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2019 international conference on applied machine learning and data science, icamlds 2019

The proceedings contain 68 papers. The topics discussed include: deterministic dual-stack query tree RFID tag anti-collision algorithm;evaluation on least square method applied to gamma spectrum de-noising;the satisfaction of entrepreneur instructors and higher vocational students majoring in economic and business administration with the cooperative instruction in the work post practice;effects of anesthetic eugenol on respiration and excretion of sinogastromyzon szechuanensis;research on gamma spectrum semi-quantitative analysis based on convolutional neural network;research on gamma spectrum semi-quantitative analysis based on convolutional neural network;and seeking for optimum culture medium for chlorella pyrenoidosa and analysis of the amino acid component.

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2020 2nd international conference on applied machine learning and data science, icamlds2020

2020 2nd International Conference on Applied Machine Learnin...

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2020 2nd international conference on applied machine learning and data science, icamlds2020

The proceedings contain 104 papers. The topics discussed include: jamming effect evaluation method based on radar behavior recognition;research on application of an improved deep convolutional neural network in handwritten character recognition;analysis of application of machine learning based on bibliometrics in pattern recognition;research on the influencing factors of Chinese medicine cultural identity of international students in Chinese medicine schools;application of SVM model to environmental resource analysis in tourism development;learning recommendation based on hybrid collaborative filtering algorithm;fruit image recognition and classification method based on improved single shot multi-box detector;the construction of a flipped classroom teaching mode of college English based on rainclass and its effectiveness analysis;and research on improved sharpening algorithm based on closed operation and binarization.

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2021 3rd international conference on applied machine learning and data science, icamlds 2021

2021 3rd International Conference on Applied Machine Learnin...

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2021 3rd international conference on applied machine learning and data science, icamlds 2021

The proceedings contain 90 papers. The topics discussed include: application of the intra cluster, characteristic of k-means clustering method in English score analysis in colleges;refined range analysis of early warning and pre-control based on multi-element spatiotemporal clustering;the effect of algorithm parameters on the number of iterations of the dual fat boundary method;research on the classification of prosperity indexes of monitoring and early warning of marine development based on grey system theory;an optimal algorithm analysis of signal transitive random signal without noise feedback;impact of government subsidies on green supply chain operation under different power structures;exploratory study on key influencing factors of successful implementation of ERP system based on the perspective of grounded theory;and research on innovation input of manage strategy based on data analysis.

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5th international conference on machine learning, Optimization, and data science, LOD 2019

5th International Conference on Machine Learning, Optimizati...

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5th international conference on machine learning, Optimization, and data science, LOD 2019

ISBN: (纸本)9783030375980

The proceedings contain 64 papers. The special focus in this conference is on machine learning, Optimization, and data science. The topics include: data Anonymization for Privacy Aware machine learning;quantitative and Ontology-Based Comparison of Explanations for Image Classification;conditional Anomaly Detection for Quality and Productivity Improvement of Electronics Manufacturing Systems;federated learning of Deep Neural Decision Forests;active learning Approach for Safe Process Parameter Tuning;on Tree-Based Methods for Similarity learning;modelling Chaotic Time Series Using Recursive Deep Self-organising Neural Networks;determining Principal Component Cardinality Through the Principle of Minimum Description Length;approximating Probabilistic Constraints for Surgery Scheduling Using Neural Networks;on the Role of Hub and Orphan Genes in the Diagnosis of Breast Invasive Carcinoma;vital Prognosis of Patients in Intensive Care Units Using an Ensemble of Bayesian Classifiers;multi-task learning by Pareto Optimality;designing Combinational Circuits Using a Multi-objective Cartesian Genetic Programming with Adaptive Population Size;trading-off data Fit and Complexity in Training Gaussian Processes with Multiple Kernels;a Chained Neural Network Model for Photovoltaic Power Forecast;load Forecasting in District Heating Networks: Model Comparison on a Real-World Case Study;restaurant Health Inspections and Crime Statistics Predict the Real Estate Market in New York City;optimal Trade-Off Between Sample Size and Precision of Supervision for the Fixed Effects Panel data Model;a New Baseline for Automated Hyper-Parameter Optimization;analysing the Overfit of the Auto-sklearn Automated machine learning Tool;Parameter Optimization of Polynomial Kernel SVM from miniCV;robust Kernelized Bayesian Matrix Factorization for Video Background/Foreground Separation;preface.

