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3rd international conference on machine learning and Computer Application, ICMLCA 2022

作者： Ren, Na Wang, Hongjiang Xia, Yan Zhang, Nan Zhang, Wenqiang Li, Zhengda Shenyang Institute of Engineering No. 18 Puchang Road Shenbei New District Shenyang110036 China

ISBN: (纸本)9781510664814

The distribution of carbon emissions varies greatly among regions and industries in China. In order to master the current situation and trend of carbon emissions in different regions and industries, and formulate and adjust relevant policies in a timely manner, it is necessary to conduct real-time, continuous, accurate and digital monitoring of carbon emissions in major regions and industries. Therefore, "carbon peaking" and "carbon neutralization" put forward higher requirements for the environmental monitoring industry and also brought greater opportunities. In this paper, the spectral information data in typical atmospheric samples are collected in real time, processed and stored structurally through the atmospheric spectral detection method based on mid far infrared spectrum and terahertz spectrum, the spectral feature extraction algorithm based on deep neural network, and the atmospheric carbon emission data analysis and visual monitoring based on big data technology;Run the spectral classification algorithm with low cost and high efficiency on the big data platform, obtain the detailed data of gas sample composition in real time, and display the analysis results visually. Through simulation experiments, the results show that the proposed method can better achieve the monitoring and early warning of atmospheric carbon emissions based on big data and spectral measurement. © 2023 SPIE.

关键词： Carbon

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machine learning (ML) based Human Activity Recognition Model using Smart Sensors in IoT Environment 12

Machine Learning (ML) based Human Activity Recognition Model...

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12th international conference on Cloud Computing, data Science and engineering, Confluence 2022

作者： Navita Mittal, Pooja M. D. University Department Of Computer Science And Applications Haryana Rohtak India

ISBN: (纸本)9781665437011

A major progression in sensor-based technologies has resulted in a fast evolution of the Internet of Things (IoT) applications for developing any real-time monitoring systems. Nowadays, an increasing number of aged people living alone dispersed worldwide, and tracking the status of their health function or activity is necessary. In this paper, an IoT-based human activity monitoring model is proposed to continuously check the activities of aged people via smart sensor-based technologies. In this model, vital data are collected by smart sensors or IoT-based devices, and analysis of that data is done by using different machine learning algorithms for detecting any risk in human activity behavior. After evaluating the proposed model, the SVM has attained the highest accuracy of 98.03% which is highly effective for our purpose. SVM outperformed all machine learning algorithms used for analysis purposes. © 2022 IEEE.

关键词： Smartphones

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machine learning in DeFi: Credit Risk Assessment and Liquidation Prediction 6

Machine Learning in DeFi: Credit Risk Assessment and Liquida...

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6th IEEE international conference on Blockchain and Cryptocurrency, ICBC 2024

作者： Palaiokrassas, Georgios Scherrers, Sandro Makri, Eftychia Tassiulas, Leandros Yale University Department of Electrical Engineering New HavenCT United States Yale University Yale Institute for Network Science New HavenCT United States Yale University School of Management New HavenCT United States

ISBN: (纸本)9798350316742

This paper investigates the application of machine learning for credit risk assessment in Multichain Decentralized Finance (DeFi). With DeFi expanding its scope, the need for effective credit risk evaluation becomes paramount. Our study utilizes a diverse dataset gathered from multiple blockchains, including Ethereum, and employs rigorous data preprocessing techniques. DeFi-specific features are extracted, capturing transaction-related statistics. machine learning models, such as Logistic Regression, Random Forest, XGBoost, CatBoost, LightGBM and a CNN, are deployed to predict wallet liquidations. Evaluation metrics, including accuracy, ROC curve and Area Under the Curve, demonstrate the efficacy of DeFi-related features in credit risk assessment. Furthermore, we analyze feature importance and inter-feature correlations, providing insights into critical risk factors within the DeFi ecosystem. This research contributes valuable insights to the DeFi landscape, offering data-driven approaches to credit risk management and investment strategies. Our findings hold significance for DeFi stakeholders seeking to navigate the evolving financial frontier while mitigating credit risk effectively. © 2024 IEEE.

关键词： Decentralized finance

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At-Risk Students Identification based on machine learning Approach: A Case Study of Computer Science Bachelor Student in Tunisia 20

At-Risk Students Identification based on Machine Learning Ap...

