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X-ray Image-Based COVID-19 Patient Detection Using Machine Learning-Based Techniques

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Computer Systems Science & Engineering 2022年第11期43卷 671-682页

作者： shabana habib Saleh Alyahya Aizaz Ahmed Muhammad Islam Sheroz Khan Ishrat Khan Muhammad Kamil Department of Information Technology College of ComputerQassim UniversityBuraidah52571Saudi Arabia Department of Electrical Engineering College of Engineering&Information TechnologyOnaizah CollegesOnaizah51911Saudi Arabia Digital Image Processing Lab Islamia CollegePeshawar25120KPKPakistan Department of Computer Science College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electrical Engineering National University of Computer and Emerging SciencesPeshawarPakistan

In early December 2019,the city of Wuhan,China,reported an outbreak of coronavirus disease(COVID-19),caused by a novel severe acute respiratory syndrome coronavirus-2(SARS-CoV-2).On January 30,2020,the World Health Organization(WHO)declared the outbreak a global pandemic crisis.In the face of the COVID-19 pandemic,the most important step has been the effective diagnosis and monitoring of infected patients.Identifying COVID-19 using Machine Learning(ML)technologies can help the health care unit through assistive diagnostic suggestions,which can reduce the health unit's burden to a certain extent.This paper investigates the possibilities of ML techniques in identifying/detecting COVID-19 patients including both conventional and exploring from chest X-ray images the effect of viral infection.This approach includes preprocessing,feature extraction,and classification.However,the features are extracted using the Histogram of Oriented(HOG)and Local Binary Pattern(LBP)feature descriptors.Furthermore,for the extracted features classification,six ML models of Support Vector Machine(SVM)and K-Nearest Neighbor(KNN)is used.Experimental results show that the diagnostic accuracy of random forest classifier(RFC)on extracted HOG plusLBP features is as high as 94%followed by SVM at 93%.The sensitivity of the K-nearest neighbour model has reached an accuracy of 88%.Overall,the predicted approach has shown higher classification accuracy and effective diagnostic performance.It is a highly useful tool for clinical practitioners and radiologists to help them in diagnosing and tracking the cases of COVID-19.

关键词： Image pre-processing detection classification x-ray images filter

Sparse Crowd Flow Analysis of Tawaaf of Kaaba During the COVID-19 Pandemic

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Computers, Materials & Continua 2022年第6期71卷 5581-5601页

作者： Durr-e-Nayab Ali Mustafa Qamar Rehan Ullah Khan Waleed Albattah Khalil Khan shabana habib Muhammad Islam Department of Computer Systems Engineering University of Engineering and TechnologyPeshawarPakistan Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Information Technology and Computer Science Pak-Austria FachhochschuleInstitute of Applied Sciences and TechnologyHaripurPakistan Department of Electrical Engineering College of Engineering and Information TechnologyOnaizah CollegesAl-QassimSaudi Arabia

The advent of the COVID-19 pandemic has adversely affected the entire world and has put forth high demand for techniques that remotely manage crowd-related tasks.Video surveillance and crowd management using video analysis techniques have significantly impacted today’s research,and numerous applications have been developed in this domain.This research proposed an anomaly detection technique applied to Umrah videos in Kaaba during the COVID-19 pandemic through sparse crowd analysis.Managing theKaaba rituals is crucial since the crowd gathers from around the world and requires proper analysis during these days of the pandemic.The Umrah videos are analyzed,and a system is devised that can track and monitor the crowd flow in Kaaba.The crowd in these videos is sparse due to the pandemic,and we have developed a technique to track the maximum crowd flow and detect any object(person)moving in the direction unlikely of the major flow.We have detected abnormal movement by creating the histograms for the vertical and horizontal flows and applying thresholds to identify the non-majority flow.Our algorithm aims to analyze the crowd through video surveillance and timely detect any abnormal activity tomaintain a smooth crowd flowinKaaba during the pandemic.

关键词： Computer vision COVID sparse crowd crowd analysis flow analysis sparse crowd management tawaaf video analysis video processing

An Optimized Approach to Vehicle-Type Classification Using a Convolutional Neural Network

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Computers, Materials & Continua 2021年第12期69卷 3321-3335页

作者： shabana habib Noreen Fayyaz Khan Department of Information Technology College of ComputerQassim UniversityBuraidah51452Saudi Arabia Department of Computer Science Islamia College UniversityPeshawarPakistan

There are numerous application areas of computing similarity between process models.It includes finding similar models from a repository,controlling redundancy of process models,and finding corresponding activities between a pair of process models.The similarity between two process models is computed based on their similarity between labels,structures,and execution behaviors.Several attempts have been made to develop similarity techniques between activity labels,as well as their execution behavior.However,a notable problem with the process model similarity is that two process models can also be similar if there is a structural variation between them.However,neither a benchmark dataset exists for the structural similarity between process models nor there exist an effective technique to compute structural similarity.To that end,we have developed a large collection of process models in which structural changes are handcrafted while preserving the semantics of the models.Furthermore,we have used a machine learning-based approach to compute the similarity between a pair of process models having structural and label differences.Finally,we have evaluated the proposed approach using our generated collection of process models.

