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Distributed Misbehavior Detection System for Cooperative Driving Networks 8

Distributed Misbehavior Detection System for Cooperative Dri...

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8th ieee international conference on Fog and Edge computing (ICFEC)

作者： Abdullahi, Abdullahi Modibbo Lalouani, Wassila Towson Univ Dept Comp & Informat Sci Towson MD 21252 USA

ISBN: (纸本)9798350361360;9798350361353

Cooperative intelligent Transportation systems (C-ITS) are foundational evolution of vehicular networks, emphasizing the seamless coordination of Vehicle-to-Vehicle communication for exchanging vital information. In C-ITS, vehicles collaborate by distributing V2V messages throughout the network. However, the potential transmission of deceptive or inaccurate information by a malicious vehicle poses a significant risk to road safety. Detecting such malicious data promptly is crucial for taking appropriate actions. Current Misbehavior Detection systems (MBS) operate with the assumption of a centralized MBS existence, but the challenge lies in empowering vehicles to make local MBS decisions as early as possible. This approach avoids the exchange of all messages with the infrastructure, reducing excessive communication overhead. In response to this challenge, we introduce an innovative distributed misbehavior detection system designed to protect vehicular networks from potential attacks. Our proposed system harnesses the scalability benefits of Federated Learning, allowing vehicles to collaboratively learn a shared model for detecting faulty and malicious messages. Recognizing the impracticality of assuming that all vehicles possess fully labeled data in real-world settings, we propose Fisher-Discriminant Analysis and model regularization to generate an effective pseudo-labeling mechanism. The model regularization proves effective when multiple models from different vehicles produce less-confidence predictions for a given sequence of Basic Safety Messages. Our approach demonstrates efficacy in identifying a broad spectrum of faults and attacks, including rare types, by learning the inherent data distribution and essential network traffic characteristics.

关键词： Secure communication intelligent transportation systems misbehavior detection system

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Constructing a Medical Domain Functional Knowledge Graph with Large Language Models 6

Constructing a Medical Domain Functional Knowledge Graph wit...

引用

6th ieee international conference on Power, intelligent computing and systems, ICPICS 2024

作者： Ji, Lintian Du, Shuxin Qiu, Yi Xu, Hongyi Guo, Xiaorui School of Engineering Huzhou University Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems Huzhou China School of Engineering Huzhou University Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems Huzhou China

ISBN: (纸本)9798350374315

The integration of medicine and artificial intelligence is creating novel opportunities to gather, analyze, and generate groundbreaking medical insights from the vast expanse of medical literature. Although these advancements hold great promise, difficulties including manual annotation and precise data retrieval, and transparency persist. advanced natural language processing (NLP) techniques and large language models (LLMs) have shown potential in addressing these issues. This paper introduces the Medical Knowledge Graph (MedKG), a comprehensive and multidisciplinary medical knowledge graph. By utilizing advanced NLP algorithms, MedKG extracts millions of entities and converts them into triples from a corpus of superior medical research papers published over the last decade. It categorizes unstructured medical information into nine distinct tags, including disease names, treatments, diagnoses, symptoms, genetics, cellular structures of diseases, patient data, medical procedures, and treatment applications, while seamlessly integrating the digital object identifier (DOI) of each paper. As the latest structured database of medical knowledge, MedKG serves as a powerful tool for accelerating medical research and lays the groundwork for constructing a more extensive medical knowledge graph using full-text medical articles. Moreover, our research establishes a foundation for practical knowledge management systems based on text mining, applicable not only to complex medical systems but also to other professional domains. © 2024 ieee.

关键词： Knowledge graph

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Atmospheric data acquisition From Ballon Satellite Using LoRa Module

Atmospheric Data Acquisition From Ballon Satellite Using LoR...

