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Crystal structural prediction of perovskite materials using machine learning: A comparative study

SOLID STATE COMMUNICATIONS 2023年第1期361卷

作者： Priyadarshini, Rojalina Joardar, Hillol Bisoy, Sukant Kishoro Badapanda, Tanmaya CV Raman Global Univ Dept Comp Sc & Engg Bhubaneswar Odisha India CV Raman Global Univ Dept Mech Engn Bhubaneswar Odisha India CV Raman Global Univ Dept Phys Bhubaneswar Odisha India

In this study, Machine Learning (ML) techniques have been exploited to classify the crystal structure of ABO3 perovskite compounds. In the present work, seven different ML algorithms are applied to the experimentally determined crystal structure data. The relevance of the data featured is measured by computing the Chi-Square test and Spearman's correlation matrix. The Z-Score value has been calculated for each attribute to confirm the existence of any outliers in the data. The Synthetic Minority Oversampling (SMOTE) technique is employed to overcome the imbalanced data set. The models' performance is calculated using the stratified k-Fold crossvalidation method. Further, to improve the accuracy of the prediction model, the conventional algorithm is supported by boosting algorithm. Comparative model efficiency on prediction of the crystal structure is presented to identify the most suitable model. As per the inferences drawn from the observations, the ensemble model using Xtreme Gradient boosting (XGBoost) algorithm when applied to the pre-processed and balanced data outperforms the other models.

关键词： Perovskite Machine learning boosting algorithms K-nearest neighborhood XGBoost

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Embedding Undersampling Rotation Forest for Imbalanced Problem

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COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2018年第1期2018卷 1-15页

作者： Guo, Huaping Diao, Xiaoyu Liu, Hongbing Xinyang Normal Univ Sch Comp & Informat Technol Xinyang 464000 Henan Peoples R China

Rotation Forest is an ensemble learning approach achieving better performance comparing to Bagging and boosting through building accurate and diverse classifiers using rotated feature space. However, like other conventional classifiers, Rotation Forest does not work well on the imbalanced data which are characterized as having much less examples of one class (minority class) than the other (majority class), and the cost of misclassifying minority class examples is often much more expensive than the contrary cases. This paper proposes a novel method called Embedding Undersampling Rotation Forest (EURF) to handle this problem (1) sampling subsets from the majority class and learning a projection matrix from each subset and (2) obtaining training sets by projecting re-undersampling subsets of the original data set to new spaces defined by the matrices and constructing an individual classifier from each training set. For the first method, undersampling is to force the rotation matrix to better capture the features of the minority class without harming the diversity between individual classifiers. With respect to the second method, the undersampling technique aims to improve the performance of individual classifiers on the minority class. The experimental results show that EURF achieves significantly better performance comparing to other state-of-the-art methods.

关键词： SHORT rotation forestry BAGGING (Textiles) boosting algorithms MATRICES PERFORMANCE

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A Survey and Study of Signal and Data-Driven Approaches for Pipeline Leak Detection and Localization

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JOURNAL OF PIPELINE SYSTEMS ENGINEERING AND PRACTICE 2024年第2期15卷

作者： Rajasekaran, Uma Kothandaraman, Mohanaprasad Vellore Inst Technol VIT Univ Sch Elect Engn SENSE Chennai 600127 Tamil Nadu India

A pipeline is critical in conveying water, oil, gas, petrochemicals, and slurry. As the pipeline ages and corrodes, it becomes susceptible to deterioration, resulting in wastage and hazardous damages depending on the material it transports. To mitigate these risks, implementing a suitable monitoring system becomes essential, enabling the early identification of damage and minimizing waste and the potential for hazardous incidents. The pipeline monitoring system can be exterior, visual/biological, and computational. This paper surveys state-of-the-art approaches and also performs experimental analyses with a few methods in signal/data-driven approaches within computational methods. More precisely, signal processing-based leak localization methods, artificial intelligence-based leak detection methods, and combined approaches are given. This paper implements five signal processing-based methods and 17 artificial intelligence-based methods. This implementation helps to compare and understand the significance of appropriate noise removal and feature extraction. The data for this analysis is collected using acousto-optic sensors from an experimental setup. After implementation, the highest observed leak localization accuracy is 99.14% with the wavelet packet adaptive independent component analysis-based generalized cross correlation, and the highest leak detection accuracy is 98.32% with the one-dimensional convolutional neural network.

