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Journal articles on the topic 'LGBM Regressor'

Author: Grafiati

Published: 7 June 2025

Last updated: 17 July 2025

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1

AMIT BIJLWAN, SHWETA POKHRIYAL, RAJEEV RANJAN, R.K SINGH, and ANKITA JHA. "Machine learning methods for estimating reference evapotranspiration." Journal of Agrometeorology 26, no. 1 (2024): 63–68. http://dx.doi.org/10.54386/jam.v26i1.2462.

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Precise estimation of evapotranspiration is crucial for optimizing crop water uses particularly in the context of agriculture and horticultural production. In this study, various machine learning techniques was used to determine reference evapotranspiration by leveraging historical weather data. The models tested include artificial neural networks (ANN), Lasso, Ridge, Random Forest, LGBM regressor, and Gradient boosting regressor. LGBM regressor emerged as the top-performing model, exhibiting exceptional accuracy with a testing R-squared of 1.0. ANN also demonstrated notable performance, achie

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Shivraj, R., S. Vikas, Abhishek MN Naga, Kumar GN Naveen, Deepak NR Dr., and B. Ompraksash. "Prediction of Stock Market Performance Analysis by Using Machine Learning Regressor Techniques." Recent Trends in Computer Graphics and Multimedia Technology 7, no. 2 (2025): 11–21. https://doi.org/10.5281/zenodo.15331553.

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<em>Stock market prediction is a widely researched and crucial topic for investors, traders, and financial analysts. Precisely predicting stock price fluctuations can aid in making informed decisions regarding the buying or selling of stocks. One approach to achieving this is through sentimental analysis that has emerged as a popular approach for predicting stock prices. The research employs machine learning methods to enhance the accuracy of stock market predictions. It focuses on analyzing the efficiency of five advanced machine learning regression model.</em> <em>Bagging Regressor, XGB Regr

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Lubis, Fachrul Rozi, and Eddy Rahman Syahputra. "Peramalan Deret Waktu untuk Bisnis : Pendekatan algoritma LGBM Regressor." Data Sciences Indonesia (DSI) 1, no. 2 (2022): 69–75. http://dx.doi.org/10.47709/dsi.v1i2.1347.

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Peramalan deret waktu adalah topik yang cukup umum di bidang data science (ilmu data). Perusahaan menggunakan model peramalan untuk mendapatkan pandangan yang lebih jelas tentang bisnis masa depan. Data masa lalu dikumpulkan dan dianalisis melalui model kuantitatif atau kualitatif sehingga pola dapat diidentifikasi dan dapat mengarahkan perencanaan bisnis di masa depan akan tetapi memilih algoritme yang tepat merupakan salah satu keputusan sulit ketika akan mengembangkan model peramalan deret waktu. Penelitian ini menyajikan hasil analisi data dengan mengadopsi kerangka kerja data science CRIS

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Geyikoğlu, Ali, and Mete Yağanoğlu. "Makine Öğrenmesi Algoritmaları ile Elektrik Dağıtım Şebekeleri Arıza Tahmini." Karadeniz Fen Bilimleri Dergisi 15, no. 1 (2025): 73–98. https://doi.org/10.31466/kfbd.1482179.

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Elektrik dağıtım şebekelerinde arıza; kaliteli ve sürekli enerji akışını engelleyici faktörler olarak tanımlanmaktadır. Arızanın meydana gelmesi sonrasında Elektrik Dağıtım Şirketleri, bakım-onarım ve yatırım çalışmaları ile düzeltici faaliyetler gerçekleştirmektedir. Meydana gelen arızalar ve sonrası düzeltici faaliyetler ile teknik kalite parametreleri sistemlerce oluşturulmaktadır. Ancak ortaya çıkan teknik veriler, herhangi bir tahminleme altyapısında kullanılmamakta, düzeltici faaliyetler genel olarak yorum ve taleplere istinaden gerçekleştirilmektedir. Bu çalışmada, sezgisel yaklaşımları

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Febriyanti, Ama, and Tomy Rizky Izzalqurny. "Predicting Credit Paying Ability with Machine Learning Algorithms." Majalah Bisnis & IPTEK 16, no. 1 (2023): 8–15. https://doi.org/10.55208/781ypr87.

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Most people still have difficulty accessing finance because of a lack or even no credit history. This study aims to develop a data model that predicts a customer's ability to pay from various aspects other than credit history. This study uses the CRSIP-DM (Cross Industry Standard Process Model for Data mining) method. The data used in this study is the Home Credit Default Risk dataset collected by documentation techniques. The data were then analyzed using data modeling analysis techniques, namely logistic regressor, decision tree classifier, random forest classifier, and lgbm classifier. This

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Febriyanti, Ama, and Tomy Rizky Izzalqurny. "Predicting Credit Paying Ability With Machine Learning Algorithms." Majalah Bisnis & IPTEK 16, no. 1 (2023): 8–15. http://dx.doi.org/10.55208/bistek.v16i1.296.

