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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wang, Chunmian Luo, Junzhou Ling, Zhen Yang, Ming Gu, Xiaodan Yao, Yu Southeast University School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350331684

The onion service is the most important mechanism of the Tor network which enables service providers to publish anonymously various TCP services, such as web services. To access the target onion services, clients first know the 56-byte onion addresses. However, randomly generated onion addresses are difficult to memorize and can be easily used by attackers to generate phishing sites with similar onion addresses. In this paper, we propose a correlated onion address generation approach which is capable of generating a unique onion address via a customized string and a root onion address. This approach enables the generated onion addresses to be computed by clients using a human-memorable string, resulting in easier access to onion services. Based on this approach, we design and implement a Tor Domain Name System (TorDNS) that allows different service providers to register anonymously and clients to access anonymous services quickly through human-memorable pseudo-onion addresses. TorDNS is compatible with existing onion service mechanism and does not introduce additional privacy and security issues. In addition, similarity detection of pseudo-onion addresses can effectively reduce the risk of phishing sites on the Tor network. © 2023 IEEE.

关键词： Web services

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AI-Driven Solutions for Predictive Maintenance in Smart Transportation Systems

AI-Driven Solutions for Predictive Maintenance in Smart Tran...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Hundekari, Sheela Mittal, Tanusha Dutt, Amit Bhavana, Munugapati Kumar, Rakesh Nijhawan, Ginni School of Computer Applications Pimpri Chinchwad University Pune India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India GLA University Department of Computer Engineering & Applications Mathura India

ISBN: (纸本)9798331512965

Predictive maintenance: A game changer in infrastructure and vehicle management, and able to cut maintenance expenses and downtime of smart transportation systems. This study explores ways in which smart transportation systems might use AI in order to plan maintenance schedules and forecast equipment breakdowns. Predictive algorithms, that can detect potential problems before raising to expensive repair or system breakdown, use real time sensor data from infrastructure and vehicles. Decision trees and neural networks are two of these models. By allowing AI driven solutions, transportation systems may transition from a reactive to a proactive maintenance practice which may support efficiency, safety, and reliability. Unsupervised learning methods are used to do anomaly detection for real time operations, and we examine the patterns in previous maintenance data with supervised learning approaches. The use of AI for predictive maintenance is also being studied for use in smart cities, where transportation networks are becoming more interconnected by IoT. The paper also considers challenges on data integration as well as the importance of scaling the algorithms and guaranteeing cybersecurity in these systems. Based on our research, smart transportation systems could benefit from AI to increase operational efficiency, reduce the cost of material and labor to maintain and repair equipment systems, and decrease the rate of disruptions. © 2025 IEEE.

关键词： Predictive maintenance

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Hybrid model with improved score level fusion for heart disease classification

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Multimedia Tools and Applications 2024年第18期83卷 54951-54987页

作者： Maithani, Ankit Verma, Garima Department of Computer Science and Engineering School of Computing DIT University Makka Wala Uttarakhand Dehradun248009 India School of Computing DIT University Uttarakhand Dehradun248009 India

The heart, an essential organ of the body, pumps blood filled with oxygen to every area of the body through a complex network of veins and arteries. Any condition affecting the heart is referred to as heart disease. It is essential to start the right treatments early on in order to reduce the death toll. Predicting cardiac disease before heart attacks or strokes is therefore a significant challenge for the healthcare industry. This article introduces a methodology for predicting heart disease which consists of the three steps: (1) pre-processing, (2) feature extraction, and (3) disease prediction. Initially, the input data is pre-processed using improved data normalization. Next, the features are extracted from the normalized data;they are higher-order statistical features, information gain-based features, mutual information-based features, and improved entropy-based features. The results are then decided utilising an improved score-level fusion procedure, which is used to the suggested hybrid classification model that incorporates models like Deep Maxout and QNN algorithms. By adjusting the Deep Maxout and QNN classifiers' ideal weights, a self-improved Beluga whale optimisation is developed for optimal training to increase the performance of the hybrid model. When using dataset 1, the Hybrid + SI-BWO produced a precision of 94.25%, while CNN, DBN, Bi-LSTM, SVM, RNN, DL, and RF all produced precisions of 75.04%, 77.38%, 80.17%, and 86.52%, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Forecasting

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Top-level design of AI monitoring system architecture based on multi-interactive metaverse strategy 7

Top-level design of AI monitoring system architecture based ...

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7th International Conference on computer Information science and Application Technology, CISAT 2024

作者： Feng, Jiangang Lin, Dan Hainan Vocational University of Science and Technology School of Information Engineering Hainan China Harbin Engineering University School of Computer Science and Technology China

ISBN: (纸本)9798350375107

The metaverse represents a groundbreaking concept for the next generation of the Internet and social forms. It maps both the real world and the virtual world into a unified digital space, creating a parallel universe that seamlessly blends virtual and physical realities. In this paper, we explores the technological foundations and applications of the metaverse across various scenarios. First, based on the multi-interaction of the metaverse strategy, the top-level design of Artificial Intelligence(AI) monitoring system is carried out. Then, the practical application cases of artificial intelligence(AI) monitoring system architecture design are presented. This architecture is an open system that requires continuous improvement and supplementation. With the development of advanced AI technologies and hardware supplements, the metaverse will become increasingly refined and comprehensive. © 2024 IEEE.

