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machine learning applied to apatite compositions for determining mineralization potential

AMERICAN MINERALOGIST 2024年第8期109卷 1394-1405页

作者： Zheng, Yu-Yu Xu, Bo Lentz, David R. Yu, Xiao-Yan Hou, Zeng-Qian Wang, Tao China Univ Geosci State Key Lab Geol Proc & Mineral Resources Beijing 100083 Peoples R China China Univ Geosci Frontiers Sci Ctr Deep Time Digital Earth Beijing 100083 Peoples R China China Univ Geosci Sch Gemol Beijing 100083 Peoples R China Univ New Brunswick Dept Earth Sci Fredericton NB E3B 5A3 Canada Chinese Acad Geol Sci Beijing SHRIMP Ctr Inst Geol Beijing 100037 Peoples R China

Apatite major and trace element chemistry is a widely used tracer of mineralization as it sensitively records the characteristics of the magmatic-hydrothermal system at the time of its crystallization. Previous studies have proposed useful indicators and binary discrimination diagrams to distinguish between apatites from mineralized and unmineralized rocks;however, their efficiency has been found to be somewhat limited in other systems and larger-scale data sets. This work applied a machine learning (ML) method to classify the chemical compositions of apatites from both fertile and barren rocks, aiming to help determine the mineralization potential of an unknown system. Approximately 13 328 apatite compositional analyses were compiled and labeled from 241 locations in 27 countries worldwide, and three apatite geochemical data sets were established for XGBoost ML model training. The classification results suggest that the developed models (accuracy: 0.851-0.992;F1 score: 0.839-0.993) are much more accurate and efficient than conventional methods (accuracy: 0.242-0.553). Feature importance analysis of the models demonstrates that Cl, F, S, V, Sr/Y, V/Y, Eu*, (La/Yb)N, and La/Sm are important variables in apatite that discriminate fertile and barren host rocks and indicates that V/Y and Cl/F ratios and the S content, in particular, are crucial parameters to discriminating metal enrichment and mineralization potential. This study suggests that ML is a robust tool for processing high-dimensional geochemical data and presents a novel approach that can be applied to mineral exploration.

关键词： Apatite major and trace element machine learning mineralization potential XGBoost

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machine-learning local resistive environments of dislocations in complex concentrated alloys from data generated by molecular dynamics simulations

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international JOURNAL OF PLASTICITY 2025年 187卷

作者： Li, Wei Ngan, Alfonso H. W. Zhang, Yuqi Univ Hong Kong Dept Mech Engn Hong Kong Peoples R China Univ Hong Kong Univ Hong Kong Shenzhen Inst Res & Innovat HKU SIR Hong Kong Peoples R China Chongqing Univ Dept Engn Mech Chongqing 400044 Peoples R China Univ Birmingham Sch Met & Mat Birmingham B15 2TT England Northeastern Univ State Key Lab Rolling & Automat Shenyang 110819 Peoples R China

Complex concentrated alloys (CCAs) differ from pure metals and conventional dilute alloys in that the multiple constituent elements are prone to develop special local atomic environments (LAEs). Due to the complexity and spatial variability of the LAEs, the resistance that they offer to travelling dislocations cannot be determined a priori by conventional strengthening theories. In this work, molecular dynamics (MD) simulations of a prototypic CCA of NiCoCr were used to generate data for dislocation features that may potentially affect dislocation resistance. Extensive analysis of these features via their Pearson correlation coefficients with dislocation velocity and ablation studies using light gradient-boosting machine learning (ML) models show that (i) the local planar fault energy (PFE), (ii) local gradient of the PFE, and (iii) dislocation core width, while all prime factors for dislocation resistance, do not have strongly linear correlation with the dislocation velocity. However, reasonably high prediction accuracy (>80 %) is achieved when all three factors are included in the ML model. Furthermore, lattice distortion, a much-discussed strengthening factor for CCAs in the literature, is also not strongly linearly correlated and its effect can be well represented by the PFE. These results indicate that CCA strength is governed not by individual dislocation-resistance factors, but a synergistic combination of these factors that goes beyond any a priori assumption. This work highlights the complexity in the nature of CCA strength, and the suitability and success of machine learning as an a posteriori approach for understanding it.

