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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 *** the face of the COVID-19 pandemic,the most important step has been the effective diagnosis and monitoring of infected *** 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 *** 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 *** approach includes preprocessing,feature extraction,and ***,the features are extracted using the Histogram of Oriented(HOG)and Local Binary Pattern(LBP)feature ***,for the extracted features classification,six ML models of Support Vector Machine(SVM)and K-Nearest Neighbor(KNN)is *** 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 *** 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

Non-Intrusive Monitoring of Everyday Behavioral Activities in an Indoor Environment

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Journal of Electronic Science and Technology 2016年第3期14卷 205-210页

作者： Atika Arshad sheroz khan A.H.M.Zahirul Alam Rumana Tasnim Department of Electrical and Computer Engineering International Islamic University MalaysiaKuala Lumpur 53100Malaysia Department of Mechatronics Engineering World University of BangladeshDhaka 1205Bangladesh

A non-intrusive design for monitoring everyday activities of an elderly person is presented. The proposed system is intended to be used in the bedroom, allowing the elders to stay at home in a safe environment. The required hardware design is simple in structure and cost effective. The sensor design is implemented by using capacitive sensors and an Arduino microcontroller unit. And a real time graphical user interface is implemented to monitor the elderly person. The performance analysis shows that the sensor design is able to differentiate between a human body and a house pet.

关键词： Index Terms-Arduino elderly home sensors senior citizens.

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 *** 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 *** is considered as the world’s largest human *** makes one of the main concerns with regards to managing the large crowds and ensuring that stampedes and other similar overcrowding accidents are *** paper presents a crowd management system using image classification and an alarm system for managing the millions of crowds during *** 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 *** core building block of CNN is is a convolutional layer obtained by the getting CNN trained with patches bearing designated features of the trainee *** algorithm is implemented,using the Conv2D layers to activate the CNN as a sequential ***,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 *** 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 *** utility of these results lies in producing appropriate signals for proving helpful in monitoring the *** pilgrims from the photos will help the authorities to determine the number of people in certain *** 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 topology and uncertain node mobility structure of FANETs do not aim to consider only one path *** different techniques are adopted to address the issues arising in FANETs,from game theory to clustering to channel estimation and other statistical *** approaches mostly employ traditional concepts for problem *** of the novel approaches that provide simpler solutions to more complex problems is to use biologically inspired *** Nature-inspired schemes address cooperation and alliance which can be used to ensure connectivity among network *** such species that resembles the dynamicity of FANETs are *** 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 ***,we envision the concept of cooperative diversity using multiple relays and present an iBAT-COOP routing protocol for *** 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 *** 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%***,the average link duration is improved by 25%compared to the BAT-FANET protocol.

关键词： Routing protocols UAVs FANETs iBATCOOP BATCOOP