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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： P Divya Prabha N Suresh Kumar N Nandhine Shree R. Sundaram H Nishanthi D Pranesh Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore Tamil Nadu India Department of Information Technology Sri Ramakrishna Engineering College Coimbatore Tamil Nadu India Department of Computer Science and Engineering Ganesh College of Engineering Salem Tamil Nadu India Department of Robotics and Automation Sri Ramakrishna Engineering College Coimbatore Tamil Nadu India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

The Study indicates the need for adequate cybersecurity safeguards in healthcare to protect patient data. The importance derives from the vulnerabilities of redundant systems, which are prone to cyber-attacks, highlighting the need for updated security standards. To strengthen the hospital’s IT infrastructure, the proposed system employs a complex technique that combines advanced encryption, strong authentication, and ongoing audits. The comprehensive technique consists of evaluating the security architecture, installing encryption, strengthening authentication, integrating intrusion detection systems, and using real-time monitoring tools. According to the results and analysis, the proposed system outperforms existing systems in terms of data security (90), cyberattack risk (85), and system integrity (90). Notable developments include 256-bit encryption, multifactor authentication, continuous vulnerability scans, and improved intrusion detection, demonstrating the proposed system’s effectiveness in dealing with cybersecurity challenges and ensuring the long-term stability of the healthcare IT infrastructure. The Study demonstrates the essential requirement for improved cybersecurity methods to provide an adequate healthcare IT infrastructure in the face of improving cyber threats.

关键词： Data security Stability criteria Authentication Intrusion detection Medical services Encryption computer security

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STARD: A Chinese Statute Retrieval Dataset with Real Queries Issued by Non-professionals

arXiv

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arXiv 2024年

作者： Su, Weihang Hu, Yiran Xie, Anzhe Ai, Qingyao Que, Zibing Zheng, Ning Liu, Yun Shen, Weixing Liu, Yiqun Department of Computer Science and Technology Tsinghua University China School of Law Tsinghua University China School of Electronics Engineering and Computer Science Peking University China Department of Automation Tsinghua University China

Statute retrieval aims to find relevant statutory articles for specific queries. This process is the basis of a wide range of legal applications such as legal advice, automated judicial decisions, legal document drafting, etc. Existing statute retrieval benchmarks focus on formal and professional queries from sources like bar exams and legal case documents, thereby neglecting non-professional queries from the general public, which often lack precise legal terminology and references. To address this gap, we introduce the STAtute Retrieval Dataset (STARD), a Chinese dataset comprising 1,543 query cases collected from real-world legal consultations and 55,348 candidate statutory articles1. Unlike existing statute retrieval datasets, which primarily focus on professional legal queries, STARD captures the complexity and diversity of real queries from the general public. Through a comprehensive evaluation of various retrieval baselines, we reveal that existing retrieval approaches all fall short of these real queries issued by non-professional users. The best method only achieves a Recall@100 of 0.907, suggesting the necessity for further exploration and additional research in this area. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Design and Application of a One-Stop Mobile Application Sharing Platform for Steel Enterprises 42

Design and Application of a One-Stop Mobile Application Shar...

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42nd Chinese Control Conference, CCC 2023

作者： Chen, Xinzhe Chen, Luefeng Hu, Jie Yang, Qingfeng Wu, Min Pedrycz, Witold School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2G7 Canada

ISBN: (纸本)9789887581543

As China's steel production accounts for an increasing share of the world's output, the intelligent transformation of the steel industry is becoming increasingly urgent. To address issues such as low levels of mobile informationization in steel enterprises and the lack of an industry-specific mobile application platform, it is of great significance to establish a shared mobile application platform for the steel industry. In this paper, the requirements of the platform were analyzed, and the platform's functions were designed. The software design of the platform was then carried out, and the entire mobile application sharing platform was developed, effectively improving the production management efficiency of steel enterprises. The results indicate that the platform can effectively meet the needs of steel enterprises and has significant engineering significance. © 2023 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Informatization

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Transformers Unveiled: A Comprehensive Evaluation of Emotion Detection in Text Transcription

Transformers Unveiled: A Comprehensive Evaluation of Emotion...