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Most people still have difficulty accessing finance because of a lack or even no credit history. This study aims to develop a data model that predicts a customer's ability to pay from various aspects other than credit history. This study uses the CRSIP-DM (Cross Industry Standard Process Model for Data mining) method. The data used in this study is the Home Credit Default Risk dataset collected by documentation techniques. The data were then analyzed using data modeling analysis techniques, namely logistic regressor, decision tree classifier, random forest classifier, and lgbm classifier. This

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Phyo, Pyae-Pyae, Yung-Cheol Byun, and Namje Park. "Short-Term Energy Forecasting Using Machine-Learning-Based Ensemble Voting Regression." Symmetry 14, no. 1 (2022): 160. http://dx.doi.org/10.3390/sym14010160.

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Meeting the required amount of energy between supply and demand is indispensable for energy manufacturers. Accordingly, electric industries have paid attention to short-term energy forecasting to assist their management system. This paper firstly compares multiple machine learning (ML) regressors during the training process. Five best ML algorithms, such as extra trees regressor (ETR), random forest regressor (RFR), light gradient boosting machine (LGBM), gradient boosting regressor (GBR), and K neighbors regressor (KNN) are trained to build our proposed voting regressor (VR) model. Final pred

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Thi Thanh Hang, Hoang, Nguyen Thi Kim Phung, Tran Trong Huy, and Lê Thị Phượng Liên. "Impact of Macro and Micro Factors on Provision for Credit Risks of Commercial Banks in Vietnam: Approach on Python Programming Platform." International Journal of Business & Management Studies 04, no. 05 (2023): 26–34. http://dx.doi.org/10.56734/ijbms.v4n5a3.

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This study uses LGBM Regressor (Light Gradient Boosting Machine Regressor) algorithm in machine learning on python platform along with SHAP (Shapley Additive exPlanation) technique to extract information from machine learning model to evaluate the macro and micro factors affecting the provision for credit risks at commercial banks in Vietnam. Data was collected from 30 commercial banks in Vietnam from 2008 to 2020. Research results show that profitability, size, bad debt, credit balance, capital adequacy ratio, economic growth and unemployment rate have an impact on the provision for credit ri

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Leleń, Michał, Katarzyna Biruk-Urban, Jerzy Józwik, and Paweł Tomiło. "Modeling and Machine Learning of Vibration Amplitude and Surface Roughness after Waterjet Cutting." Materials 16, no. 19 (2023): 6474. http://dx.doi.org/10.3390/ma16196474.

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This study focused on analyzing vibrations during waterjet cutting with variable technological parameters (speed, vfi; and pressure, pi), using a three-axis accelerometer from SEQUOIA for three different materials: aluminum alloy, titanium alloy, and steel. Difficult-to-machine materials often require specialized tools and machinery for machining; however, waterjet cutting offers an alternative. Vibrations during this process can affect the quality of cutting edges and surfaces. Surface roughness was measured by contact methods after waterjet cutting. A machine learning (ML) model was develope

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Nguyen, Huu Nam, Quoc Thanh Tran, Canh Tung Ngo, Duc Dam Nguyen, and Van Quan Tran. "Solar energy prediction through machine learning models: A comparative analysis of regressor algorithms." PLOS ONE 20, no. 1 (2025): e0315955. https://doi.org/10.1371/journal.pone.0315955.

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Solar energy generated from photovoltaic panel is an important energy source that brings many benefits to people and the environment. This is a growing trend globally and plays an increasingly important role in the future of the energy industry. However, it intermittent nature and potential for distributed system use require accurate forecasting to balance supply and demand, optimize energy storage, and manage grid stability. In this study, 5 machine learning models were used including: Gradient Boosting Regressor (GB), XGB Regressor (XGBoost), K-neighbors Regressor (KNN), LGBM Regressor (Ligh

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Phoeuk, Menghay, and Minho Kwon. "Accuracy Prediction of Compressive Strength of Concrete Incorporating Recycled Aggregate Using Ensemble Learning Algorithms: Multinational Dataset." Advances in Civil Engineering 2023 (May 17, 2023): 1–23. http://dx.doi.org/10.1155/2023/5076429.

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The use of alternative materials and recycling in construction has gained popularity in recent years as part of the industry’s commitment to sustainability. One such material, recycled aggregates, has been extensively studied over the past two decades for its potential to replace natural aggregates in cement-based composites. However, the unique properties of recycled aggregates make traditional concrete mix design methods ineffective in determining their target compressive strength. To address this challenge, four machine learning models based on ensemble learning algorithms, including CatBoo

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Mostafa, Nihal N., Ahmed Tolba, and Mohamed Abouhawwash. "Application of Deep Learning Initiatives for CO2 Emissions Forecasting." Climate Change Reports 1 (January 29, 2024): 19–29. http://dx.doi.org/10.61356/j.ccr.2024.1209.

