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An Automated Real-Time Face Mask Detection System Using Transfer Learning with Faster-RCNN in the Era of the COVID-19 Pandemic

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computers, Materials & Continua 2022年第5期71卷 4151-4166页

作者： Maha Farouk S.Sabir Irfan Mehmood Wafaa Adnan Alsaggaf Enas Fawai Khairullah Samar Alhuraiji Ahmed S.Alghamdi Ahmed A.Abd El-Latif Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversitySaudi Arabia Centre for Visual Computing Faculty of Engineering and InformaticsUniversity of BradfordBradfordU.K Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahP.O.Box 23713Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahSaudi Arabia Department of Mathematics and Computer Science Faculty of ScienceMenoufia University32511Egypt

Today,due to the pandemic of COVID-19 the entire world is facing a serious health *** to the World Health Organization(WHO),people in public places should wear a face mask to control the rapid transmission of *** governmental bodies of different countries imposed that wearing a face mask is compulsory in public ***,it is very difficult to manually monitor people in overcrowded *** research focuses on providing a solution to enforce one of the important preventative measures of COVID-19 in public places,by presenting an automated system that automatically localizes masked and unmasked human faces within an image or video of an area which assist in this outbreak of *** paper demonstrates a transfer learning approach with the Faster-RCNN model to detect faces that are masked or *** proposed framework is built by fine-tuning the state-of-the-art deep learning model,Faster-RCNN,and has been validated on a publicly available dataset named Face Mask Dataset(FMD)and achieving the highest average precision(AP)of 81%and highest average Recall(AR)of 84%.This shows the strong robustness and capabilities of the Faster-RCNN model to detect individuals with masked and un-masked ***,this work applies to real-time and can be implemented in any public service area.

关键词： COIVD-19 deep learning faster-RCNN object detection transfer learning face mask

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Human Gait Recognition:A Deep Learning and Best Feature Selection Framework

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computers, Materials & Continua 2022年第1期70卷 343-360页

作者： Asif Mehmood Muhammad Attique Khan Usman Tariq Chang-Won Jeong Yunyoung Nam Reham R.Mostafa Amira ElZeiny Department of Computer Science COMSATS University IslamabadWah Campus47080Pakistan Department of Computer Science HITEC University TaxilaTaxila47040Pakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Medical Convergence Research Center Wonkwang UniversityIksanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Department of Information Systems Faculty of Computers and Information SciencesMansoura UniversityMansoura35516Egypt Department of Information Systems Faculty of Computers and InformationDamietta UniversityDamiettaEgypt

Background—Human Gait Recognition(HGR)is an approach based on biometric and is being widely used for *** is adopted by researchers for the past several *** factors are there that affect the system performance such as the walking variation due to clothes,a person carrying some luggage,variations in the view ***—In this work,a new method is introduced to overcome different problems of HGR.A hybrid method is proposed or efficient HGR using deep learning and selection of best *** major steps are involved in this work-preprocessing of the video frames,manipulation of the pre-trained CNN model VGG-16 for the computation of the features,removing redundant features extracted from the CNN model,and *** the reduction of irrelevant features Principal Score and Kurtosis based approach is proposed named *** that,the features of PSbK are fused in one ***,this fused vector is fed to the One against All Multi Support Vector Machine(OAMSVM)classifier for the final ***—The system is evaluated by utilizing the CASIA B database and six angles 00◦,18◦,36◦,54◦,72◦,and 90◦are used and attained the accuracy of 95.80%,96.0%,95.90%,96.20%,95.60%,and 95.50%,***—The comparison with recent methods show the proposed method work better.

关键词： Human gait recognition deep features extraction features fusion features selection

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Fractional-order binary bat algorithm for feature selection on high-dimensional microarray data

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Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7453-7467页

作者： Esfandiari, Ahmad Farivar, Faezeh Khaloozadeh, Hamid Department of Computer and Mechatronics Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Systems and Control Faculty of Electrical Engineering K. N. Toosi University of Technology Seyed-Khandan Shariati Ave. Tehran16317-14191 Iran