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Wireless and Optical Technologies (GCWOT), 2020 Global Conference on

作者： Sara Ali Bushra Naz Sanam Narejo Sahil Ali Jitander Kumar Pabani National Center of Robotics and Automation – CMS Lab Mehran University of Engineering and Technology Jamshoro Pakistan Department of Computer Systems Engineering Mehran University of Engineering and Technology Jamshoro Pakistan National Center of Rorotics and Automation - CMS Lab Mehran University of Engineering and Technology Jamshoro Pakistan Department of Telecommunication Engineering Dawood University of Engineering and Technology Karachi Pakistan

ISBN: (数字)9798331534271

ISBN: (纸本)9798331534288

The fast growth of digital communication via smartphones and social media has made textual data as one of the most essential forms of people's expressions. Accurate emotion discovery from text is thus important for applications in customer sentiment analysis and interactive technologies, be it a web, desktop, or mobile application, an intelligent robot, or minor connected devices. This paper is intended to evaluate the performance of five state-of-the-art transformer models, including BERT, ALBERT, DistilBERT, Transformer XL, and RoBERTa, for multiclass emotion detection using the IEMOCAP dataset. From the results, it is observed that DistilBERT has the highest accuracy of 79.2%, implying an effective approach in balancing model size and performance. These research show that smaller models like DistilBERT often outperform larger models by reducing their overfitting and computational demands. The findings also highlights the need for task-specific fine-tuning and optimization in emotion detection.

关键词： Wireless communication Emotion recognition Adaptation models Visualization Accuracy Social networking (online) Computational modeling Transfer learning Transformers Context modeling

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Blockchain-based Pseudonym Management for Vehicle Twin Migrations in Vehicular Edge Metaverse

arXiv

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arXiv 2024年

作者： Kang, Jiawen Luo, Xiaofeng Nie, Jiangtian Wu, Tianhao Zhou, Haibo Wang, Yonghua Niyato, Dusit Mao, Shiwen Xie, Shengli The School of Automation Guangdong University of Technology China The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Electronic Science and Engineering Nanjing University China The Department of Electrical and Computer Engineering Auburn University United States

Driven by the great advances in metaverse and edge computing technologies, vehicular edge metaverses are expected to disrupt the current paradigm of intelligent transportation systems. As highly computerized avatars of Vehicular Metaverse Users (VMUs), the Vehicle Twins (VTs) deployed in edge servers can provide valuable metaverse services to improve driving safety and on-board satisfaction for their VMUs throughout journeys. To maintain uninterrupted metaverse experiences, VTs must be migrated among edge servers following the movements of vehicles. This can raise concerns about privacy breaches during the dynamic communications among vehicular edge metaverses. To address these concerns and safeguard location privacy, pseudonyms as temporary identifiers can be leveraged by both VMUs and VTs to realize anonymous communications in the physical space and virtual spaces. However, existing pseudonym management methods fall short in meeting the extensive pseudonym demands in vehicular edge metaverses, thus dramatically diminishing the performance of privacy preservation. To this end, we present a cross-metaverse empowered dual pseudonym management framework. We utilize cross-chain technology to enhance management efficiency and data security for pseudonyms. Furthermore, we propose a metric to assess the privacy level and employ a Multi-Agent Deep Reinforcement Learning (MADRL) approach to obtain an optimal pseudonym generating strategy. Numerical results demonstrate that our proposed schemes are high-efficiency and cost-effective, showcasing their promising applications in vehicular edge metaverses. © 2024, CC BY.

关键词： Reinforcement learning

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Optimizing Route Efficiency in Formula One (F1) Vehicles Using Reinforcement Learning Algorithms

Optimizing Route Efficiency in Formula One (F1) Vehicles Usi...

