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16th International Conference on information technology and Electrical Engineering, ICITEE 2024

作者： Bachmid, Muhammad Avied Anshari Haq, Muhammad Yasir Mumtaz, Muhammad Rafly Pinandito, Aryo Universitas Brawijaya Faculty of Computer Science Media Game and Mobile Laboratory Department of Informatics Engineering Malang Indonesia Universitas Brawijaya Faculty of Computer Science Malang Indonesia Universitas Brawijaya Faculty of Computer Science Media Game and Mobile Laboratory Department of Information System Malang Indonesia

ISBN: (纸本)9798350375817

Prior study has developed the RouteSegmentation algorithm to identify the perimeter area surrounding a route. In this study, a comparative experiment was carried out to investigate the performance of the RouteSegmentation algorithm implementation in contrast to the RouteBoxer algorithm. Both algorithms play a crucial role in geographical information systems (GIS), particularly in enhancing navigational tools by identifying points of interest along specified routes. Through systematic experimentation using various perimeter distance values and types of route, the trade-offs between processing efficiency and the granularity of the area identified by these algorithms were evaluated. The results demonstrate that RouteSegmentation maintained relatively consistent performance across different perimeter distance values and statistically significantly faster processing time than RouteBoxer. Furthermore, while the RouteBoxer algorithm benefits from larger perimeter distance values by reducing processing time, it compromises the coverage results of the algorithm and potentially undermines its usefulness in practical scenarios. This study not only provides insights into the optimal use of perimeter distance values for each algorithm but also guides users in selecting the appropriate algorithm based on their specific application needs, balancing detailed geographical analysis and runtime efficiency. © 2024 IEEE.

关键词： Transportation routes

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Analyzing Employee Retention Factors using Machine Learning 4

Analyzing Employee Retention Factors using Machine Learning

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Rahman, Moshiur Islam, Md Rashedul Bala, Partho Sattar, Abdus Daffodil International University Department of Management Information System Daffodil Smart City Dhaka Bangladesh Daffodil Institute of Information and Technology Department of Computer Science and Engineering Dhanmondi Dhaka Bangladesh Bangabandhu Sheikh Mujibur Rahman Science and Technology University Department of Computer Science and Engineering Dhaka8100 Bangladesh Daffodil International University Department of Computer Science and Engineering Daffodil Smart City Dhaka Bangladesh

ISBN: (纸本)9798350343670

This research proposal aims to employ machine learning techniques to analyze employee retention factors in Software Companies, recognizing its crucial role in organizational success and the potential costs of high turnover rates. Through Watson Analytics' advanced analytics capabilities, the study seeks to identify key factors contributing to employee attrition and retention, culminating in the development of a predictive model using machine learning algorithms. The outcomes are expected to include actionable recommendations to improve retention strategies, insights into the relative importance of different factors, and the creation of a data- driven, proactive talent management approach for Software Companies. By empowering organizations to retain top talent and fostering a positive work environment, this research envisions a transformative impact on long-term success and performance. © 2024 IEEE.

关键词： Artificial Intelligence Data science Deep Learning Machine Learning Monitoring Smartphone Sensors

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Assessing the Impact of Drink and Driving on Fatalities in the United States using the Fatality Analysis Reporting system Database 8

Assessing the Impact of Drink and Driving on Fatalities in t...

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8th IEEE International Conference for Convergence in technology, I2CT 2023

作者： Baryab, Mohammad Hussain, Muhammad Zunnurain Hasan, Muhammad Zulkifl Hasan, Abdullah Information Technology University of Punjab Department of Economics Lahore Pakistan Bahria University Dept. of Computer Science Lahore Pakistan University of Central Punjab Faculty of Computer Science and Information Technology Pakistan

ISBN: (纸本)9798350334012

To examine the impact of drinking and driving-related motor vehicle crash on fatalities in the US. The paper used cross-sectional data of the US covering 50 states, District of Columbia and Puerto Rico of 2020. The paper used linear regression to estimate the likelihood of death from drinking and driving-related motor vehicle crashes. Some age groups have also been made to check which age groups have a likelihood to die from drinking and driving-related motor vehicle crashes in the US. Moreover, it was also analyzed which sex was more involved in drinking and driving-related crashes of motor vehicles and that it resulted in their death. Historical violation and its relationship with drinking and driving-related motor vehicle crashes have also been examined. The results show Drink and drive has a positive relationship with deaths The study shows a positive relationship between drinking and driving with death from a drinking and driving-related motor vehicle crash. Some preventive measures such as check-posts should be there to stop or reduce prevalence of drinking and driving. © 2023 IEEE.

