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13th IEEE international conference on Communications, Circuits and Systems (ICCCAS)

作者： Zhu, Yuedi Li, Chao Wang, Yong Shanghai Univ Elect Power Coll Comp Sci & Technol Shanghai Peoples R China State Grid Digital Technol Holdings Co LTD Beijing Peoples R China

ISBN: (纸本)9798350386288;9798350386271

The ongoing evolution of digital transformation poses new challenges for our society. In the domain of networks, we confront a series of challenges related to dependence and the implementation of security through design. As a result, strategies centered around data and machine learning techniques become effective options for ensuring the security of extensive network systems. However, in the field of network security, solutions based on machine learning encounter challenges regarding generalization across different contexts and privacy. In other words, solutions relying on specific network data often encounter limitations in terms of performance when applied to different networks. The paper introduces a federated learning (FL) approach tailored for Network Intrusion Detection Systems (NIDS). By integrating the Energy Flow Classifier with the Gaussian Mixture Model clustering algorithm into the federated learning strategy, the proposed method performs well when handling non-IID (Non-Independent Identically Distributed) data and provides a viable solution for achieving generalization across diverse networks.

关键词： Network intrusion detection Federated learning Energy Flow Classifier Gaussian Mixture Model

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A comparative study of machine learning in diabetes classification: Decision trees, logistic regression, and SVM 2

A comparative study of machine learning in diabetes classifi...

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2nd international conference on Big data, Computational Intelligence, and Applications, BDCIA 2024

作者： Zhu, Xinglong Liao, Huitong Zhang, Tingting Zhang, Lujuan Li, Xueri Department of Computer Science Guangdong University of Science and Technology Dongguan China

ISBN: (纸本)9781510689053

As the application of computer science in healthcare continues to expand, machine learning techniques have become an important tool for disease diagnosis. In this study, we trained and predicted diabetes datasets by preprocessing, standardising and normalising the data, and then using three machine learning models, aiming to improve the accuracy of diabetes prediction and the value of clinical application. The experimental results show that these machine learning models exhibit a high degree of accuracy and reliability in the field of diabetes diagnosis, among which the SVM performs the best on normalised data, with the highest values of accuracy, precision, recall, F1-score, and AUC evaluation metrics, showing the good performance of the support vector machine in predicting diabetes. The decision tree model performed consistently on raw and standardised data, while the logistic regression model performed best on AUC values. This study not only verifies the effectiveness of machine learning models in diabetes prediction, but also provides clinicians with accurate diagnostic assistance, which is of significant academic and practical significance. © 2025 SPIE.

关键词： Logistic regression

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Reinforcement learning as an Improvement Heuristic for Real-World Production Scheduling 23

Reinforcement Learning as an Improvement Heuristic for Real-...

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23rd IEEE international conference on machine learning and Applications, ICMLA 2024

作者： Müller, Arthur Vollenkemper, Lukas Fraunhofer IOSB-INA Department of Machine Intelligence Lemgo32657 Germany Center for Applied Data Science Bielefeld University of Applied Sciences and Arts Gütersloh33330 Germany

ISBN: (纸本)9798350374889

The integration of Reinforcement learning (RL) with heuristic methods is an emerging trend for solving optimization problems, which leverages RL's ability to learn from the data generated during the search process. One promising approach is to train an RL agent as an improvement heuristic, starting with a suboptimal solution that is iteratively improved by applying small changes. We apply this approach to a real-world multiobjective production scheduling problem. Our approach utilizes a network architecture that includes Transformer encoding to learn the relationships between jobs. Afterwards, a probability matrix is generated from which pairs of jobs are sampled and then swapped to improve the solution. We benchmarked our approach against other heuristics using real data from our industry partner, demonstrating its superior performance. © 2024 IEEE.

关键词： Reinforcement learning

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A Trusted Mechanism for Participant Screening and Verification in Federated learning 4

A Trusted Mechanism for Participant Screening and Verificati...

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4th international conference on machine learning and Intelligent Systems Engineering (MLISE)

作者： Xie, Yun Wu, Yucheng Wang, Hongyan Li, Ang Nanjing Vocat Coll Informat Technol Jiangsu Prov Serv Customizat Network Applicat Eng Nanjing Peoples R China Nanjing Vocat Coll Informat Technol Nanjing Peoples R China Nanjing Univ Posts & Telecommun Nanjing Univ Sci & Technol Zijin Coll Nanjing Peoples R China

ISBN: (纸本)9798350375084;9798350375077

Federated learning(FL) solves the problem of "data Silos" achieving the dual-purpose of data retention and remote sharing, which is widely applied in fields such as healthcare, transportation, and manufacturing. Local participants (LPs) are the main entities in FL, contributing resources such as data, computing, communication, and energy. The actual contribution of LPs directly affects the performance of federated learning. Existing research has mostly focused on how to design efficient algorithms for LPs, while neglecting the credibility of them. Obviously, highly trusty LPs will provide high-quality data sources and model medium parameters, which are the core factors affecting the performance of FL. This paper designs a new mechanism based on the joining protocol to verify the legitimacy of LPs, and combines subjective logical models to evaluate the reputation of participants. It solves the credibility evaluation and screening problems of participants, as well as the fairness of rewards.