关键词： Generalized cross correlation (GCC) Basic cross-correlation (BCC) boosting algorithms Clustering algorithms Convolutional neural network Leak detection and localization

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Comparative Analysis of Machine Learning and Deep Learning Based Water Pipeline Leak Detection Using EDFL Sensor

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JOURNAL OF PIPELINE SYSTEMS ENGINEERING AND PRACTICE 2023年第4期14卷 04023026-04023026页

作者： Rajasekaran, Uma Kothandaraman, Mohanaprasad Vellore Inst Technol VIT Univ Sch Elect Engn SENSE Chennai 600127 Tamil Nadu India

A pipeline is the most efficient way to transport water from one place to another. Due to aging, corrosion, and external factors, the pipeline is prone to damage, which causes leaks. Many machine learning (ML) and deep learning (DL) methods are available to address this issue. This paper does an experimental study on available methods in ML and DL for leak detection for the collected data using an acousto-optic sensor. The experimental setup comprises of an acousto-optic sensor made of an erbium-doped fiber laser (EDFL), galvanized iron pipeline, a tank, a pump, and a data acquisition unit. The dimensions of the galvanized pipeline looped with the water tank are a length of 40 m, an inner diameter of 89 mm, and an outer diameter of 90 mm. The diameter of the simulated leak aperture is 5 mm. The methods analyzed in this study are k-means, k-medoids, Naive Bayes (NB), support vector machines (SVM), k-nearest neighbor (KNN), decision tree (DT), categorical boosting (CatBoost), random forest (RF), XGBoost, AdaBoost, and one-dimensional convolutional neural network (1DCNN). ML algorithms need a feature extraction technique because the data collected from the experiment is too large and contains redundant information. Feature extraction reduces the data size by extracting essential information. This paper extracts ten features from raw data. Among the ML algorithms, AdaBoost gives the highest prediction accuracy of 98.02%. This paper also implements eight models of 1DCNN, and Model 1 shows the best prediction accuracy of 98.16%, which is the highest compared with all the other classifiers in ML and DL for one-dimensional time series acousto-optic sensor data.

关键词： One-dimensional convolutional neural network (1DCNN) boosting algorithms Decision tree (DT) Feature extraction Clustering algorithms Naive Bayes (NB) Support vector machines (SVM)

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Customer Transaction Prediction System

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Procedia Computer Science 2020年 168卷 49-56页

作者： Devendra Prakash Jaiswal Srishti Kumar Partha Mukherjee Pennsylvania State University 30 E. Swedesford Road Great Valley Malvern 19355 USA

In this data-driven world, every innovation is targeted towards the attainment of a better future where we can sustain ourselves in the easiest and most comfortable of ways. The desire of this utopia has induced numerous inventions which has led us all to this era where Artificial Intelligence has become a very crucial part of our daily lives. One such implementation which we discuss here, pertains to building a customer transaction predictions system using a completely anonymized dataset. We incorporate deep learning methodologies along with a gradient boosting tree-based algorithm which would help us in identifying the best approach for our problem. This implementation aims to enhance human intuition through techniques, which would be capable of helping the institutions in achieving customer satisfaction and in identifying important features from the anonymized dataset.

