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A Multi-Task Deep Learning Framework for Simultaneous Detection of Thoracic Pathology through Image Classification

Journal of Computer and Communications 2024年第4期12卷 153-170页

作者： Nada Al Zahrani Ramdane Hedjar Mohamed Mekhtiche Mohamed Bencherif taha Al Fakih Fattoh Al-Qershi Muna Alrazghan Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia Department of Information System College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

Thoracic diseases pose significant risks to an individual's chest health and are among the most perilous medical diseases. They can impact either one or both lungs, which leads to a severe impairment of a person’s ability to breathe normally. Some notable examples of such diseases encompass pneumonia, lung cancer, coronavirus disease 2019 (COVID-19), tuberculosis, and chronic obstructive pulmonary disease (COPD). Consequently, early and precise detection of these diseases is paramount during the diagnostic process. Traditionally, the primary methods employed for the detection involve the use of X-ray imaging or computed tomography (CT) scans. Nevertheless, due to the scarcity of proficient radiologists and the inherent similarities between these diseases, the accuracy of detection can be compromised, leading to imprecise or erroneous results. To address this challenge, scientists have turned to computer-based solutions, aiming for swift and accurate diagnoses. The primary objective of this study is to develop two machine learning models, utilizing single-task and multi-task learning frameworks, to enhance classification accuracy. Within the multi-task learning architecture, two principal approaches exist soft parameter sharing and hard parameter sharing. Consequently, this research adopts a multi-task deep learning approach that leverages CNNs to achieve improved classification performance for the specified tasks. These tasks, focusing on pneumonia and COVID-19, are processed and learned simultaneously within a multi-task model. To assess the effectiveness of the trained model, it is rigorously validated using three different real-world datasets for training and testing.

关键词： Pneumonia Thoracic Pathology COVID-19 Deep Learning Multi-Task Learning

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The Effect of Core Eccentricity on the Structural Behavior of Concrete Tall Buildings

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Journal of Civil Engineering and Architecture 2024年第3期18卷 112-117页

作者： Tarek Anwar taha Awida Civil and Architecture Engineering Department College of EngineeringInternational University of Kuwait(IUK)Ardiya Government AreaKuwait

The main purpose of this paper is to investigate the effect of core eccentricity on the structural behavior of concrete tall *** buildings of 55 floors with plan dimensions 48.0×48.0 m2 were *** cases of main core locations are studied:centric(A),eccentric by one sixth(B)and one third(C)of building *** three-dimensional finite element method has been used in conducting structural analysis through ETABS *** and lateral(wind and seismic)loadings are applied to all building *** has been concluded that the core location is the prime parameter governing the structural behavior of tall *** the first two cases(A,B)have acceptable and similar structural behaviors conforming to code limits,in the third case(C),the building behavior came beyond code *** author introduced remedial action by adding two secondary cores in the opposite direction of the main core(C-R)to restore the building behavior to the code *** results of this action were satisfactory.

关键词： Concrete tall buildings core eccentricity structural behavior gravity wind seismic loadings

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Insights into the anorexic mechanism of Khat:an integrated in vivo,ex vivo,and in silico investigations

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Traditional Medicine Research 2024年第10期9卷 36-51页

作者： Ahmed Ali Jerah Manal Mohamed Elhassan taha Abdullah Farasani Sivakumar Sivagurunathan Moni Emad Sayed Shaheen Andleeb Khan Ibrahim Abdo Khardali Magbool Oraiby Heyam Mohamed Ali Sidahmed Siddig Ibrahim Abdelwahab Department of Medical Laboratory Technology College of Applied Medical SciencesJazan UniversityJazan 45142Saudi Arabia Health Research Centre Jazan UniversityJazan 45142Saudi Arabia Department of Pharmaceutics College of PharmacyJazan UniversityJazan 45142Saudi Arabia Department of Biosciences Faculty of ScienceIntegral UniversityLucknow 226026India Poison Control and Medical Forensic Chemistry Center General Directorate of Health AffairsJazan 45142Saudi Arabia Department of Pharmaceutical Chemistry Faculty of PharmacyAl-Neelain UniversityKhartoum 16458Sudan

