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Prediction of Covid-19 Based on Chest X-Ray Images Using Deep Learning with CNN

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Computer Systems Science & Engineering 2022年第6期41卷 1223-1240页

作者： Anika Tahsin Meem Mohammad Monirujjaman Khan Mehedi Masud sultan aljahdali Department of Electrical and Computer Engineering North South UniversityBashundharaDhaka1229Bangladesh Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia

The COVID-19 pandemic has caused trouble in people’s daily lives andruined several economies around the world, killing millions of people thus far. Itis essential to screen the affected patients in a timely and cost-effective manner inorder to fight this disease. This paper presents the prediction of COVID-19 withChest X-Ray images, and the implementation of an image processing systemoperated using deep learning and neural networks. In this paper, a Deep Learning,Machine Learning, and Convolutional Neural Network-based approach for predicting Covid-19 positive and normal patients using Chest X-Ray pictures is proposed. In this study, machine learning tools such as TensorFlow were used forbuilding and training neural nets. Scikit-learn was used for machine learning fromend to end. Various deep learning features are used, such as Conv2D, Dense Net,Dropout, Maxpooling2D for creating the model. The proposed approach had aclassification accuracy of 96.43 percent and a validation accuracy of 98.33 percentafter training and testing the X-Ray pictures. Finally, a web application has beendeveloped for general users, which will detect chest x-ray images either as covidor normal. A GUI application for the Covid prediction framework was run. Achest X-ray image can be browsed and fed into the program by medical personnelor the general public.

关键词： Covid-19 prediction covid-19 coronavirus normal deep learning convolutional neural network image processing chest x-ray

Healthcare Monitoring Using Ensemble Classifiers in Fog Computing Framework

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Computer Systems Science & Engineering 2023年第5期45卷 2265-2280页

作者： P.M.Arunkumar Mehedi Masud sultan aljahdali Mohamed Abouhawwash Department of Computer Science and Engineering Karpagam College of EngineeringCoimbatore641032India Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Nowadays,the cloud environment faces numerous issues like synchronizing information before the switch over the data *** requirement for a centralized internet of things(IoT)-based system has been restricted to some *** to low scalability on security considerations,the cloud seems *** healthcare networks demand computer operations on large amounts of data,the sensitivity of device latency evolved among health networks is a challenging *** comparison to cloud domains,the new paradigms of fog computing give fresh alternatives by bringing resources closer to users by providing low latency and energy-efficient data processing *** fog computing frameworks have various flaws,such as overvaluing response time or ignoring the accuracy of the result yet handling both at the same time compromises the network *** this proposed work,Health Fog is integrated with the Optimized Cascaded Convolution Neural Network framework for diagnosing heart ***,the data is collected,and then pre-processing is done by Linear Discriminant *** the features are extracted and optimized using Galactic Swarm *** optimized features are given into the Health Fog framework for diagnosing heart disease *** uses ensemble-based deep learning in edge computing devices,which automatically monitors real-life health networks such as heart disease ***,the classifiers such as bagging,boosting,XGBoost,Multi-Layer Perceptron(MLP),and Partitions(PART)are used for classifying the *** the majority voting classifier predicts the *** work uses FogBus architecture and evaluates the execution of power usage,bandwidth of the network,latency,execution time,and accuracy.

关键词： Healthfog FogBus cascaded convolution neural network cloud computing heart disease automatic health monitoring internet of things

SVM and KNN Based CNN Architectures for Plant Classification

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Computers, Materials & Continua 2022年第6期71卷 4257-4274页