关键词： Accidents

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Beyond administrative reports: a deep learning framework for classifying and monitoring crime and accidents leveraging large-scale online news

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Neural Computing and Applications 2025年第10期37卷 7183-7205页

作者： Tuarob, Suppawong Tatiyamaneekul, Phonarnun Pongpaichet, Siripen Tawichsri, Tanisa Noraset, Thanapon Faculty of Information and Communication Technology Mahidol University Nakhon Pathom Thailand Department of Computer Science University College London London United Kingdom Puey Ungphakorn Institute for Economic Research Bank of Thailand Bangkok Thailand

The escalating prevalence of violent crimes and accidents underscores the urgent need for efficient and timely monitoring systems. Traditional methods reliant on administrative reports often suffer from significant delays. This paper proposes CRIMSON, a novel framework that leverages large-scale online news to provide real-time insights into crime and accident trends. CRIMSON utilizes a multi-label classification technique that leverages a fine-tuned, pre-trained, cross-lingual language model to accurately categorize news articles. Our experimental results, conducted on a substantial dataset of Thai news articles, demonstrate superior performance, achieving an average F1 score of 86%. Beyond classification, CRIMSON aggregates categorized news into real-time statistics, revealing strong correlations between news-reported incidents and official crime data. This study pioneers online news as a reliable and timely crime and accident monitoring source, offering valuable insights for law enforcement, policymakers, and researchers. © The Author(s) 2025.

关键词： Deep learning

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Advance Path Loss Model for Distance Estimation Using LoRaWAN Network's Received Signal Strength Indicator (RSSI)

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IEEE Access 2024年 12卷 83205-83216页

作者： Vo, Hoang Hoang Long Nguyen, Van Tran, Van Lic Ferrero, Fabien Lee, Fang-Yi Tsai, Meng-Hsun The University of Danang-University of Science and Technology Faculty of Advance Science and Technology Da Nang550000 Viet Nam The University of Danang-University of Science and Technology Department of Electronic and Telecommunication Engineering Da Nang550000 Viet Nam LEAT CNRS Universite Cote d'Azur Sophia Antipolis06903 France National Cheng Kung University Department of Computer Science and Information Engineering Tainan70101 Taiwan National Yang Ming Chiao Tung University Department of Computer Science Hsinchu30010 Taiwan

This work introduces a novel approach to improve the precision of distance estimation in localization systems by using existing LoRaWAN and RSSI-based techniques. Despite the benefits of range and power efficiency, these systems exhibit limited accuracy in practical situations. To address the limitation, this study provides an innovative technique that greatly improves the precision of distance estimations, particularly in urban environments. The fundamental basis of this approach lies in the use of a dynamic path loss model. An additional element is to accommodate the varied and dynamic conditions of signal transmission in metropolitan areas. A better Kalman filter is also used in the study. This is important because it reduces the effects of multipath fading and environmental noise that often make RSSI-based localization in LoRaWAN networks less accurate. The study further examines the influence of the environmental exponent, also known as the path loss exponent, on the RSSI results and the precision of the distance measurements. This methodology achieves the average error under 1 meters for indoor environments and under 7 meters for outdoor environments. Finally, the Cumulative Density Function (CDF) shows 90 % of the distance estimation algorithm error for indoor environment is lower than 1.08 meters while for outdoor environment is lower than 7.55 meters. Based on these improvements, the introduced methodology not only enhances and improves existing approaches but also optimizes the precision and dependability of urban localization technologies, with substantial implications for a variety of practical applications. © 2013 IEEE.

关键词： Kalman filters

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IoT based Line of Control Monitoring system 2

IoT based Line of Control Monitoring System

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2nd International Conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Petkar, Taniya Faculty of Engineering and Technology Department of Computer Science And Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

This paper presents a novel line-of-control (LoC) monitoring system that leverages the Internet of Things (IoT) to improve border security. The system creates a strong infrastructure for real-time monitoring throughout critical border regions, especially the Line of Control, by utilizing IoT devices, sensors, and communication technologies. The coverage, timeliness, and data quality of conventional border surveillance systems are frequently compromised. As opposed to this, the suggested system makes use of a wirelessly networked network of intelligent sensors, such as infrared, acoustic, and video cameras. By utilizing sophisticated image processing algorithms and machine learning to analyze the gathered data, the system is able to differentiate between regular activities and any security risks. To help security staff make decisions quickly, real-time updates are transmitted to a centralized control center. Integration with cloud computing also makes data analysis, retrieval, and storage simpler and guarantees data accessibility for forensic investigations and trend research. © 2025 IEEE.