Background:Chewing Khat(Catha edulis)releases cathine and cathinone,which may reduce appetite via an unknown *** study investigated the peripheral and central effects of fresh leaves and buds of Catha edulis water extract(CEWE)on appetite biomarkers,gene expression,and body weight,using in vivo,ex vivo,and in silico ***:Rats of both sexes were orally administered CEWE at different doses and durations in three different *** chromatography-mass spectroscopy(LC-MS)-MS was used to detect cathinone and cathine in the murine *** effect of Khat on serotonin receptors was studied in isolated rat fundus *** of the two Khat ligands was performed on G(The 5-hydroxytryptamine-type 2C receptor(5-HT2C)in an agonist-bound active conformation)and H(5-HT2C in an antagonist-bound inactive conformation)proteins to determine which ligands are most likely to act as agonists or ***:Significant differences(P0.05).The expression of the ghrelin and leptin genes was also unaffected,but the expression of the 5-hydroxytryptamine(5-HT)gene decreased(P<0.05)with CEWE *** antagonizes 5-HT receptors in isolated rat fundus *** findings indicated that the khat ligands bound to 5-HT2C *** and cathinone levels in rat plasma were ***:Khat extract may suppress appetite by antagonizing the 5-HT *** research is required to understand its mechanism and potential applications.

关键词： Catha edulis appetite leptin ghrelin 5-hydroxytryptamine molecular docking

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Neuroprotective potential of traditional Ayurvedic Kadha:age-related neurological disorders:a comprehensive literature review

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Traditional Medicine Research 2024年第9期9卷 51-77页

作者： Sarvin Sadreddini Mohammad Javad Emami Kazemabad Sogand Sheikholeslami Kosar Chitzan-Zadeh Mohammad taha Kouchaki Midia Rashidi Amir Abbas Ghayerin Shahin Sabaghi Ghazal Tavakoli Sepehr Nanbakhsh Tina Mansourian Arefeh Tabashiri Fariba Arbab Mojeni Mohammed Namiq Amin Zahra Taheri Seyed Esmaeil Sedighi Taraneh Tarkashvand Parham Amirazodi Mobina Fathi Shirin Yaghoobpoor Niloofar Deravi School of Medicine Tabriz University of Medical SciencesTabriz ***Iran School of Medicine Qom University of Medical SciencesQom ***Iran School of Medicine Hamadan University of Medical SciencesHamadan ***Iran School of Medicine Ahvaz Jundishapur University of Medical SciencesAhvaz ***Iran Faculty of Medicine Tabriz University of Medical SciencesTabriz ***Iran School of Medicine Iran University of Medical SciencesTehran ***Iran School of Medicine Shahid Beheshti University of Medical SciencesTehran ***Iran School of Medicine Mazandaran University of Medical SciencesSari ***Iran School of Medicine Kermanshah University of Medical SciencesKermanshah ***Iran Faculty of Pharmacy Pharmaceutical Sciences BranchIslamic Azad UniversityTehran ***Iran Department of Biology Faculty of SciencePayamNoor UniversityTehran ***Iran Faculty of Medicine Islamic Azad University of Medical ScienceTehran 193951495Iran International Campus Shahid Sadoughi University of Medical SciencesYazd ***Iran

Despite modern medicine’s advancements,age-related neurological diseases like Alzheimer’s disease and Parkinson’s disease remain challenging due to high costs,side effects,and limited ***,a traditional Indian medicine system,offers Kadha tea as a potential herbal *** review explores Kadha’s components(basil(Ocimum basilicum L.),black pepper(Piper nigrum L.),Cinnamon(Cinnamomum verum ***),ginger(Zingiber officinale Roscoe),and raisin(Vitis vinifera L.))and their interaction with various neurological *** suggest Kadha exhibits anti-inflammatory,antioxidant,and antiviral properties,potentially impacting Alzheimer’s disease,Parkinson’s disease,neurotoxicity,neuroinflammation,and brain *** focusing on specific disease mechanisms and Kadha’s intergrade effects,this review aims to elucidate its potential role in managing age-related neurological disorders.

