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Unleashing the Potential of boosting Techniques to Optimize Station-Pairs Passenger Flow Forecasting

Procedia Computer Science 2024年 235卷 32-44页

作者： Madhuri Patel Samir B. Patel Debabrata Swain Siddharth Shah Department of Computer Engineering Pandit Deendayal Energy University Gandhingar India Department of Information Technology L D College of Engineering Ahmedabad India

Station-pair passenger flow forecast modeling is crucial for public transportation to address emerging needs. The accurate prediction and estimation will provide backbone support for various features of transport, viz. (a) Demand forecasting for resource allocation during peak travel periods; (b) Service planning to determine transportation frequency on specific routes; (c) Resource allocation for vehicles and capacity adjustments, including planning for new fleet size; (d) Infrastructure planning to identify areas requiring additional development; (e) Enhancing passenger experience with ample seating and amenities; and (f) Emergency planning for crowd control and passenger safety. Recent advancements in transit information station-pair estimation methods can be implemented with Data Science. Over the years, there has been significant progress in developing innovative modeling approaches to estimate travel behaviour, taking into account various factors such as timeslots, holidays, and other relevant features. In this study, we have synthesized ticket data from Thane Municipal Transport (TMT), collected from e-ticket machines, spanning from January 1 to June 23, 2023, covering all routes and comprising more than 2.80 crore tickets. This paper presents a comprehensive station-pair short-term passenger flow forecast by utilizing three boosting models: extreme Gradient boosting (XGBoost), Light Gradient boosting model (LightGBM), and Categorical boosting model (CatBoost). Their implementation is well-suited based on statistical parameters. The study concludes by comparing the results of the models, considering computation time and statistical parameters, to validate their superiority. By looking into day to day requirements, the LightGBM model is most appropriate for implementation with an accurate result. The research findings contribute to providing valuable data support and a robust scientific foundation for managing passenger flow through route planning and dispatch

关键词： Public transport Station Pair passenger flow prediction boosting algorithms LightGBM

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A hybrid model based on bidirectional long short-term memory neural network and Catboost for short-term electricity spot price forecasting

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2022年第2期73卷 301-325页

作者： Zhang, Fan Fleyeh, Hasan Bales, Chris Dalarna Univ Dept Microdata Anal S-79188 Falun Sweden Dalarna Univ Dept Energy Technol Falun Sweden Dalarna Univ Dept Comp Engn Falun Sweden

Electricity price forecasting plays a crucial role in a liberalised electricity market. Generally speaking, long-term electricity price is widely utilised for investment profitability analysis, grid or transmission expansion planning, while medium-term forecasting is important to markets that involve medium-term contracts. Typical applications of medium-term forecasting are risk management, balance sheet calculation, derivative pricing, and bilateral contracting. Short-term electricity price forecasting is essential for market providers to adjust the schedule of production, i.e., balancing consumers' demands and electricity generation. Results from short-term forecasting are utilised by market players to decide the timing of purchasing or selling to maximise profits. Among existing forecasting approaches, neural networks are regarded as the state of art method due to their capability of modelling high non-linearity and complex patterns inside time series data. However, deep neural networks are not studied comprehensively in this field, which represents a good motivation to fill this research gap. In this article, a deep neural network-based hybrid approach is proposed for short-term electricity price forecasting. To be more specific, categorical boosting (Catboost) algorithm is used for feature selection and a bidirectional long short-term memory neural network (BDLSTM) will serve as the main forecasting engine in the proposed method. To evaluate the effectiveness of the proposed approach, 2018 hourly electricity price data from the Nord Pool market are invoked as a case study. Moreover, the performance of the proposed approach is compared with those of multi-layer perception (MLP) neural network, support vector regression (SVR), ensemble tree, ARIMA as well as two recent deep learning-based models, gated recurrent units (GRU) and LSTM models. A real-world dataset of Nord Pool market is used in this study to validate the proposed approach. Mean percentage error (MAP

关键词： Bidirectional long short-term memory neural network deep learning electricity price forecasting machine learning boosting algorithms energy market

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Assessing the performance of state-of-the-art machine learning algorithms for predicting electro-erosion wear in cryogenic treated electrodes of mold steels