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20th ACS/IEEE international conference on Computer Systems and Applications (AICCSA)

作者： Khalifa, Amani BenSaid, Fatma Kacem, Yessine Hadj Jridi, Zouhaier Univ Gafsa Fac Sci Gafsa Gafsa Tunisia Univ Sfax REGIM Lab Res Grp Intelligent Machines Sfax Tunisia Univ Sfax Natl Engn Sch Sfax CES Lab Sfax Tunisia

ISBN: (纸本)9798350319439

Various data mining techniques, like prediction and clustering, can be applied on educational data in order to study the student's performance and behavior. Predicting academic results is one of the methods that aim at monitoring student progress and anticipating students that are at risk of failure in their academic career. In this paper, we propose a machine learning (ML) based Educational data Mining (EDM) approach, named ARSITUN, for the identification of at-risk students. Using ARSITUN, an early intervention can be performed for the detected students in order to lower the risk of their failure. The proposed approach was developed and tested using student's data that were collected from the Tunisian administration system for bachelors and masters called "Salima". We created a new dataset, named GCSD, that concerns 358 students from the faculty of Sciences of Gafsa during the school years period 2014-2022. The experimental results showed that our EDM model reaches an accuracy of 90.44% for computer science bachelors' grade prediction (Tunisian case study).

关键词： Educational data mining At-risk student identification machine learning Student's performance Bachelors grade prediction

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data Valuation in machine learning: "Ingredients", Strategies, and Open Challenges 31

Data Valuation in Machine Learning: "Ingredients", Strategie...

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31st international Joint conference on Artificial Intelligence (IJCAI)

作者： Sim, Rachael Hwee Ling Xu, Xinyi Low, Bryan Kian Hsiang Natl Univ Singapore Dept Comp Sci Singapore Singapore

ISBN: (纸本)9781956792003

data valuation in machine learning (ML) is an emerging research area that studies the worth of data in ML. data valuation is used in collaborative ML to determine a fair compensation for every data owner and in interpretable ML to identify the most responsible, noisy, or misleading training examples. This paper presents a comprehensive technical survey that provides a new formal study of data valuation in ML through its "ingredients" and the corresponding properties, grounds the discussion of common desiderata satisfied by existing data valuation strategies on our proposed ingredients, and identifies open research challenges for designing new ingredients, data valuation strategies, and cost reduction techniques.

关键词： machine learning

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Deep learning Approaches for Image Classification Techniques

Deep Learning Approaches for Image Classification Techniques

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IEEE international conference on Electrical engineering, Big data and Algorithms (EEBDA)

作者： Guan, Youyou Han, Yuxuan Liu, Siqi Shandong Univ Dept Comp Sci & Technol Jinan Shandong Peoples R China China Agr Univ Int Coll Beijing Beijing Peoples R China Shanghai Univ Engn Sci Elect & Elect Engn Shanghai Peoples R China

ISBN: (纸本)9781665416061

Neural network is a popular and significant research direction in machine learning, which is widely used in classification. regression, pattern recognition and other fields. Based on the current direction of academic research, the application of neural network in image classification has great research value. However, due to a large number of such articles, involving too wide a range of aspects, it is difficult to grasp the main idea quickly when quoting. We have selected and sorted out some representative basic articles and innovative cuttingedge articles. After systematic analysis and integration, we give each a better algorithm for some of the previous algorithms. This upgrade is reflected in performance, efficiency and other aspects.

关键词： Image classification Computer vision Deep learning CNN FNN RNN

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How Do ML Jobs Fail in datacenters? Analysis of a Long-Term dataset from an HPC Cluster 14

How Do ML Jobs Fail in Datacenters? Analysis of a Long-Term ...

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14th Annual ACM/SPEC international conference on Performance engineering, ICPE 2023

作者： Chu, Xiaoyu Talluri, Sacheendra Versluis, Laurens Iosup, Alexandru Vrije Universiteit Amsterdam Amsterdam Netherlands

ISBN: (纸本)9798400700729

Reliable job execution is important in High Performance Computing clusters. Understanding the failure distribution and failure pattern of jobs helps HPC cluster managers design better systems, and users design fault tolerant systems. machine learning is an increasingly popular workload for HPC clusters are used for. But, there is little information on machine learning job failure characteristics on HPC clusters, and how they differ from the previous workload such clusters were used for. The goal of our work is to improve the understanding of machine learning job failures in HPC clusters. We collect and analyze job data spanning the whole of 2022, and over 2∼million jobs. We analyze basic statistical characteristics, the time pattern of failures, resource waste caused by failures, and their autocorrelation. Some of our findings are that machine learning jobs fail at a higher rate than non-ML jobs, and waste much more CPU-time per job when they fail. © 2023 Owner/Author.

