine applications.

关键词： Telemedicine ISDN Telehealth Telecommunication Healthcare delivery

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Evolutionary algorithsm with Machine Learning Based Epileptic Seizure Detection Model

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Computers, Materials & Continua 2022年第9期72卷 4541-4555页

作者： Manar ahmed Hamza Noha Negm Shaha al-Otaibi Amel Aalhussan Mesfer al Duhayyim Fuad ali mohammed al-Yarimi mohammed Rizwanullah Ishfaq Yaseen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz University16278AlKharjSaudi Arabia Department of Computer Science College of Science and ArtsKing Khalid UniversityMahayilAsir62529Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz University16278Saudi Arabia

Machine learning (ML) becomes a familiar topic among decisionmakers in several domains, particularly healthcare. Effective design of MLmodels assists to detect and classify the occurrence of diseases using healthcaredata. Besides, the parameter tuning of the ML models is also essentialto accomplish effective classification results. This article develops a novelred colobuses monkey optimization with kernel extreme learning machine(RCMO-KELM) technique for epileptic seizure detection and *** proposed RCMO-KELM technique initially extracts the chaotic, time,and frequency domain features in the actual EEG signals. In addition, the minmax normalization approach is employed for the pre-processing of the EEGsignals. Moreover, KELM model is used for the detection and classificationof epileptic seizures utilizing EEG signal. Furthermore, the RCMO techniquewas utilized for the optimal parameter tuning of the KELM technique insuch a way that the overall detection outcomes can be considerably *** experimental result analysis of the RCMO-KELM technique has beenexamined using benchmark dataset and the results are inspected under severalaspects. The comparative result analysis reported the better outcomes of theRCMO-KELM technique over the recent approaches with the accuy of 0.956.

关键词： Epileptic seizures eeg signals machine learning kelm parameter tuning rcmo algorithm

Deep Transfer Learning Driven Oral Cancer Detection and Classification Model

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Computers, Materials & Continua 2022年第11期73卷 3905-3920页

作者： Radwa Marzouk Eatedal alabdulkreem sami Dhahbi Mohamed K.Nour Mesfer al Duhayyim Mahmoud Othman Manar ahmed Hamza Abdelwahed Motwakel Ishfaq Yaseen mohammed Rizwanullah Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam Bin Abdulaziz 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

Oral cancer is the most commonly occurring‘head and neck cancers’across the *** of the oral cancer cases are diagnosed at later stages due to absence of awareness among *** earlier identification of disease is essential for improved outcomes,Artificial Intelligence(AI)and Machine Learning(ML)models are used in this *** this background,the current study introduces Artificial Intelligence with Deep Transfer Learning driven Oral Cancer detection and Classification Model(AIDTLOCCM).The primary goal of the proposed AIDTL-OCCM model is to diagnose oral cancer using AI and image processing *** proposed AIDTL-OCCM model involves fuzzy-based contrast enhancement approach to perform data *** by,the densely-connected networks(DenseNet-169)model is employed to produce a useful set of deep ***,Chimp Optimization algorithm(COA)with Autoencoder(AE)model is applied for oral cancer detection and ***,COA is employed to determine optimal parameters involved in AE model.A wide range of experimental analyses was conducted on benchmark datasets and the results were investigated under several *** extensive experimental analysis outcomes established the enhanced performance of AIDTLOCCM model compared to other approaches with a maximum accuracy of 90.08%.

关键词： Oral cancer detection image classification artificial intelligence machine learning deep learning