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Towards Intelligent Detection and Classification of Rice Plant Diseases Based on Leaf Image Dataset

Computer Systems Science & Engineering 2023年第11期47卷 1385-1413页

作者： Fawad Ali Shah Habib Akbar Abid Ali Parveen Amna maha Aljohani Eman A.Aldhahri Harun Jamil Department of IT The University of HaripurHaripur22620Pakistan Department of Computer Science University of Engineering and TechnologyTaxila47080Pakistan Department of Computer Science GANK(S)DC KTS HaripurHaripur22620Pakistan College of Pharmacy Gachon University Medical CampusNo.191HambakmoeroYeonsu-guIncheon21936Korea Software Engineering Department College of Computer Science and EngineeringUniversity of JeddahJeddah23218Saudi Arabia College of Computer Sciences and Engineering Department of Computer Science and Artificial IntelligenceUniversity of JeddahJeddahSaudi Arabia Department of Computer Engineering Jeju National UniversityJejusi63243Korea

The detection of rice leaf disease is significant because,as an agricultural and rice exporter country,Pakistan needs to advance in production and lower the risk of *** this rapid globalization era,information technology has increased.A sensing system is mandatory to detect rice diseases using Artificial Intelligence(AI).It is being adopted in all medical and plant sciences fields to access and measure the accuracy of results and detection while lowering the risk of *** Neural Network(DNN)is a novel technique that will help detect disease present on a rice leave because DNN is also considered a state-of-the-art solution in image detection using sensing *** in this paper,the adoption of the mixed-method approach Deep Convolutional Neural Network(Deep CNN)has assisted the research in increasing the effectiveness of the proposed *** CNN is used for image recognition and is a class of deep-learning neural *** is popular and mostly used in the field of image recognition.A dataset of images with three main leaf diseases is selected for training and testing the proposed *** the image acquisition and preprocessing process,the Deep CNN model was trained to detect and classify three rice diseases(Brown spot,bacterial blight,and blast disease).The proposed model achieved 98.3%accuracy in comparison with similar state-of-the-art techniques.

关键词： Rice plant disease detection convolution neural network image classification biological classification

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Expression levels of pro-inflammatory interleukin-8 and certain antimicrobial peptides in concurrent with bacterial conjunctivitis

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International Journal of Ophthalmology(English edition) 2021年第5期14卷 666-675页

作者： Alaa El-Dien Shawky Hosny Zeinab El-Demerdash El-Bazza Mohammed Abdelhalim Ramadan maha Ahmed Shafik Mahmoud Ahmed Shafeek Rania Abdelmonem Khattab Microbiology and Immunology Department Faculty of PharmacyCairo UniversityKasr Al-Aini 11562CairoEgypt Drug Radiation Research Department National Center for Radiation Research and TechnologyAtomic Energy AuthorityCairo 9621Egypt Ophthalmology Department Faculty of medicineAL-Azhar UniversityCairo 11754Egypt

AIM:To detect the quantitative expression levels of the pro-inflammatory interleukin-8(IL8),antimicrobial peptides human beta defense-2(HBD2),and human beta defense-3(HBD3)genes in bacterial ***:The human conjunctival epithelial cells were obtained using the impression cytology technique from healthy controls and *** genes expression levels were determined utilizing a reverse transcription quantitative polymerase chain reaction(RT-q PCR).The contribution of causative agent type,the number of isolates and severity of clinical features,in the increase of genes expression was also ***:The RT-q PCR showed that IL8,HBD2,and HBD3 expression increased in bacterial conjunctivitis as compared to healthy control(P<0.001).In gram-negative bacterial conjunctivitis,HBD2 was highly up-regulated(P<0.001)compared to other types of bacterial *** mixed bacterial conjunctivitis,a direct correlation between HBD2 up-regulation and HBD3 up-regulation was observed(P<0.05).The severity of clinical features was related to the up-regulation of IL8 and HBD2(P<0.05).CONCLUSION:IL8,HBD2,and HBD3 are immuneeffectors in infectious ***2 is active during different bacterial conjunctivitis but is more released with gram-negative bacteria compared to gram-positive ***3 is an obvious defender in different bacterial conjunctivitis.