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2024 international conference on intelligent systems for Cybersecurity, ISCS 2024

作者： Venkat, Yalamandala Sravan Yadav, Thopula Venkatesh Anitha, Yerram Akhil, Madaka Praveena, Hirald Dwaraka Sree Vidyanikethan Engineering College Dept. of Ece Tirupati India Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College Dept. of Ece Tirupati India

ISBN: (纸本)9798350375237

A method for real-time atmospheric data acquisition from a balloon satellite employs a LoRa module and an Arduino microcontroller to interface with sensors and control the data acquisition process. This system leverages long-range (LoRa) technology to transmit weather data wirelessly over long distances, providing real-time monitoring capabilities even in challenging environments. The observation includes the gathering of air pressure, temperature, humidity, altitude, light intensity, and precipitation. The integration of hardware components in the system offers a cost-effective and efficient solution for real-time atmospheric data acquisition, which implicates meteorological research, environmental monitoring, and disaster management. By leveraging LoRa technology, it overcomes the limitations of traditional wired systems and extends the reach of weather monitoring to previously inaccessible areas. © 2024 ieee.

关键词： data acquisition

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intelligent Controller Using Genetic Algorithm Applied on Wind Energy Conversion systems: Design and Comparison Phases 6

Intelligent Controller Using Genetic Algorithm Applied on Wi...

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

作者： El Fadili, Yattou Boumhidi, Ismail Faculty of Sciences Dhar El Mahraz Department of Physics Fez Morocco

ISBN: (纸本)9798350351200

This present paper suggests an intelligent nonlinear controller based on the powerful law which is the sliding mode (SM) combined with the fractional calculus (FC) and optimized using the genetic algorithm (GA). This improved controller is applied to the three-blade horizontal-Axis variable-speed wind power (WP) to control its electromagnetic torque using the two-mass model of the mechanical part of this WP connected to the grid. The main purpose of this developed controller is to minimize the high peak of the electromagnetic torque and minimize the loads on the drive train thanks to mitigating the chattering phenomenon (noting that the chatter is the major disadvantage of the majority of the controllers existing in the literature due to the high-frequency oscillations that damage the system). The benefits earned from this advanced controller are the high tracking quality based on the maximum power point tracking (MPPT) algorithm, the enlarged lifespan of WP thanks to mitigating the chatter, reducing the peak value of the electromagnetic torque and ensuring stability. The emulation results are presented under MATLAB software using two wind speed profile scenarios to prove these cited benefits. © 2024 ieee.

关键词： MATLAB

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Promoting Open data Utilization in Infrastructure Management Through an Integrated System of data Processing, Cloud Storage, and Visualization 13

Promoting Open Data Utilization in Infrastructure Management...

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Joint 13th international conference on Soft computing and intelligent systems and 25th international Symposium on advanced intelligent systems, SCIS and ISIS 2024

作者： Zhang, Zhenlong Kawamura, Kei Su, Yiming Emoto, Hisao Sawamura, Shuji Nguyen, C.K. Graduate School of Sciences and Technology for Innovation Yamaguchi University Ube Japan Tottori University Social Systems and Civil Engineering Faculty of Engineering Tottori Japan Yamaguchi Prefecture Government Department of Construction Yamaguchi Japan MienTrung University of Civil Engineering 195 Ha Huy Tap Phu Yen Tuy Hoa620000 Viet Nam

ISBN: (纸本)9798350373332

During the rapid economic growth of Japan in the 1960s, the country's infrastructure development progressed swiftly. Since 2014, the Japanese government has mandated regular inspections of major urban infrastructure. This policy has resulted in the annual accumulation of substantial amounts of infrastructure inspection data. Two years prior to implementing this policy, the Japanese government also launched an open data initiative to encourage industry developers to utilize open data. However, this study observes that inconsistencies in data formatting and standards across different regions pose significant challenges to the utilization of this data. To address the obstacles hindering the use of open data, this study proposes an integrated system(Service Link Platform) for effectively managing and utilizing periodic transportation infrastructure inspection data which encompasses data collection, data format processing, cloud storage and transmission, and data utilization. This article will layout the manner in which the system employs a method for distributing data in JSON (JavaScript Object Notation) format to publicly manage bridge inspection data and ensure compatibility of data formats. © 2024 ieee.