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ADVANCED ENGINEERING INFORMATICS 2024年 61卷

作者： Cetin, Abdurrahman Atali, Gokhan Erden, Caner Ozkan, Sinan Serdar Sakarya Univ Appl Sci Vocat Sch Sakarya Machinery & Met Technol Sakarya Turkiye Sakarya Univ Appl Sci Fac Technol Dept Mechatron Engn Sakarya Turkiye Sakarya Univ Appl Sci Fac Technol Dept Comp Engn Sakarya Turkiye Sakarya Univ Appl Sci AI Res & Applicat Ctr Sakarya Turkiye

In manufacturing, predicting and reducing electro-erosion wear during the electric discharge machining (EDM) process is critical to minimize delays, financial losses and product defects. Achieving this requires developing and evaluating accurate machine learning models. In our study, we focus on cryogenically treated mold steel electrodes to investigate the potential of different machine learning algorithms to predict EDM wear. We considered five machine learning algorithms-artificial neural networks, ensemble learning, boosting algorithms, tree-based algorithms, and k-nearest neighbors-to evaluate their ability to predict wear patterns accurately. Each algorithm was trained and tested using actual experimental data from EDM processes. Our results show that the machine learning models demonstrated exceptional accuracy, accurately predicting EDM wear in training and testing datasets with almost 99% accuracy. In addition, we identified the most influential characteristics that affect wear patterns, including operating current, cryogenic process parameters, and electrode composition. Based on these findings, manufacturers can gain valuable insight into the factors that cause EDM wear and optimize their EDM processes accordingly to improve productivity, reduce wear-related costs, and increase production quality across multiple manufacturing industries. Furthermore, this research provides insights into the possibilities of implementing these models in real manufacturing contexts and motivates future research on this topic. Ultimately, integrating advanced computing techniques and prudent decision-making strategies will shape the future of manufacturing operations management and promote sustainable and profitable business growth.

关键词： Electro-erosion wear Cryogenic treatment Mold steel Machine learning algorithms boosting algorithms Ensemble learning

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Classification of Flood Disaster Risks with the Use of Gradient boosting Algorithm 22

Classification of Flood Disaster Risks with the Use of Gradi...

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Proceedings of the 2022 5th International Conference on Computational Intelligence and Intelligent Systems

作者： John Paul Quilingking Tomas Gabriela Andes Razmin Bernadette Ellazar Ayesha Keith Santos School of Information Technology Mapua University Philippines

ISBN: (纸本)9781450397612

This study used base and ensemble approaches to classify the flood disaster risks in a local provincial capital in the Philippines using an intelligent methodology based on machine learning. It focused on Gradient boosting Algorithm with Decision Trees as base classifiers/estimators. The researchers consulted with experts to determine the weights of causative factors to fluvial flooding, which were then classified into four (4) risk levels using the Quantile Method and the Exponential Regression for missing value imputation. The K-fold cross-validation was used to validate the proposed algorithms. The experiment shows that Gradient boosting Algorithm is the most appropriate model for the disaster data with the score of 80.00%, more than 70% in all the classification criteria (accuracy, precision, recall f1-score), respectively.

关键词： Gradient boosting boosting algorithms Classification

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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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A Comparative Study of Machine Learning algorithms for Predicting Weight Range of Neonate

A Comparative Study of Machine Learning Algorithms for Predi...

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International Conference on Decision Aid Sciences and Applications (DASA)

作者： Adeeba, Saleem Banujan, Kuhaneswaran Kumara, B. T. G. S. Prasanth, Senthan Sabaragamuwa Univ Sri Lanka Dept Comp & Informat Syst Belihuloya Sri Lanka Sabaragamuwa Univ Sri Lanka Dept Phys Sci & Technol Belihuloya Sri Lanka