关键词： interleukin-8 human beta defense-2 human beta defense-3 infectious conjunctivitis real-time polymerase chain reaction

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Inference Procedures on the Generalized Poisson Distribution from Multiple Samples: Comparisons with Nonparametric Models for Analysis of Covariance (ANCOVA) of Count Data

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Open Journal of Statistics 2021年第3期11卷 420-436页

作者： maha Al-Eid Mohamed M. Shoukri Department of Biostatistics Epidemiology and Scientific Computing King Faisal Specialist Hospital and Research Center Riyadh Saudi Arabia Department of Epidemiology and Biostatistics Schulich School of Medicine and Dentistry University of Western Ontario London Ontario Canada

Count data that exhibit over dispersion (variance of counts is larger than its mean) are commonly analyzed using discrete distributions such as negative binomial, Poisson inverse Gaussian and other models. The Poisson is characterized by the equality of mean and variance whereas the Negative Binomial and the Poisson inverse Gaussian have variance larger than the mean and therefore are more appropriate to model over-dispersed count data. As an alternative to these two models, we shall use the generalized Poisson distribution for group comparisons in the presence of multiple covariates. This problem is known as the ANCOVA and is solved for continuous data. Our objectives were to develop ANCOVA using the generalized Poisson distribution, and compare its goodness of fit to that of the nonparametric Generalized Additive Models. We used real life data to show that the model performs quite satisfactorily when compared to the nonparametric Generalized Additive Models.

关键词： Count Regression Over Dispersion Generalized Linear Models Analysis of Covariance Generalized Additive Models

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Validated stability indicating methods for determination of nitazoxanide in presence of its degradation products

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Journal of Pharmaceutical Analysis 2012年第2期2卷 105-116页

作者： Nouruddin W.Ali Samah Sayed Abbas Hala El-Sayed Zaazaa maha Mohamed Abdelrahman Mohamed Abdelkawy Analytical Chemistry Department Faculty of PharmacyBeni-Suef UniversityHussin El-Shafey StreetBeni-Suef CityEgypt Analytical Chemistry Department Faculty of PharmacyCairo UniversityKasr El-Aini Street11562CairoEgypt

Three sensitive,selective and reproducible stability-indicating methods are presented for determination of nitazoxanide (NTZ),a new anti-protozoal drug,in presence of its degradation *** A utilizes the first derivative of ratio spectra spectrophotometry by measurement of the amplitude at 364.4 nm using one of the degradation products as a *** B is a chemometric-assisted spectrophotometry,where principal component regression (PCR) and partial least squares (PLS) were *** two approaches were successfully applied to quantify NTZ in presence of degradation products using the information included in the absorption spectra in the range 260-360 *** C is based on the separation of NTZ from its degradation products followed by densitometric measurement of the bands at 254 *** separation was carried out on silica gel 60F 254,using chloroform-methanol-ammonia solution-glacial acetic acid (95:5:1:1 by volume,pH=5.80) as a developing *** methods are suitable as stability-indicating methods for the determination of NTZ in presence of its degradation products either in bulk powder or in pharmaceutical *** analysis of the results has been carried out revealing high accuracy and good precision.

关键词： Nitazoxanide Degradation Derivative spectrophotometry Chemometrics TLC-densitometry Stability-indicating methods

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Federated Learning with Blockchain Assisted Image Classification for Clustered UAV Networks

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Computers, Materials & Continua 2022年第7期72卷 1195-1212页

作者： Ibrahim Abunadi maha M.Althobaiti Fahd N.Al-Wesabi Anwer Mustafa Hilal Mohammad Medani Manar Ahmed Hamza Mohammed Rizwanullah Abu Serwar Zamani Department of Information Systems Prince Sultan UniversityRafha StreetRiyadh11586Saudi Arabia Department of Computer Science College of Computing and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’a61101Yemen Department of Computer and Self Development Prince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia

The evolving“Industry 4.0”domain encompasses a collection of future industrial developments with cyber-physical systems(CPS),Internet of things(IoT),big data,cloud computing,***,the industrial Internet of things(IIoT)directs data from systems for monitoring and controlling the physical world to the data processing system.A major novelty of the IIoT is the unmanned aerial vehicles(UAVs),which are treated as an efficient remote sensing technique to gather data from large *** are commonly employed in the industrial sector to solve several issues and help decision *** the strict regulations leading to data privacy possibly hinder data sharing across autonomous *** learning(FL)becomes a recent advancement of machine learning(ML)which aims to protect user *** this aspect,this study designs federated learning with blockchain assisted image classification model for clustered UAV networks(FLBIC-CUAV)on IIoT *** proposed FLBIC-CUAV technique involves three major processes namely clustering,blockchain enabled secure communication and FL based image *** UAV cluster construction process,beetle swarm optimization(BSO)algorithm with three input parameters is designed to cluster the UAVs for effective *** addition,blockchain enabled secure data transmission process take place to transmit the data from UAVs to cloud ***,the cloud server uses an FL with Residual Network model to carry out the image classification process.A wide range of simulation analyses takes place for ensuring the betterment of the FLBIC-CUAV *** experimental outcomes portrayed the betterment of the FLBIC-CUAV approach over the recent state of art methods.

关键词： UAV networks clustering secure communication blockchain federated learning image classification

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On the Index of Repeatability: Estimation and Sample Size Requirements

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Open Journal of Statistics 2019年第4期9卷 530-541页

作者： maha Al-Eid Mohamed M. Shoukri Department of Biostatistics Epidemiology and Scientific Computing King Faisal Specialist Hospital and Research Center Riyadh KSA Department of Epidemiology and Biostatistics Schulich School of Medicine and Dentistry London Ontario Canada

Background: Repeatability is a statement on the magnitude of measurement error. When biomarkers are used for disease diagnoses, they should be measured accurately. Objectives: We derive an index of repeatability based on the ratio of two variance components. Estimation of the index is derived from the one-way Analysis of Variance table based on the one-way random effects model. We estimate the large sample variance of the estimator and assess its adequacy using bootstrap methods. An important requirement for valid estimation of repeatability is the availability of multiple observations on each subject taken by the same rater and under the same conditions. Methods: We use the delta method to derive the large sample variance of the estimate of repeatability index. The question related to the number of required repeats per subjects is answered by two methods. In first methods we estimate the number of repeats that minimizes the variance of the estimated repeatability index, and the second determine the number of repeats needed under cost-constraints. Results and Novel Contribution: The situation when the measurements do not follow Gaussian distribution will be dealt with. It is shown that the required sample size is quite sensitive to the relative cost. We illustrate the methodologies on the Serum Alanine-aminotransferase (ALT) available from hospital registry data for samples of males and females. Repeatability is higher among females in comparison to males.

关键词： Measurements Errors Functions of Variance Components Delta Method Lagrange Multiplier Bootstrap Technique

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“I’m Just Guiding You”: An Exploration of Software Design Mentorship within a Software Engineering Firm

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Journal of Software Engineering and Applications 2018年第6期11卷 299-317页

作者： Leshell Hatley maha Al-Freih Brenda Bannan Department of Mathematics & Computer Science Coppin State University Baltimore USA Department of Educational Technology Princess NourahBint Abdulrahman University Riyadh Saudi Arabia Instructional Technology/Learning Technologies Design Research George Mason University Fairfax USA

The case study presented here uses an interpretivist (qualitative, humanistic) approach to illustrate and describe a range of interactions and behaviors that occur during design meetings where mentoring and design simultaneously occur within a software engineering firm, during a portion of the design phase for a software project. It attempts to examine the interaction between two design team members (one novice and one expert) and describes how these observations intersect with the theoretical and applied literature and actual design processes. Taking cues from two theoretical descriptions of the design process, the study presented here suggests that modes and models of mentorship should be added, when applicable, as a descriptive portion of the design process.

关键词： Software Design Mentorship Design Meeting Collaboration

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Predictors of the Aggregate of COVID-19 Cases and Its Case-Fatality: A Global Investigation Involving 120 Countries

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Open Journal of Statistics 2021年第2期11卷 259-277页

作者： Sarah Al-Gahtani Mohamed Shoukri maha Al-Eid Department of Internal Medicine King Fahd Medical City Riyadh Saudi Arabia Department of Epidemiology and Biostatistics Schulich School of Medicine and Dentistry University of Western Ontario London Ontario Canada Department of Biostatistics Epidemiology and Scientific Computing King Faisal Specialist Hospital and Research Center Riyadh Saudi Arabia