关键词： Local resistive environments Planar fault energy distribution Dislocation core width distribution machine learning High entropy alloys

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Using machine learning to predict student science achievement based on science curriculum type in TIMSS 2019

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international JOURNAL OF science EDUCATION 2024年第9期47卷

作者： Song, Yajie Cutumisu, Maria McGill Univ Fac Educ Dept Educ & Counselling Psychol Montreal PQ Canada Fac Educ Dept Educ & Counselling Psychol 530 Educ Bldg3700 McTavish St Montreal PQ H3A 1Y2 Canada

Most educational systems use either an integrated or a separated science curriculum. However, it is unclear which of these science curricula benefits students more author and existing research provides insufficient information about the implementation details of the curriculum employed. Therefore, this study compares the effects of two science curricula on students' science literacy, drawing on socio-ecological theory and employing educational data mining techniques. Results from Grade 8 science students in 44 countries sampled in the Trends in international Mathematics and science Study (TIMSS) 2019 showed that (1) the integrated curricula benefitted students marginally more than the separated curricula;(2) curriculum type was not essential in directly predicting students' academic performance;and (3) random forest outperformed linear regression, lasso regression, decision trees, and neural networks in predicting student science achievement. This study advances our understanding of the predictors of student science performance, demonstrates that machine learning techniques can be applied successfully to examine curriculum effects, and provides directions for implementing integrated science curricula.

关键词： science curriculum TIMSS educational data mining machine learning propensity score matching

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Research on fault prediction optimization of CNC machine tools based on ensemble learning 5

Research on fault prediction optimization of CNC machine too...

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5th international conference on Signal Processing and Computer science, SPCS 2024

作者： Yao, Li Wan, Yuwen Damjanovic, Srdjan Xin, Zhengfei Shanghai Polytechnic University 2360 Jinhai Road Pudong Shanghai China University of East Sarajevo Faculty of Business Economics Bijeljina Sarajevo Bosnia and Herzegovina

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

As the manufacturing industry develops towards high precision and intelligence, CNC machine tools play an important role in production. The occurrence of failures not only reduces production efficiency but also increases costs. In order to improve the accuracy and efficiency of fault prediction, this paper establishes an integrated learning model for CNC machine tool fault prediction by stacking ensemble learning algorithms and combining decision trees, support vector machine (SVM), random forests and other algorithms. The fault-related features are optimized through data preprocessing and feature engineering, and the results are finally obtained. The experimental results show that the ensemble learning model is superior to the single model in result indicators, especially recall rate and F1 score, reaching 0.6393 and 0.7027. This verifies that the ensemble learning model proposed in this paper has better performance in improving the performance of CNC machine tool fault prediction and can better solve the problems related to prediction in CNC machine tool faults. © 2025 SPIE.

关键词： Prediction models

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Exploring Robust DDoS Detection: A machine learning Analysis with the CICDDoS2019 dataset 5

Exploring Robust DDoS Detection: A Machine Learning Analysis...

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5th IEEE India Council international Subsections conference, INDISCON 2024

作者： Saluja, Kamal Bagchi, Susama Solanki, Vikas Khan, Muhammad Numan Ali Dhamija, Esha Debnath, Sanjoy Kumar Chitkara University Institite of Engineering and Technology Chitkara University Punjab India University of Technology and Applied Sciences College of Computing and Information Sciences Shinas Oman

ISBN: (纸本)9798350376753

The ever-present threat posed by Distributed Denial of Service attack is highlighted. The significance of building robust cybersecurity measures that are not just inventive but also flexible. The purpose of this study paper is to investigate the effectiveness of an algorithm based on machine learning in detecting distributed denial of service assault to strengthen the resilience of Internet infrastructure. Random forest, decision tree, naïve bayes, and KNN are some of the machine learning techniques that are investigated in this study. The dataset used for this investigation is the CICDDoS 2019 data, Moreover, the research broadened its approach to identify the role of Artificial Intelligence in the Cybersecurity Framework. It does this by providing insight into the relative merits of various algorithms. The study, in its essence, highlights the critical role that adaptive and intelligent defense systems play in decreasing the impact of distributed denial of service assaults (DDoS). This provides a foundation for prepared strategies in the constantly shifting world of cybersecurity. © 2024 IEEE.