关键词： Virtual reality

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Logistics distribution and inspection of unmanned vehicles based on improved YOLOv5 9

Logistics distribution and inspection of unmanned vehicles b...

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9th International Conference on Electromechanical Control Technology and Transportation, ICECTT 2024

作者： Hu, Jiayu Sun, Zhijin Li, Dalin School of Computer Science Zhuhai College of Science and Technology Zhuhai China School of Electronic Information Engineering Zhuhai College of Science and Technology Zhuhai China College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)9781510682399

With the continuous development and application of unmanned vehicle technology, more and more unmanned vehicles must work in a variety of different bad weather and environmental conditions, which also brings higher requirements to the research of unmanned vehicle perception systems. Especially in foggy days or other severe weather conditions, the perception of complex road states becomes more difficult. Therefore, to improve the perception effect of unmanned vehicles in bad weather, this paper proposes a multi-functional unmanned vehicle visual perception system based on YOLOv5. This paper proposes three aspects to improve the problem of the perception effect of unmanned vehicles in bad weather. First, the model balance between computational efficiency and accuracy is improved by including the Ghost Bottleneck module. Secondly, the CBATM module is used to enhance the target perception ability of the model, especially the detection accuracy in foggy scenarios. Finally, the MSR algorithm is combined to enhance the robustness of the model in foggy scenarios and improve the ability of the model to perceive targets in a complex environment. This YOLOv5-based multi-functional unmanned vehicle visual perception system has a wide application prospect in the application of multifunctional unmanned vehicles integrating distribution and inspection and provides strong support for the realization of intelligent perception and decision-making. © 2024 SPIE.

关键词： Medical imaging

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A Lightweight, Secure, and Scalable Blockchain-Fog-IoMT Healthcare Framework with IPFS Data Storage for Healthcare 4.0

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SN computer science 2024年第1期5卷 198页

作者： Mallick, Soubhagya Ranjan Lenka, Rakesh Kumar Tripathy, Pradyumna Kumar Rao, D. Chandrasekhar Sharma, Suraj Ray, Niranjan Kumar Computer Science and Engineering International Institute of Information Technology Odisha Bhubaneswar 751003 India Computer Science and Engineering Silicon Institute of Technology Odisha Bhubaneswar 751024 India Information Technology Veer Surendra Sai University of Technology Odisha Burla 768018 India Computer Science and Engineering Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur 495009 India School of Computer Science and Engineering Kalinga Institute of Industrial Technology Odisha Bhubaneswar 751024 India

The gathering, processing, transmission, sharing, and storage of healthcare data was the core idea behind Healthcare 4.0. Currently, most of the existing solutions for offering smart healthcare services rely on cloud-based platforms. The main issues with current healthcare systems include storage overhead, processing speed, scalability, single points of failure, bandwidth requirements, and device connectivity. The recent challenges in the healthcare system motivated us to develop a system by integrating the Internet of Medical Things (IoMT), Blockchain, fog computing, and InterPlanetary File System (IPFS) to achieve decentralization, scalability, security, immutability, and data privacy. In this paper, we propose a novel IoMT and Blockchain framework with fog node computing to reduce latency and speed up processing, as well as to reduce the network congestion, bandwidth requirements, and the main Blockchain network overload. Untrusted devices are connected to the system using a proxy monitor, which records the device’s activity. In addition, the interplanetary file system (IPFS) is integrated with the Blockchain network to store patient data and files in order to ensure the system’s decentralization, scalability, security, and privacy. Moreover, we evaluate the proposed framework in terms of security, scalability, latency, storage, efficiency, and performance. The experimental analysis shows that the proposed framework makes access, searches, uploads, and downloads faster and more secure. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Blockchain Efficiency Fog computing IoMT IPFS Scalability Security

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Wi-Fi Based Smart Attendance Monitoring System 7

Wi-Fi Based Smart Attendance Monitoring System

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7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023

作者： Ramakrishnan, Nithin Ajil, A. Ali, Anooja Meenakshi Sundaram, A. Sanju, V. School of Computer Science and Engineering Reva University Bengaluru India

ISBN: (纸本)9798350343144

The attendance monitoring system provides a convenient way to track the presence of faculty members. In the past, faculty attendance was marked using traditional methods under the supervision of higher authorities such as the principal or department head. However, these methods were often outdated and prone to errors, leading to discrepancies in attendance records over the years. To address these issues, a new system was implemented using modern technology, specifically finger-punching and face recognition. This system replaced the old record books and other outdated methods, offering a more efficient and accurate way to manage faculty attendance. Additionally, a mobile application is developed for faculty or staff members to mark their attendance using their smartphones. This application utilizes Wi-Fi and location services to record attendance on campus. The recorded attendance data is securely stored in a database, allowing the administrator or main authority to access and modify the records as needed. This integrated system not only simplifies the attendance process but also ensures data integrity and provides real-time monitoring capabilities. © 2023 IEEE.

