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machine learning insights into CaCO3 phase transitions: Synthesis and phase prediction

CERAMICS international 2024年第13期50卷 23284-23295页

作者： Huang, Yanqi De Spiegeleer, Bart Parakhonskiy, Bogdan Skirtach, Andre G. Univ Ghent Fac Biosci Engn Nanobiotechnol Grp B-9000 Ghent Belgium Univ Ghent DruQuaR Grp Fac Pharmaceut Sci B-9000 Ghent Belgium

machine learning (ML) extends rapidly in many research areas including design of novel processing routines, imaging, and material science. Particularly, ML enables design of new materials with complex structures and shorten the development cycle through model prediction from existing database. Calcium carbonate (CaCO3) particles are regarded as promising candidates for drug delivery, biomedical, food, and industrial filler applications due to their good physicochemical properties and biocompatibility. However, a prerequisite for these applications is the production of particles with desired morphology, size, and phase compositions. Here, it is shown that the crystal growth and phase transition induced the transformation of spherical particles into spindlelike, square and needle-like morphologies with increasing temperature, and the increase of concentration increased this transition temperature. Furthermore, it is found that the concentration of the reacting salt solutions shifted the phase transition temperatures to higher values. Subsequently, ML is applied to precisely investigate and predict the polymorph formation of CaCO3 particles based on the experimental data obtained under 85 conditions, which would enable us to track crystallization trends, aiding in the identification of optimal conditions for generating monophase samples, and provide a feasible scheme for learning similar materials.

关键词： machine learning Calcium carbonate Surface morphology Phase transition Model prediction

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A semisupervised fault frequency analysis method for rotating machinery based on restricted self-attention network

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STRUCTURAL HEALTH MONITORING-AN international JOURNAL 2025年第3期24卷 1522-1537页

作者： Zhang, Huaqin Hong, Jichao Yang, Haixu Zhang, Xinyang Liang, Fengwei Zhang, Chi Huang, Zhongguo Univ Sci &Technol Beijing Sch Mech Engn 30 Coll Rd Beijing 100083 Peoples R China Univ Sci & Technol Beijing Shunde Innovat Sch Foshan Peoples R China

With the development of informatization and digitalization, condition monitoring has been applied to industrial equipment such as rotating machinery. Collecting and storing large amounts of equipment operating data enable the detection of mechanical equipment faults using historical operational data. This article proposes a semisupervised data-driven approach to analyze the fault frequencies of rotating machinery. Frequency band information and the degree of association with faults are obtained through the variance of attention values. To address the inherent issue of decoupling information between data segments in deep learning, restrictive layers are proposed. These layers prevent the flow of information between data segments from rendering interpretable information ineffective. Bearing and gearbox datasets are used to validate the proposed method. The fault frequencies extracted by this method correspond to actual faults. The preferred deep learning framework achieves an accuracy exceeding 99% on both datasets. The method is compared with various signal processing methods and identifies fault frequencies that are difficult to identify using traditional methods. Furthermore, the unreliability of traditional deep learning in fault diagnosis is also exposed. In this study, semisupervised deep learning fault frequency extraction is achieved for the first time.

关键词： Rotating machinery semisupervised machine learning fault diagnosis frequency analysis self-attention network

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Predictive Analysis of machine learning Frameworks in the review of Multivariate datasets for Crop Yield Prediction 4

Predictive Analysis of Machine Learning Frameworks in the re...

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4th IEEE international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Sedimo, Kutlwano Kuthadi, Venumadhav Dinakenyane, Othapile Selvaraj, Rajalakshmi Botswana International University of Science and Technology Department of Computing and Informatics Palapye Botswana

ISBN: (纸本)9798331518981

Crop Yield Prediction (CYP) is crucial for optimizing agricultural practices globally. This study conducts an in-depth review of machine learning (ML) techniques applied to multivariate datasets for crop yield forecasting. We examine the performance of models including deep learning, ensemble classifiers, and hybrid approaches, emphasizing their ability to integrate diverse data sources and adapt to varying climatic conditions. Despite significant progress, challenges such as sparse data handling and generalization across regions persist. This paper not only identifies these challenges but also proposes advanced solutions for improving prediction accuracy, offering a roadmap for future research in agricultural ML frameworks. © 2024 IEEE.

关键词： Adversarial machine learning

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Digitization Workflow for data Mining in Production Technology applied to a Feed Axis of a CNC Milling machine 5

Digitization Workflow for Data Mining in Production Technolo...