关键词： Cybersecurity

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SSDP DDoS Attacks Detection with machine learning Classification Algorithms 5th

SSDP DDoS Attacks Detection with Machine Learning Classifica...

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

作者： Dasari, Kishorebabu Reddy, Siddharth Shirley, G. Sharon Keshav Mem Inst Technol Hyderabad Telangana India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Nowadays, a major cyber-attack is known as a Distributed Denial of Service (DDoS). It disrupts the legitimate users to access the server or network resources. Server or network resource is unavailable is not a small issue these days, because everything is online. So DDoS attacks detection is the key research area to find the best DDoS attack detection. This study evaluate the Logistic Regression, Naive Bayes, Decision Tree, KNN, Random Forest, Ada Boost, Gradient Boost, Multi-Layer Perceptron classification algorithms in order to detect SSDP DDoS attack. CIC-DDoS2019 dataset is used in this study. Classification algorithms evaluated for SSDP DDoS attack detection with different uncorrelated features subsets selected by different correlation methods of Pearson, Kendall and Spearmanmethods. Classification algorithms evaluated by different metrics of accuracy, Log-loss value, ROC-AUC score, K-Fold cross validation accuracy. MLP, KNN classification algorithms gives the best classification results to detect SSDP DDoS attacks.

关键词： DDoS Attack SSDP attack Correlation methods

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Super-Teaching in machine learning 8th

Super-Teaching in Machine Learning

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8th international Symposium on Cyber Security, Cryptology, and machine learning, CSCML 2024

作者： Barak-Pelleg, Dina Berend, Daniel Kontorovich, Aryeh Sami Shamoon College of Engineering Beer Sheva84108 Israel Departments of Mathematics and of Computer Science Ben-Gurion University Beer Sheva84105 Israel Department of Computer Science Ben-Gurion University Beer Sheva84105 Israel

ISBN: (纸本)9783031769337

In machine learning, efficiently leveraging large datasets is essential, particularly when computational resources are limited. Sample compression techniques, which involve using carefully chosen subsets of data to train models, provide significant reductions in runtime and memory requirements while preserving generalization performance. This paper investigates a concept known as "super-teaching," where a knowledgeable teacher selectively provides an optimal subset of training data to a learner, thereby enhancing learning outcomes. Building on the work of Ma et al. (2018), who demonstrated that a teacher with complete knowledge of the data distribution could significantly improve a learner’s performance, our study extends this idea beyond the scenario in their paper. We present a more robust and generalized approach, offering insights into how super-teaching can be effectively applied to a broader range of machine learning problems, potentially leading to better training efficiency and improved generalization. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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A Comprehensive Approach of Algorithmic Trading System based on Technical Analysis

A Comprehensive Approach of Algorithmic Trading System based...

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2024 IEEE international conference on Cybernetics and Innovations, ICCI 2024

作者： Jongpermwattanapol, Apisan Jongkohpo, Khathathorn Worasucheep, Chukiat Hiruncharoenvate, Chaya Krongtong, Tassanee King Mongkut's University Of Technology Engineering Science Classroom Bangkok Thailand King Mongkut's University Of Technology Applied Computer Science Bangkok Thailand Securities And Exchange Commission Bangkok Thailand The Institute For The Promotion Of Teaching Science The Institute And Technology Technology Unit Bangkok Thailand

ISBN: (纸本)9798350370096

machine learning researchers have been attempting to accurately predict stock price trends. Moreover, financial researchers use Technical Analysis with optimization models [1] to achieve the same task by extracting and combining important features for the model. Despite many Algorithmic Trading systems (ATS) available, modelling the stock market has still been proven difficult since stock prices are nonlinear and often appear random [2]. This work focuses on leveraging the advantages of both by employing the Technical Analysis approach to create features for training machine learning models. The models were evaluated using data from nine stocks listed on the Stock Exchange of Thailand (SET) from 2019 to 2023. The performances of three machine learning models: Logistic Regression, K-Nearest Neighbors, and Support Vector machines were compared with Differential Evolution and the buy-and-hold strategies. The results showed that the Differential Evolution can significantly improve the return on investment in comparison to the machine learning models and the buy-and-hold strategies. © 2024 IEEE.