作者： Sukanta Ghosh Amar Singh Kavita N.Z.Jhanjhi Mehedi Masud sultan aljahdali Lovely Professional University Jalandhar144005India Department of Computer Science and Engineering Chandigarh UniversityMohaliIndia School of Computer Science and Engineering Tailor’s UniversitySubang Jaya47500Malaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Automatic plant classification through plant leaf is a classical problem in Computer *** classification is challenging due to the introduction of new species with a similar pattern and *** efforts are made to automate plant classification using plant leaf,plant flower,bark,or *** much effort,it has been proven that leaf is the most reliable source for plant *** it is challenging to identify a plant with the help of leaf structure because plant leaf shows similarity in morphological variations,like sizes,textures,shapes,and ***,it is required to normalize all plant leaves into the same size to get better *** Neural Networks(CNN)provides a fair amount of accuracy when leaves are classified using this *** the performance can be improved by classifying using the traditional approach after applying *** this paper,two approaches,namely CNN+Support Vector Machine(SVM)and CNN+K-Nearest Neighbors(kNN)used on 3 datasets,namely LeafSnap dataset,Flavia Dataset,and MalayaKew *** datasets are augmented to take care all the *** assessments and correlations of the predetermined feature extractor models are ***+kNN managed to reach maximum accuracy of 99.5%,97.4%,and 80.04%,respectively,in the three datasets.

关键词： Plant leaf classification artificial intelligence SVM kNN deep learning deep CNN training epoch

A Monte Carlo Based COVID-19 Detection Framework for Smart Healthcare

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Computers, Materials & Continua 2022年第2期70卷 2365-2380页

作者： Tallat Jabeen Ishrat Jabeen Humaira Ashraf Nz Jhanjhi Mamoona Humayun Mehedi Masud sultan aljahdali Research Center for Modelling and Simulation(RCMS)NUST Islamabad44000Pakistan Department of Computer Science and Software Engineering International Islamic UniversityIslamabad44000Pakistan School of Computer Science and Engineering SCE Taylor’s UniversitySubang Jaya47500Malaysia College of Computer Science and Information Technology Jouf UniversityAl JoufSaudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

COVID-19 is a novel coronavirus disease that has been declared as a global pandemic in *** affects the whole world through personto-person *** virus spreads by the droplets of coughs and sneezing,which are quickly falling over the ***,anyone can get easily affected by breathing in the vicinity of the COVID-19 ***,vaccine for the disease is under clinical investigation in different pharmaceutical *** now,multiple medical companies have delivered health monitoring ***,a wireless body area network(WBAN)is a healthcare system that consists of nano sensors used to detect the real-time health condition of the *** proposed approach delineates is to fill a gap between recent technology trends and healthcare *** COVID-19 affected patient is monitored through WBAN sensors and network,a physician or a doctor can guide the patient at the right timewith the correct possible *** scenario helps the community to maintain social distancing and avoids an unpleasant environment for hospitalized patients Herein,a Monte Carlo algorithm guided protocol is developed to probe a secured cipher *** cipher helps to avoid wireless network issues like packet loss,network attacks,network interference,and routing *** Carlo based covid-19 detection technique gives 90%better results in terms of time complexity,performance,and *** indicate that Monte Carlo based covid-19 detection technique with edge computing idea is robust in terms of time complexity,performance,and efficiency and thus,is advocated as a significant application for lessening hospital expenses.

关键词： COVID-19 coronavirus cryptography Monte Carlo algorithm edge computing

Deep Learning Based Automated Detection of Diseases from Apple Leaf Images

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Computers, Materials & Continua 2022年第4期71卷 1849-1866页

作者： Swati Singh Isha Gupta Sheifali Gupta Deepika Koundal sultan aljahdali Shubham Mahajan Amit Kant Pandit Department of Electronics and Communication Engineering University Institute of TechnologyHimachal Pradesh UniversityShimla171005India Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjabIndia Department of Systemics School of Computer ScienceUniversity of Petroleum and Energy StudiesDehradunIndia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia School of Electronics&Communication Shri Mata Vaishno Devi UniversityKatra185320India