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This work models and forecasts vehicle CO2 emissions, a major source of atmospheric changes and climate disruptions, using cutting-edge artificial intelligence. The CO2 emission by vehicle dataset from Kaggle, which includes several features such vehicle class, engine size, cylinder transmission, fuel type, fuel consumption, city, highway, comb, and CO2 emissions, was used to build the model. To predict CO2 emissions, a hybrid model (CNN-LSTM-MLP) was developed based on long short-term memory network (LSTM), convolution neural network (CNN), and multi-layer perceptron (MLP). The proposed model

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Bairamova, Diana. "PREDICTING COURSE GRADES OF STUDENTS’ ACADEMICPERFORMANCE USING THE LIGHTGBM REGRESSOR." Suleyman Demirel University Bulletin Natural and Technical Sciences 62, no. 1 (2024): 34–47. https://doi.org/10.47344/sdubnts.v62i1.952.

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In the modern world, using all available opportunities andtechnologies, special attention should be paid to the development of theeducation system of students, since education serves as the basis for thedevelopment of the future generation. Nowadays, thanks to the use of availableArtificial Intelligence methods, it is possible to predict various events, anomaliesor other important things. With the help of machine learning, it is possible topredict at an early stage of a student's education whether he will finish the coursesuccessfully or not. In this study, it is proposed to predict the final

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Kumar, Vijendra, Naresh Kedam, Kul Vaibhav Sharma, Darshan J. Mehta, and Tommaso Caloiero. "Advanced Machine Learning Techniques to Improve Hydrological Prediction: A Comparative Analysis of Streamflow Prediction Models." Water 15, no. 14 (2023): 2572. http://dx.doi.org/10.3390/w15142572.

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The management of water resources depends heavily on hydrological prediction, and advances in machine learning (ML) present prospects for improving predictive modelling capabilities. This study investigates the use of a variety of widely used machine learning algorithms, such as CatBoost, ElasticNet, k-Nearest Neighbors (KNN), Lasso, Light Gradient Boosting Machine Regressor (LGBM), Linear Regression (LR), Multilayer Perceptron (MLP), Random Forest (RF), Ridge, Stochastic Gradient Descent (SGD), and the Extreme Gradient Boosting Regression Model (XGBoost), to predict the river inflow of the Ga

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Wang, Hongwei, Yuanbo Ding, Yu Kong, Daoyuan Sun, Ying Shi, and Xin Cai. "Predicting the Compressive Strength of Sustainable Portland Cement–Fly Ash Mortar Using Explainable Boosting Machine Learning Techniques." Materials 17, no. 19 (2024): 4744. http://dx.doi.org/10.3390/ma17194744.

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Unconfined compressive strength (UCS) is a critical property for assessing the engineering performances of sustainable materials, such as cement–fly ash mortar (CFAM), in the design of construction engineering projects. The experimental determination of UCS is time-consuming and expensive. Therefore, the present study aims to model the UCS of CFAM with boosting machine learning methods. First, an extensive database consisting of 395 experimental data points derived from the literature was developed. Then, three typical boosting machine learning models were employed to model the UCS based on th

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Bijalwan, Priya, Ashulekha Gupta, Anubhav Mendiratta, Amar Johri, and Mohammad Asif. "Predicting the Productivity of Municipality Workers: A Comparison of Six Machine Learning Algorithms." Economies 12, no. 1 (2024): 16. http://dx.doi.org/10.3390/economies12010016.

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One of the most significant areas of local government in the world is the municipality sector. It provides various services to the residents and businesses in their areas, such as water supply, sewage disposal, healthcare, education, housing, and transport. Municipalities also promote social and economic development and ensure democratic and accountable governance. It also helps in encouraging the involvement of communities in local matters. Workers of Municipalities need to maintain their services regularly to the public. The productivity of the employees is just one of the main important fac

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Prashant Bhuva, Ankur Bhogayata. "Predicting Compressive Strength of Self-Compacting Concrete Using Machine and Deep Learning Models." Journal of Information Systems Engineering and Management 10, no. 28s (2025): 334–47. https://doi.org/10.52783/jisem.v10i28s.4334.

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This paper discusses the compressive strength prediction for self-compacting concrete (SCC) by a host of machine learning (ML) and deep learning (DL) models is discussed in this research work. Random Forest (RF), Keras Regressor (KR), Extremely Randomized Trees (ERT), Extreme Gradient Boosting (XGB), Gradient Boosting (GB), Light Gradient Boosting Machine (LGBM), and Category Boosting (CB) are some of the many ensemble methods until now. In addition, the ability of several models to predict the compressive strength of SCC was examined with generalized additive models like Gradient Boosting Reg

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Nikhil, Uppugunduri Vijay, Athiya M. Pandiyan, S. P. Raja, and Zoran Stamenkovic. "Machine Learning-Based Crop Yield Prediction in South India: Performance Analysis of Various Models." Computers 13, no. 6 (2024): 137. http://dx.doi.org/10.3390/computers13060137.

