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Cybernetics and Innovations (ICCI), International Conference on

作者： Thutchaphong Pongkhan Phutanate Khoosirirat Pokpong Songmuang Rachada Kongkachandra Data Science and Innovation College of Interdisciplinary Studies Thammasat University Thailand Department of Computer Science Faculty of Science and Technology Thammasat University Thailand

ISBN: (数字)9798350370096

ISBN: (纸本)9798350370102

Gaming communities are groups of people from different nations, religions, genders, and ages who come to play games and have discussions about the games. However, some gaming communities are plagued by toxic behavior of people in communities, which negatively affects to other players. A Conversation Sentiment Analysis in the League of Legends Game Community is about analyzing the toxicity of the League of Legends game community by using the Sentiment Analysis methods which have Bag-of-Words and Weight Score method, and then comparing it with three machine learning techniques: Logistic Regression, Support Vector Machine (SVM), and CatBoost, and showing how members in this community have interacted with other members in the community by using the Conversation Mapping Interactive method.

关键词： Support vector machines Sentiment analysis Humanities Technological innovation Logistic regression Toxicology Social networking (online)

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Enabling Protection for Critical Infrastructure through Security and Privacy in the Industrial Internet of Things 9

Enabling Protection for Critical Infrastructure through Secu...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Yasotha, B. Vijayanandh, T. Thirumoorthy, P. Priya, M.Sangeetha Sasidaran, S. Prakalya, S.B. Srmist Department of Data Science and Business Systems Kattankulathur India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Avadi Tamil Nadu Chennai India Dhanalakshmi College of Engineering Department of Electrical and Electronics Engineering Chennai India Chennai India Panimalar Engineering College Department of Information Technology Chennai600123 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350377972

Industrial Internet of Things can improve critical infrastructure in energy, transportation, and manufacturing. However, IIoT device and system integration opened security weaknesses that bad actors may exploit, causing huge disruptions and financial losses. IoT network security and privacy must be prioritized to protect key infrastructure. IIoT security issues and solutions are examined in this research. First, we take a look at the state of security and the unique risks that industrial IoT devices are up against, including hacking, illegal access, and cyber-physical assaults. Then, to lessen the impact of these dangers, we investigate cutting-edge security solutions, such as authentication, encryption, and access control systems. Building a robust security architecture specifically for IIoT applications is one of the main goals of the project. To improve safety and anonymity, this system uses cutting-edge innovation like blockchain, AI, and edge computing. We also talk about how privacy-preserving methods like differential privacy and anonymization are important for keeping sensitive information safe while allowing data sharing for analysis and operations. We assess the efficacy of the suggested security protocols and procedures using a battery of case studies and simulations. Reducing the attack surface and improving the resilience of IIoT systems can be achieved by a multi-layered security approach that includes effective privacy protections. We conclude by outlining our research's larger implications, which include the importance of standardization, regulatory compliance, and stakeholder collaboration in ensuring the safe and privacy-protecting deployment of IIoT technologies in vital infrastructure. Protecting vital infrastructure from cyber threats is our ultimate goal, and our work helps build IIoT systems that are more secure and dependable. © 2024 IEEE.

关键词： Industrial internet of things (IIoT)

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Evidential Reasoning Rule Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Zhou, Zhiguo Liu, Hui Department of Biostatistics & Data Science Kansas CityKS United States University of Kansas Medical Center Department of Biostatistics & Data Science Kansas CityKS United States University of Central Missouri Department of Computer Science and Cybersecurity WarrensburgMO United States