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5th international conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Drowatzky, Lucas Maelzer, Mauritz Wejlupek, Kim A. Wiemer, Hajo Ihlenfeldt, Steffen TUD Dresden Univ Technol IMD D-01062 Dresden Germany

Condition monitoring and predictive maintenance applications receive ongoing scientific attention in production technology. Larger companies, especially machine and component manufacturers, already offer related products. Small and medium-sized enterprises (SMEs) in particular show interest in developing and offering solutions in this market themselves to gain economic advantages, to improve resource utilization of their machines or to be able to offer these advantages to their own customers. In the development process, however, they often encounter problems already in the digitization of the machines. The first hurdle is to obtain an analysiscapable data set. This is due to the fact that common and established general data mining development process models, such as CRISP-DM, do not focus on production technology, causing difficulties for engineers during deployment. A problem with existing process models is the limited practicality in the engineering domain due to restricted adaptability. In a previous paper, a guideline for engineers for data mining suitable digitization of production machines was developed in order to solve these problems. The related results were provided in the context of a project for condition monitoring of mixing machines. In this paper, the proposed method is applied to components of a 5-axis CNC milling machine in three different monitoring use cases. A complete workflow is presented, including effect analysis, sensor selection, formulation of predictive scenarios, data preparation, training of machine learning algorithms and vizualization. data and documentation are provided alongside this publication. (c) 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 5th international conference on Industry 4.0 and Smart Manufacturing.

关键词： Condition Monitoring machine learning Digitization Production Engineering Production Technology Anomaly Detection Classification Usable Artificial Intelligence UAI data Mining

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Practical Considerations of Fully Homomorphic Encryption in Privacy-Preserving machine learning

Practical Considerations of Fully Homomorphic Encryption in ...

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2024 IEEE international conference on Big data, Bigdata 2024

作者： Lo, Dan Chia-Tien Shi, Yong Shahriar, Hossain Deng, Bobin Zhang, Xinyue Chen, Mei-Lan Kennesaw State University Department of Computer Science MariettaGA United States University of West Florida The Center for Cybersecurity PensacolaFL United States Georgia State University School of Nursing AtlantaGA United States

ISBN: (纸本)9798350362480

machine learning has been successfully applied to big data analytics across various disciplines. However, as data is collected from diverse sectors, much of it is private and confidential. At the same time, one of the major challenges in machine learning is the slow training speed of large models, which often requires high-performance servers or cloud services. To protect data privacy while still allowing model training on such servers, privacy-preserving machine learning using Fully Homomorphic Encryption (FHE) has gained significant attention. However, its widespread adoption is hindered by performance degradation. This paper presents our experiments on training models over encrypted data using FHE. The results show that while FHE ensures privacy, it can significantly degrade performance, requiring complex tuning to optimize. © 2024 IEEE.

关键词： Adversarial machine learning

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Bayesian Back Analysis of Spatial Variability with machine learning Surrogates

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ASCE-ASME JOURNAL OF RISK AND UNCERTAINTY IN ENGINEERING SYSTEMS PART A-CIVIL ENGINEERING 2025年第2期11卷

作者： Chen, Xiaoying Wang, Shen Yang, Hao-Qing Zhang, Lulu Tang, Chao-Sheng Nanjing Univ Sch Earth Sci & Engn 163 Xianlin Ave Nanjing 210023 Peoples R China Southwest Jiaotong Univ SWJTU Leeds Joint Sch 999 Xian Rd Chengdu 611756 Peoples R China Univ Leeds Sch Civil Engn Woodhouse Ln Leeds LS2 9JT England Nanyang Technol Univ Sch Civil & Environm Engn 50 Nanyang Ave Singapore 639798 Singapore Shanghai Jiao Tong Univ Dept Civil Engn State Key Lab Ocean Engn Shanghai 200240 Peoples R China

Geotechnical sensors provide the advantage of directly monitoring model responses that accurately reflect field conditions. Within these field monitoring data lies the latent potential to glean insights into soil parameters. Beyond relying solely on site-investigation data, the incorporation of field monitoring data serves as a valuable complementary strategy. It aids in evaluating soil spatial variability and addressing uncertainties related to field responses. In this study, a surrogate-based Bayesian back-analysis method is proposed to assess the spatial variability in ground profiles and the uncertainty of field responses. The surrogate models are constructed using machine learning algorithms. To validate the effectiveness of the proposed approach and select the optimal machine learning surrogates, a hypothetical example involving an unsaturated soil slope subjected to rainfall infiltration is first employed. The proposed method is further applied to a hydraulic monitoring project in Hong Kong. The results demonstrate the promising potential of Gaussian process regression with the Matern 5/2 kernel based on 100 training samples for training surrogate models. The saturated hydraulic conductivity obtained from the maximum a posterior (MAP) and borehole logs exhibit similarity, and the MAP estimate accurately captures the observed spatial variation in the dynamic probe test. The proposed method can effectively estimate the soil spatial variability and provides reasonable uncertainty predictions of pore pressure head.

关键词： Back analysis Spatial variability machine learning Surrogate model Bayesian inference

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machine learning-based Predictive Framework for Road Accident Severity Classification and Real-Time Risk Assessment

Machine Learning-based Predictive Framework for Road Acciden...

