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Design and Development of Multipurpose data acquisition for Effective Transportation 9

Design and Development of Multipurpose Data Acquisition for ...

引用

9th international conference on advanced computing and Communication systems, ICACCS 2023

作者： Diwakaran, S. Kumar, Baswarajula Sai Kumar, Konduru Sushmitha, Katugunta Chandra Siddhardha Reddy, Kondama Reddy Kalasalingam Academy of Research and Education Department of ECE Krishnankoil626126 India

ISBN: (纸本)9798350397376

The most pressing problem in contemporary cities is traffic. In order to offer people with efficient, quiet driving and a life free of pollutants, traffic regulation becomes more essential. The work is intended to create a system in which all modules are connected to a single control unit that can control, detect, and report any system faults as well as to develop a single module that can improve the efficiency of vehicle transportation on roads, control energy-efficient LED street lights, and design a module that can detect emergency vehicles and alert other vehicles on the road so that emergency vehicles can arrive at their destination in time. © 2023 ieee.

关键词： Energy efficiency

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Social Media data Analytics Using K-Means Clustering 3

Social Media Data Analytics Using K-Means Clustering

引用

3rd international conference on Ubiquitous computing and intelligent Information systems, ICUIS 2023

作者： Sai Teja Veeramallu, Veera Venkata Venkata Sandeep, Adari Leela Shivling, Badrinath Burra, Dattatreya Bulla, Suneetha Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350306989

In the recent technologically advanced world, internet serves as a vast repository of data. The exponential growth of online activities has resulted in an abundance of data, making it crucial for businesses to harness this information to gain meaningful insights for enabling effective decision-making. One significant source of data is social media platforms such as WhatsApp, Twitter, and Facebook. However, the sheer volume of data generated on these platforms presents challenges in terms of data handling and deriving valuable insights. The primary objective of this research work is to analyze social media data and extract valuable insights using advanced analytics techniques. By leveraging social media APIs, data will be collected from various platforms and the clustering algorithms are applied to uncover patterns, trends, and relationships within the dataset. The outcomes of the performed analysis offer valuable insights to drive their strategies towards achieving greater success. By utilizing social media data analytics, organizations gain a deeper understanding of their target audience, identify emerging market trends, and make data-driven decisions. This research aims to assist businesses in improving their strategies, enhancing customer engagement, and achieving better outcomes. © 2023 ieee.

关键词： data Analytics

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RAPID: Integrating AI and Multi-Agent systems for Enhanced Traffic Management Framework with YOLOv9

RAPID: Integrating AI and Multi-Agent Systems for Enhanced T...

引用

2024 international conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2024

作者： Baroud, Shadia Yahya Yahaya, Nor Adnan Department of Computing University Malaysia of Computer Science and Engineering Petaling Jaya Malaysia

ISBN: (纸本)9798331533137

Traffic accidents pose a major risk to human life and have a considerable economic impact worldwide. In this study, we present the RAPID framework, an innovative approach that integrates Artificial Intelligence (AI) and Multi-Agent systems (MAS) to enhance traffic management. The core of the RAPID framework is the YOLOv9 model, a "state-of-the-art" deep learning (DL) algorithm for real-time object detection and classification, which we employ to detect and classify traffic accidents accurately. The RAPID framework addresses key challenges in modern traffic management, including data heterogeneity, scalability and real-time processing. Our system leverages a multi-layered architecture: data is acquired from various sources, including traffic cameras and CCTV and then transmitted to a cloud data center for storage and processing. Within the cloud infrastructure, the data undergoes pre-processing and is analyzed by the YOLOv9 model. The MAS coordinates various components of the framework, distributing tasks among specialized agents responsible for data acquisition, preprocessing, model training and real-time detection. We evaluated the performance of the RAPID framework using a dataset of 3000 labeled traffic accident images. The YOLOv9 model demonstrated 95.4% accuracy, 93.8% precision, 92.6% recall and 93.2% F1-score. The experimental results indicate that the RAPID framework significantly improves traffic accident detection and response times compared to traditional systems, highlighting its potential for real-world applications. The RAPID framework contributes to the field of intelligent transportation systems (ITS) by providing a robust, scalable and efficient solution for real-time traffic management. ©2024 ieee.

关键词： Multi agent systems

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A Financial Decision Support Optimization System Based on Artificial Intelligence and data Mining Algorithms 3

A Financial Decision Support Optimization System Based on Ar...