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Agriculture is one of the most important activities that produces crop and food that is crucial for the sustenance of a human being. In the present day, agricultural products and crops are not only used for local demand, but globalization has allowed us to export produce to other countries and import from other countries. India is an agricultural nation and depends a lot on its agricultural activities. Prediction of crop production and yield is a necessary activity that allows farmers to estimate storage, optimize resources, increase efficiency and decrease costs. However, farmers usually pred

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Vu, Van-Hieu, Binh Ngo-Van, and Tung Hoang Do Thanh. "TWO-PHASE COMBINED MODEL TO IMPROVE THE ACCURACY OF INDOOR LOCATION FINGERPRINTING." Journal of Computer Science and Cybernetics 38, no. 4 (2022): 377–403. http://dx.doi.org/10.15625/1813-9663/38/4/17592.

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Wi-Fi Fingerprinting based Indoor Positioning System (IPS) aims to help locate and navigate users inside buildings. It has become a popular research topic in recent years. For the most parts, authors use the traditional machine learning algorithms to enhance the accuracy of locationing. Their methods involve using a standalone algorithm or a combination of different algorithms in only one phase, producing results with an acceptable accuracy. In this paper, we present a different approach applying a machine learning model that combines many algorithms in two phases, and propose a feature reduct

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Guo, Xiangyu, Jinye Chen, and Gexuan Ren. "Accuracy of Green Bond Issuance Predictor." International Journal of Global Economics and Management 4, no. 1 (2024): 127–42. http://dx.doi.org/10.62051/ijgem.v4n1.19.

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Climate change is affecting the development of many industries in different aspects. These impacted enterprises transform into sustainable enterprises to avoid the risks, and by doing so they enter into the green bond market. The current literature provides effective reference indicators for participants in the green bond market. These indicators illustrate the funding size of the green bonds in different dimensions to the participants. As for the improvement of the policies about environmental protection there also emerge some new indicators such as ESG score. Besides, machine learning is an

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Sopasakis, Alexandros, Maria Nilsson, Mattias Askenmo, Fredrik Nyholm, Lillemor Mattsson Hultén, and Victoria Rotter Sopasakis. "Machine learning evaluation for identification of M-proteins in human serum." PLOS ONE 19, no. 4 (2024): e0299600. http://dx.doi.org/10.1371/journal.pone.0299600.

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Serum electrophoresis (SPEP) is a method used to analyze the distribution of the most important proteins in the blood. The major clinical question is the presence of monoclonal fraction(s) of antibodies (M-protein/paraprotein), which is essential for the diagnosis and follow-up of hematological diseases, such as multiple myeloma. Recent studies have shown that machine learning can be used to assess protein electrophoresis by, for example, examining protein glycan patterns to follow up tumor surgery. In this study we compared 26 different decision tree algorithms to identify the presence of M-p

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Ha, Hoang, Tran Thi Thu Trang, Dam Duc Nguyen, et al. "Hybrid Model of LightGBM Regression and Grid Search Optimization for the Estimation of Permanent Deformation of Asphalt Mixtures Pavements." Engineering, Technology & Applied Science Research 15, no. 3 (2025): 22901–7. https://doi.org/10.48084/etasr.10419.

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This study aimed to estimate the permanent deformation of Asphalt Mixtures (AMs) of pavements (Fn) utilizing a hybrid LGBM-GSO machine learning model, which combines Light Gradient-Boost Machine (LGBM) regression and Grid Search Optimization (GSO). In this study, input physical parameters, namely Filler (FP), fine aggregate (S), coarse aggregate (C), bitumen percent (BP), Marshall stability (M), Voids in Mineral Aggregate (VMA), air voids (Va), and Marshall flow (F) were used to predict Fn. Laboratory data from 118 AMs were analyzed. Model validation was carried out using various standard eval

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Alotaibi, Muteb Zarraq, and Mohd Anul Haq. "Customer Churn Prediction for Telecommunication Companies using Machine Learning and Ensemble Methods." Engineering, Technology & Applied Science Research 14, no. 3 (2024): 14572–78. http://dx.doi.org/10.48084/etasr.7480.

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This study investigates customer churn, which is a challenge in the telecommunications sector. Using a dataset of telecom customer churn, multiple classifiers were employed, including Random Forest, LGBM, XGBoost, Logistic Regression, Decision Trees, and a custom ANN model. A rigorous evaluation was conducted deploying cross-validation techniques to capture nuanced customer behavior. The models were optimized by hyperparameter tuning, improving their customer churn prediction results. An ensemble averaging method was also adopted, achieving an accuracy of 0.79 and a recall of 0.72 in the test

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Sengkey, Daniel Febrian, and Angelina Masengi. "Regression Algorithms in Predicting the SARS-CoV-2 Replicase Polyprotein 1ab Inhibitor: A Comparative Study." Journal of Electronics, Electromedical Engineering, and Medical Informatics 6, no. 1 (2023): 1–10. http://dx.doi.org/10.35882/jeeemi.v6i1.338.