关键词： data assimilation

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Enabling intelligent Decision Making: Harnessing advanced Management Strategies for Enhanced Organizational Efficiency and Effectiveness 4

Enabling Intelligent Decision Making: Harnessing Advanced Ma...

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4th ieee international conference on computing, Communication, and intelligent systems, ICCCIS 2023

作者： Usmani, Usman Ahmad Usmani, Mohammed Umar Usmani, Abdullah Yousuf Aligarh Muslim University India University of Malaysia Pahang Malaysia Auburn University United States

ISBN: (纸本)9798350306118

In today's dynamic and complex business landscape, effective management and decision-making are crucial for organizations to thrive and adapt. The integration of intelligent technologies has revolutionized traditional management practices by offering data-driven insights and automated solutions. This research paper explores the intersection of intelligent Management and Decision Making, delving into the symbiotic relationship between advanced technologies and strategic decision processes. We examine how artificial intelligence, machine learning, big data analytics, and predictive modeling contribute to informed decision-making across various industries. Drawing from real-world case studies and empirical research, we analyze the benefits and challenges associated with adopting intelligent management systems. Furthermore, we discuss the ethical considerations entailed in entrusting critical decisions to automated systems. By synthesizing theoretical frameworks and practical applications, this paper provides valuable insights for organizational leaders, researchers, and practitioners seeking to navigate the evolving landscape of intelligent management and decision-making, © 2023 ieee.

关键词： Decision making

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Cloud data Anomaly Detection using Machine Learning 4

Cloud Data Anomaly Detection using Machine Learning

引用

4th international conference on Ubiquitous computing and intelligent Information systems, ICUIS 2024

作者： Karthik Kumar, G.V. Achary, Rathnakar Shelke, Chetan J. Alliance School of Advanced Computing Alliance University Bangalore India

ISBN: (纸本)9798331529635

In cybersecurity, anomaly detection has one of the most important roles in discovering threats that are a menace to an organization's information security. This paper describes the implementation of an anomaly detection system employing Isolation Forests of the Azure Function kind. Utilizing the possibility of the cloud service offered by Azure, the solution provides an effective anomaly detection. Raw data is normalized, features are reconstructed to address cases of missing data and the model then applied is the Isolation Forest with the purpose of differentiating normal and anomalous behavior. Standard performance measures, like accuracy, are used to measure the performance of the model. Furthermore, we give a clearer understanding of the model through the confusion matrix and the correlation heat map, respectively. That is why our approach shows good results in anomaly detection with a low percentage of false positives, which makes it appropriate for use in cloud systems. The integration with Azure Functions further confirms the simplicity, efficiency, and accessibility of serverless computing in more complex cybersecurity solutions. © 2024 ieee.

关键词： Cybersecurity

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Leveraging GNSS and Onboard Visual data from Consumer Vehicles for Robust Road Network Estimation

Leveraging GNSS and Onboard Visual Data from Consumer Vehicl...

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2024 international conference on intelligent Robots and systems

作者： Opra, Balazs Le Dem, Betty Walls, Jeffrey M. Lukarski, Dimitar Stachniss, Cyrill Woven Toyota Inc Tokyo Japan Univ Bonn Bonn Germany Univ Bonn Ctr Robot Bonn Germany Lamarr Inst Machine Learning & Artificial Intelli Bonn Germany

ISBN: (纸本)9798350377712;9798350377705

Maps are essential for diverse applications, such as vehicle navigation and autonomous robotics. Both require spatial models for effective route planning and localization. This paper addresses the challenge of road graph construction for autonomous vehicles. Despite recent advances, creating a road graph remains labor-intensive and has yet to achieve full automation. The goal of this paper is to generate such graphs automatically and accurately. Modern cars are equipped with onboard sensors used for today's advanced driver assistance systems like lane keeping. We propose using global navigation satellite system (GNSS) traces and basic image data acquired from these standard sensors in consumer vehicles to estimate road-level maps with minimal effort. We exploit the spatial information in the data by framing the problem as a road centerline semantic segmentation task using a convolutional neural network. We also utilize the data's time series nature to refine the neural network's output by using map matching. We implemented and evaluated our method using a fleet of real consumer vehicles, only using the deployed onboard sensors. Our evaluation demonstrates that our approach not only matches existing methods on simpler road configurations but also significantly outperforms them on more complex road geometries and topologies. This work received the 2023 Woven by Toyota Invention Award.