引用

3rd ieee international conference on Electrical Engineering, Big data and Algorithms, EEBDA 2024

作者： Cao, Natalie National University of Singapore 117597 Singapore

ISBN: (纸本)9798350380989

Effective financial decision-making is crucial for organizational growth, yet it is often impeded by limitations in information availability and subjective biases. While traditional computer-based financial decision support systems (FDSS) have marginally enhanced decision quality, they still need further improvement with data inaccuracies, limited analytical capabilities, and slow response times. This paper examines the potential of advanced Artificial Intelligence (AI) and data mining algorithms to revolutionize FDSS. We critically analyze the shortcomings of current FDSS and introduce an innovative approach incorporating AI-driven data mining into electronic financial decision support frameworks. This integration aims to create more sophisticated, agile, and intelligent tools, addressing the evolving demands of financial decision-makers. Our study highlights the transformative impact of AI and data mining on FDSS and sets the stage for future advancements in intelligent financial decision-making. © 2024 ieee.

关键词： artificial intelligence data mining algorithms financial optimization systems

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Design and Simulation of an intelligent Traffic Management System Using Vehicular Networks 3

Design and Simulation of an Intelligent Traffic Management S...

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3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Tapan Datta, P. Lalith Kumar, D. Tanoj, S. Rajagopal, Shinu M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

In this study, the reasearch is focused on the simulation of vehicular communication networks, especially intelligent traffic systems, to enhance dependability, safety, and optimality in communication. The simulation is performed using the OMNeT++ simulator and is integrated with the Veins framework that uses the ieee 802.11p protocol for V2V and V2I communications. Importing real-world traffic scenarios into the framework is obtained via the use of SUMO through TraCIScenarioManager. Other features of the framework include the definition of mobility models, dynamic obstacles, and detailed radio propagation modeling. TraCIDemo11p is used for mobility and communication by both RSUs and vehicles with applications in beaconing and channel switching. The whole framework analyzes smart traffic management solutions, providing as much insight into their performance based upon the communication reliability and overall efficiency in vehicular networks. © 2025 ieee.

关键词： Highway administration

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Design and Implementation of Power System Simulation Platform Based on Cloud computing 4

Design and Implementation of Power System Simulation Platfor...

引用

4th international conference on Energy, Power and Electrical Engineering, EPEE 2024

作者： Lu, Yuanhong Guo, Haiping Zhao, Yanjun Huang, Libin Hu, Binjiang Luo, Hui Zhang, Jingyue National Energy Power Grid Technology R&d Centre Guangzhou China State Key Laboratory of Hvdc Electric Power Research Institute China Southern Power Grid Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China Csg Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (纸本)9798331518066

Power system simulation involves simulating the operating states and dynamic processes of power systems using mathematical models and computer technology to analyze and predict system behavior. Traditional simulation methods struggle to meet the needs of large-scale, complex power grids due to significant demands for computational resources. Cloud computing, with its high scalability, resource pooling, and on-demand services, is ideally suited for the extensive simulation calculations required by modern power systems. Despite its advantages, the application of cloud computing in power system simulation poses challenges related to data security and privacy, particularly in collaborative simulations involving multiple power entities. This paper presents the design and implementation of a cloud computing-based power system simulation platform. The proposed platform leverages the distributed computing capabilities of cloud computing, integrates federated learning to ensure data privacy, and utilizes advanced power system simulation technologies. This approach significantly enhances simulation efficiency and accuracy while providing data security and privacy protection. The platform offers reliable decision support for power system planning, operation, and control. © 2024 ieee.

关键词： Cloud platforms

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Dynamic Checkpointing: Fault Tolerance in High-Performance computing 3

Dynamic Checkpointing: Fault Tolerance in High-Performance C...

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3rd international conference on Communication, Control, and intelligent systems, CCIS 2024

作者： Bhowmik, Biswajit Verma, Tushar Dineshbhai, Navdeep Dadhania Reddy, Manideep Raya Vsrs Girish, K.K. National Institute of Technology Karnataka Maharshi Sushrut Cas Lab Brics Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Maharshi Kanad Qc Lab Brics Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Ishwarchandra Vidyasagar Ait Lab Brics Laboratory Dept. of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798331528201

Parallel computing has become a cornerstone of modern computational systems, enabling the rapid processing of complex tasks by utilizing multiple processors simultaneously. However, the efficiency and reliability of these systems can be significantly compromised by inherent challenges such as hardware failures, communication delays, and uneven workload distribution. These issues not only slow down computations but also threaten the dependability of applications reliant on parallel processing. To address these challenges, researchers have developed strategies like dynamic checkpointing and load balancing, which are crucial for enhancing fault tolerance and optimizing performance. Dynamic checkpointing periodically saves the computational state, allowing for recovery from failures without significant data loss, while load balancing ensures that tasks are evenly distributed across processors, preventing bottlenecks and underutilization of resources. By integrating these mechanisms, this paper proposes a robust framework that improves the reliability and efficiency of parallel systems, particularly in high-performance computing environments where the ability to handle large-scale data processing with minimal downtime is critical. © 2024 ieee.

