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Deep Learning Based Face Mask Detection in Religious Mass Gathering During COVID-19 Pandemic

computer systems science & Engineering 2023年第8期46卷 1863-1877页

作者： Abdullah S AL-Malaise AL-Ghamdi Sultanah MAlshammari Mahmoud Ragab Information Systems Department Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department HECI SchoolDar Alhekma UniversityJeddahSaudi Arabia Center of Excellence in Smart Environment Research King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser CityCairo11884Egypt

Notwithstanding the religious intention of billions of devotees,the religious mass gathering increased major public health concerns since it likely became a huge super spreading event for the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).Most attendees ignored preventive measures,namely maintaining physical distance,practising hand hygiene,and wearing *** a face mask in public areas protects people from spreading *** intelligence(AI)based on deep learning(DL)and machine learning(ML)could assist in fighting covid-19 in several *** study introduces a new deep learning-based Face Mask Detection in Religious Mass Gathering(DLFMD-RMG)technique during the COVID-19 *** DLFMD-RMG technique focuses mainly on detecting face masks in a religious mass *** accomplish this,the presented DLFMD-RMG technique undergoes two pre-processing levels:Bilateral Filtering(BF)and Contrast *** face detection,the DLFMD-RMG technique uses YOLOv5 with a ResNet-50 *** addition,the face detection performance can be improved by the seeker optimization algorithm(SOA)for tuning the hyperparameter of the ResNet-50 module,showing the novelty of the *** last,the faces with and without masks are classified using the Fuzzy Neural Network(FNN)*** stimulation study of the DLFMD-RMG algorithm is examined on a benchmark *** results highlighted the remarkable performance of the DLFMD-RMG model algorithm in other recent approaches.

关键词： Religious mass gathering Hajj and Umrah covid-19 pandemic face mask computer vision deep learning

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Effective and Efficient Generation of Candidate Networks for Top-k Keyword Search in Relational Database 9

Effective and Efficient Generation of Candidate Networks for...

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9th International Conference on Web Research, ICWR 2023

作者： Khalifeh, Fatemeh Taheri, Mohammad Shiraz University Computer Science Engineering and Information Technology Department Shiraz Iran

ISBN: (纸本)9798350399691

Todays' data over web are retrieved from Relational Database Management systems (RDBMS). Web retrieval and content analysis by keyword search in relational database has become the preferential way to search in todays' era of Big Data. Any keyword search system has two main parts including candidate network generation and evaluation. Most research's attention is on providing an efficient candidate network evaluation algorithm and relies on non-efficient candidate network generation paradigm. In this work, a novel probabilistic candidate network generation algorithm is proposed. From the outcome of our investigation on benchmark databases including new ISC database, it is possible to conclude that the proposed candidate network generation approach enhances the efficiency of databases keyword search. © 2023 IEEE.

关键词： Search engines

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A Review on Deep Learning Techniques for EEG-Based Driver Drowsiness Detection systems

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Informatica (Slovenia) 2024年第3期48卷 359-378页

作者： Latreche, Imene Slatnia, Sihem Kazar, Okba Barka, Ezedin Harous, Saad Department of Computer Science University of Mohamed Khider Biskra Algeria College of Arts Sciences & Information Technology University of Kalba Sharjah United Arab Emirates Department of Information Systems and Security UAE University Al Ain United Arab Emirates Computer Science Department College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