关键词： Federated learning Reputation mechanism Subject logic Join protocol

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Enhancing Road Safety: Predicting Severity of Accidents 5th

Enhancing Road Safety: Predicting Severity of Accidents

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5th international conference on data science, machine learning and Applications

作者： Deepthi, A. Chaithrika, B. Teena, Ch. Manvitha, G. Keerthana, G. Sree G Narayanamma Inst Technol & Sci Dept CSE AI&ML Hyderabad Telangana India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Road accidents are a pervasive global issue with profound consequences for individuals, communities, and economies. This research investigates the diverse impacts of traffic accidents on human lives, healthcare systems, and economic development. Accurate accident severity analysis is crucial for effective management and prevention. To enhance prediction accuracy, this study explores the integration of machine learning methods, including Random Forest, Support Vector machine, K-Nearest Neighbours, and Decision Tree [1]. Utilizing the Road Traffic Accident dataset, the research focuses on feature extraction and selection, aiming to classify accident severity into three levels: minor, severe, and fatal. Despite the dataset's real-world basis and inherent imbalance, this study contributes valuable insights to the discourse on road safety and accident severity prediction.

关键词： Accident Severity Prediction machine learning Ensemble Methods Logistic Regression Classification and Regression Trees.

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CrowdAL: Towards a Blockchain-empowered Active learning System in Crowd data Labeling 20

CrowdAL: Towards a Blockchain-empowered Active Learning Syst...

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20th IEEE international conference on E-science (E-science)

作者： Hou, Shaojie Wang, Yuandou Zhao, Zhiming Univ Amsterdam Multiscale Networked Syst Amsterdam Netherlands Vrije Univ Amsterdam Amsterdam Netherlands

ISBN: (纸本)9798350365627;9798350365610

Active learning (AL) is a machine learning technique where the model selectively queries the most informative data points for labeling by human experts. Integrating AL with crowdsourcing leverages crowd diversity to enhance data labeling but introduces challenges in consensus and privacy. This poster presents CrowdAL, a blockchain-empowered crowd AL system designed to address these challenges. CrowdAL integrates blockchain for transparency and a tamper-proof incentive mechanism, using smart contracts to evaluate crowd workers' performance and aggregate labeling results, and employs zero-knowledge proofs to protect worker privacy.

关键词： Active learning Crowdsourcing Blockchain Zero Knowledge Proofs

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State-of-the-art applications of machine learning in the life cycle of solid waste management

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Frontiers of Environmental science & Engineering 2023年第4期17卷 53-69页

作者： Rui Liang Chao Chen Akash Kumar Junyu Tao Yan Kang Dong Han Xianjia Jiang Pei Tang Beibei Yan Guanyi Chen School of Environmental Science and Engineering Tianjin UniversityTianjin 300350China School of Mechanical Engineering Tianjin University of CommerceTianjin 300134China Tianjin Key Laboratory of Biomass Wastes Utilization/Tianjin Engineering Research Center of Bio Gas/Oil Technology Tianjin 300072China School of Science Tibet UniversityLhasa 850012China

Due to the superiority of machine learning(ML)data processing,it is widely used in research of solid waste(SW).This study analyzed the research and developmental progress of the applications of ML in the life cycle of *** analyses were undertaken on the literature published between 1985 and 2021 in the science Citation Index Expanded and Social sciences Citation Index to provide an overview of the *** on the articles considered,a rapid upward trend from 1985 to 2021 was found and international cooperatives were found to have *** three topics of ML,namely,SW categories,ML algorithms,and specific applications,as applied to the life cycle of SW were *** has been applied during the entire SW process,thereby affecting its life *** was used to predict the generation and characteristics of SW,optimize its collection and transportation,and model the processing of its energy ***,the current challenges of applying ML to SW and future perspectives were *** goal is to achieve high economic and environmental benefits and carbon reduction during the life cycle of *** plays an important role in the modernization and intellectualization of SW *** is hoped that this work would be helpful to provide a constructive overview towards the state-of-the-art development of SW disposal.

关键词： machine learning(ML) Solid waste(SW) Bibliometrics SW management Energy utilization Life cycle

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Deriva-ML: A Continuous FAIRness Approach to Reproducible machine learning Models 20

Deriva-ML: A Continuous FAIRness Approach to Reproducible Ma...