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AI in Cybersecurity (ICAIC), International Conference on

作者： Sanam Narejo Muhammad Taimoor Khan Muhammad Zakir Shaikh Lubna Luxmi Dhirani Bhawani Shankar Chowdhery Department of Computer Systems Mehran Univerity of Engineering and Technology Jamshoro Pakistan National Centre for Robotics Automation and Artificial Intelligence Mehran Univerity of Engineering and Technology Jamshoro Pakistan Mechanical Engineering and energy efficiency School of Industrial Engineering University of Malaga Malaga Spain Department of Electronic and Computer Engineering University of Limerick Limerick Ireland

ISBN: (数字)9798331518882

ISBN: (纸本)9798331518899

This research explores the application of reinforcement learning (RL) to enhance route efficiency and performance of a Formula One (F1) car within a simulation environment. The simulation is implemented using Python, NEAT (NeuroEvolution of Augmenting Topologies), and PyGame to create a dynamic system where neural networks control the car’s navigation. RL enables the F1 car, acting as an agent, to learn optimal decisions through a fitness-based reward mechanism by interacting with its environment. Equipped with radar sensors to detect obstacles and measure distances, the virtual car adjusts its speed and steering to avoid collisions and optimize movement. Over successive generations, the RL algorithm refines the car’s driving ability, improving speed and directional control to maximize distance covered and minimize lap times. A fitness-based evaluation system tracks progress, providing metrics such as best and average fitness scores, which highlight the car’s evolving performance. Results demonstrate the effectiveness of RL in enhancing autonomous driving capabilities, enabling the car to navigate complex environments and improve decision-making across generations.

关键词： Navigation Tracking Neural networks Decision making Reinforcement learning Radar tracking Topology Sensors Automobiles Vehicle dynamics

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Study of Ripening Stages of Mango by Image Processing Techniques 3

Study of Ripening Stages of Mango by Image Processing Techni...

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3rd International Conference on Intelligent Robotics, Mechatronics and automation Systems, IRMAS 2023

作者： Sumathi, V. Archana, A. Senthil, Rethinam Adarsh, J.K. Nabeel, Muhammed Centre for Automation School of Electrical Engineering VIT Chennai Tamil Nadu Chennai India School of Electrical Engineering VIT Chennai Tamil Nadu Chennai India Department of Pharmacology Saveetha Dental College and Hospitals SIMATS Tamil Nadu Chennai600077 India Electrical and Computer Engineering NYU Abu Dhabi United Arab Emirates

Mango reaches its maturity in different stages which comprise both size and color. These maturity stages are well-known by the farmer in the field. On the basis of these different stages of maturity or commonly say 'ripening of Mango' one can predict when these are perfect for harvesting. The stages comprise a distinct pattern in which Mango gets ripped. In this paper, a study is carried out on the different stages of ripening of Mangoes based on their color. Image processing techniques are used to predict the mango's different ripening stages by taking the Mango's image. © Published under licence by IOP Publishing Ltd.

关键词： Image processing

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Experimental and Numerical Study of Airborne Contaminants Transmission in Hospital Isolation Rooms Equipped with Protected Zone Ventilation

SSRN

引用

SSRN 2024年

作者： Fečer, Tomáš Aganovic, Amar Bi, Yang Larkermani, Elyas Georges, Laurent Plášek, Josef Rekstad, Inge Håvard Mathisen, Hans Martin Cao, Guangyu Department of Energy and Process Engineering Norwegian University of Science and Technology - NTNU Trondheim Norway Department of Automation and Process Engineering UiT The Arctic University of Norway Tromsø Norway Institute of Computer Aided Engineering and Computer Science Brno University of Technology Brno Czech Republic

Experimental tests have been conducted in the small test chamber at the Norwegian University of Science and Technology. The purpose has been to examine air flow path and airborne pollutant distribution, in the case study of an airborne infection isolation room (AIIR) configuration. This configuration uses the concept of protected zone ventilation (PZV) including a combination of a localised air supply system and an air curtain at four air change rates per hour (6, 9, 12, 15) and two negative pressure differences between the outside and inside of the isolation room (2.5 Pa and 5 Pa). In this study, three scenarios, (a) without an air supply curtain, (b) with the 45° and (c) with the 90° air supply curtain, were investigated experimentally and numerically. The N2O tracer gas was employed to simulate airborne transmission of infectious agents throughout the experiment. In the CFD simulation, viscous effects have been included using the (RNG) k–Ε turbulence model with species transport model. The simulated domain has been meshed using the structured mesh with hexagonal cells and with a uniform resolution of 0.0175m, resulting in a total of over 6 million cells. The scenario with the 45° supply air curtain significantly decreased the gas concentration already at 6 air change rates per hour. At the same time, the negative pressure difference of 2.5 Pa had the lower gas concentration in two scenarios (a) and (b) while 5.0 Pa in scenario (c). © 2024, The Authors. All rights reserved.