关键词： Machine learning intelligent data management similarities of process models structural metrics dataset graph edit distance process matching artificial intelligence

Hajj Crowd Management Using CNN-Based Approach

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Computers, Materials & Continua 2021年第2期66卷 2183-2197页

作者： Waleed Albattah Muhammad Haris Kaka Khel shabana habib Muhammad Islam Sheroz Khan Kushsairy Abdul Kadir Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Electronic Section Universiti Kuala Lumpur British Malaysian InstituteMalaysia Department of Electrical Engineering Onaizah CollegesSaudi Arabia Department of Electrical and Computer Engineering International Islamic UniversityMalaysia

Hajj as the Muslim holy pilgrimage,attracts millions of humans to Mecca every year.According to statists,the pilgrimage has attracted close to 2.5 million pilgrims in 2019,and at its peak,it has attracted over 3 million pilgrims in 2012.It is considered as the world’s largest human gathering.Safety makes one of the main concerns with regards to managing the large crowds and ensuring that stampedes and other similar overcrowding accidents are avoided.This paper presents a crowd management system using image classification and an alarm system for managing the millions of crowds during Hajj.The image classification system greatly relies on the appropriate dataset used to train the Convolutional neural network(CNN),which is the deep learning technique that has recently attracted the interest of the research community and industry in varying applications of image classification and speech recognition.The core building block of CNN is is a convolutional layer obtained by the getting CNN trained with patches bearing designated features of the trainee mages.The algorithm is implemented,using the Conv2D layers to activate the CNN as a sequential network.Thus,creating a 2D convolution layer having 64 filters and drop out of 0.5 makes the core of a CNN referred to as a set of KERNELS.The aim is to train the CNN model with mapped image data,and to make it available for use in classifying the crowd as heavily-crowded,crowded,semi-crowded,light crowded,and normal.The utility of these results lies in producing appropriate signals for proving helpful in monitoring the pilgrims.Counting pilgrims from the photos will help the authorities to determine the number of people in certain areas.The results demonstrate the utility of agent-based modeling for Hajj pilgrims.

关键词： Crowd management CNN approach Hajj

An Improved iBAT-COOP Protocol for Cooperative Diversity in FANETs

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Computers, Materials & Continua 2021年第5期67卷 2527-2546页

作者： Shahzad Hameed Qurratul-Ain Minhas Sheeraz Ahmed shabana habib Mohammad Kamrul Hasan Muhammad Islam Sheroz Khan Quaid-i-Azam University IslamabadPakistan Iqra National University PeshawarPakistan Network and Communication Technology Lab Center for Cyber SecurityUKMBangiMalaysia Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Electrical Engineering Onaizah College of Engineering&Information TechnologyAl-QassimSaudi Arabia

Flying ad hoc networks(FANETs)present a challenging environment due to the dynamic and highly mobile nature of the network.Dynamic network topology and uncertain node mobility structure of FANETs do not aim to consider only one path transmission.Several different techniques are adopted to address the issues arising in FANETs,from game theory to clustering to channel estimation and other statistical schemes.These approaches mostly employ traditional concepts for problem solutions.One of the novel approaches that provide simpler solutions to more complex problems is to use biologically inspired schemes.Several Nature-inspired schemes address cooperation and alliance which can be used to ensure connectivity among network nodes.One such species that resembles the dynamicity of FANETs are Bats.In this paper,the biologically inspired metaheuristic technique of the BAT Algorithm is proposed to present a routing protocol called iBATCOOP(Improved BAT Algorithm using Cooperation technique).We opt for the design implementation of the natural posture of bats to handle the necessary flying requirements.Moreover,we envision the concept of cooperative diversity using multiple relays and present an iBAT-COOP routing protocol for FANETs.This paper employs cooperation for an optimal route selection and reflects on distance,Signal to Noise Ratio(SNR),and link conditions to an efficient level to deal with FANET’s routing.By way of simulations,the performance of iBAT-COOP protocol outperforms BAT-FANET protocol and reduces packet loss ratio,end-to-end delay,and transmission loss by 81%,21%,and 82%respectively.Furthermore,the average link duration is improved by 25%compared to the BAT-FANET protocol.

关键词： Routing protocols UAVs FANETs iBATCOOP BATCOOP