关键词： Anonymized Dataset Handling Machine Learning Artificial Intelligence Artificial Neural Networks boosting algorithms XGBoost Data Cleaning Pre-processing

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Smart Robust Feature Selection (SoFt) for imbalanced and heterogeneous data

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KNOWLEDGE-BASED SYSTEMS 2022年第0期236卷 107197-107197页

作者： Kasim, Henry King, Stephen Lee, Gary Kee Khoon Sirigina, Rajendra Prasad How, Shannon Shi Qi Hung, Terence Gih Guang Cent Technol & Strategy Grp Future Intelligence Technol Rolls Royce Singapore Singapore Nanyang Technol Univ Rolls RoyceNTU Corp Lab Singapore Singapore Cranfield Univ Transport & Mfg IVHM Ctr Sch Aerosp Bedford MK43 0AL England

Designing a smart and robust predictive model that can deal with imbalanced data and a heterogeneous set of features is paramount to its widespread adoption by practitioners. By smart, we mean the model is either parameter-free or works well with default parameters, avoiding the challenge of parameter tuning. Furthermore, a robust model should consistently achieve high accuracy regardless of any dataset (imbalance, heterogeneous set of features) or domain (such as medical, financial). To this end, a computationally inexpensive and yet robust predictive model named smart robust feature selection (SoFt) is proposed. SoFt involves selecting a learning algorithm and designing a filtering-based feature selection algorithm named multi evaluation criteria and Pareto (MECP). Two state-of-the-art gradient boosting methods (GBMs), CatBoost and H2O GBM, are considered potential candidates for learning algorithms. CatBoost is selected over H2O GBM due to its robustness with both default and tuned parameters. The MECP uses multiple parameter-free feature scores to rank the features. SoFt is validated against CatBoost with a full feature set and wrapper-based CatBoost. SoFt is robust and consistent for imbalanced datasets, i.e., average value and standard deviation of log loss are low across different folds of K-fold cross-validation. Features selected by MECP are also consistent, i.e., features selected by SoFt and wrapper-based CatBoost are consistent across different folds, demonstrating the effectiveness of MECP. For balanced datasets, MECP selects too few features, and hence, the log loss of SoFt is significantly higher than CatBoost with a full feature set. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Class-imbalanced data Heterogeneous features boosting algorithms Feature Selection CatBoost H2O GBM

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A Machine Learning-Based Framework for Water Quality Index Estimation in the Southern Bug River

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WATER 2023年第20期15卷 3543页

作者： Masood, Adil Niazkar, Majid Zakwan, Mohammad Piraei, Reza Jamia Millia Islamia Dept Civil Engn New Delhi 110025 India Free Univ Bozen Bolzano Fac Engn Piazza Univ 5 I-39100 Bolzano Bolzano Italy Maulana Azad Natl Urdu Univ Sch Technol Hyderabad 500032 Telangana India Shiraz Univ Dept Civil Engn Shiraz *** Iran

River water quality is of utmost importance because the river is not only one of the key water resources but also a natural habitat serving its surrounding environment. In a bid to address whether it has a qualified quality, various analytics are required to be considered, but it is challenging to measure all of them frequently along a river reach. Therefore, estimating water quality index (WQI) incorporating several weighted analytics is a useful approach to assess water quality in rivers. This study explored applications of ten machine learning (ML) models to estimate WQI for the Southern Bug River, which is the second-longest river in Ukraine. The ML methods considered in this study include artificial neural networks (ANNs), Support Vector Regressor (SVR), Extreme Learning Machine, Decision Tree Regressor, random forest, AdaBoost (AB), Gradient boosting Regressor, XGBoost Regressor (XGBR), Gaussian process (GP), and K-nearest neighbors (KNN). Each data measurement consists of nine analytics (NH4, BOD5, suspended solids, DO, NO3, NO2, SO4, PO4, Cl), while the quantity of data is more than 2700 data points. The results indicated that all ML models demonstrate satisfactory performance in predicting WQI. However, GP outperformed the other models, followed by XGBR, SVR, and KNN. Furthermore, ANN and AB demonstrated relatively weaker performance. Moreover, a reliability assessment conducted on both training and testing datasets also confirmed the results of the comparative analysis. Overall, the results enhance the assertion that ML models can sufficiently predict WQI, thereby enhancing water quality management.