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Due to its extensive steps and trials, drug discovery is a long and expensive process. In the last decade, as also hard pressed by the COVID-19 pandemic, the screening process could be assisted with the advancement in computational technology including the application of Machine Learning. The classification task in Machine Learning has become one of the major approaches for drug discovery. Unfortunately, this practice uses discretized labels that might lead to the loss of quantitative properties that could be meaningful. Therefore, in this paper, we aim to compare various Machine Learning regr

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Taufiq Hakimi bin Mohamad Suffian and M. Bhuyan. "Analysis in Materials Science by Predicting Concrete Compressive Strength Using Machine Learning." Graduate Journal of Interdisciplinary Research, Reports and Reviews 2, no. 01 (2024): 54–62. https://doi.org/10.34256/gjir3.v2i01.11.

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Future developments in materials science engineering will be greatly influenced by the application of machine learning for determining the properties of concrete, especially its compressive strength. This research predicts the compressive strength of concrete with eight independent variables, including cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and age using supervised machine learning (ML) techniques of linear regression (LR) and light gradient boosting machine (LGBM). The ML models are fed a total of 1030 datasets using a 70:30 split ratio

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Kull, Ryan M., Joseph G. Kosciw, and Emily A. Greytak. "Preparing School Counselors to Support LGBT Youth: The Roles of Graduate Education and Professional Development." Professional School Counseling 20, no. 1a (2017): 1096–2409. http://dx.doi.org/10.5330/1096-2409-20.1a.13.

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This study examined whether school counselors’ LGBT-related graduate education and professional development predicted more frequent efforts to support LGBT students, and whether their LGBT-related self-efficacy mediated the relationship between their training experiences and supportive efforts. Results from ordinary least squares (OLS) regression analyses indicated that more exposure to LGBT-related graduate education and professional development predicted more frequent engagement in LGBT-related practices among school counselors. Results from OLS regression-based path analysis further indicat

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Aktaş, Tuğba, İsmail Mert Temel, and Ahmet Saygılı. "Comparative Analysis of Diabetes Diagnosis with Machine Learning Methods." International Scientific and Vocational Studies Journal 8, no. 1 (2024): 22–32. http://dx.doi.org/10.47897/bilmes.1447878.

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Diabetes is a disease that occurs when the body cannot regulate the level of sugar (glucose) in the blood. Early diagnosis of this disease is important in preventing more serious diseases that may arise later. Within the scope of this study, an attempt was made to optimize the diabetes data set for use by training it with different models. At the very beginning of the study, Logistic Regression, KNN, SVM (Support Vector Machine), CART (Classification and Regression Trees), RF (Random Forest), Adaboost, GBM (Gradient Boosting Machines), XGBoost (Extreme Gradient Boosting), LGBM (Light Gradient

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Dr. S. Sankar Ganesh, Anugu Durga Bhavani, Mettu Sujitha Reddy, and Kuchipudi Neha. "COGNISENSE: A MACHINE LEARNING FRAMEWORK FOR COGNITIVE LOAD DETECTION VIA HUMAN-TECHNOLOGY INTERACTION." Scientific Digest : Journal of Applied Engineering 13, no. 7(1) (2025): 29–36. https://doi.org/10.70864/joae.2025.v13.i7(1).pp29-36.

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Mental workload classification plays a vital role in enhancing human performance, particularly in high-stakes environments where excessive cognitive load can decrease multitasking efficiency by up to 30%. However, existing models often face challenges such as noisy EEG signals and small datasets, leading to overfitting and limited generalizability. Traditional approaches like Linear Discriminant Analysis (LDA) and Ridge Regression are insufficient for capturing the complex nonlinear characteristics inherent in EEG data. To overcome these limitations, this work introduces a novel Locally Cascad

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Delfita, Wiwi, Neviyarni S., and Riska Ahmad. "The Contribution of Sexual Identity Towards the Students’ Perception of Lesbian, Gay, Bisexual, and Transgender." Journal of Educational and Learning Studies 2, no. 2 (2019): 120. http://dx.doi.org/10.32698/0642.