Objective: Since the identification of COVID-19 in December 2019 as a pandemic, over 4500 research papers were published with the term “COVID-19” contained in its title. Many of these reports on the COVID-19 pandemic suggested that the coronavirus was associated with more serious chronic diseases and mortality particularly in patients with chronic diseases regardless of country and age. Therefore, there is a need to understand how common comorbidities and other factors are associated with the risk of death due to COVID-19 infection. Our investigation aims at exploring this relationship. Specifically, our analysis aimed to explore the relationship between the total number of COVID-19 cases and mortality associated with COVID-19 infection accounting for other risk factors. Methods: Due to the presence of over dispersion, the Negative Binomial Regression is used to model the aggregate number of COVID-19 cases. Case-fatality associated with this infection is modeled as an outcome variable using machine learning predictive multivariable regression. The data we used are the COVID-19 cases and associated deaths from the start of the pandemic up to December 02-2020, the day Pfizer was granted approval for their new COVID-19 vaccine. Results: Our analysis found significant regional variation in case fatality. Moreover, the aggregate number of cases had several risk factors including chronic kidney disease, population density and the percentage of gross domestic product spent on healthcare. The Conclusions: There are important regional variations in COVID-19 case fatality. We identified three fac

关键词： Intraclass Correlation Coefficient Hierarchical Data Structure Negative Binomial Regression Data Splitting Mixed Effects Linear Regression Model

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An Automated System for Early Prediction of Miscarriage in the First Trimester Using Machine Learning

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Computers, Materials & Continua 2023年第4期75卷 1291-1304页

作者： Sumayh S.Aljameel Malak Aljabri Nida Aslam Dorieh M.Alomari Arwa Alyahya Shaykhah Alfaris maha Balharith Hiessa Abahussain Dana Boujlea Eman S.Alsulmi Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Computer Science Department College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Computer Engineering College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Obstetrics and Gynecology College of MedicineImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia

Currently, the risk factors of pregnancy loss are increasing andare considered a major challenge because they vary between cases. The earlyprediction of miscarriage can help pregnant ladies to take the needed careand avoid any danger. Therefore, an intelligent automated solution must bedeveloped to predict the risk factors for pregnancy loss at an early stage toassist with accurate and effective diagnosis. Machine learning (ML)-baseddecision support systems are increasingly used in the healthcare sector andhave achieved notable performance and objectiveness in disease predictionand prognosis. Thus, we developed a model to help obstetricians predictthe probability of miscarriage using ML. And support their decisions andexpectations about pregnancy status by providing an easy, automated way topredict miscarriage at early stages using ML tools and techniques. Althoughmany published papers proposed similar models, none of them used Saudiclinical data. Our proposed solution used ML classification algorithms tobuild a miscarriage prediction model. Four classifiers were used in this study:decision tree (DT), random forest (RF), k-nearest neighbor (KNN), andgradient boosting (GB). Accuracy, Precision, Recall, F1-score, and receiveroperating characteristic area under the curve (ROC-AUC) were used to evaluatethe proposed model. The results showed that GB overperformed the otherclassifiers with an accuracy of 93.4% and ROC-AUC of 97%. This proposedmodel can assist in the early identification of at-risk pregnant women to avoidmiscarriage in the first trimester and will improve the healthcare sector inSaudi Arabia.

关键词： Miscarriage pregnancy abortion machine learning gradient boosting

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Paralleled DC-DC Power Converters Sliding Mode Control with Dual Stages Design

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Journal of Power and Energy Engineering 2014年第2期2卷 1-10页

作者： Bashar Khasawneh maha Sabra Mohamed A. Zohdy Department of Electrical and Computer Engineering Oakland University Rochester Michigan USA.

This paper proposes the new cascaded series parallel design for improved dynamic performance of DC-DC buck boost converters by a new Sliding Mode Control (SMC) method. The converter is controlled using Sliding Mode Control method that utilizes the converter’s duty ratio to determine the skidding surface. System modeling and simulation results are presented. The results also showed an improved overall performance over typical PID controller, and there was no overshoot or settling time, tracking the desired output nicely. Improved converter performance and robustness were expected.

关键词： DC-DC Converters Paralleled Converter DC-DC Control Efficiency Improvement of DC-DC Sliding Mode Control (SMC) Hyper Plane Design Variable Structure Control (VSC)

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