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A Game-Theoretic Approach to Safe Crowd Evacuation in Emergencies

computers, Materials & Continua 2024年第4期79卷 1631-1657页

作者： Maria Gul Imran Ali Khan Gohar Zaman Atta Rahman Jamaluddin Mir Sardar Asad Ali Biabani May IssaAldossary Mustafa Youldash Ashraf Saadeldeen Maqsood Mahmud Asiya Abdus Salam Dania Alkhulaifi Abdullah AlTurkey31Department of Computer Science COMSATS University IslamabadAbbottabad CampusAbbottabad22010Pakistan Department of Computer Science Abbottabad University of Science and TechnologyAbbottabad22010Pakistan Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Science and Technology Unit Umm Al-Qura UniversityMakkah21955Saudi Arabia Deanship of Scientific Research Umm Al-Qura UniversityMakkah21955Saudi Arabia Department of Computer Information Systems College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Engineering College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Science Faculty of Computer Science and Information TechnologyOmdurman Islamic UniversityOmdurman14415Sudan Business Analytic Program Department of Management and MarketingCollege of Business AdministrationUniversity of BahrainSakhir32038Bahrain

Obstacle removal in crowd evacuation is critical to safety and the evacuation system efficiency. Recently, manyresearchers proposed game theoreticmodels to avoid and remove obstacles for crowd evacuation. Game theoreticalmodels aim to study and analyze the strategic behaviors of individuals within a crowd and their interactionsduring the evacuation. Game theoretical models have some limitations in the context of crowd evacuation. Thesemodels consider a group of individuals as homogeneous objects with the same goals, involve complex mathematicalformulation, and cannot model real-world scenarios such as panic, environmental information, crowds that movedynamically, etc. The proposed work presents a game theoretic model integrating an agent-based model to removethe obstacles from exits. The proposed model considered the parameters named: (1) obstacle size, length, andwidth, (2) removal time, (3) evacuation time, (4) crowd density, (5) obstacle identification, and (6) route *** proposed work conducts various experiments considering different conditions, such as obstacle types, obstacleremoval, and several obstacles. Evaluation results show the proposed model’s effectiveness compared with existingliterature in reducing the overall evacuation time, cell selection, and obstacle removal. The study is potentially usefulfor public safety situations such as emergency evacuations during disasters and calamities.

关键词： Safe crowd evacuation public safety emergency transition probability cooperation

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Revolutionalize Your Learning Experience with EQU ACCESS 4

Revolutionalize Your Learning Experience with EQU ACCESS

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Raveenthiran, Gawsan Sivarajah, Kaanushan Mohamed Riyal, Akash Ahamed Kugathasan, Vethurja Chandrasiri, Sanjeevi Rajendran, Karthiga Sri Lanka Institute of Information Technology Department of Computer Science & Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350395914

This paper introduces a novel approach aimed at enhancing online education by placing a central focus on students' emotional well-being and improving their learning experiences. The approach integrates four key machine learning technologies: behavioral expression analysis, a personalized chatbot for emotional support, voice stress detection, and visual content description. Through empirical findings, the study illustrates the effectiveness of these methods in bolstering students' emotional well-being and academic performance. By providing a roadmap for the advancement of online education and emotional support, this research holds promise for delivering substantial benefits to learners worldwide. The study showcases notable advancements in online education, reporting a 30% rise in perceived emotional support and a 25% increase in overall satisfaction. The personalized emotional support chatbot achieved an 85% accuracy in addressing students' emotional needs, while voice stress detection boasted a 90% accuracy in identifying anxiety. Additionally, visual content description led to a 20% improvement in comprehension. These findings highlight the approach's potential to elevate both emotional well-being and academic performance in online learners. © 2024 IEEE.

关键词： Contrastive Learning

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A novel ontological-based trust aware hybrid key management scheme (OTAHKMS) to enhance network lifetime and energy usage in wireless sensor networks (WSNs)

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International Journal of information technology (Singapore) 2024年第3期16卷 1429-1435页

作者： Barve, Amit Pallavi, R. Deepak, Shashikant Murugan, R. Yadav, Dhananjay Singh, Ashutosh Kr. Sharma, Meenakshi Shalini, S. Department of Computer Science and Engineering Faculty of Engineering and Technology Parul University Gujarat Vadodara India Department of Computer Science Engineering Presidency University Karnataka Bangalore India Department of UGDX ATLAS SkillTech University Mumbai India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Noida India Department of Electronics and Communication Engineering Noida Institute of Engineering and Technology UP Noida India Department of Education Sanskriti University UP Mathura India Department of Computer Science and Engineering Dayananda Sagar Academy of Technology and Management Bangalore India