ISBN: (纸本)9781665495011

Birth weight is a crucial measure of pregnancy outcome, and it indicates a neonate's chances of longevity, growth, long-lived health, and mental development. In epidemiological studies, it is commonly regarded as the causative pathway to these health consequences. Scanning the fetus's weight before birth is associated with difficulties and is typically calculated using empirical calculations based on doctors' clinical experience. In this research, the authors proposed an approach to predict the neonate weight class with minimum errors and costs during six months of pregnancy, utilising existing pregnant women's data. Authors recognised 16 attributes after an extreme literature survey and expert opinions. The haemoglobin level of the mother, mother's pre-pregnancy weight and height, pregnant woman's weight each month until 6 are some attributes identified. For the Machine learning (ML) models to work, a dataset of 1000 pregnant women was obtained and preprocessed. Then dataset was prepared to apply ML models. This research uses eight different ML algorithms to predict the neonate weight class at six months of pregnancy. Also, this research provides an approach to compare different eight ML models to find an effective algorithm for the neonate weight prediction. The Linear Discriminant Analysis (LDA) model has higher performance than other models. Accuracy, precision, recall and F1-score for LDA are 81.94 %, 83.05%, 94.23% and 88.28 % respectively.

关键词： neonate weight boosting algorithms machine learning

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boosting methods for multi-class imbalanced data classification: an experimental review

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JOURNAL OF BIG DATA 2020年第1期7卷 1-47页

作者： Tanha, Jafar Abdi, Yousef Samadi, Negin Razzaghi, Nazila Asadpour, Mohammad Univ Tabriz Fac Elect & Comp Engn POB 51666-16471 Tabriz Iran

Since canonical machine learning algorithms assume that the dataset has equal number of samples in each class, binary classification became a very challenging task to discriminate the minority class samples efficiently in imbalanced datasets. For this reason, researchers have been paid attention and have proposed many methods to deal with this problem, which can be broadly categorized into data level and algorithm level. Besides, multi-class imbalanced learning is much harder than binary one and is still an open problem. boosting algorithms are a class of ensemble learning methods in machine learning that improves the performance of separate base learners by combining them into a composite whole. This paper's aim is to review the most significant published boosting techniques on multi-class imbalanced datasets. A thorough empirical comparison is conducted to analyze the performance of binary and multi-class boosting algorithms on various multi-class imbalanced datasets. In addition, based on the obtained results for performance evaluation metrics and a recently proposed criteria for comparing metrics, the selected metrics are compared to determine a suitable performance metric for multi-class imbalanced datasets. The experimental studies show that the CatBoost and LogitBoost algorithms are superior to other boosting algorithms on multi-class imbalanced conventional and big datasets, respectively. Furthermore, the MMCC is a better evaluation metric than the MAUC and G-mean in multi-class imbalanced data domains.

关键词： boosting algorithms Imbalanced data Multi-class classification Ensemble learning

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Design of concrete-filled steel tubular columns using data-driven methods

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JOURNAL OF CONSTRUCTIONAL STEEL RESEARCH 2023年 200卷

作者： Degtyarev, Vitaliy V. Thai, Huu-Tai New Millennium Bldg Syst LLC 3700 Forest Dr Suite 501 Columbia SC 29204 USA Univ Melbourne Dept Infrastruct Engn Parkville Vic 3010 Australia

By leveraging the merits of structural steel and concrete materials, concrete-filled steel tubular (CFST) struc-tures have been increasingly used in the composite construction of bridges and high-rise buildings. However, their design equations are more complicated than those of steel and reinforced concrete (RC) structures, especially for circular columns under eccentric loading. Therefore, the use of emerging data-driven approaches will help structural engineers ease the design process. This paper explores the use of data-driven design methods as alternatives to conventional mechanics-based design models. Five boosting algorithms, including adaptive boosting (AdaBoost), gradient boosting machine (GBR), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical gradient boosting (CatBoost), are employed to develop predictive models for four different types of CFST columns (i.e., circular columns, rectangular columns, circular beam-columns, and rectangular beam-columns). These predictive models are trained using the most up-to-date and comprehensive database collected from over 3,200 test specimens. Reliability analysis is conducted to calibrate the resistance reduction factors for three different design frameworks (i.e., the US, Eurocode, and Australian frameworks) to ensure that the newly developed predictive models meet the target reliability indices required by different design frameworks. A web-based design tool is also developed to promote the practical use of data-driven methods for the design of CFST columns.

关键词： CFST columns Machine learning boosting algorithms Structural design Resistance reduction factor Reliability analysis

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

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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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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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