关键词： Ayurveda Kadha neuroprotective neurological disorders traditional medicine

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A Hybrid Meta-Classifier of Fuzzy Clustering and Logistic Regression for Diabetes Prediction

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Computers, Materials & Continua 2022年第6期71卷 6089-6105页

作者： Altyeb Altaher taha Sharaf Jameel Malebary Department of Information Technology Faculty of Computing and Information Technology in RabighKing Abdulaziz UniversityJeddahSaudi Arabia

Diabetes is a chronic health condition that impairs the body’s ability to convert food to energy,recognized by persistently high levels of blood *** diabetes can cause many complications,including retinopathy,nephropathy,neuropathy,and other vascular *** learning methods can be very useful for disease identification,prediction,and *** paper proposes a new ensemble learning approach for type 2 diabetes prediction based on a hybrid meta-classifier of fuzzy clustering and logistic *** proposed approach consists of two ***,a baselearner comprising six machine learning algorithms is utilized for predicting ***,a hybrid meta-learner that combines fuzzy clustering and logistic regression is employed to appropriately integrate predictions from the base-learners and provide an accurate prediction of *** hybrid metalearner employs the Fuzzy C-means Clustering(FCM)algorithm to generate highly significant clusters of predictions from *** predictions of base-learners and their fuzzy clusters are then employed as inputs to the Logistic Regression(LR)algorithm,which generates the final diabetes prediction *** were conducted using two publicly available datasets,the Pima Indians Diabetes Database(PIDD)and the Schorling Diabetes Dataset(SDD)to demonstrate the efficacy of the proposed method for predicting *** compared with other models,the proposed approach outperformed them and obtained the highest prediction accuracies of 99.00%and 95.20%using the PIDD and SDD datasets,respectively.

关键词： Ensemble learning fuzzy clustering diabetes prediction machine learning

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Anticoagulant use before COVID-19 diagnosis prevent COVID-19 associated acute venous thromboembolism or not:A systematic review and meta-analysis

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World Journal of Methodology 2024年第3期14卷 141-162页

作者： Kinza Iqbal Akshat Banga taha Bin Arif Sawai Singh Rathore Abhishek Bhurwal Syeda Kisa Batool Naqvi Muhammad Mehdi Pankaj Kumar Mitali Madhu Salklan Ayman Iqbal Jawad Ahmed Nikhil Sharma Amos Lal Rahul Kashyap Vikas Bansal Juan Pablo Domecq Department of Internal Medicine Dow Medical CollegeKarachi 74200Pakistan Department of Internal Medicine Sawai Man Singh Medical CollegeJaipur 302004India Department of Internal Medicine Dr.Sampurnanand Medical CollegeJodhpur 342003RajasthanIndia Department of Gastroenterology and Hepatology Rutgers Robert Wood Johnson School of MedicineNew BrunswickNJ 08901United States Department of Internal Medicine Pandit Bhagwat Dayal Sharma Post Graduate Institute of Medical SciencesRohtak 124001HaryanaIndia Department of Nephrology and Hypertension Mayo ClinicRochesterMN 55905United States Department of Medicine Division of Pulmonary and Critical Care MedicineMayo ClinicRochesterMN 55905United States Department of Research Wellspan HealthYorkPA 17403United States

BACKGROUND Coagulopathy and thromboembolic events are associated with poor outcomes in coronavirus disease 2019(COVID-19)*** is conflicting evidence on the effects of chronic anticoagulation on mortality and severity of COVID-19 *** To summarize the body of evidence on the effects of pre-hospital anticoagulation on outcomes in COVID-19 *** A Literature search was performed on LitCovid PubMed,WHO,and Scopus databases from inception(December 2019)till June 2023 for original studies reporting an association between prior use of anticoagulants and patient outcomes in adults with *** primary outcome was the risk of thromboembolic events in COVID-19 patients taking *** outcomes included COVID-19 disease severity,in terms of intensive care unit admission or invasive mechanical ventilation/intubation requirement in patients hospitalized with COVID-19 infection,and *** random effects models were used to calculate crude and adjusted odds ratios(aORs)with 95%confidence intervals(95%CIs).RESULTS Forty-six observational studies met our inclusion *** unadjusted analysis found no association between prior anticoagulation and thromboembolic event risk[n=43851,9 studies,odds ratio(OR)=0.67(0.22,2.07);P=0.49;I2=95%].The association between prior anticoagulation and disease severity was non-significant[n=186782;22 studies,OR=1.08(0.78,1.49);P=0.64;I2=89%].However,pre-hospital anticoagulation significantly increased all-cause mortality risk[n=207292;35 studies,OR=1.72(1.37,2.17);P<0.00001;I2=93%].Pooling adjusted estimates revealed a statistically non-significant association between pre-hospital anticoagulation and thromboembolic event risk[aOR=0.87(0.42,1.80);P=0.71],mortality[aOR=0.94(0.84,1.05);P=0.31],and disease severity[aOR=0.96(0.72,1.26);P=0.76].CONCLUSION Prehospital anticoagulation was not significantly associated with reduced risk of thromboembolic events,improved survival,and lower disease seve