High-dimensional microarray data suffer from the confounding effects of irrelevant, redundant and noisy genes on the scalability and efficiency of classification algorithms. In order for an effective dimensionality reduction and the selection of informative genes, this paper introduces a novel approach using fractional calculus concepts. This study proposes a modified version of binary the bat algorithm named fractional-order binary bat algorithm (FBBA) able to control the convergence process using more historical memory of bat behaviors. The gene selection technique contains a two-stage hybrid filter/wrapper method which employs a new correlation-based feature clustering (CFC) algorithm in the filter stage and the FBBA in the wrapper stage. The CFC-FBBA is evaluated on ten microarray gene expression datasets by employing the support vector machine classifier with a k-fold Monte Carlo cross validation data partitioning model. Furthermore, the results show that the CFC-FBBA, while minimizing the size of the gene subset, achieves the highest classification accuracy in most cases compared to several state-of-art hybrid techniques. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Classification (of information)

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Perception Gaps Between Hashtag Stakeholders on X (Twitter) - Application on the Australian National Disability Insurance Scheme (#NDIS) 20

Perception Gaps Between Hashtag Stakeholders on X (Twitter) ...

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20th IEEE International Conference on e-Business engineering, ICEBE 2024

作者： Zhang, Yu Bui, Hang Thanh Zipperle, Michael Buick, Fiona Chand, Satish Hussain, Omar Wang, Min Mo, Huadong Zhang, Wenjie School of Business Unsw Canberra Canberra Australia University of Canberra Faculty of Science and Technology Canberra Australia School of Systems & Computing Unsw Canberra Canberra Australia School of Computer Science and Engineering Unsw Sydney Sydney Australia

ISBN: (纸本)9798350365856

A hashtag on X (Twitter) can attract a range of stakeholders with diverse perceptions on the hashtag. Under-standing and considering the differences between these stake-holders' perceptions on specific hashtags can be highly valuable for policy making in both public and private sectors. This paper proposes a framework to analyse the depth and width of the perception gap between stakeholders of a hashtag using sentiment analysis and zero-shot learning on large scale Twitter data. The framework was applied to the hashtag '#NDIS', the National Disability Insurance Scheme, Australia's pilot personalised disability service which provides funding to support people with disability. Over 155,000 tweets were collected and analysed to identify and assess the perception gap between the two most significant stakeholders (NDIS participants and service providers) on six main topics during the implementation of the NDIS. The study verified the effectiveness of the proposed framework, revealing a widespread perception gap between the two NDIS stakeholders, as well as differences in their online behaviours using Twitter. The results suggest that the NDIS policymakers and service providers should pay attention to the utilisation of social media, including hearing voice from the broader public and creating channels for transparent engagement with other NDIS stakeholders. © 2024 IEEE.

关键词： Tweets

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Catalyzing EEG signal analysis: unveiling the potential of machine learning-enabled smart K nearest neighbor outlier detection

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International Journal of Information Technology (Singapore) 2024年 1-12页

作者： Aymen, Abid Khediri, Salim El Thaljaoui, Adel Miladi, Moahmed Kachouri, Abdennaceur Computer & amp Embedded Systems Lab National Engineering School of Sfax University of Sfax Sfax Tunisia Department of Information Technology College of Computer Qassim University Buraydah Saudi Arabia Department of Computer Science Faculty of Science of Gafsa University of Gafsa Gafsa Tunisia Preparatory Institute for Engineering Studies of Gafsa Gafsa Tunisia Department of Computer Science and Information College of Science at Zulfi Majmaah University Al-Majmaah 11952 Saudi Arabia Higher Institute of Computer Science and Multimedia of Sfax University of Sfax Sfax Tunisia Advanced Fluid Dynamics Energetics and Environment Lab National Engineering School of Sfax University of Sfax Sfax Tunisia

Electroencephalogram (EEG) data are susceptible to artifacts, such as lapses in concentration or poor imagination, which can significantly impact the accuracy of disease diagnosis in e-health applications. To mitigate this issue, the use of machine learning (ML) and potentially artificial intelligence (AI) solutions to accurately identify outliers becomes crucial. Unlike many AI methods that incorporate unnecessary or redundant input variables, our study focuses on detecting anomalous values in EEG data through the K nearest neighbor (KNN) process and Euclidean distance metric. Our proposed unsupervised non-parametric algorithm, known as the smart KNN outlier detector (SKOD), eliminates the need for initial parameter configurations such as the number of neighbors (K), while achieving high performance. Evaluation of SKOD using real EEG data from 140 trials demonstrated sensitivity and specificity exceeding 60%, with nearly perfect accuracy in detecting outliers reaching close to 100%. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Data analysis EEG signal Healthcare Knowledge-based systems Machine learning Outlier detection

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Intelligent Energy Management Energy Storage systems Using Machine Learning

Intelligent Energy Management Energy Storage Systems Using M...