Driver Drowsiness is considered one of the significant causes of road accidents and fatal injuries. Due to this, creating systems that can monitor drivers and detect early drowsiness has become an important field of research and a challenging task in recent years. Several research attempts were proposed to solve this problem based on several approaches and techniques. The Electroencephalogram (EEG) is one of the most efficient and reliable method, among the physiological signals-based monitoring approaches. In this area, many Machine Learning (ML) techniques have been used to detect EEG-based driver drowsiness. However, due to the limitations of ML techniques, many researchers have shifted their focus to the use of deep learning (DL) techniques, which have demonstrated superior performance in many fields including the physiological signals classification tasks. This paper reviews and discusses numerous new research papers that have proposed and implemented driver drowsiness detection systems based on EEG and deep learning techniques. In addition, we have outlined the limitations and difficulties of the existing works and highlighted and proposed some propositions that will help future field researchers enhance and generalize the results. Based on our thorough analysis, we have determined that the latest advancements in detecting driver drowsiness have employed the convolutional neural network (CNN) technique, which has demonstrated effective performance in classifying signals. Furthermore, the primary issue encountered in all works is developing a more precise and accurate method. Nevertheless, we seek a precise system capable of swiftly identifying a state of drowsiness while using minimal spatial memory and processing resources. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Electroencephalography

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Enabling Robust Security in MQTT-Based IoT Networks with Dynamic Resource-Aware Key Sharing 4

Enabling Robust Security in MQTT-Based IoT Networks with Dyn...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Kaganurmath, Sharadadevi Cholli, Nagaraj Department of Computer Science Engineering Global Academy of Technology Bengaluru India Department of Information Science Engineering RV College of Engineering Bengaluru India

This paper presents a novel approach aimed at developing a secure secret key-sharing system optimized for resource-constrained Internet of Things (IoT) devices. Focusing on the MQTT protocol, the research endeavors to establish secure communication channels between IoT devices and brokers, thereby enhancing the overall security of MQTT-based IoT deployments. This research introduces a novel Dynamic Lightweight Authentication for MQTT (DLA-MQTT) mechanism designed to the unique needs of IoT devices operating under the MQTT protocol. The DLA-MQTT mechanism leverages an innovative lightweight Generalized Feedback Shift Register (GFSR)-based Pseudo-Random Number Generator (PRNG) to generate ephemeral keys, ensuring secure communication while addressing the limitations of computational power and energy resources. Through a detailed comparative analysis with existing cryptographic solutions, the DLA-MQTT mechanism demonstrates superior performance in terms of computational overhead, energy consumption, and execution time, while maintaining robust security against common attacks such as Man-in-the-Middle (MitM) and Denial of Service (DoS). he proposed algorithm's adaptability and scalability are validated using the Cooja simulator, where simulated IoT networks are subjected to various threat scenarios. The results confirm the DLA-MQTT mechanism's efficacy, showcasing a significant reduction in resource utilization without compromising the strength of the security provided. © 2025 The Authors. Published by Elsevier B.V.

关键词： Shift registers

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An IoT-Assisted Real-Time Urban Air Pollution Data Collection system 5th

An IoT-Assisted Real-Time Urban Air Pollution Data Collectio...

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5th International Conference on Computing and Network Communications, CoCoNet 2023

作者： Ghose, Banani Minsar, Sk. Ghosh, Udayan Arif Mohammad, Sk. Mallick, Adil Hossain Rehena, Zeenat Department of Information Technology Haldia Institute of Technology West Bengal Haldia India Department of Computer Science and Engineering Aliah University West Bengal Kolkata India

ISBN: (纸本)9789819745395

Air pollution is one of the innumerable menaces an individual comes across every moment. Countless means to make urban lives smoother are indebted to witnessing air pollution and other types of pollution. It is contributed by unrestricted urbanization, traffic, industrial emissions, etc., causing life-threatening diseases;even death. Monitoring air pollution should be the primary objective of any society. For an effective air pollution monitoring system to run and deliver a real-time Air Quality Index (AQI) a prompt IoT-enabled air pollution sensor-based data collection system is a prerequisite. In this letter, a real-time hardware-based IoT-enabled air pollution-related data collection module is designed. This will be portable and will be recording both pollutant concentrations and meteorological factors. It consists of a number of sensors and a Wi-Fi module. Collected data will be directly sent to the cloud for further analysis. Thus, as a part of the air pollution monitoring system, this module will be capable of recording real-time data, that will lead to real-time air health monitoring. It will assist urban residents and policymakers in taking decisions that will guide them to have healthier lives. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Air quality

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How Can CoPs in Scaled Agile Settings Look Like? Toward a Taxonomy for CoPs in Large-Scale Agile Software Development 58

How Can CoPs in Scaled Agile Settings Look Like? Toward a Ta...