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1st international conference on data science, machine learning and Applications, 2019

1st International Conference on Data Science, Machine Learni...

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1st international conference on data science, machine learning and Applications, 2019

ISBN: (纸本)9789811514197

The proceedings contain 203 papers. The special focus in this conference is on data science, machine learning and Applications. The topics include: A Survey on Stock Market Prediction Using machine learning Techniques;improve the Efficiency of the Classifiers Using Resample Technique on Image Segmentation dataset;Minimization of Deformation of Sensor data with Enhanced Probabilistic Maximum like Hood Selection Process (PMSP);analyzing the Understandability of data Warehouse Conceptual Model Using Fuzzy Logic Techniques;a Survey on Reversible data Hiding Techniques;predictive Analytics in Mining of Educational data: A Research Travelogue;machine learning and Deep learning in Cyber Security for IoT;K-NN SEMANTIC Inquiry on Scrambled Social Information BASE;a Study on Mining and Analysis of Trajectory databases with Multi-dimensional Feature Sets;Idiom Recommendation Using POS Tagging and Sentence Parsing;Prediction of Housing Prices Using machine learning, Time Series ARIMA Model and Artificial Neural Network;PRSSDF: Page Rank Specific Student Discussion Forum;a Comparative Study of Recommender Systems;an Overview on Blockchain Technology and Its Applications;Performance Analysis of 8-Bit Vedic Multipliers Using HDL Programming;CMOS Implementation of Comparators for ADCs;capable and Verification Protocol for Restricting Information Storage in Cloud Computing;outlying Info Integrity-Checking Protocol with Augmented Stability to Secure Storage in Cloud;challenges and Uses of Big data Analytics for Social Media;sub-scene Target Detection and Recognition Using Deep learning Convolution Neural Networks;indian and European Script Identification: A Review;ioT Based Garbage Disposer for Educating Rural India;Face Recognition Using Open CV with Deep learning;biometric Recognition Using Fusion.

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Carbon footprint mapping of urban areas in Türkiye using hyperparameter-optimized machine learning techniques

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international JOURNAL OF ENVIRONMENTAL science AND TECHNOLOGY 2025年 1-24页

作者： Atasever, U. H. Bozdag, A. Univ Erciyes Fac Engn Dept Geomat Engn Kayseri Turkiye Univ Nigde Omer Halisdemir Fac Engn Dept Geomat Engn Nigde Turkiye

This study predicted changes in carbon footprints at the city level in T & uuml;rkiye using the Bayesian method and hyper-optimized machine learning techniques with high accuracy and analyzed their spatial distribution. The dataset for 80 provinces in T & uuml;rkiye was searched. However, only the parameter data affecting the carbon concentrations in 2019-2022 and the carbon concentration amount dataset for 2019 could be accessed. The available dataset concerning parameters that affect carbon concentrations obtained from 80 cities in T & uuml;rkiye for 2019 was trained using hyper-optimized machine learning algorithms by the Bayesian technique (Ensemble Regression, Gaussian Process Regression, Gaussian Kernel Regression, Support Vector machine Regression, Linear Regression, and Binary Decision Regression). Carbon footprint values were predicted for 2019, 2020, 2021, and 2022, and performance metrics were presented. In the application, while the ensemble regression technique obtained the lowest mean squared error value (2.31) for the training data, support vector machine regression obtained the lowest mean squared error value (13.8) in the test data. The Bayesian optimization algorithm significantly improved the success of all regression techniques applied in the study. After hyperparameter optimization, the regression methods' performance improved by 0.14 on average in terms of the coefficient of determination metric, 34.5 on average in the mean squared error metric, and 0.032 on average in the mean absolute relative error. The study will assist local governments in obtaining more accurate carbon emission estimates using less data and implementing climate action plans.