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2024 International Conference on Intelligent systems for Cybersecurity, ISCS 2024

作者： Govindasamy, Chinnaraj Dayana, T.R Nisha Adkar, Siddhika Dilip Prakash, D. Shyam Gupta, A. Shilpa Trivedi, Sanjeev Kumar Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamilnadu Chennai602105 India Vels Institute of Science Technology and Advance Studies Department of Computer Science Chennai India School of Computer Science Engineering and Applications D y Patil International University Akurdi India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore India Department of Electrical and Electronics Engineering Aurora's Technological Research Institute Uppal Hyderabad India Khwaja Moinuddin Chishti Language University Faculty of Engineering and Technology Department of Electronics Lucknow India

ISBN: (纸本)9798350375237

A nevertheless-emerging generation called cloud computing permits customers to pay for services on a usage-based foundation. Internet-primarily based IT offerings are supplied through cloud computing, at the same time as virtualization enables the availability of PC sources. The muse of cloud computing is the information center, which is made up of networked computers, cables, electricity components, and different components that host and shop corporate records. With cloud facts centers, high overall performance has continually been the most critical concern, yet it compromises strength utilization. The key hassle is to lessen power consumption at the same time as preserving provider nice and performance a good way to stability device performance and strength intake. A detailed grasp of strength use styles within the cloud environment is needed for our suggested technique. We look at power consumption tendencies and reveal how, through the use of the right optimization standards based on our strength intake models, we can keep more strength in cloud records facilities. All through the prediction section, tablet optimization, which has a 97 percent accuracy rate, permits this era to provide extra correct future price forecasts. © 2024 IEEE.

关键词： Machine learning

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Thalassemia Screening by Sentiment Analysis on Social Media Platform Twitter

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computers, Materials & Continua 2023年第7期76卷 665-686页

作者： Wadhah Mohammed M.Aqlan Ghassan Ahmed Ali Khairan Rajab Adel Rajab Asadullah Shaikh Fekry Olayah Shehab Abdulhabib Saeed Alzaeemi Kim Gaik Tay Mohd Adib Omar Ernest Mangantig School of Computer Sciences Universiti Sains MalaysiaUSM11800PenangMalaysia College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia86400JohorMalaysia IPPT Universiti Sains MalaysiaUSM11800PenangMalaysia

Thalassemia syndrome is a genetic blood disorder induced by the reduction of normal hemoglobin production,resulting in a drop in the size of red blood *** severe forms,it can lead to *** genetic disorder has posed a major burden on public health wherein patients with severe thalassemia need periodic therapy of iron chelation and blood transfusion for ***,controlling thalassemia is extremely important and is made by promoting screening to the general population,particularly among thalassemia *** Twitter is one of the most influential social media platforms for sharing opinions and discussing different topics like people’s health conditions and major public health *** individuals’sentiments in these tweets helps the research centers to formulate strategies to promote thalassemia screening to the *** effective Lexiconbased approach has been introduced in this study by highlighting a classifier called valence aware dictionary for sentiment reasoning(VADER).In this study applied twitter intelligence tool(TWINT),Natural Language Toolkit(NLTK),and VADER constitute the three main *** represents a gold-standard sentiment lexicon,which is basically tailored to attitudes that are communicated by using social *** contribution of this study is to introduce an effective Lexicon-based approach by highlighting a classifier calledVADERto analyze the sentiment of the general population,particularly among thalassemia carriers on the social media platform *** this study,the results showed that the proposed approach achieved 0.829,0.816,and 0.818 regarding precision,recall,together with F-score,*** tweets were crawled using the search keywords,“thalassemia screening,”thalassemia test,“and thalassemia diagnosis”.Finally,results showed that India and Pakistan ranked the highest in mentions in tweets by the public’s conversations on thalassemia screening with 181 and 164 tweets,respectively.

关键词： Social media platform Twitter screening thalassemia lexicon-based VADER

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Early Detection of Alzheimer's Disease Using Gamified Self Administrative Gerocognitive Exam and Deep Learning

Early Detection of Alzheimer's Disease Using Gamified Self A...