关键词： Face recognition

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Detection and Navigation of Unmanned Vehicles in Wooded Environments Using Light Detection and Ranging Sensors

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Sensors and Materials 2023年第11期35卷 3637-3654页

作者： Zhang, Zhiwei Jhang, Jyun-Yu Lin, Cheng-Jian School of Electronic Information Engineering Hebei University of Technology Tianjin300401 China Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung404 Taiwan Department of Computer Science & Information Engineering National Chin-Yi University of Technology Taichung411 Taiwan

With the advancement of automatic navigation, navigation control has become an indispensable core technology in the movement of unmanned vehicles. In particular, research on navigation control in outdoor wooded environments, which are more complex, less controlled, and more unpredictable than indoor environments, has received widespread attention. To realize the movement control and obstacle avoidance of unmanned vehicles in unknown environments, in this study, we use light detection and ranging (LiDAR) sensors to sense the surrounding environment. By plane meshing the point cloud reflected from LiDAR, we can instantly establish feasible regions. At the same time, using the artificial potential field algorithm, a stable obstacle avoidance and navigation path is planned for use in an unknown environment. In an actual woods navigation experiment to evaluate our proposed LiDAR detection method, we used an independently developed unmanned vehicle with Ackermann steering geometry. Experimental results indicate that the proposed method can effectively detect obstacles. The accuracy requirement is within 30 cm from the navigation target, and the experimental results show that the average navigation success rate of the proposed method is as high as 85%. The experimental results demonstrate that the system can stably and safely navigate in scenarios with different unknown environments. © MYU K.K.

关键词： Navigation

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RIS-Aided MIMO Downlink Transmission for Ultradense LEO Satellite-Terrestrial Networks

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IEEE Internet of Things Journal 2025年第11期12卷 15304-15318页

作者： Zhang, Xin Qin, Xiaohan Zhang, Zitian Cai, Lin X. Zhou, Haibo Zhuang, Weihua Nanjing University School of Electronic Science and Engineering Nanjing210023 China Zhejiang Gongshang University School of Information and Electronic Engineering Hangzhou310018 China Illinois Institute of Technology Department of Electrical and Computer Engineering ChicagoIL60616 United States University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Ultradense low-Earth orbit (LEO) satellite-terrestrial network (ULSN) has evolved as a new paradigm to provide ubiquitous and high-capacity communications in next generation wireless networks. However, the direct LEO satellite broadband connectivity faces significant challenges in urban environments due to the masking effect, which limits the reliability and availability of communication links in ULSNs. To address this, reconfigurable intelligent surface (RIS) is emerging as a promising solution in ULSNs. In this article, we investigate RIS-aided downlink data transmission in urban environments of multiusers in ULSNs. We set up a mixed-integer programming (MIP) model for maximizing the sum rate of terrestrial users in ULSNs. To solve the complex MIP problem, we propose a two-phase joint optimization algorithm with a deep learning phase and an alternative optimization (AO) phase. In the deep learning phase, a deep neural network (DNN) algorithm is employed to obtain the optimal user association matrix based on the positions of terrestrial users and LEO satellites. Then in the AO phase, successive convex approximation is utilized to transform the nonconvex subproblems of beamforming and RIS phase design into convex formulations and iteratively solve them. Simulation results demonstrate that the proposed algorithm outperforms other baseline algorithms. © 2014 IEEE.

关键词： Integer programming

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Modeling of radiative free cooling for data centers 25

Modeling of radiative free cooling for data centers

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25th International Congress on Modelling and Simulation, MODSIM 2023

作者： Long, Wenjun Aili, Ablimit School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9780987214300

Demand for data centers with high-performance computing is growing rapidly. However, large energy consumption in data centers (DC) is increasingly becoming a major challenge. As cooling systems are a key part of data centers accounting for a substantial fraction of energy consumption, improving their energy efficiency remains an important goal. Here, we demonstrate cooling energy saving in a data center with a radiative free cooling system that operates as part of the DC chiller system. Using physics-based models of the radiative free cooling and chiller systems, we simulate chiller energy consumption with and without free cooling. We specifically study the effects of DC outlet air temperature, free cooling system size, cooling tower water flow rate, free cooling system flow rate, relative humidity and ambient temperature on the system performance such as coefficient of performance, power consumption, and energy and water savings. Through a series of sensitivity analyses, we identify operation parameters of the integrated cooling system that must be optimized to achieve the best energy-saving state. Energy and water savings are positively corelated with cooling power and size of the free cooling system, cooling tower flow rate and relative humidity. We show that 7.5% cooling energy and 7.6% water consumption can be saved for the annual average weather conditions of Singapore. © 2023 Proceedings of the International Congress on Modelling and Simulation, MODSIM. All rights reserved.

关键词： Energy utilization

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