关键词： machine learning

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Enhancing elderly care services through integrated sentiment analysis and knowledge reasoning: A deep learning approach

International Journal of Cognitive Computing in Engineering

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international Journal of Cognitive Computing in engineering 2025年 6卷 477-494页

作者： Ai, Yongguan Chu, Shiwei Wang, Juan Xu, Nianfang School of Public Administration Anhui Vocational and Technical College Anhui Hefei230011 China School of Electronic Information Engineering Anhui University Hefei230039 China School of Computer and Information Technology Anhui Vocational and Technical College Anhui Hefei230011 China

This study proposes a pioneering integrated care model for elderly care service robots that integrates sentiment analysis and knowledge reasoning through a deep learning framework. The primary objective of this research is to address the limitations of current elderly care robots in providing emotionally intelligent and personalized care. The model utilizes advanced deep learning techniques, such as Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs), to analyze multimodal data comprising speech, facial expressions and body language. This enables the model to provide a comprehensive understanding of an elderly individual's emotional and health status. The efficacy of the model is demonstrated by its ability to enhance the precision of care decisions, improve the quality of care, user satisfaction, and system reliability. The experimental results demonstrate substantial improvements in sentiment recognition accuracy (96.5 %), reasoning accuracy (93.7 %), decision execution time (3.2 s), user satisfaction (4.9 points), and system stability (98.4 %), highlighting the transformative potential of the model in revolutionizing elderly care services. © 2025

关键词： Adversarial machine learning

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A Congruence-based Approach to Active Automata learning from Neural Language Models 16

A Congruence-based Approach to Active Automata Learning from...

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16th international conference on Grammatical Inference (ICGI)

作者： Mayr, Franz Yovine, Sergio Carrasco, Matias Pan, Federico Vilensky, Federico Univ ORT Uruguay Fac Ingn Montevideo Uruguay

The paper proposes an approach for probably approximately correct active learning of probabilistic automata (PDFA) from neural language models. It is based on a congruence over strings which is parameterized by an equivalence relation over probability distributions. The learning algorithm is implemented using a tree data structure of arbitrary (possibly unbounded) degree. The implementation is evaluated with several equivalences on LSTM and Transformer-based neural language models from different application domains.

关键词： MAT-PAC active learning Neural language models PDFA

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ANOMALY SCORING MODEL FOR DIAGNOSIS ON machine CONDITION AND HEALTH MANAGEMENT 17

ANOMALY SCORING MODEL FOR DIAGNOSIS ON MACHINE CONDITION AND...

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ASME 17th international Manufacturing Science and engineering conference (MSEC) held jointly with the 50th North American Research conference (NAMRC)

作者： Joung, Byung Gun Li, Zhongtian Sutherland, John W. Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9780791885819

The reliability of manufacturing equipment is critical for ensuring the productivity and energy efficiency of a manufacturing facility. An unexpected machine breakdown may lead to unexpected downtime, disruption of manufacturing schedule, lower production efficiency, higher operation and maintenance cost. The recent development in machine learning and artificial intelligence enables data-driven Predictive Maintenance (PdM) by means of perceiving the dynamics of manufacturing systems and abstracting them into learnable features to provide a better interpretation of machine failures or unplanned downtimes. PdM, often translated to Prognostics and Health Management (PHM), aims to continue the optimal/normal operation of manufacturing systems. Often, vibration is used as a proxy of an early indicator of impending failure. In this study, tri-axial acceleration data collected from the two different machines are utilized. PdM-based strategies for machine condition monitoring and smart scheduling of equipment maintenance using an anomaly scoring model are discussed for two critical elements in a manufacturing system: 1) Chiller 2) Compressor. An anomaly scoring model is developed to extract meaningful information from the vibration data.

关键词： Smart Manufacturing machine learning Anomaly Detection PdM PHM

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