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Some students perceive lesbian, gay, bisexual, and transgender (LGBT) positively, even though LGBT is a sexual deviation that is not appropriate with values and norms. There are several factors that influence an individual's perception of LGBT, including sexual identity. This study aims at looking at the contribution of sexual identity to student perceptions about LGBT. This research used a quantitative approach with a descriptive method and a simple linear regression analysis. The sample of this research was 385 taken from 15.752 undergraduate students of Universitas Negeri Padang which the s

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Ou, Shuo-Ming, Kuo-Hua Lee, Ming-Tsun Tsai, Wei-Cheng Tseng, Yuan-Chia Chu, and Der-Cherng Tarng. "Artificial Intelligence for Risk Prediction of Rehospitalization with Acute Kidney Injury in Sepsis Survivors." Journal of Personalized Medicine 12, no. 1 (2022): 43. http://dx.doi.org/10.3390/jpm12010043.

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Sepsis survivors have a higher risk of long-term complications. Acute kidney injury (AKI) may still be common among sepsis survivors after discharge from sepsis. Therefore, our study utilized an artificial-intelligence-based machine learning approach to predict future risks of rehospitalization with AKI between 1 January 2008 and 31 December 2018. We included a total of 23,761 patients aged ≥ 20 years who were admitted due to sepsis and survived to discharge. We adopted a machine learning method by using models based on logistic regression, random forest, extra tree classifier, gradient boosti

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Ganji, Arun, D. Usha, and P. S. Rajakumar. "Enhanced Early Diagnosis of Liver Diseases Using Feature Selection and Machine Learning Techniques on the Indian Liver Patient Dataset." Scalable Computing: Practice and Experience 26, no. 3 (2025): 1104–15. https://doi.org/10.12694/scpe.v26i3.4254.

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Liver diseases are a significant global health concern, with timely diagnosis crucial for effective treatment and prevention of further damage. This study addresses the challenge of early liver disease detection using machine learning techniques applied to the Indian Liver Patient Dataset (ILPD). Our proposed method comprises a four-phase approach: (1) initial model training using five machine learning algorithms - Multilayer Perceptron (MLP), Support Vector Machine (SVM), Decision Trees (DT-CART), Light Gradient Boosting Machine (LGBM), and Logistic Regression (LR) - on the original dataset;

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Li, Zan, and Nairen Zhang. "Short-Term Demand Forecast of E-Commerce Platform Based on ConvLSTM Network." Computational Intelligence and Neuroscience 2022 (July 14, 2022): 1–10. http://dx.doi.org/10.1155/2022/5227829.

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Based on real sales data, this article constructed LGBM and LSTM sales prediction models to compare and verify the performance of the proposed models. In this article, we forecast the product sales of stores in the future T + 3 days and use MAPE as the evaluation index. The experiment shows that the product sales prediction model based on the convolutional LSTM (ConvLSTM) network has better prediction accuracy. From a store point of view, ConvLSTM prediction model MAPE was 0.42 lower than the long short-term memory (LSTM) network and 0.68 lower than LGBM. From the perspective of commodity cate

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Gacilo, Jesus, Brigitte Steinheider, Thomas H. Stone, Vivian Hoffmeister, I. M. Jawahar, and Tara Garrett. "The double-edged sword of having a unique perspective." Equality, Diversity and Inclusion: An International Journal 37, no. 3 (2018): 298–312. http://dx.doi.org/10.1108/edi-03-2017-0060.

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Purpose Drawing on social identity theory and the concept of perceived organizational support, the authors conducted an online, exploratory survey of 150 lesbian, gay, bisexual, and transgender (LGBT) workers in 28 countries to examine whether being LGBT provides a unique perspective in the workplace, if they perceive their employer appreciates this perspective, and what effects this has on perceived discrimination and perceived career advancement. Collectively these questions have implications for work engagement and career prospects of LGBT workers. The paper aims to discuss these issues. De

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Li, Chien-Ching, Alicia Matthews, and XinQi Dong. "Psychological Distress Among Older LGBT and Non-LGBT Asian Americans: The Influence of Minority Stress." Innovation in Aging 4, Supplement_1 (2020): 624. http://dx.doi.org/10.1093/geroni/igaa057.2125.

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Abstract Emerging data from epidemiological studies have confirmed elevated prevalence rates for mental health conditions among the lesbian, gay, bisexual and transgender (LGBT) populations. An estimated 2.8% of Asian Americans identify as LGBT and 26% of Asian LGBT are 40 years or older. This study analyzed the California Health Interview Survey to examine differences in psychological distress between LGBT and non-LGBT older Asian Americans, and further evaluated the role of discrimination in medical care and intimate violence on psychological distress. Regression results showed older LGBT As

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Wie, Jeong Ha, Se Jin Lee, Sae Kyung Choi, et al. "Prediction of Emergency Cesarean Section Using Machine Learning Methods: Development and External Validation of a Nationwide Multicenter Dataset in Republic of Korea." Life 12, no. 4 (2022): 604. http://dx.doi.org/10.3390/life12040604.