In recent decades, artificial intelligence (AI), particularly machine learning (ML), has achieved remarkable advancements. However, the practical implementation of ML in critical real-world applications remains constrained due to the concerns on building reliable ML model. To address this challenge, ensuring model reliability requires a focus on three major aspects: credibility, adaptability, and interpretability. To achieve these objectives in a unified way, a new reliable learning pattern which consists of training, adaptation, testing and second decision stage was proposed to extend the traditional training-testing pattern. Based on this framework, a new evidential reasoning rule learning (ER2L) strategy was developed, where individual-reliability is defined to tune the trained model for fitting the test sample better in adaptation stage and overall-reliability is introduced to measure the credibility of the model outputs for the second decision. Furthermore, due to ER2’s nature as a probabilistic inference engine, the model is interpretable. Building on ER2L, a new ML approach termed Naïve ER2 (NER2) was proposed. The experimental studies demonstrated that NER2 can obtain better performance by introducing individual-reliability for model tuning, whilst the overall-reliability can evaluate the credibility of model output reasonably. © 2020 IEEE.

关键词： Engineering education

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Generative Adversarial Networks for Semi-Supervised Learning in Clinical Decision Support Systems 3rd

Generative Adversarial Networks for Semi-Supervised Learning...

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3rd International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Sengan, Sudhakar Katbar, Nek Muhammad Kothalanka, Amarendra Shelke, Mayura Chinnathambi, Kalimuthan Sharma, Dilip Kumar Ammasai, Viswanathan Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627451 India School of Mathematics and Statistics Central South University Hunan Changsha410083 China Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of AI & DS AISSMS IOIT Pune411001 India Deprtment of Information Technology V.S.B. College of Engineering Technical Campus Tamil Nadu Coimbatore642109 India Department of Mathematics Jaypee University of Engineering and Technology Madhya Pradesh Guna473226 India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

ISBN: (纸本)9789819783540

Clinical Decision Support Systems (CDSS) have emerged as crucial tools in healthcare, leveraging vast amounts of data to aid clinicians in their decision-making processes. However, the challenge of efficiently utilizing limited labelled medical data remains. This paper introduces a novel approach using Semi-supervised DC-GAN to address this challenge. Building upon the foundational principles of Generative Adversarial Networks (GANs), this proposed model augments the capabilities of traditional semi-supervised methods, optimizing their performance in the CDSS landscape. Through extensive evaluations, they compared this approach with three widely accepted semi-supervised algorithms: MixMatch, Mean Teacher, and Π-Model. The results indicate that Semi-supervised DC-GAN enhances the data utilization process and significantly improves model training, especially when working with limited labelled datasets. This research underscores the potential of GANs in revolutionizing semi-supervised learning within CDSS, paving the way for more efficient and informed clinical decisions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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Convergence of the Inexact Langevin Algorithm and Score-based Generative Models in KL Divergence

arXiv

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arXiv 2022年

作者： Yang, Kaylee Yingxi Wibisono, Andre Department of Statistics and Data Science Yale University United States Department of Computer Science Yale University United States

We study the Inexact Langevin Dynamics (ILD), Inexact Langevin Algorithm (ILA), and Score-based Generative Modeling (SGM) when utilizing estimated score functions for sampling. Our focus lies in establishing stable biased convergence guarantees in terms of the Kullback-Leibler (KL) divergence. To achieve these guarantees, we impose two key assumptions: 1) the target distribution satisfies the log-Sobolev inequality (LSI), and 2) the score estimator exhibits a bounded Moment Generating Function (MGF) error. Notably, the MGF error assumption we adopt is more lenient compared to the L∞ error assumption used in existing literature. However, it is stronger than the L2 error assumption utilized in recent works, which often leads to unstable bounds. We explore the question of how to obtain a provably accurate score estimator that satisfies the MGF error assumption. Specifically, we demonstrate that a simple estimator based on kernel density estimation fulfills the MGF error assumption for sub-Gaussian target distribution, at the population level. Copyright © 2022, The Authors. All rights reserved.