关键词： water quality index rivers machine learning support vector machine extreme learning machine boosting algorithms

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People and Mobile Robot Classification Through Spatio-Temporal Analysis of Optical Flow

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2015年第6期29卷 1550021-1550021页

作者： Moreno, Plinio Figueira, Dario Bernardino, Alexandre Santos-Victor, Jose Univ Lisbon Inst Super Tecn LARSyS Inst Syst & Robot ISR IST P-1699 Lisbon Portugal

The goal of this work is to distinguish between humans and robots in a mixed human-robot environment. We analyze the spatio-temporal patterns of optical flow-based features along several frames. We consider the Histogram of Optical Flow (HOF) and the Motion Boundary Histogram (MBH) features, which have shown good results on people detection. The spatio-temporal patterns are composed of groups of feature components that have similar values on previous frames. The groups of features are fed into the FuzzyBoost algorithm, which at each round selects the spatio-temporal pattern (i.e. feature set) having the lowest classification error. The search for patterns is guided by grouping feature dimensions, considering three algorithms: (a) similarity of weights from dimensionality reduction matrices, (b) Boost Feature Subset Selection (BFSS) and (c) Sequential Floating Feature Selection (SFSS), which avoid the brute force approach. The similarity weights are computed by the Multiple Metric Learning for large Margin Nearest Neighbor (MMLMNN), a linear dimensionality algorithm that provides a type of Mahalanobis metric Weinberger and Saul, J. MaCh. Learn. Res. 10 (2009) 207-244. The experiments show that FuzzyBoost brings good generalization properties, better than the GentleBoost, the Support Vector Machines (SVM) with linear kernels and SVM with Radial Basis Function (RBF) kernels. The classifier was implemented and tested in a real-time, multi-camera dynamic setting.

关键词： Human robot environments boosting algorithms people versus robot detection optical flow-based features

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SVM based Ensemble Learning for Spatial and Temporal Air Pollution Analysis

SVM based Ensemble Learning for Spatial and Temporal Air Pol...

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2014 International Conference on Information Technology and Management Engineering(ITME 2014)

作者： Shahid ALI Anthony LAI Department of Computing Unitec Department of Electrotechnology Unitec

The paper aims to propose spatial and temporal air pollution data analysis by using Support Vector Machine(SVM) ensemble method along with single SVM, Bagging and Adaboost M1 algorithms to identify three objectives of our research. First, to ascertain a computation method that could handle large amount of spatial and temporal air pollution data. Secondly, to discover a high percentage of classification accuracy of proposed method by deploying ensemble algorithms, i.e. single SVM, Bagging and boosting algorithms. Thirdly, to find best speed of classification of proposed method in terms of computation time. Finally, we compare the ensemble model accuracy of single SVM, Bagging and Adaboost M1 by using Weka 3.6.5 software in order to choose best model to classify air pollution data with high accuracy.

关键词： Single SVM Bagging boosting algorithms

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International agricultural trade forecasting using machine learning

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DATA & POLICY 2021年第1期3卷 e1-e1页

作者： Gopinath, Munisamy Batarseh, Feras A. Beckman, Jayson Kulkarni, Ajay Jeong, Sei Univ Georgia Dept Agr & Appl Econ Athens GA 30602 USA Virginia Polytech Inst & State Univ Bradley Dept Elect & Comp Engn Virginia Tech Arlington VA 24061 USA USDA Econ Res Serv Washington DC USA George Mason Univ Coll Sci Fairfax VA 22030 USA

Focusing on seven major agricultural commodities with a long history of trade, this study employs data-driven analytics to decipher patterns of trade, namely using supervised machine learning (ML), as well as neural networks. The supervised ML and neural network techniques arc trained on data until 2010 and 2014, respectively. Results show the high relevance of ML models to forecasting trade patterns in near- and long-term relative to traditional approaches, which arc often subjective assessments or time-series projections. While supervised ML techniques quantified key economic factors underlying agricultural trade flows, neural network approaches provide better fits over the long term.

关键词： agriculture boosting algorithms forecasting machine learning trade flows

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