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This study was a multicenter retrospective cohort study of term nulliparous women who underwent labor, and was conducted to develop an automated machine learning model for prediction of emergent cesarean section (CS) before onset of labor. Nine machine learning methods of logistic regression, random forest, Support Vector Machine (SVM), gradient boosting, extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), k-nearest neighbors (KNN), Voting, and Stacking were applied and compared for prediction of emergent CS during active labor. External validation was performed using

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Song, Yufei, Shiwu Li, Zhiguo Liu, Yuekui Zhang, and Nan Shen. "Analysis on Chlorophyll Diagnosis of Wheat Leaves Based on Digital Image Processing and Feature Selection." Traitement du Signal 39, no. 1 (2022): 381–87. http://dx.doi.org/10.18280/ts.390140.

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Crop nutrition measurement is of great significance in agricultural practice, especially in variable rate fertilization. The chlorophyll content, an important indicator of nitrogen nutrition in crops, largely depends on crop growth and development, photosynthesis, and crop yield, and plays an important role in the monitoring of crop growth. This paper tries to detect the chlorophyll content of wheat quickly, using the digital image processing technology. Specifically, a feature selection method was developed based on wrapper and light gradient boosting machine (LGBM), and combined with logisti

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Zlateva, Nina, Stanislav Ivanov, and Boris Popshterev. "The impacts of LGBT inclusion on business processes: The case of Bulgaria." Journal of General Management 48, no. 1 (2022): 98–112. http://dx.doi.org/10.1177/03063070211063067.

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This study surveyed 178 managers and employees of public and private organisations in Bulgaria to evaluate their perceptions towards the effects of LGBT inclusion on the business processes in their organisations, namely, human resource management, operations management and marketing. Non-parametric tests, factor and regression analyses were used to evaluate the role of various demographic and company characteristics on managers’ perceptions. The general attitudes of managers towards the LGBT community are positive. Managers seem to embrace the inclusion of LGBT employees in the workplace, but

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Rodríguez Pérez, José Gabriel. "Estrés minoritario LGBT+ sin hogar." HUMAN REVIEW. International Humanities Review / Revista Internacional De Humanidades 16, no. 6 (2023): 1–14. https://doi.org/10.37819/revhuman.v16i6.1532.

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Introduction: LGBT+ youth are at high risk of homelessness in addition to experiencing sexual minorities stress. Objective: to know the perception of stress in homeless LGBT+ minorities in their risk of social exclusion. Methodology: linear probability regression model to determine the marginal effects of the items with the probability of social exclusion risk. The significance of the partial effects and the reliability of the degree of classification from the model are evaluated. Results: different types of LGBT+ minority stress influence the risk of social exclusion. Conclusions: future rese

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Kim, Heeran, and Jong Suk Kim. "A Machine Learning Model-based Exploratory Study on Predicting Dropout Intentions of University Students: Utilizing 2019-2023 KEDI NASEL Data." Korean Association For Learner-Centered Curriculum And Instruction 25, no. 5 (2025): 585–602. https://doi.org/10.22251/jlcci.2025.25.5.585.

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Objectives The purpose of this study is to develop a machine learning-based model that predicts the intention to withdraw of university students nationwide using NASEL integrated data from 2019 to 2023 from the Korea Education Development Institute and to explore important predictors. Methods The research model consisted of 55 independent variables and three classes of dependent variables (‘no intention to withdraw’, ‘non-academic intention to withdraw’, and ‘academic intention to withdraw’. In addition, undersampling and oversampling were applied to solve the class imbalance problem, and a to

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Xia, Zili, Junxiao Guo, Zixiang Yue, Youliang Ding, Zhiwen Wang, and Shouwang Sun. "Digital Model of Deflection of a Cable-Stayed Bridge Driven by Deep Learning and Big Data Optimized via PCA-LGBM." Sustainability 15, no. 12 (2023): 9623. http://dx.doi.org/10.3390/su15129623.

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Based on big data, we can build a regression model between a temperature field and a temperature-induced deflection to provide a control group representing the service performance of bridges, which has a positive effect on the full life cycle maintenance of bridges. However, the spatial temperature information of a cable-stayed bridge is difficult to describe. To establish a regression model with high precision, the improved PCA-LGBM (principal component analysis and light gradient boosting machine) algorithm is proposed to extract the main temperature features that can reflect the spatial tem

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Maula, Sugha Faiz Al, Nicoletta Almira Dyah Setiawan, Elly Pusporani, and Sa'idah Zahrotul Jannah. "MODELING HOUSE SELLING PRICES IN JAKARTA AND SOUTH TANGERANG USING MACHINE LEARNING PREDICTION ANALYSIS." BAREKENG: Jurnal Ilmu Matematika dan Terapan 19, no. 1 (2025): 107–18. https://doi.org/10.30598/barekengvol19iss1pp107-118.