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58th Hawaii International Conference on system sciences, HICSS 2025

作者： Tobisch, Franziska Schmidt, Johannes Matthes, Florian Technical University of Munich School of Computation Information and Technology Department of Computer Science Garching Germany

ISBN: (纸本)9780998133188

Fast-changing business environments and customer needs require organizations to react quickly and adapt to changes. While agile software development methods can support organizations in achieving this goal, applying these methods in a large context, like multi-team projects, entails high complexity. Several scaling agile frameworks recommend Communities of Practice (CoP), groups of people with a shared interest that exchange with each other on a regular basis, to support the application of agile at scale, as they can foster, e.g., inter-team collaboration and alignment. Still, no clear overview of aspects that must be considered when establishing and cultivating CoPs in scaled agile settings exists. Therefore, we propose a taxonomy for CoPs in this context, building on literature and findings of an interview study. The taxonomy can support practitioners and researchers, offering a structured overview of aspects relevant to establishing and cultivating CoPs in large-scale agile settings and future research directions. © 2025 IEEE computer Society. All rights reserved.

关键词： Software design

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Hybrid deep Ensemble for Fine-Grained Race Estimation

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Multimedia Tools and Applications 2024年 1-24页

作者： Ahmed, Mazida A. Choudhury, Ridip Dev Sarma, Shikhar Kr. Borbora, Khurshid A. Bhuyan, Manash P. Barman, Utpal Department of Information Technology Gauhati University Assam Guwahati India Krishna Kanta Handiqui State Open University Assam Guwahati India Gauhati University Institute of Distance and Open Learning Gauhati University Assam Guwahati India Department of Computer Science Dibru College Assam Dibrugarh India Faculty of Computer Technology Assam down town University Assam Guwahati India

Race identification has made advances over the past few years, and finds application in numerous areas including surveillance, law enforcement, and even in administrative policy. It is, yet, limited to the groups with major diversifications like Asian, African, Caucasian, and Indian and is still under-explored for sub-racial categorization probably due to the unavailability of sufficient data. This article is aimed at estimating the recognized sub-groups of the ‘Indian’ class. An exclusive dataset (North East India Dataset) has been created for this purpose. Also, a hybrid ensemble blending the concepts of Bagging and Stacking (Super Learner) with minute customizations has been put forward that trains on specialized augmented patterns of data along with intermediate features. Extensive experiments have been conducted and the findings reveal the potential effectiveness of the method especially in lowering the loss. The model has also been validated against another benchmark dataset, FairFace. On the North East India and FairFace set, the proposed model achieves an accuracy of 91% and 80% respectively, outperforming the base models by at least 4%. The proposed ensemble performs better than the standard classical methods such as Bagging and Boosting (AdaBoost) on both datasets. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Adaptive boosting

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Opticurve: an optimized informer-curvelet framework for enhanced hyperspectral image segmentation and classification

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International Journal of information technology (Singapore) 2025年 1-16页

作者： Shaw, Kailash Wou Onn, Choo Li, Baihua Department of Computer Science and Engineering (AIML) Vishwakarma Institute of Technology Maharashtra Pune 411037 India Faculty of Data Science and Information Technology INTI International University Persiaran Perdana BBN Putra Nilai Negeri Sembilan Nilai 71800 Malaysia Department of Computer Science Loughborough University Loughborough LE11 3TU United Kingdom