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2025 international conference on Visual Analytics and data Visualization, ICVADV 2025

作者： Kumar, V.Aswin Radhan, GR.Habi Amshavalli, R.S. Rajalakshmi, R. Shafana, N. Jeenath Sathyabama Institute of Science and Technology Dept of Computer Science and Engineering Chennai600119 India

ISBN: (纸本)9798331521394

Road accidents are a major public safety issue and thus, appropriate predictive models for predicting severity of such accidents are of great interest. In this study, we propose a machine learning based predictive framework that classifies road accident severity into three classes, i.e., Low, Medium and High. The dataset contains important driver and environmental factors such as the age and gender of the driver, road surface condition, and light condition. A number of machine learning models (Random Forest, Gradient Boosting, Logistic Regression, Decision Tree, and Support Vector machine (SVM)) are implemented and compared to determine which was the most effective classifier. Structured data preprocessing, encoding, and feature selection techniques are applied on the models, which are trained and being evaluated. % Accuracy of most models is 84%, with a small variation. A real time interactive widget interface is also developed for user input and for instant severity predictions. The proposed system can offer important information about accident prevention strategy and policy making to improve road safety by data driven analytics. © 2025 IEEE.

关键词： Highway accidents

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The Limitations of data, machine learning and Us 24

The Limitations of Data, Machine Learning and Us

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ACM international conference on Management of data (SIGMOD)

作者： Baeza-Yates, Ricardo Northeastern Univ Inst Experiential San Jose CA 95113 USA Univ Chile Dept Comp Sci Miami FL 33172 USA Univ Chile Dept Comp Sci Santiago Chile

ISBN: (纸本)9798400704222

machine learning (ML), particularly deep learning, is being used everywhere. However, not always is applied well or has ethical and/or scientific issues. In this keynote we first do a deep dive in the limitations of supervised ML and data, its key input. We cover small data, datification, bias, and evaluating success instead of harm, among other limitations. The second part is about ourselves using ML, including different types of social limitations and human incompetence such as cognitive biases, pseudoscience, or unethical applications. These limitations have harmful consequences such as discrimination, misinformation, and mental health issues, to mention just a few. In the final part we discuss regulation on the use of AI and responsible principles that can mitigate the problems outlined above.

关键词： ML evaluation bias AI ethics pseudoscience legitimacy

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Research on the application of machine learning in corporate public opinion monitoring and management 24

Research on the application of machine learning in corporate...

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3rd international conference on Signal Processing, Computer Networks and Communications, SPCNC 2024

作者： Ma, Xiaohui School of Management Shanghai University Shanghai China

ISBN: (纸本)9798400710834

This article makes a point of the overall application of corporate public opinion monitoring and management technology based on machine learning. This work proves that by means of case analysis, the basic theories and methods of machine learning, it elaborates on how to use effective machine learning for large-scale public opinion data collection, processing, and analysis, and prediction. Considerable attention is paid to the way natural language processing techniques and sentiment analysis are applied in processing text data and how the models of machine learning, among other support vector machines, decision trees, and neural networks, bear upon implementation in real enterprise environments. Research results show that the level of efficiency and prediction accuracy will be greatly improved if the machine learning technique is applied during public opinion data processing, therefore effectively optimizing the strategy of public opinion response and management of the company. The effectiveness and application scenarios of each prediction model in public opinion analysis were verified through comparative analysis with multiple prediction models, which provided the scientific data support for the decision-making reference of the company. © 2024 Copyright held by the owner/author(s).

关键词： Prediction models

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Enhanced NDVI prediction accuracy in complex geographic regions by integrating machine learning and climate data-a case study of Southwest basin

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international JOURNAL OF applied EARTH OBSERVATION AND GEOINFORMATION 2025年 139卷

作者： Zhou, Zehui Jin, Jiaxin Yong, Bin Huang, Weidong Yu, Lei Yang, Peiqi Sun, Dianchen Nanjing Univ Posts & Telecommun Sch Management Nanjing 210003 Peoples R China Hohai Univ Coll Geog & Remote Sensing Nanjing 211100 Peoples R China Hohai Univ Natl Key Lab Water Disaster Prevent Nanjing Peoples R China Nanjing Hydraul Res Inst Natl Key Lab Water Disaster Prevent Nanjing 210029 Peoples R China Nanjing Normal Univ Sch Geog Nanjing Peoples R China

The normalized difference vegetation index (NDVI) is a vital metric for assessing vegetation growth, yet accurate prediction remains challenging, particularly in regions with complex geographic and climatic conditions. machine learning methods offer promise but are often hindered by sensitivity to model structure, input parameters, and training samples. To address these limitations, this study developed an NDVI time-series prediction optimization model, LSKRX, which integrates multiple machine learning algorithms with local geographic and climatic data. Using the Southwest Basin of China as a case study, dominant climatic factors were identified through sub-basin analysis, and machine learning models were constructed to link NDVI with these factors. The LSKRX model demonstrated significant improvements in prediction accuracy compared to single-model approaches, with the most notable enhancement in BIAS. Spatially, the model's predictions aligned closely with observed values, particularly in the middle and lower reaches of the Yarlung Zangbo River. The model performed exceptionally well in winter (CC: 0.964) and summer (CC: 0.918) and achieved optimal accuracy in alpine regions at altitudes of 4000-5000 m (CC: 0.900). By leveraging the strengths of multiple machine learning models, the LSKRX model enhances NDVI prediction reliability under complex mountainous and alpine conditions, providing a robust tool for precise ecological assessment and management.

关键词： Complex geographic regions Climate factors machine learning Normalized difference vegetation index (NDVI) Prediction-Optimization Coupled Model

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