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20th IEEE international conference on E-science (E-science)

作者： Li, Zhiwei Kesselman, Carl D'Arcy, Mike Pazzani, Michael Xu, Benjamin Yizing Univ Southern Calif Inst Informat Sci Viterbi Sch Engn Marina Del Rey CA 90292 USA Univ Southern Calif Keck Sch Med Dept Ophthalmol Los Angeles CA 90007 USA

ISBN: (纸本)9798350365627;9798350365610

Increasingly, artificial intelligence (AI) and machine learning (ML) are used in escience applications [9]. While these approaches have great potential, the literature has shown that ML-based approaches frequently suffer from results that are either incorrect or unreproducible due to mismanagement or misuse of data used for training and validating the models [12, 15]. Recognition of the necessity of high-quality data for correct ML results has led to data-centric ML approaches that shift the central focus from model development to creation of high-quality data sets to train and validate the models [14, 20]. However, there are limited tools and methods available for data-centric approaches to explore and evaluate ML solutions for escience problems which often require collaborative multidisciplinary teams working with models and data that will rapidly evolve as an investigation unfolds [1]. In this paper, we show how data management tools based on the principle that all of the data for ML should be findable, accessible, interoperable and reusable (i.e. FAIR [26]) can significantly improve the quality of data that is used for ML applications. When combined with best practices that apply these tools to the entire life cycle of an ML-based escience investigation, we can significantly improve the ability of an escience team to create correct and reproducible ML solutions. We propose an architecture and implementation of such tools and demonstrate through two use cases how they can be used to improve ML-based escience investigations.

关键词： data management FAIR data data-centric machine learning reproducibility

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Applying data science and machine learning for Predictive Analytics in Organizational Decision-Making 10th

Applying Data Science and Machine Learning for Predictive An...

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10th international conference on Technologies and Innovation

作者： Vergara-Lozano, Vanessa Lagos-Ortiz, Katty Sinche, Andrea Medina-Moreira, Jose Rochina Garcia, Christian Univ Agraria Ecuador Fac Ciencias Agrarias Ave 25 Julio Guayaquil Ecuador Univ Bolivariana Ecuador Duran Ecuador Univ Internac Ecuador Fac Ciencias Empresari Ave Las Aguas Guayaquil Ecuador

ISBN: (纸本)9783031757013;9783031757020

This article examines current and future trends in data science and machine learning (ML) within organizational settings using data from the 2020 Kaggle data science and machine learning Survey, which collected over 20,000 responses. The survey provides a detailed view of the field, addressing demographics, employment trends, preferred tools and methods, and relevant educational backgrounds. Through in-depth descriptive analysis, the study reveals key patterns and concepts illustrating how organizations are adopting and integrating these technologies into their operations. Additionally, the implications of these findings for the future of data science andML are explored, emphasizing the anticipated evolution in terms of business practices and technological advancements. This approach provides a crucial strategic vision for those seeking to understand and anticipate the changing dynamics in the field of data science and machine learning in the corporate context. The study employs advanced predictive modeling techniques to analyze the dataset, using generative neural networks and optimization methods such as Adam. These tools allow for the exploration of complex relationships and hidden patterns in the data, providing deep insights that go beyond traditional descriptive analyses.

关键词： data science machine learning Demographics Labor trends Tools and methods Educational environments

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Hybrid Horizons: Advancing Water Potability Prediction Through Hybrid machine learning 15

Hybrid Horizons: Advancing Water Potability Prediction Throu...

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15th international conference on Ubiquitous and Future Networks (ICUFN)

作者： Biju, Jovita Badgujar, Chetan Poulose, Alwin Indian Inst Sci Educ & Res Thiruvananthapuram IIS Sch Data Sci Thiruvananthapuram 695551 Kerala India Univ Tennessee Biosyst Engn & Soil Sci Knoxville TN 37996 USA

ISBN: (纸本)9798350385304;9798350385298

Ensuring access to safe drinking water is a critical global concern with significant implications for public health. This paper investigates the application of the hybrid machine learning model in assessing water potability, offering a comprehensive review of current methodologies and prospects. With water quality assessment a critical component of public health management, integrating machine learning techniques shows promising avenues for improving accuracy, efficiency, and predictive capabilities. This paper synthesizes existing literature on machine learning models in water quality analysis, highlighting various approaches, such as supervised and hybrid machine learning models utilized for water potability assessment. Furthermore, it examines using diverse data sources, including the pH level of the water, water hardness, total dissolved solids in the water, Chloramines concentration, sulfate concentration, electrical conductivity, organic carbon content, Trihalomethanes concentration, and turbidity level to enhance model performance and robustness. Our experiment results on the Water Quality and Potability dataset show that the proposed hybrid machine learning model achieved 68% classification accuracy compared to traditional supervised machine learning techniques. By critically evaluating the strengths and limitations of supervised and hybrid machine learning models, our research contributes to the ongoing discourse on leveraging technology to safeguard water quality and public health, ultimately fostering sustainable water management practices.

关键词： Hybrid machine learning Water Potability Prediction Water Quality and Potability dataset Sustainable Water Management

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