Hyperspectral image (HSI) classification is crucial for applications in climate action, land use analysis, disaster risk reduction, and informed decision-making, given the complex spatial and spectral variations inherent in HSI data. Traditional methods struggle with accurately capturing these variations, necessitating more advanced techniques. This work introduces an Optimized Linformer-Curvelet (OptiCurve) Framework that integrates CNN-based feature extraction, Curvelet Transform for spatial detail capture, and Linformer for efficient feature representation. By combining these techniques, the model enhances HSI segmentation and classification, supporting improved outcomes in critical areas like environmental monitoring and disaster response. The framework is validated on four standard HSI datasets-Indian Pines, Pavia University, Kennedy Space Center, and Houston University-showing significant performance improvements over existing methods. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Climate action Disaster risk reduction Feature extraction Hyperspectral image classification Land use Linformer-Curvelet transform

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A privacy-preserving approach for detecting smishing attacks using federated deep learning

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International Journal of information technology (Singapore) 2025年第1期17卷 547-553页

作者： Remmide, Mohamed Abdelkarim Boumahdi, Fatima Ilhem, Bousmaha Boustia, Narhimene LRDSI Laboratory Department of Computer Science Faculty of Sciences University of Blida 1 Blida Algeria Electronics Department Faculty of Technology University of Blida 1 Blida Algeria

Smishing is a type of social engineering attack that involves sending fraudulent SMS messages to trick recipients into revealing sensitive information. In recent years, it has become a significant threat to mobile communications. In this study, we introduce a novel smishing detection method based on federated learning, which is a decentralized approach ensuring data privacy. We develop a robust detection model within a federated learning framework based on deep learning methods such as Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM). Our experiments show that the federated learning method using Bi-LSTM achieves an accuracy of 88.78%, highlighting its effectiveness in tackling smishing detection while preserving user privacy. This approach not only offers a promising solution to smishing attacks but also lays the groundwork for future research in mobile security and privacy-preserving machine learning. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Bi-LSTM Data privacy Federated learning Smishin detection Social engineering

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Enhancing Security and Privacy in Health Care Using Cyber-physical systems Through Machine Learning 8th

Enhancing Security and Privacy in Health Care Using Cyber-ph...

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8th International Conference on information and Communication technology for Intelligent systems, ICTIS 2024

作者： Takale, Dattatray G. Gawali, Piyush P. Deshmukh, Gopal B. Mahalle, Parikshit N. Mehta, Pradnya S. Kashid, Shraddha S. Sule, Bipin Derle, Deepak R. Department of Computer Engineering Vishwakarma Institute of Information Technology SPPU Pune India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology SPPU Pune India Department of Computer Engineering MIT ADT University Pune India and Humanities Vishwakarma Institute of Technology Pune India Department of Computer Science and Application KTHM College Nashik India

ISBN: (纸本)9789819766772

The importance of securing cyber-physical systems (CPS) is paramount to safeguarding the interconnected networks and devices, especially in sectors such as blockchain, health care, and the "Internet of Things" (IoT). The importance of authentication for users and devices in CPS security is highlighted in this paper. As a result of the intricate interconnectivity of networks, the integration of cyber-physical systems (CPS) in health care has revolutionized the delivery of medical services in an era of rapidly advancing technology. There are, however, serious concerns surrounding the privacy and security of sensitive healthcare data as a result of this integration. In this paper, a revolutionary approach to address these concerns is presented by utilizing machine learning techniques to address these problems. To enhance security measures within healthcare CPSs and protect privacy, Random Forest, a powerful ensemble learning algorithm, is employed as an integral part of the security measures. The proposed system utilizes a combination of anomaly detection, adaptive learning, and multi-attribute feature selection in order to identify potential security breaches while simultaneously ensuring patient confidentiality. As an additional security precaution, encryption protocols are implemented to ensure that the transmission of data is secure, preventing unauthorized access to the data as well. "As a comprehensive evaluation of the system's performance, a number of evaluation metrics are used, including Accuracy, Precision, Recall, F1 Score, and AUC-ROC". Based on these results, it is evident that this approach is effective in providing robust security and privacy protections in healthcare CPS, thereby establishing a new paradigm for delivering safe and confidential health care in the digital age. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cybersecurity

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