关键词： advanced driver assistance systems

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Digital Health systems: Ontology-Based Universal Dialog Service for Hybrid E-Rehabilitation Activities Support 12

Digital Health Systems: Ontology-Based Universal Dialog Serv...

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12th ieee international conference on intelligent data acquisition and advanced computing systems: Technology and Applications, IDAACS 2023

作者： Palagin, Oleksandr Kaverinsky, Vladislav Petrenko, Mykola Malakhov, Kyrylo Glushkov Institute of Cybernetics National Academy of Sciences of Ukraine Microprocessor Technology Lab Kyiv03187 Ukraine The I.M. Frantsevic Institute for Problems in Material Science National Academy of Sciences of Ukraine Kyiv03142 Ukraine

ISBN: (纸本)9798350358056

The medical rehabilitation system in Ukraine encountered a set of crucial challenges that demanded immediate attention and action. The primary objective revolves around rehabilitating patients with Combat stress reaction. Ukraine possesses a network of medical and preventive institutions that cater to the psycho-physiological rehabilitation needs of military personnel. These institutions employ contemporary rehabilitation technologies. Nonetheless, not all individuals have access to long-term rehabilitation within these centers. Hence, the integration of telerehabilitation technology becomes crucial for patients dealing with post-traumatic stress disorder and related conditions. This integration, combined with objective monitoring of the functional state, holds significant importance. Remote patient-centered rehabilitation emerges as one of the most effective approaches within the realm of medical rehabilitation assistance. Moreover, there is a need for efficient methods that support the 'Physical therapist - Patient - Multidisciplinary team' system in the field of rehabilitation. Hence, in this paper, we not only explore conventional rehabilitation techniques but also present and elucidate the following advancements: a revised and comprehensive understanding of the hybrid e-rehabilitation concept and its underlying principles, an enhanced formalization notion of the Smart-system for remote support in hybrid e-rehabilitation services and activities, and the conceptual framework and software implementation of the ontology-based universal dialog service within the Smart-system. © 2023 ieee.

关键词： Computational linguistics hybrid e-rehabilitation Ontology engineering Telerehabilitation Transdisciplinary research Universal dialog service

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SurrealDriver: Designing LLM-powered Generative Driver Agent Framework based on Human Drivers' Driving-thinking data

SurrealDriver: Designing LLM-powered Generative Driver Agent...

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2024 international conference on intelligent Robots and systems

作者： Jin, Ye Yang, Ruoxuan Yi, Zhijie Shen, Xiaoxi Peng, Huiling Liu, Xiaoan Qin, Jingli Li, Jiayang Xie, Jintao Gao, Peizhong Zhou, Guyue Gong, Jiangtao Tsinghua Univ Inst AI Ind Res Beijing Peoples R China

ISBN: (纸本)9798350377712;9798350377705

Leveraging advanced reasoning capabilities and extensive world knowledge of large language models (LLMs) to construct generative agents for solving complex real-world problems is a major trend. However, LLMs inherently lack embodiment as humans, resulting in suboptimal performance in many embodied decision-making tasks. In this paper, we introduce a framework for building human-like generative driving agents using post-driving self-report driving-thinking data from human drivers as both demonstration and feedback. To capture high-quality, natural language data from drivers, we conducted urban driving experiments, recording drivers' verbalized thoughts under various conditions to serve as chain-of-thought prompts and demonstration examples for the LLM-Agent. The framework's effectiveness was evaluated through simulations and human assessments. Results indicate that incorporating expert demonstration data significantly reduced collision rates by 81.04% and increased human likeness by 50% compared to a baseline LLM-based agent. Our study provides insights into using natural language-based human demonstration data for embodied tasks. The driving-thinking dataset is available at https://***/AIR-DISCOVER/Driving-Thinking-dataset.

关键词： Demonstrations

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