关键词： Errors

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An Empirical Analysis and Challenging Era of Blockchain in Green Society 1

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4th International Conference on Communication and Intelligent Systems, ICCIS 2022

作者： Senthil, G.A. Prabha, R. Divya, B. Sathya, A. Department of Information Technology Agni College of Technology Chennai India Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Computer Science and Engineering Sri Sai Ram Institute of Technology Chennai India Department of Artificial Intelligence and Data Science Sri Sai Ram Institute of Technology Chennai India

ISBN: (数字)9789819923229

ISBN: (纸本)9789819923212

Blockchain is one of the new inventions that can help develop sustainable and sustainable solutions as it is able to provide accountability, transparency, online tracking and robustness, and improve the efficiency of global collaboration. A massive increase in electricity demand has created a new dilemma. Power businesses, startups, commercial banks, policymakers, and researchers are all interested in blockchain technology, which has become the most generally acknowledged IoT Technology. Therefore, blockchain technology efforts from both industry and education have been included in the study. Recently, the blockchain has been employed as a new approach for developing and enhancing green societies. A full examination of all the constraints and potential of the blockchain through technological ideas is required. In this article, tremendous attempts are underway to thoroughly investigate the complete foundation of the green community and how the blockchain contributes to the green community. Finally, this article also highlights the applications and challenges of the green chain-driven community. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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Cognizant Care Machine Learning-Assisted Smart Records for Improved Healthcare 2

Cognizant Care Machine Learning-Assisted Smart Records for I...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Mageshwari, G. Latha, J. Naveen, G. Murugan, S. Karthick Joseph Gilbert Andrews, Allen R.M.K.College of Engineering and Technology Department of Artificial Intelligence and Data Science Tiruvallur Dist Puduvayal India R.M.K.College of Engineering and Technology Department of Computer Science and Engineering Tiruvallur Dist Kavaraipettai India Madha Institute of Engineering and Technology Department of Electronics and Communication Engineering Tiruvallur Dist Kavaraipettai India R. M. D. Engineering College Department of Computer Science and Engineering Tiruvallur Dist Kavaraipettai India Deloitte Consulting LLP 200 Sterling Road MechanicsburgPA United States

ISBN: (纸本)9798350317558

In the era of evolving healthcare demands, the requirement for innovation in record-keeping systems is undeniable. Traditional methods of documenting have difficulties in terms of efficiency, accuracy, and flexibility. The paper addresses an urgent need for a revolutionary solution by proposing "Cognizant Care,"a unique system that employs machine learning to build Smart Records, therefore revolutionizing healthcare documentation and decision-making processes. The inefficiencies of legacy systems, which include time-consuming manual data entry and limited predictive capabilities, highlight the need for a novel concept. Cognizant Care's architecture can be incorporated with existing Electronic Health Record systems, providing intelligent data extraction, predictive analytics, decision support, and automated reporting. The results and analysis show a significant improvement demonstrates superior performance with accuracy at 94%, precision of 92%, recall of 88%, and an F1 score of 90%, proving the system's ability to mitigate the drawbacks of existing systems. Cognizant Care emerges as an essential method signifying an evolving era in which the combination of human knowledge and artificial intelligence drives healthcare into a realm of unparalleled achievements. © 2024 IEEE.

关键词： Cognizant Care Cost Savings Decision Support Electronic Health Records Healthcare Innovation Personalized Patient Care

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Machine Learning Models for Speech-Based Depression Screening

Machine Learning Models for Speech-Based Depression Screenin...

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Cybernetics and Innovations (ICCI), International Conference on

作者： Kunanon Kongchatree Ongon Suriyo Natvara Pichedpan Rachada Kongkachandra Pokpong Songmuang Data Science and Innovation College of Interdisciplinary Studies Thammasat University Bangkok Thailand Department of Computer Science Thammasat University Bangkok Thailand

ISBN: (数字)9798331533267

ISBN: (纸本)9798331533274

Mental health issues significantly affect society, requiring long-term treatment that is both costly and impacts personal relationships. This research paper proposes a speech-based method to screen for depression risk as an accessible alternative. The research included five machine learning models-Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Naive Bayes, and Linear Discriminant Analysis (LDA)-were used to analyze speech data from 25 participants with varying mental health conditions. Three data preparation techniques-Leave-One-Out Cross-Validation, SMOTE, and raw data-were applied to optimize the models. Results showed that SVM with SMOTE achieved the highest accuracy (98 %), emphasizing the importance of effective data preparation. This research demonstrates the potential of using speech analysis as a cost-effective tool for early mental health screening and provides a foundation for practical applications in healthcare and beyond.