关键词： Prior anticoagulation COVID-19 Prehospital anticoagulation Chronic anticoagulation Mortality Severity Thromboembolic events

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Phenotypic Analysis of Diseased Plant Leaves Using Supervised and Weakly Supervised Deep Learning

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Plant Phenomics 2023年第2期5卷 139-148页

作者： Lei Zhou Qinlin Xiao Mohanmed Farag taha Chengjia Xu Chu Zhang College of Mechanical and Electronic Engineering Nanjing Forestry UniversityNanjingChina College of Biosystems Engineering and Food Science Zhejiang UniversityZhejiangChina Department of Soil and Water Sciences Faculty of Environmental Agricultural SciencesArish UniversityNorth Sinai45516Egypt School of Information Engineering Huzhou UniversityHuzhouChina

Deep learning and computer vision have become emerging tools for diseased plant *** previous studies focused on image-level disease *** this paper,pixel-level phenotypic feature(the distribution of spot)was analyzed by deep ***,a diseased leaf dataset was collected and the corresponding pixel-level annotation was contributed.A dataset of apple leaves samples was used for training and *** set of grape and strawberry leaf samples was used as an extra testing ***,supervised convolutional neural networks were adopted for semantic ***,the possibility of weakly supervised models for disease spot segmentation was also ***-CAM combined with ResNet-50(ResNet-CAM),and that combined with a few-shot pretrained U-Net classifier for weakly supervised leaf spot segmentation(WSLSS),was *** were trained using image-level annotations(healthy versus diseased)to reduce the cost of annotation *** showed that the supervised DeepLab achieved the best performance(IoU=0.829)on the apple leaf *** weakly supervised WSLSS achieved an IoU of *** processing the extra testing dataset,WSLSS realized the best IoU of 0.511,which was even higher than fully supervised DeepLab(IoU=0.458).Although there was a certain gap in IoU between the supervised models and weakly supervised ones,WSLSS showed stronger generalization ability than supervised models when processing the disease types not involved in the training ***,the contributed dataset in this paper could help researchers get a quick start on designing their new segmentation methods in future studies.

关键词： Deep computer weakly

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Effect of Diabetes Self-Management Education on Glycaemic Control in Sudanese Adults with Type 2 Diabetes

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Journal of Biosciences and Medicines 2024年第3期12卷 316-327页

作者： Sahar Moawia Balla Elnour Tayseer Abdelmotalib Ahmed taha Haiam Abdalla Wadatalla Ziryab Zainelabdin Mohamed Elmahdi Marwah Isam Abdulmajeed Mohammedahmed Rowa Abdelmonem Sidig Hamadto Nahla Yousif Osman Mohammed Saeed Omnia Mubarak Saad Abdallah Sulafa Abdelbagi Mustafa Ahmed Hanady Abdelhameed Ahmed Mohamed Sarah Khalil Fathi Khalil Family Medicine Health Gates Primary Care Riyadh SAU Family Medicine Dhaman Health Assurance Hospitals Company Kuwait KWT Family Medicine First Health Cluster Riyadh SAU Emergency Department Sheikh Khalifa Medical City Abudahbi ARE Family Medicine University of Medical Sciences and Technology Khartoum SDN Family Medicine Sudan Medical Specialization Board-Sudan Khartoum SDN Family Medicine Ultra Care Home Care Company Riyadh SAU Medicine Department Health Gates Primary Care Riyadh SAU Family Medicine Mouwasat Hospital Madinah SAU