关键词： Ventilation

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A two-level classification diagnosis method for AC arc faults based on data random fusion and MC-MGCNN network

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Measurement: Journal of the International Measurement Confederation 2024年 224卷

作者： Gao, Wei Rao, Junmin Cui, Fengxin Wai, Rong-Jong College of Electrical Engineering and Automation Fuzhou University Fujian Fuzhou350108 China Department of Electrical Engineering Fuzhou University Zhicheng College Fuzhou350002 China Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei106 Taiwan

The series arc faults of the electric lines in the low-voltage distribution network can lead to devastating fires, posing a significant threat to the lives and safety of residents. Aiming at the problems that arc faults are challenging to identify due to the variety of load types in the lines, a new diagnosis method of two-level classification for arc faults based on data random fusion and MC-MGCNN network is proposed. Firstly, the first level of load classification is carried out. Extreme weighted fuzzy entropy is calculated to separate the dimmer load that could easily lead to misjudgment from the total load pool, and the concepts of simple load pool and complex load pool are established. Secondly, the corresponding classifiers are designed for the two different load pools for the second level of diagnosis classification. Specifically, the time–frequency domain features are extracted for the load waveforms in a simple load pool, and extreme gradient boosting (XGBoost) is used for rapid diagnosis. On the contrary, for the nonlinear load waveforms and multi-load combination waveforms in the complex load pool, the random fusion mechanism of data is applied for feature enhancement, and a new one-dimensional data set is constructed. Finally, the latest data is input into the multi-channel convolution neural network combining multi-head attention and gated recurrent unit (MC-MGCNN) network for arc fault diagnosis. The experimental results show that the first level of load classification can effectively reduce the number of misjudgment samples in the complex load pool and improve the overall binary classification accuracy by 7.54 %. The new data set gains different degrees of improvement in the identification accuracy of other comparative networks. In addition, the new network has good feature learning ability and diagnostic accuracy of arc faults. The accuracy of the multi-classification is up to 99.5 %, and the binary classification accuracy is up to 99.94 %. The test

关键词： Classification (of information)

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Sentiment Analysis on Drug Reviews Using Diverse Classification Techniques 3

Sentiment Analysis on Drug Reviews Using Diverse Classificat...

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3rd International Conference on Communication, Computing and Industry 4.0, C2I4 2022

作者： Duraisamy, Premkumar Natarajan, Yuvaraj Preethaa, K.R.Sri Mouthami, K. KPR Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Research Professor Intelligent Construction Automation Center Kyungpook National University Deagu Korea Republic of

ISBN: (纸本)9781665474979

Free text reviews are abundantly distributed over the internet among the standard population of the world. Although online doctors were consulted during the pandemic, many drugs are taken without prescription by ordinary people. Reviews are distributed about the medicines through online feeds, posts and blogs, which are merely helpful in identifying the side effects and their adverse reactions in combination with another drug or standalone adverse reactions. Spelling mistakes, jargon and short names are normalized throughout the medical terminologies using Natural language processing (NLP) and RxNorm from the reviews extracted from these sources. The concept of Bidirectional Encoder Representations from Transformers (BERT) is used to mine the association rules for the drug interactions when used in conjugation with another drug, and the idea of Adaptive Fuzzy logic neural networks is used to validate the results of association rules derived from the BERT. Inconsistencies identified by the regulations generated were rejected efficiently by the Support vector machines (SVM), Radial Basis Function (RBF)and the system shows 60.11 percentage of accuracy with 10 cross-fold validation, whereas the results are equivalent to the accuracy obtained using SVM with RBF kernel. The data set is reduced from its dimension using Linear discriminant analysis (LDA) by applying the mining process and BERT on two drugs, namely 'Azithromycin' in conjugation with Buprenorphine has Type 'A' side effects, and Prozac in conjugation with Metronidazole shows different side effects. Deplin and Zoloft give very positive symptoms and are independent of dosage interactions. © 2022 IEEE.

关键词： Bidirectional Encoder Representations from Transformers (BERT) Linear discriminant analysis (LDA) Radial Basis Function (RBF) Support vector machines (SVM)

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