关键词： Support vector machines Analytical models Accuracy Speech analysis data preprocessing Mental health Machine learning Predictive models Depression data models

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Collaborative Neurodynamic Algorithms for Solving Sudoku Puzzles 12

Collaborative Neurodynamic Algorithms for Solving Sudoku Puz...

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12th International Conference on Information science and Technology, ICIST 2022

作者： Li, Hongzong Wang, Jun City University of Hong Kong Department of Computer Science Kowloon Hong Kong School of Data Science City University of Hong Kong Kowloon Hong Kong

ISBN: (数字)9781665485821

ISBN: (纸本)9781665485821

In this article, Sudoku is formulated as a quadratic unconstrained binary optimization, and a variables reduction algorithm is proposed based on given elements. Collaborative neurodynamic optimization algorithms based on discrete Hopfield networks or Boltzmann machines are developed for solving the formulated optimization problem. A population of discrete Hopfield networks or Boltzmann machines operating concurrently are employed for scatter search. A particle swarm optimization rule is used to re-initialize the initial states of discrete Hopfield networks or Boltzmann machines upon their local convergence. Experimental results on five Sudoku instances are elaborated to demonstrate the efficacy of the proposed collaborative neurodynamic optimization algorithms for solving Sudoku puzzles. © 2022 IEEE.

关键词： Particle swarm optimization (PSO)

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Speed Bump Detection Through Inertial Sensors and Deep Learning in a Multi-contextual Analysis

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SN computer science 2023年第1期4卷 1-20页

作者： Menegazzo, Jeferson von Wangenheim, Aldo Graduate Program in Computer Science (PPGCC) Image Processing and Computer Graphics Lab (LAPIX) Brazilian National Institute for Digital Convergence (INCoD) Department of Informatics and Statistics (INE) Federal University of Santa Catarina (UFSC)-Florianópolis Santa Catarina Brazil

Speed bumps are vertical raisings of the road pavement used to force drivers to slow down to ensure greater safety in traffic. However, these obstacles have disadvantages in terms of efficiency and safety, where the presence of speed bumps can affect travel time and fuel consumption, cause traffic jams, delay emergency vehicles, and cause vehicle damage or accidents when not properly signaled. Due to these factors, the availability of geolocation information for these obstacles can benefit several applications in Intelligent Transportation System (ITS), such as Advanced Driver Assistance Systems (ADAS) and autonomous vehicles, allowing to trace more efficient routes or alert the driver of the presence of the obstacle ahead. Speed bump detection applications described in the literature employ cameras or inertial sensors, represented by accelerometers and gyroscopes. While camera-based solutions are mature with evaluation in different contextual conditions, those based on inertial sensors do not offer multi-contextual analyses, being mostly simple applications of proof of concept, not applicable in real-world scenarios. For this reason, in this work, we propose the development of a reliable speed bump detection model based on inertial sensors, capable of operating reliably in contextual variations: different vehicles, driving styles, and environments in which vehicles can travel to. For the model development and validation, we collect nine datasets with contextual variations, using three different vehicles, with three different drivers, in three different environments, in which there are three different surface types, in addition to variations in conservation state and the presence of obstacles and anomalies. The speed bumps are present in two different pavement types, asphalt and cobblestone. We use the collected data in experiments to evaluate aspects such as the influence of the placement of the sensors for vehicle data collection and the data window size. Afterwar

关键词： Autonomous vehicles Deep learning Inertial sensors Intelligent transport systems Road conditions Speed bump

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