关键词： Financial markets

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Direct entanglement detection of quantum systems using machine learning

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NPJ QUANTUM INFORMATION 2025年第1期11卷 1-14页

作者： Huang, Yulei Che, Liangyu Wei, Chao Xu, Feng Nie, Xinfang Li, Jun Lu, Dawei Xin, Tao Southern Univ Sci & Technol Shenzhen Inst Quantum Sci & Engn Shenzhen Peoples R China Southern Univ Sci & Technol Dept Phys Shenzhen Peoples R China Shenzhen Univ Inst Quantum Precis Measurement Coll Phys & Optoelect Engn State Key Lab Radio Frequency Heterogeneous Integr Shenzhen Peoples R China Quantum Sci Ctr Guangdong Hong Kong Macao Greater Shenzhen Peoples R China Int Quantum Acad Shenzhen Peoples R China

Entanglement plays a crucial role in advancing quantum technologies and exploring quantum many-body simulations. Here, we introduce a protocol aided by neural networks for measuring entanglement in both equilibrium and non-equilibrium states of local Hamiltonians, with a favorable amount of training data. Our numerical simulations across various Hamiltonian models and qubit configurations reveal that this approach can predict comprehensive entanglement metrics, such as R & eacute;nyi entropy, for up to 100 qubits using only single-qubit and two-qubit Pauli measurements. Excitingly, future entanglement dynamics beyond the measurement window can be predicted based solely on previous single-qubit traces. Experimentally, we utilize a nuclear spin quantum processor and a neural network to measure entanglement in the ground and dynamical states of a one-dimensional spin chain. The results demonstrate the feasibility of our method in practical experiments. Therefore, our approach offers a promising method for experimentally measuring entanglement in systems with dozens to hundreds of qubits.

关键词： Qubits

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A novel geospatial machine learning approach to quantify non-linear effects of land use/land cover change (LULCC) on carbon dynamics

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international JOURNAL OF applied EARTH OBSERVATION AND GEOINFORMATION 2024年 128卷

作者： Kang, Jing Zhang, Bailing Dang, Anrong Hiroshima Univ Grad Sch Adv Sci & Engn Higashihiroshima 7398529 Japan Tsinghua Univ Sch Architecture Beijing 100084 Peoples R China Waseda Univ Waseda Inst Adv Study Tokyo 1698050 Japan

Land Use and Land Cover Change (LULCC) introduces considerable uncertainties in its relationship with carbon emissions, posing challenges to nature-based climate mitigation. The effective establishment and protection of carbon sinks through land use management lack clarity. To address this challenge, this study utilized top -down interannual grided CO2 data from satellite observations, revealing a 7 % decline in carbon sinks in mainland China from 2016 to 2019. Faced with this anomaly, we proposed a novel approach that combined machine learning with traditional regression analysis, to investigate the nonlinear relationship between annual spatiotemporal variations in net carbon exchange and LULCC. Sentinel-2 imagery was employed for high-resolution (10 m) LULC classification based on uniform rules. Particularly, time-series LULC class probabilities were considered and estimated using a deep learning framework via the Google Earth Engine (GEE) cloud platform, which allows us to access the effects of dynamic LULCC. GIS methods were applied to enhance machine learning interpretability, integrating multi -source remote sensing datasets, particularly for capturing nonlinear features in spatiotemporal aspects of LULCC and carbon emissions. Threshold effects revealed how LULCC transformed areas were associated with carbon sinks or sources. The results mapped carbon sink shrinkage locations, highlighting correlations with significant reductions in snow cover (-6.25 %), changing water patterns (-1.3 %), urban expansion (1 %), and mixed forest changes (regrowth of 4 % and deforestation of -1%). This research aims to advance understanding of carbon emissions through remote sensing, bridging different Earth observation data within a geostatistical context, and expects to provide a new validation method for the bottom -up approach.

关键词： GEE Sentinel-2 Top-downCO2 observations Net carbon exchange Land use/land cover machine learning Non-linear Carbon neutrality

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