Research Background: The high prevalence of diabetes in Sudan, estimated at 16%, highlights the importance of effective health education in diabetes management. Diabetes self-management education has been identified as a crucial tool in enhancing the knowledge, attitudes, and abilities necessary for self-management among individuals with diabetes. Aim: To assess the impact of diabetes self-management education on medication adherence and glycemic control in Sudanese adults with type 2 diabetes before and 3 months after the DSME intervention. Method: The study was conducted in Sudan between September 2022 and March 2023, it was an interventional, one-group, pre- and post-test study that aimed to assess the impact of diabetes self-management education (DSME) on medication adherence and diabetes control in Sudanese adults with type 2 diabetes. The research was conducted in primary health care centers in six cities in Sudan and involved 244 participants. The data entry and statistical analysis were conducted using the Statistical Package for Social Sciences version 27.0. A paired t test was used for analysis. Results: The study included 244 participants, 67% of whom were males. The age mean ± SD was 48.6 ± 9.3 years, and 85.3% of participants were married. Age at onset of diabetes mean ± SD was 40.60 ± 7.81 years;44.6% had diabetes for less than 5 years;and 84.1% had a positive family history of diabetes mellitus. The levels of poor, low, and partial adherence to medication decreased by 8.2%, 4%, and 20.6%, respectively, after the intervention. The levels of good and high medication regime adherence increased by 13% and 19.8%, respectively;BMI decreased by 1.1 ± 0.73 kg/m2 (p = 0.005). The fasting blood sugar decreased by 69 ± 32.9 mg/dl (p = 0.049), and the glycated hemoglobin decreased by 1.21 ± 0.28% (p = 0.001). Conclusions: The findings of this study reinforce the importance of patient education in improving glycemic control and enhancing self-management

关键词： Sudan Adherence Intervention Education Self-Management Diabetes

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Conditional Probability Approach for Fault Detection in Photovoltaic Energy Farms

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Computer Systems Science & Engineering 2022年第9期42卷 1109-1120页

作者： Nagy I.Elkalashy Ibrahim B.M.taha Electrical Engineering Department Faculty of EngineeringMenoufia University32511Egypt Electrical Engineering Department College of EngineeringTaif University21944Saudi Arabia

Detection of electric faults in photovoltaic (PV) farms enhances a sustainable service continuity of farm energy generation. In this paper, a probabilisticfunction is introduced to detect the faults in the PV farms. The conditional probability functions are adopted to detect different fault conditions such as internalstring faults, string-to-string faults, and string-to-negative terminal faults. As thediodes are important to make the PV farms in-service safely during the faults,the distribution currents of these faults are evaluated with different concepts ofdiode consideration as well as without considering any diode installation. Thispart of the study enhances the diode utilization in the PV farms concerning theprotection point of view. The PV string currents are used as inputs to the conditional probability detection algorithms. However, the setting of the fault detectiontechnique is not portable for the other PV systems due to broad ranges of PV system ratings. To accordingly generalize the proposed fault detection algorithm, thePV string currents are first normalized to the total array current for universallyapplying the detection function at different PV string ratings. The limiting faultresistances are evaluated to show the sensitivity of the proposed fault *** results ensure the application of the proposed probabilistic detection functionfor PV farms.

关键词： Photovoltaic farm fault detection conditional probability diodes

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Real and Altered Fingerprint Classification Based on Various Features and Classifiers

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Computers, Materials & Continua 2023年第1期74卷 327-340页

作者： Saif Saad Hameed Ismail taha Ahmed Omar Munthir Al Okashi College of Computer Sciences and Information Technology University of AnbarAnbarIraq

Biometric recognition refers to the identification of individuals through their unique behavioral features(e.g.,fingerprint,face,and iris).We need distinguishing characteristics to identify people,such as fingerprints,which are world-renowned as the most reliablemethod to identify *** recognition of fingerprints has become a standard procedure in forensics,and different techniques are available for this *** current techniques lack interest in image enhancement and rely on high-dimensional features to generate classification ***,we proposed an effective fingerprint classification method for classifying the fingerprint image as authentic or altered since criminals and hackers routinely change their fingerprints to generate fake *** order to improve fingerprint classification accuracy,our proposed method used the most effective texture features and *** Analysis(DCA)and Gaussian Discriminant Analysis(GDA)are employed as classifiers,along with Histogram of Oriented Gradient(HOG)and Segmentation-based Feature Texture Analysis(SFTA)feature vectors as *** performance of the classifiers is determined by assessing a range of feature sets,and the most accurate results are *** proposed method is tested using a Sokoto Coventry Fingerprint Dataset(SOCOFing).The SOCOFing project includes 6,000 fingerprint images collected from 600 African people whose fingerprints were taken ten *** distinct degrees of obliteration,central rotation,and z-cut have been performed to obtain synthetically altered replicas of the genuine *** proposal achieved massive success with a classification accuracy reaching 99%.The experimental results indicate that the proposed method for fingerprint classification is feasible and *** experiments also showed that the proposed SFTA-based GDA method outperformed state-of-art approaches in feature dimension and classification accuracy.

关键词： Fingerprint classification HOG SFTA discriminant analysis(DCA)classifier gaussian discriminant analysis(GDA)classifier SOCOFing

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