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A Machine Learning Approach to Identifying Employees at Risk of.Workplace Injury 2

A Machine Learning Approach to Identifying Employees at Risk...

引用

2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Pramanik, Bablu Modak, Santanu Brainware University Dept. of Computer Science & Engineering India Bengal College of Engineering and Technology Dept. of Information Technology India

ISBN: (纸本)9798350360523

Workplace injuries are a critical concern, with millions occurring annually, leading to substantial human and economic costs. This study focuses on a qualitative analysis based on statistics from 2013 to 2017, with a specific emphasis on fatal and non-fatal workplace accidents within the European Union (EU) and Romania. The primary objective is to assess Romania's workplace safety status in the context of.the EU and to propose strategic measures for improvement. To achieve this goal, four key indicators and statistical datasets are utilized, sourced from the National Institute of.Statistics (NIS) of.Romania, the Eurostat database of.the European Commission, and the Romanian Labor Inspection. These indicators include the rate of.incidence frequency index for occupational accidents, the average duration index, the frequency index for fatal accidents, and the severity index, enabling a comprehensive evaluation of.accident frequency and severity. The rate of.incidence, measuring injuries per 100,000 workers, is a pivotal indicator. Additionally, the study calculates the frequency index of.non-fatal accidents (injuries per 1,000 employees) and the fatal accident frequency index (injuries per 1,000 workers). Statistical findings are rigorously validated through ANOVA analysis and T-tests. Following data evaluation, the study of.ers strategic recommendations informed by national and European strategies, including the National Occupational Safety and Health (OSH) Strategy for 2017-2020 and the "EU Strategic Framework on Health and Safety at Work"spanning 2014 to 2020. These recommendations aim to guide efforts for workplace accident control and prevention in Romania and the broader European *** summary, this study's core objective is to comprehensively analyze workplace accidents in Romania and the EU, employing various indicators and statistical data. Its primary aim is to assess the current status and propose evidence-based strategies for improvement, aligning with

关键词： Machine learning

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Exploring the Potential of.MANETs: A Study on Raspberry Pi and UAV Integration 3

Exploring the Potential of MANETs: A Study on Raspberry Pi a...

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of.Things, IDCIoT 2025

作者： Archana, B. Mahalakshmi, A. Revathi, K. Ashok Kumar, K. Hema, A.B. Dept. of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore India Dept. of Information Technology SNS College of Engineering Coimbatore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

This research investigates the feasibility of.utilizing Mobile Ad Hoc Networks (MANETs) in conjunction with Raspberry Pi-equipped Unmanned Aerial Vehicles (UAV) swarms. The primary objective is to overcome the limitations of.traditional communication infrastructures in dynamic and decentralized environments. Specifically, we address the challenge of.establishing reliable and efficient communication links within a swarm of.UAVs engaged in various applications, including disaster response, military reconnaissance, and environmental monitoring. By integrating Raspberry Pi devices with UAVs, we aim to create a self-organizing communication network that enables seamless data transmission, coordinated swarm operations, and enhanced mission effectiveness. The research explores different synchronization strategies, including master-slave and decentralized approaches, to optimize swarm coordination. Furthermore, we investigate the potential of.incorporating advanced technologies such as machine learning, computer vision, and swarm intelligence to further enhance the autonomy, adaptability, and overall performance of.the UAV swarm. © 2025 IEEE.

关键词： Mobile ad hoc networks

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Generating High-Quality Distributed Representations of.Words Using Wiktionary and Sentence-BERT 16

Generating High-Quality Distributed Representations of Words...

引用

16th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2024

作者： Nishiura, Ryota Tsuchiya, Seiji Watabe, Hirokazu Graduate School of Science and Engineering Doshisha University Dept. of Information and Computer Science Kyotanabe Kyoto Japan Doshisha University Faculty of Science and Engineering Dept. of Intelligent Information Engineering and Science Kyotanabe Kyoto Japan

ISBN: (纸本)9798350377903

We propose an improved method to generate high-quality distributed representations of.words. In our previous paper, we introduced a novel method to obtain distributed representations of.words with BERT using Wiktionary as semantic sources of.words. The previous method outperformed a Wrd2Vec model in a word similarity task, while there still had to be some room to improve in term of.semantic accuracy. In this paper, our new method introduces one of.Sentence-BERT models to text-embed the semantic sources instead of.the original BERT model. We demonstrate that our proposed method outperforms both our previous method and the Word2Vec model in the tasks. © 2024 IEEE.