关键词： Network security

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Mobile Edge Computing for Efficient Vehicle Management in Smart City 14

Mobile Edge Computing for Efficient Vehicle Management in Sm...

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14th IEEE International Conference on Communication systems and Network Technologies, CSNT 2025

作者： Chanu, Asem Debala Shelar, Saylee Nath, Shubha Brata Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati Guwahati India Department of Computer Science and Engineering Ajeenkya DY Patil University Pune India

ISBN: (纸本)9798331531935

Mobile edge computing (MEC) promises to be a useful technology to enable applications in a smart city. However, the task offloading becomes challenging as the MEC infrastructure has limited resources. To solve the challenges of MEC, we have proposed a system architecture with an optimization algorithm to minimize the task delay in MEC. A Genetic Algorithm based task allocation algorithm finds the optimal set of edge servers for task deployment and processing. The proposed system efficiently optimizes the task delay in MEC as analyzed from the experiments performed by taking virtual machines as MEC servers. We show the usefulness of the proposed system by taking an application scenario for efficient vehicle management in a smart city. © 2025 IEEE.

关键词： Genetic algorithms

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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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Online Machine Learning Approach to Detect and Mitigate Low-Rate DDoS Attacks in SDN-Based Networks 5

Online Machine Learning Approach to Detect and Mitigate Low-...

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5th IEEE International Conference on Artificial Intelligence in Engineering and technology, IICAIET 2023

作者： Alashhab, Abdussalam Ahmed Mohd Zahid, Mohd Soperi Alashhab, Mohamed Alashhab, Shehabuldin Alasmarya Islamic University Faculty of Information Technology Department of Computer Science Zliten Libya Universiti Teknologi Petronas Department of Computer and Information Science Seri Iskandar Malaysia Universiti Sultan Azlan Shah Faculty of Management and Information Technology Kuala Kangsar Malaysia

ISBN: (纸本)9798350304152

Software Defined Networking (SDN) technology provides rapid configuration, scalability, and management through its dynamic, programmable architecture that outperforms traditional network architecture with limitations on scalability and management. However, the threat of Distributed Denial of Service (DDoS) attacks remains difficult to detect and threatens traditional and SDN-based networks. Fortunately, Machine Learning (ML) and Deep Learning (DL) technologies along with SDN have proven to have a superior potential to deal with these threats effectively. However, most of the previous studies focused on resolving high-rate DDoS attacks, and only a few dealt with the detection of Low-rate DDoS attacks that are difficult to detect due to their similarity to legitimate traffic. In addition, these studies do not utilize up to date data sets that contain the new features. To address this issue, we propose an online machine learning model that utilizes Stochastic Gradient Descent (SGD) optimizer and Explainable Boosting Machine (EBM) classifier to detect LDDoS attacks in SDN-based networks. Our model is designed to process large amounts of network traffic data in real-time and updates the model parameters incrementally to continually train the model on expected DDoS attacks, as the attack may change and appear differently. We evaluated the proposed approach in an SDN-simulated environment using Mininet and the Ryu controller. Our experimental results show that the proposed EBM model achieves high accuracy and outperforms existing methods, with 99% accuracy on the training data. The proposed system effectively counters LDDoS attacks and adapts to future mutations and zero-day DDoS attacks. © 2023 IEEE.

关键词： Stochastic systems

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RehabPal: Automated Physical Therapy Assistance

RehabPal: Automated Physical Therapy Assistance

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International Conference on Computing and Machine Learning, CML 2024

作者： Arya, P. Sharma, Treya Jatain, Aman Bhaskar, Shalini Bajaj Manju Kumar, Sandeep Department of Computer Science & Technology Amity University Gurgaon India School of Engineering & Technology K R Mangalam University Gurgaon India Department of Computer Science & Engineering and Technology Jaypee Institute of Information Technology Noida India CHRIST University Bengaluru India

ISBN: (纸本)9789819778386

This innovative research project transforms automated physical therapy support by combining 2D and 3D pose assessment approaches with tailored feedback systems. Utilizing Mediapipe technology, the apparatus attentively observes patients’ movements in a two-dimensional space and gives realtime data regarding their gait, exercise compliance, and range of motion. Accurate data on joint angles and body segment alignment are provided by the system's sophisticated 3D posture estimation algorithms, which enhance tracking precision. The system incorporates customized feedback systems that include individual patients’ goals, progress, and conditions and delivery and reward schemes. By increasing user engagement and adherence, the integration of gamification elements has the potential to revolutionize automated physical therapy support. This comprehensive approach aims to enhance patients’ quality of life while simultaneously enhancing long-term rehabilitation outcomes by providing more conveniently accessible, affordably priced, and specially designed therapies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Patient rehabilitation

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