关键词： Parallel processing systems

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Deep Reinforcement Learning Actor-Critic Algorithm based Autonomous Lane Change Decisions in Static Environments 6

Deep Reinforcement Learning Actor-Critic Algorithm based Aut...

引用

6th international conference on intelligent computing in data Sciences, ICDS 2024

作者： Safae, Belkhyr Jaouad, Boumhidi Nikolov Nikola, S. University Sidi Mohamed Ben LISAC Laboratory Computer Science Departement Faculty of Science Dhar EI Mahraz Abdellah Fez Morocco University of Limerick Department of Computer Science and Information Systems Limerick Ireland

ISBN: (纸本)9798350351200

Deep Reinforcement Learning (DRL) has emerged as a pivotal technology in modern transportation systems, offering considerable potential to enhance driving safety and optimize performance. This study underscores the significance of DRL in autonomous driving (AD) and introduces the Advantage Actor-Critic (A2C) approach for lane change decision-making. A2C, representing policy-based methods, is compared with the Prioritized Replay Deep Q-Network (PRDQN), a value-based approach previously used in similar scenarios. Both approaches were evaluated using the CARLA (Car Learning to Act) simulation environment, focusing on static obstacle scenarios. The comparative analysis aims to assess the performance differences between A2C and PRDQN, highlighting the effectiveness of A2C in improving decision-making accuracy, safety, and adaptability. The results demonstrate the superior performance of A2C in improving safety and decision-making within autonomous driving systems. © 2024 ieee.

关键词： Deep reinforcement learning

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Assessment and Management of Security Risks in Accounting Information systems from a Lifecycle Perspective

Assessment and Management of Security Risks in Accounting In...

引用

2024 international conference on Mechatronics and intelligent Control, ICMIC 2024

作者： Gong, Lijiahui Business School Wuhan Huaxia Institute of Technology Wuhan430223 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

As the carrier of information assets, the importance of accounting information systems is increasing with the development of informatization. Meanwhile, information systems are facing more security threats. How to effectively ensure the security of information systems and prevent information assets from being stolen or damaged has become an inevitable challenge. Therefore, conducting research on the security issues of information systems is of great significance. In response to the data security risks in accounting information systems, this article first establishes a risk indicator system and proposes an information system risk assessment model based on adaptive fuzzy neural networks, which scores the risk indicator attributes during the assessment process. In addition, we propose a fine-grained weight adaptive adjustment algorithm in the model, which provides a quantitative method for the risk indicators of accounting information systems. The feasibility and applicability of the model have been verified through simulation testing. © 2025 SPIE.

关键词： Information security Risk assessment Network security Computer security computing systems Fuzzy logic Situational awareness sensors Neural networks systems modeling Prototyping

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Research on Abnormal Work Order Closed-Loop Management System for Power acquisition Operation and Maintenance 2

Research on Abnormal Work Order Closed-Loop Management Syste...

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2nd international conference on data Analytics, computing and Artificial Intelligence, ICDACAI 2023

作者： Xiaoqin, Ma Xiaohe, Guo Hongjiao, Luo Yufeng, Xue Haiying, Ma State Grid Qinghai Information Telecommunication Company Xining China State Grid Qinghai Electric Economic Research Institute Xining China

ISBN: (纸本)9798350339765

This paper introduces geographic information system display technology, distributed big data processing architecture, measuring equipment anomaly self-learning analysis technology and other advanced technologies to achieve the graphical panoramic operation and maintenance control and metering equipment online monitoring and intelligent diagnosis. At the same time, this paper constructs the corresponding software application system. Then, an anomaly diagnosis and processing method based on power acquisition operation and maintenance system is proposed. The method firstly obtains the diagnosis rules through association mining, and then converts the monitored data into event elements and performs rule matching to obtain the diagnosis results. Finally, confirm and feedback the diagnosis result through on-site troubleshooting. The practical application results show that the above system can effectively reduce the burden of operation and maintenance personnel, shorten the time of fault handling and enrich the means of marketing audit. The system can improve the efficiency of collection operation and maintenance and the service level of power marketing and reduce the economic loss of enterprises. © 2023 ieee.

关键词： data acquisition

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