关键词： Embeddings

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Reconsider Time Series Analysis for Insider Threat Detection

Reconsider Time Series Analysis for Insider Threat Detection

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Chen, Chia-Cheng Pao, Hsing-Kuo National Taiwan University of Science and Technology Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350362480

Insider threat detection (ITD) presents a significant challenge in cybersecurity, particularly within large and complex organizations. Traditionally, ITD has been overshadowed by the focus of.external threats, resulting in less attention and development in this critical area. Conventional ITD approaches of.en rely heavily on event-driven approaches. On top of.that, researchers developed various rule-based methods to conquer the tasks. Based on that, we of.en ignore the intrinsic temporal relationships that are naturally built in between events that occur in different moments. For instance, we may easily understand events with causality such as one anomalous event followed by another specific event to complete a malicious action;however, may not be aware of.events that occur around 9 am every morning during working hours. In our opinion, we attempt to re-consider the temporal behavior to extract the information hidden in cyberspace activities. Specifically, some effective sentence embeddings can assist us in providing informative internal representations to summarize temporal behaviors in the temporal activity sequences to make the right judgment on insider threat detection. In this paper, we propose a novel methodology for insider threat detection that emphasizes temporal relationship modeling on top of.already-matured event sequence analysis to effectively catch insider threats. The proposed approach leverages contrastive sentence embeddings to learn users' intentions in sequences, followed by the deployment of.a user-level and event-level Contrastive Learning (euCL) model to incorporate temporal behaviors with user behavior embeddings. To validate the proposed methodology, we conduct extensive analyses and experiments using the publicly available CERT dataset. The results demonstrate the effectiveness and robustness of.the proposed method in detecting insider threats and identifying malicious scenarios, highlighting its potential for enhancing cybersecurity measur

关键词： Contrastive Learning

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Enhanced Adaptive Video Streaming for Resource-Constrained UAVs through Dynamic Region-of.Interest Tile Swapping 10

Enhanced Adaptive Video Streaming for Resource-Constrained U...

引用

10th International Conference on Applied System Innovation, ICASI 2024

作者： Hsu, Tz-Heng Ju, Rui-Han Horng, Gwo-Jiun Southern Taiwan University of Science and Technology Dept. of Computer Science and Information Engineering Tainan Taiwan

ISBN: (纸本)9798350394924

Real-time video transmission via unmanned aerial vehicles (UAVs) is significantly impacted by latency issues. Using Region of.Interest (ROI) tile segmentation methods, video streaming techniques can dynamically adjust the encoding bitrate, assign various priorities to distinct video tiles, and specify different quality quantization factors. High-efficiency video tile encoding techniques, combined with ROI-based streaming methods, can meet various Quality of.Service (QoS) criteria. However, dynamically assigning quantization factors for different tiles has the disadvantage of.increasing the encoding system's complexity. This study presents a method to dynamically swap ROI tiles to lower the complexity of.the encoding system, making it suitable for UAVs with limited computational capacities. By adopting the proposed adaptive video streaming transmission method with tile-swapping strategy, it is feasible to enhance the visual quality in critical areas for resource-constrained UAV devices. © 2024 IEEE.

关键词： Video streaming

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Active Element Arrangement and Prediction Model for Channel Estimation in IRS-Assisted Systems

Active Element Arrangement and Prediction Model for Channel ...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Tsuchiya, Yoshihiko Suga, Norisato Uruma, Kazunori Fujisawa, Masaya Tokyo University of Science Dept. of Information and Computer Technology Tokyo Japan Shibaura Institute of Technology Dept. of Information and Communication Engineering Tokyo Japan Kogakuin University Dept. of Computer Science Tokyo Japan

ISBN: (纸本)9798350350821

Intelligent reflecting surfaces (IRS) that can dynamically control the phase of.radio waves and reflect them are attracting attention to realize non line-of.sight communication in the high-frequency band. Channel state information (CSI) is estimated to properly control the phase, however the overhead of.the estimation reduces communication efficiency. To reduce the overhead, IRS systems with active elements that perform channel estimation are considered. One approach treats the partial CSI obtained from the active elements as an image and utilizes a super-resolution technique to predict the entire CSI. However, because the active elements are arranged in grid, prediction is difficult when the active elements spacing and the CSI fluctuation period match. In this study, we propose a new arrangement that gathers the active elements in the center of.the IRS. Furthermore, we regard prediction as an image-inpainting problem that recovers missing pixels from known pixel values, and we propose applying the U-Net to solve this problem. Numerical experiments show that the proposed method outperforms conventional methods in prediction accuracy and spectral efficiency. © 2024 IEEE.