In Agriculture Sciences, detection of diseases is one of the mostchallenging tasks. The mis-interpretations of plant diseases often lead towrong pesticide selection, resulting in damage of crops. Hence, the automaticrecognition of the diseases at earlier stages is important as well as economicalfor better quality and quantity of fruits. Computer aided detection (CAD)has proven as a supportive tool for disease detection and classification, thusallowing the identification of diseases and reducing the rate of degradationof fruit quality. In this research work, a model based on convolutional neuralnetwork with 19 convolutional layers has been proposed for effective andaccurate classification of Marsonina Coronaria and Apple Scab diseases fromapple leaves. For this, a database of 50,000 images has been acquired bycollecting images of leaves from apple farms of Himachal Pradesh (H.P)and Uttarakhand (India). An augmentation technique has been performedon the dataset to increase the number of images for increasing the *** performance analysis of the proposed model has been compared with thenew two Convolutional Neural Network (CNN) models having 8 and 9 layersrespectively. The proposed model has also been compared with the standardmachine learning classifiers like support vector machine, k-Nearest Neighbour, Random Forest and Logistic Regression models. From experimentalresults, it has been observed that the proposed model has outperformed theother CNN based models and machine learning models with an accuracy of99.2%.

关键词： Deep learning convolutional neural network apple leaves apple scab support vector machine

MRMR Based Feature Vector Design for Efficient Citrus Disease Detection

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Computers, Materials & Continua 2022年第9期72卷 4771-4787页

作者： Bobbinpreet sultan aljahdali Tripti Sharma Bhawna Goyal Ayush Dogra Shubham Mahajan Amit Kant Pandit Department of Electronics&Communication Engineering Chandigarh UniversityMohali140413India Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Ronin Institute Mont ClairNJ07043USA School of Electronics&Communication Engineering Shri Mata Vaishno Devi UniversityKatra182320India

In recent times,the images and videos have emerged as one of the most important information source depicting the real time *** images nowadays serve as input for many applications and replacing the manual methods due to their capabilities of 3D scene representation in 2D *** capabilities of digital images along with utilization of machine learning methodologies are showing promising accuracies in many applications of prediction and pattern *** of the application fields pertains to detection of diseases occurring in the plants,which are destroying the widespread *** the disease detection process was done by a domain expert using manual examination and laboratory *** is a tedious and time consuming process and does not suffice the accuracy *** creates a room for the research in developing automation based methods where the images captured through sensors and cameras will be used for detection of disease and control its *** digital images captured from the field’s forms the dataset which trains the machine learning models to predict the nature of the *** accuracy of these models is greatly affected by the amount of noise and ailments present in the input images,appropriate segmentation methodology,feature vector development and the choice of machine learning *** ensure the high rated performance of the designed system the research is moving in a direction to fine tune each and every stage separately considering their dependencies on subsequent *** the most optimum solution can be obtained by considering the image processing methodologies for improving the quality of image and then applying statistical methods for feature extraction and *** training vector thus developed is capable of presenting the relationship between the feature values and the target *** this article,a highly accurate system model for detecting the diseases occurring in citrus fruits

关键词： Citrus diseases classification feature vector design plant disease detection redundancy reduction

Machine Learning and Artificial Neural Network for Predicting Heart Failure Risk

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Computer Systems Science & Engineering 2023年第1期44卷 757-775页

作者： Polin Rahman Ahmed Rifat MD.IftehadAmjad Chy Mohammad Monirujjaman Khan Mehedi Masud sultan aljahdali Department of Electrical and Computer Engineering North South UniversityBashundharaDhaka1229Bangladesh Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia

Heart failure is now widely spread throughout the *** disease affects approximately 48%of the *** is too expensive and also difficult to cure the *** research paper represents machine learning models to predict heart *** fundamental concept is to compare the correctness of various Machine Learning(ML)algorithms and boost algorithms to improve models’accuracy for *** supervised algorithms like K-Nearest Neighbor(KNN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF),Logistic Regression(LR)are considered to achieve the best *** boosting algorithms like Extreme Gradient Boosting(XGBoost)and Cat-Boost are also used to improve the prediction using Artificial Neural Networks(ANN).This research also focuses on data visualization to identify patterns,trends,and outliers in a massive data *** and Scikit-learns are used for *** Flow and Keras,along with Python,are used for ANN model *** DT and RF algorithms achieved the highest accuracy of 95%among the ***,KNN obtained a second height accuracy of 93.33%.XGBoost had a gratified accuracy of 91.67%,SVM,CATBoost,and ANN had an accuracy of 90%,and LR had 88.33%accuracy.