关键词： Carbon footprint Hyperparameter optimization Bayesian optimization machine learning techniques City carbon map

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A Systematic Review on Application of Multimodal learning and Explainable AI in Tuberculosis Detection

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IEEE ACCESS 2025年 13卷 62198-62221页

作者： Nansamba, Barbara Nakatumba-Nabende, Joyce Katumba, Andrew Kateete, David Patrick Makerere Univ Dept Comp Sci Kampala Uganda Makerere Univ Dept Elect & Comp Engn Kampala Uganda Makerere Univ Dept Immunol & Mol Biol Kampala Uganda

Physicians rely on various data sources when diagnosing Tuberculosis (TB). This includes the patient's historical data, demographic data, clinical laboratory results, and imaging data. Traditionally, the application of machine learning and deep learning in detecting TB has focused more on using single modes of data. This constrains the capabilities of the artificial intelligence (AI) techniques to replicate the clinical practice of incorporating multiple sources of information in decision-making. Recent advancements in deep learning and machine learning have enabled the integration of multimodal data which has led to the development of applications that more accurately reflect the clinician's approach. However, the operations of deep learning techniques are still blackbox in nature, which makes it hard to understand their internal work mechanisms. As a result, it is necessary to incorporate explainable AI techniques to assist AI model users understand how the models make decisions. In this paper, we carried out a systematic review of two areas: First, we reviewed recent studies on the application of multimodal learning in TB detection. Here we have provided a summary of the public datasets used in the studies, data modalities used, the fusion techniques, and finally identified AI techniques that can be used with multimodal data. Then we looked at papers that used explainable AI techniques in TB diagnosis and prognosis. This study followed PRISMA guidelines to ensure replicability and accurate reporting of the main findings of the reviewed studies. To stay up-to-date with the state of the art, we specifically examined papers published between 2019 and June 2024. We reviewed thirty-one journal and conference papers we found using Web of science, Scopus and Pubmed databases. The review indicated that models trained on multiple data modalities outperformed those trained on single data modalities. This is due to the additional information extracted from each data modalit

关键词： Tuberculosis Artificial intelligence Explainable AI Deep learning Systematic literature review databases Prognostics and health management Accuracy Guidelines Decision making explainable AI multimodal learning machine learning tuberculosis detection

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DWE: Research on Water Quality Inversion and Scalability in Small Watersheds Using UAV Remote Sensing data

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IEEE ACCESS 2025年 13卷 58680-58698页

作者： Li, Hao Xie, Zhijun Jin, Xing Peng, Changchun Ren, Cheng Mao, Yunfei Zhou, Kefan Li, Yingying Ningbo Univ Fac Elect Engn & Comp Sci Ningbo 315211 Peoples R China Ningbo Univ Zhejiang Engn Res Ctr Adv Mass Spectrometry & Clin Ningbo 315211 Peoples R China Ningbo Urban Drainage Co Ltd Ningbo 315211 Peoples R China

Urban rivers are faced with serious water quality deterioration, and an efficient, comprehensive and accurate dynamic monitoring method for urban water quality is urgently needed. Remote sensing water quality inversion model based on spectral features has been widely used in water quality monitoring. However, traditional water quality inversion models ignore the complex optical properties of water bodies, resulting in the performance of models that are often limited and cannot be reused across time and space. Therefore, this study applies the idea of integrated modeling to remote sensing inversion of water quality parameters (WQPs), and proposes an adaptive dynamic weighted ensemble learning (DWE) model, aiming to integrate the advantages of different machine learning models, improve the accuracy of WQP inversion, and adaptively assign dynamic weights to different machine learning models, thereby achieving the scalability of the model in time and space. In addition, to address the limitations of using drone data to infer small-scale water WQP due to spatial resolution limitations and limited input features, we propose an ASO-GSA feature selection method for selecting the optimal feature combination from potential feature set data. The results show that the DWE model based on optimal features has high accuracy and robustness in WQP prediction, with coefficients of determination (R-2) of 0.8472, 0.8547, 0.8973, and 0.8393 for chlorophyll a (Chl-a), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP), respectively, which improved the prediction accuracy by nearly 30% compared with the traditional inversion model. In order to evaluate the spatiotemporal scalability of the model, we applied the model to unmanned aerial vehicle (UAV) remote sensing data acquired from the Nanxintang River during the first half of the year from 2024 to 2025 (nearly one year later) and the Shangheng River between 2023 and 2024 to assess the scalability of the model.

关键词： Water quality Remote sensing Rivers data models Monitoring Autonomous aerial vehicles Accuracy Predictive models machine learning Adaptation models UAV remote sensing ensemble learning machine learning water quality inversion

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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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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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