关键词： Channel estimation

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Reinforcement Learning for Adaptive Traffic Signal Control Using Deep Q-Networks 1

Reinforcement Learning for Adaptive Traffic Signal Control U...

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1st International Conference on Sustainable Energy Technologies and Computational Intelligence, SETCOM 2025

作者： Bohra, Asha Rani Selvi, Thamarai Vishal, V. Sunkara, Pawan Sai Sunku, Balaji Jignesh, Bhavsar Nitya New Horizon College of Engineering Dept. of Computer Science and Engineering Karnataka Bengaluru India New Horizon College of Engineering Dept. of Information Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798331520540

Traffic congestion is a critical issue in urban areas, contributing to increased travel time, fuel consumption, and environmental pollution. Traditional traffic signal control methods, such as fixed-time systems, cannot adapt to real-time changes in traffic conditions. This paper presents a novel approach using Deep Q-Networks (DQN) to control traffic signals based on realtime traffic data dynamically. Our simulation environment models a four-way intersection, where vehicle densities and wait times are continuously monitored. The DQN agent optimizes green light durations by minimizing vehicle wait times and maximizing traffic throughput. Extensive simulation results demonstrate that our system significantly reduces congestion and improves traffic flow compared to traditional fixed-time systems. Furthermore, the approach shows potential for real-world applications in urban traffic control systems, particularly in smart cities equipped with Internet of.Things (IoT) devices. © 2025 IEEE.

关键词： Traffic congestion

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Framework for Cloud-Based Data Management in Autonomous Vehicles

Framework for Cloud-Based Data Management in Autonomous Vehi...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Pallapothu, Harshith Gopi Krishna, G. Sunil, Digvijay Teja Yadav, S. Nagasundari, S. Pes University Dept. of Computer Science & Engineering Bengaluru India Research Center for Information Security Forensics and Cyber Resilience Pes University Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798331529833

The increasing adoption of.autonomous vehicles has driven the need for robust data management solutions that support real-time operations and ensure vehicle safety and efficiency. This work introduces a cloud-based framework for management of.sensor data from autonomous vehicles, focusing on optimizing payload transmission rates. The framework leverages AWS IoT Core and AWS IoT Analytics to ensure efficient data flow from vehicle sensors to cloud storage. This framework demonstrates its potential for scalable deployment in real-world autonomous vehicle networks, contributing to the evolution of.connected vehicle technologies and intelligent transport systems. This framework ensures reliable data transmission up to 250 payloads per second, beyond which data loss occurs. © 2025 IEEE.

关键词： Cloud platforms

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IRS Controlling Method With Machine Learning Based on Position information 13

IRS Controlling Method With Machine Learning Based on Positi...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

ISBN: (纸本)9798350355079

Intelligent Reflecting Surfaces (IRS) is attracting attention for wireless communications at high-frequency band. IRS can control radio propagation by reflecting radio waves and shifting their phase. As one of.the methods to control IRS, location-based methods have been proposed. However, to achieve highly accurate IRS control by the methods, the assumed propagation model must match the actual propagation environment. This study proposes a method to control the IRS from location information adapted to the propagation environment by using machine learning. Numerical experiments confirm that the proposed method can appropriately control IRS regardless of.the propagation environment. © 2024 IEEE.

关键词： Adversarial machine learning

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A 4x4 MIMO Antenna Array for the Wi-Fi7Access Point on a Metal Wall Application

A 4x4 MIMO Antenna Array for the Wi-Fi7Access Point on a Met...

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2024 International Symposium on Antennas and Propagation, ISAP 2024

作者： Su, Hsin-Lung Zhan, You-Sheng Dept. of Computer and Communication National Pingtung University Taiwan Dept. of Computer Science and Information Engineering National Pingtung University Taiwan

ISBN: (纸本)9798350364774

A 4×4 CPW MIMO Antenna array with an artificial magnetic conductor (AMC) reflector is presented for Wi-Fi7 access point on a metal wall application in the study. The 10-dB impedance of.the integrated MIMO antenna array covers the Wi-Fi 7 operating bands, and the transmission coefficients are under -15 dB in the whole Wi-Fi7 bands. This proposed integrated MIMO antenna is low prof.le and has good total radiation efficiency (over 60%). The height of.the integrated MIMO antenna array is 6.325 mm. © 2024 IEEE.

关键词： Wireless local area networks (WLAN)

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