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International Conference on computer and Communications Technologies (ICCCT)

作者： Jayanthi M Shereya P V Israd I Faculty of Department of Computer Science and Business Systems Rajalakshmi Engineering College Chennai India Student of Department of Computer Science and Business Systems Rajalakshmi Engineering College Chennai India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

This study uses a convolutional neural network (CNN) for multiclass classification in a gamified version of the SAGE (Self-Administered Gerocognitive Examination) test to introduce a distinctive approach for the early identification of Alzheimer's disease (AD). The application features three interactive games: a card flipping and matching game, a “set the time” game with a maximum of four attempts, and a timed challenge to name 12 animals within three minutes, verified through an API (Application Programming Interface). These engaging activities reduce anxiety associated with traditional assessments while providing valuable insights into cognitive states. Results are collected, and if a specific threshold is reached, the system recommends further evaluation via MRI (Magnetic Imaging Resonance) testing. Additionally, the application can analyze MRI scans to monitor disease progression. Key benefits include early detection through non-invasive methods, enhanced user engagement, real-time feedback for timely intervention, and comprehensive monitoring of cognitive health. Our findings suggest that combining gamification with advanced deep learning (DL) techniques can significantly improve early detection strategies for cognitive decline.

关键词： Deep learning Databases Magnetic resonance imaging Transfer learning Games Convolutional neural networks Alzheimer's disease Monitoring Testing Application programming interfaces

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Applying Apache Spark on Streaming Big Data for Health Status Prediction

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computers, Materials & Continua 2022年第2期70卷 3511-3527页

作者： Ahmed Ismail Ebada Ibrahim Elhenawy Chang-Won Jeong Yunyoung Nam Hazem Elbakry Samir Abdelrazek Information Systems Department Faculty of Computers and InformationMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computers and InformationEl-Zagazig UniversityZagazigSharqiyah44519Egypt Medical Convergence Research Center Wonkwang UniversityIksanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Big data applications in healthcare have provided a variety of solutions to reduce costs,errors,and *** work aims to develop a real-time system based on big medical data processing in the cloud for the prediction of health *** the proposed scalable system,medical parameters are sent to Apache Spark to extract attributes from data and apply the proposed machine learning *** this way,healthcare risks can be predicted and sent as alerts and recommendations to users and healthcare *** proposed work also aims to provide an effective recommendation system by using streaming medical data,historical data on a user’s profile,and a knowledge database to make themost appropriate real-time recommendations and alerts based on the sensor’s *** proposed scalable system works by tweeting the health status attributes of *** cloud profile receives the streaming healthcare data in real time by extracting the health attributes via a machine learning prediction algorithm to predict the users’health ***,their status can be sent on demand to healthcare ***,machine learning algorithms can be applied to stream health care data from wearables and provide users with insights into their health *** algorithms can help healthcare providers and individuals focus on health risks and health status changes and consequently improve the quality of life.

关键词： Big data streaming processing healthcare data machine learning IoT data processing Apache Spark

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Optical Evanescent Field for Direct Observation of Sub-100nm Particle Motion on Glass Surface 1

Optical Evanescent Field for Direct Observation of Sub-100nm...

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1st International Conference on Robotics, engineering, science, and Technology, RESTCON 2024

作者： Khajornrungruang, Panart Permpatdechakul, Thitipat Suzuki, Keisuke Blattler, Aran Terayama, Yutaka Goto, Daiki Phaisalpanumas, Pipat Bun-Athuek, Natthaphon Phunopas, Amornphun Kyushu Institute of Technology Iizuka-Shi Mechanical Science and Technology Division Dept. of Intelligent and Control System Faculty of Computer Science and Systems Engineering Fukuoka Japan Kyushu Institute of Technology Iizuka-Shi Mechanical Information Science and Technology Division Dept. of Interdisciplinary Informatics Graduate School of Computer Science and Systems Engineering Fukuoka Japan King Mongkut's University of Technology North Bangkok Bangsue Faculty of Engineering Dept. of Production and Robotics Engineering Bangkok Thailand Kyushu Institute of Technology Iizuka-Shi Mechanical Science and Technology Course Dept. of Intelligent and Control System Graduate School of Computer Science and Systems Engineering Fukuoka Japan King Mongkut's University of Technology North Bangkok Bangsue Faculty of Engineering Dept. of Industiral Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Bangsue College of Industrial Technology Dept. of Mechanical Engineering Technology Bangkok Thailand

ISBN: (纸本)9798350348842

To directly investigate the dynamic nanoscale phenomenon on the surface being processed in wet conditions such as precision polishing, and cleaning in semiconductor industrial, an optical method for visualization and observation of each sub-100 nm sized particle that is moving on an interface such as a silica glass surface by applying an evanescent field have been proposing. Subsequently, we developed an experimental apparatus equipped with an optical microscopy system for verifying the moving particle observation method in a laboratory scale. This article introduces some experimentally direct observation results of duplicated wet processes. © 2024 IEEE.

关键词： Visualization

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