关键词： Heart failure prediction data visualization machine learning k-nearest neighbors support vector machine decision tree random forest logistic regression xgboost and catboost artificial neural network

Sentiment Analysis with Tweets Behaviour in Twitter Streaming API

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Computer Systems Science & Engineering 2023年第5期45卷 1113-1128页

作者： Kuldeep Chouhan Mukesh Yadav Ranjeet Kumar Rout Kshira Sagar Sahoo NZ Jhanjhi Mehedi Masud sultan aljahdali Computer Science and Engineering I.T.S Engineering CollegeGreater Noida201310India Computer Science and Engineering DPG Institute of Technology and ManagementGurgaon122004India Computer Science and Engineering National Institute of Technology SrinagarJammu and KashmirIndia Department of Computer Science and Engineering SRM UniversityAmaravatiAndhra Pradesh522240India School of Computer Science SCS Taylor’s UniversitySubang Jaya47500Malaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia

Twitter is a radiant platform with a quick and effective technique to analyze users’perceptions of activities on social *** researchers and industry experts show their attention to Twitter sentiment analysis to recognize the stakeholder *** sentiment analysis needs an advanced level of approaches including adoption to encompass data sentiment analysis and various machine learning *** assessment of sentiment analysis in multiple fields that affect their elevations among the people in real-time by using Naive Bayes and Support Vector Machine(SVM).This paper focused on analysing the distinguished sentiment techniques in tweets behaviour datasets for various spheres such as healthcare,behaviour estimation,*** addition,the results in this work explore and validate the statistical machine learning classifiers that provide the accuracy percentages attained in terms of positive,negative and neutral *** this work,we obligated Twitter Application Programming Interface(API)account and programmed in python for sentiment analysis approach for the computational measure of user’s perceptions that extract a massive number of tweets and provide market value to the Twitter account *** distinguish the results in terms of the performance evaluation,an error analysis investigates the features of various stakeholders comprising social media analytics researchers,Natural Language Processing(NLP)developers,engineering managers and experts involved to have a decision-making approach.

关键词： Machine learning Naive Bayes natural language processing sentiment analysis social media analytics support vector machine Twitter application programming interface

Dissolution mechanism and kinetics ofβ(Mg_(17)Al_(12))phases in AZ91 magnesium alloy

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Journal of Magnesium and Alloys 2024年第4期12卷 1581-1592页

作者： sultan Alomairy Department of Physics College of ScienceTaif UniversityTaif 21944Saudi Arabia

In this study,the phase transformations,crystallization kinetics and dissolution mechanism ofβphase(Mg_(17)Al_(12))in magnesium alloy AZ91 were investigated by optical microscopy,X-ray diffraction,differential scanning calorimetry and differential *** results indicate that this AZ91 alloy undergoes a phase transformation during aging,a discontinuous precipitation of theβphase(Mg_(17)Al_(12))at 150℃at the grain boundaries and another continuous at 350℃within the *** activation energy of the dissolution reaction of theβphase(Mg_(17)Al_(12))under non-isothermal conditions is 116.781 kJ/mol,while it is 129.7383 kJ/mol under isothermal *** Avrami coefficient,n,relevant for the dissolution kinetics of theβphase(Mg_(17)Al_(12))is 1.152 and 1.211 in the non-isothermal and isothermal conditions *** numerical coefficients m and Avrami n are 0.993 and 1.152.

关键词： AZ91 kinetics β(Mg_(17)Al_(12)) Activation energy Discontinuous precipitation Continuous precipitation DSC