Security, heterogeneity, and interoperability are the prominent concerns in wireless sensor networks (WSNs) with restricted amounts of resources such as energy, transmission range, and processing of packets. Moreover, secured and trusted communication among WSNs is required to enhance network lifetime and security, thereby reducing energy consumption in the network. Thus, the given paper presents an ontological-based trust aware hybrid key management scheme (OTAHKMS) for WSNs. The proposed approach is a two-fold approach, where the first phase involves the encryption process for maintaining data confidentiality and integrity, while the second phase involves the use of ontology for capturing trust relationships and representing contextual information among several nodes in the network. The performance of the proposed trusted approach is validated and compared with existing cited works based on evaluation metrics such as communication trust value (CTV), network throughput (NT), packet delivery ratio (PDR), and energy consumption (EC). The results show that the proposed ontological-based trusted approach efficiently outperforms existing protocols, thereby enhancing network lifetime and energy usage in WSNs. © 2024, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Encryption Packets transmission Security Trust validation Wireless sensor networks (WSNs)

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A New Hybrid Model for Segmentation of the Skin Lesion Based on Residual Attention U-Net

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computers, Materials & Continua 2023年第6期75卷 5177-5192页

作者： Saleh Naif Almuayqil Reham Arnous Noha Sakr Magdy M.Fadel Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaSaudi Arabia Department of Communications and Electronics Delta Higher Institute for Engineering and Technology(DHIET)MansouraEgypt Computers Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansouraEgypt

Skin segmentation participates significantly in various biomedical applications,such as skin cancer identification and skin lesion *** paper presents a novel framework for segmenting the *** framework contains two main stages:The first stage is for removing different types of noises from the dermoscopic images,such as hair,speckle,and impulse noise,and the second stage is for segmentation of the dermoscopic images using an attention residual U-shaped Network(U-Net).The framework uses variational Autoencoders(VAEs)for removing the hair noises,the Generative Adversarial Denoising Network(DGAN-Net),the Denoising U-shaped U-Net(D-U-NET),and Batch Renormalization U-Net(Br-U-NET)for remov-ing the speckle noise,and the Laplacian Vector Median Filter(MLVMF)for removing the impulse *** the second main stage,the residual attention u-net was used for *** framework achieves(35.11,31.26,27.01,and 26.16),(36.34,33.23,31.32,and 28.65),and(36.33,32.21,28.54,and 27.11)for removing hair,speckle,and impulse noise,respectively,based on Peak Signal Noise Ratio(PSNR)at the level of(0.1,0.25,0.5,and 0.75)of *** framework also achieves an accuracy of nearly 94.26 in the dice score in the process of segmentation before removing noise and 95.22 after removing different types of *** experiments have shown the efficiency of the used model in removing noise according to the structural similarity index measure(SSIM)and PSNR and in the segmentation process as well.

关键词： Skin tumor speckle noise impulse noise hair noise deep learning segmentation

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Labor Safety Analysis Using Object Recognition and Deep Learning 1

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2nd International Conference on Human-Centric Smart Computing, ICHCSC 2023

作者： Evangeline, D. Jatin, B. Manoj, S. Pannaga, N. Sanjeev, G. Parkavi, A. Department of Information Science and Engineering Ramaiah Institute of Technology Bangalore India Department of Computer Science and Engineering Ramaiah Institute of Technology Bangalore India

ISBN: (数字)9789819977116

ISBN: (纸本)9789819977109

Occupational safety is a major concern in any industry or workplace, and the use of advanced technologies such as object recognition and deep learning can greatly enhance safety measures. This paper proposes a labor safety analysis system that utilizes object recognition and deep learning algorithms to detect potential hazards and ensure safe working conditions. The proposed system can recognize various types of safety gear and analyze their positions and movements to detect potential safety in real time. The system can also provide alerts and warnings to workers and supervisors in case of the absence of safety gear. Experimental results demonstrate the effectiveness and efficiency of the proposed work. This can significantly improve the safety and productivity of the workplace. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Object recognition

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Performance Evaluation of Meta-data features for Spam SMS Classification using Sequential Models

Performance Evaluation of Meta-data features for Spam SMS Cl...

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2024 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2024

作者： Prakash, K. Sakthi Abirami, A.M. Singh, Supriya Ramanujam, E. Thiagarajar College of Engineering Department of Information Technology Madurai India National Institute of Technology Silchar Department of Computer Science and Engineering Assam India

ISBN: (纸本)9798350348460

The escalating threat of cybercrime, particularly in the form of spam, fraudulent messages, and phishing attacks, poses a substantial risk to individuals who may unknowingly divulge sensitive information to malicious user. This paper addresses the urgent need for a robust spam filtering model capable of detecting and preventing these deceptive messages before reaching users' inboxes or SMS folders. Focusing on the English spam SMS dataset from the UCI repository, we propose a sequential learning-based spam message filtering framework using the meta-data feature of SMS message. The performance of the meta-data features are compared with the various other methods such as vectorization, sequential model with embedding layers. Performance shown that the Meta-data feature vectors doesn't depend on any parameters for the process of vectorization and it leverages the performance of the sequential models. © 2024 IEEE.

关键词： Deep Learning Ham hand-crafted features meta-data Sequential models SMS Spam

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A Review on Amalgamation of Textile-based Components for Standardization of Smart Fabrics 5

A Review on Amalgamation of Textile-based Components for Sta...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Kumar, Sandeep Haripriya Taneja, Neeti Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Science and Information Technology Bangalore India Noida Institute of Engineering and Technology Department of Computer Science Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350383522

This article provides an overview of the fundamental ideas and methodologies utilized in the construction of fabric sensors, along with the frequently employed materials and procedures in the field of electronic textiles. It illustrates that depending on the level of integration within the fabric platform, sensing capabilities may be accomplished by either intrinsic or external modifications to textile substrates. The present study showcases the adaptability of fabric sensors in quantifying force, pressure, chemicals, humidity, and temperature fluctuations. The materials, connections, fabric circuits, interconnect, encapsulation, and manufacturing processes used in fabric technologies are very adaptable and diverse. However, they are not as durable as the components used in traditional electronics. The survey results indicate that it is feasible to create a comprehensive smart fabric system by combining various types of functional elements based on textiles. This effort aims to initiate the standardization of smart fabric sensing techniques and e-textile production procedures. © 2024 IEEE.

关键词： e-textiles Fabric sensors textile sensors

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rafPS:A shapley-based visual analytics approach to interpret traffic

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Computational Visual Media 2024年第6期10卷 1101-1119页

作者： Zezheng Feng Yifan Jiang Hongjun Wang Zipei Fan Yuxin Ma Shuang-Hua Yang Huamin Qu Xuan Song Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 18055China Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong KongChina Department of Computer Science University of ReadingBerkshire RG66AHUK Center for Spatial Information Science the University of TokyoTokyo 113-00331Japan Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet Southern University of Science and TechnologyShenzhen 518055China

Recent achievements in deep learning(DL)have demonstrated its potential in predicting traffic *** predictions are beneficial for understanding the situation and making traffic control ***,most state-of-the-art DL models are consi-dered“black boxes”with little to no transparency of the underlying mechanisms for end *** previous studies attempted to“open the black box”and increase the interpretability of generated ***,handling complex models on large-scale spatiotemporal data and discovering salient spatial and temporal patterns that significantly influence traffic flow remain *** overcome these challenges,we present TrafPS,a visual analytics approach for interpreting traffic prediction outcomes to support decision-making in traffic management and urban *** measurements region SHAP and trajectory SHAP are proposed to quantify the impact of flow patterns on urban traffic at different *** on the task requirements from domain experts,we employed an interactive visual interface for the multi-aspect exploration and analysis of significant flow *** real-world case studies demonstrate the effectiveness of TrafPS in identifying key routes and providing decision-making support for urban planning.

关键词： data visualization model interpretation urban planning urban visual analytics

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Kinmen Wind Lion Face Generation Based on the Deep Convolutional Generative Adversarial Network 13

Kinmen Wind Lion Face Generation Based on the Deep Convoluti...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Wu, Chia-Huan Zhao, Yu-Xiang Chou, Cheng-Hou Hsieh, Yi-Zeng National Taiwan University of Science and Technology Department of Electrical and Computer Engineering Taiwan National Quemoy University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350355079

Under the advancements of science and technology at present, artificial intelligence has become widely applied in daily life. Hence, deep learning has attracted much attention in recent years and has been widely used in recognition, classification and generation. The results generated by generative adversarial networks have become increasingly realistic and are now being applied to human face generation and reconstruction. Among the various technologies, the deep convolutional generative adversarial network (DCGAN) is more widely used. Based on the trained DCGAN of human and cat faces, this paper collected Kinmen wind lion data by YOLOv3 and crawler technology and transferred the data to the DCGAN of wind lions through network-based transfer learning to generate complete wind lion faces. The experimental process consisted of four parts. The wind lion dataset was directly used to train the DCGAN in the first two parts, and the DCGAN of the human and cat faces was used for transfer learning in the second two parts. According to the results, the training effect of human face-based transfer learning was the best and was the closest to Kinmen's wind lions. © 2024 IEEE.

关键词： Generative adversarial networks

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Suicide Risk Prediction Using Random Forest and Imbalanced Data Management Approach 10

Suicide Risk Prediction Using Random Forest and Imbalanced D...

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10th International Conference on Artificial Intelligence and Robotics, QICAR 2024

作者： Rabbani, Fatemeh Masoumi, Behrooz Keyvanpour, Mohammad Reza Department of Computer and Information Technology Engineering Qazvin Branch Islamic Azad University Qazvin Iran Department of Computer Engineering Alzahra University Tehran Iran

ISBN: (纸本)9798350348873

Predicting suicide risk is crucial for reducing suicide-related deaths. Machine learning techniques offer the potential for early and precise identification of individuals at risk. A primary challenge in this realm is the imbalanced nature of data, which can undermine model efficiency. In this study, a suicide risk prediction model was developed using the random forest algorithm and was compared its performance with other methods. To address data imbalance, two distinct balancing techniques were applied. Subsequently, these balanced datasets were fed into the model, and their prediction outcomes were assessed in tandem. Our findings revealed an overall prediction accuracy of 0.99, backed by a sensitivity of 0.99 and a specificity of 0.98. It's evident that leveraging the random forest algorithm, combined with data balancing methods, significantly enhances the evaluation metrics, setting a new performance standard in predictive modeling. ©2024 IEEE.

关键词： information management

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