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The increasing demand for housing in urban agglomerations, particularly in areas like Jakarta, has made homeownership a significant challenge for many, especially first-time buyers and the lower-middle class. Post-pandemic shifts have further influenced housing preferences, driving interest towards suburban areas with green spaces. Despite government efforts through mortgage subsidy programs, affordability remains a concern, particularly in peripheral regions. This study aims to analyze housing prices in various Jakarta regions using machine learning models, including Multiple Linear Regressio

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Inui, Atsuyuki, Fumiaki Takase, Stefano Lucchina, and Takako Kanatani. "Prediction of Electrophysiological Severity and Carpal Tunnel Syndrome Instrument Changes After Carpal Tunnel Release Using Machine Learning Model." Applied Sciences 15, no. 4 (2025): 1812. https://doi.org/10.3390/app15041812.

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Introduction: The severity of carpal tunnel syndrome (CTS) is evaluated by electrophysiological examination as well as a patient-oriented questionnaire. We hypothesized that machine learning could predict postoperative electrophysiological severity as well as the scores of patient-oriented questionnaires. In this study, we developed machine learning models to predict postoperative changes in electrophysiological severity and changes in the Carpal Tunnel Syndrome Instrument (CTSI). Materials and Methods: Data from four hundred and twenty hands of individuals who had been diagnosed with CTS and

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Zhang, Zhanquan. "A well production prediction method based on blending heterogeneous ensemble learning optimized by OOA." Journal of Physics: Conference Series 2835, no. 1 (2024): 012002. http://dx.doi.org/10.1088/1742-6596/2835/1/012002.

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Abstract Oil well production prediction is an important research content in oilfield development, and constructing a scientific and good prediction model is a key issue. In this paper, a blending ensemble learning oil well production prediction model combining Random Forest, LGBM, and TCN is established and optimized by the Osprey optimization algorithm. Firstly, ANOVA and Pearson’s correlation coefficient is used to filter the relevant features affecting the well yield prediction, the autocorrelation coefficient is used to determine the lag order of the well yield prediction, and RF, LGBM, an

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Mukhanova, Ayagoz, Madiyar Baitemirov, Azamat Amirov, et al. "Forecasting creditworthiness in credit scoring using machine learning methods." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 5 (2024): 5534. http://dx.doi.org/10.11591/ijece.v14i5.pp5534-5542.

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This article provides an overview of modern machine learning methods in the context of their active use in credit scoring, with particular attention to the following algorithms: light gradient boosting machine (LGBM) classifier, logistic regression (LR), linear discriminant analysis (LDA), decision tree (DT) classifier, gradient boosting classifier and extreme gradient boosting (XGB) classifier. Each of the methods mentioned is subject to careful analysis to evaluate their applicability and effectiveness in predicting credit risk. The article examines the advantages and limitations of each met

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Nguyen, Hung Viet, and Haewon Byeon. "Prediction of Parkinson’s Disease Depression Using LIME-Based Stacking Ensemble Model." Mathematics 11, no. 3 (2023): 708. http://dx.doi.org/10.3390/math11030708.

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Depression symptoms are comparable to Parkinson’s disease symptoms, including attention deficit, fatigue, and sleep disruption, as well as symptoms of dementia such as apathy. As a result, it is difficult for Parkinson’s disease caregivers to diagnose depression early. We examined a LIME-based stacking ensemble model to predict the depression of patients with Parkinson’s disease. This study used the epidemiologic data of Parkinson’s disease dementia patients (EPD) from the Korea Disease Control and Prevention Agency’s National Biobank, which included 526 patients’ information. We used Logistic

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Yong, Stephen Luo Sheng, Jing Lin Ng, Yuk Feng Huang, and Chun Kit Ang. "Estimation of Reference Crop Evapotranspiration with Three Different Machine Learning Models and Limited Meteorological Variables." Agronomy 13, no. 4 (2023): 1048. http://dx.doi.org/10.3390/agronomy13041048.

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Precise reference crop evapotranspiration (ET0) estimation plays a key role in agricultural fields as it aids in the proper operation and management of irrigation scheduling. However, reliable ET0 estimation poses a challenge when there is insufficient or incomplete long-term meteorological data at the East Coast Economic Region (ECER), Malaysia, where the economy is highly dependent on agricultural crop production. This study evaluated the performances of different standalone machine learning (ML) models, namely, the light gradient boosting machine (LGBM), decision forest regression (DFR), an

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Li, Zhenrui, Qisheng Zhang, and Yixiao Wang. "Quantifying and Predicting Tennis Match Momentum: An LGBM-Based Analysis and Visualization Model." Highlights in Science, Engineering and Technology 98 (May 16, 2024): 470–78. http://dx.doi.org/10.54097/jzqwgp87.

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In any competitive games, the most exciting thing for the audience is the change of the scores of both players, and quantifying the trend of the scores of both players can help coaches provide timely guidance to the players during the games and also help the players in the preparation for the games. The author firstly obtains the data of the players' psychological state, physical reserve and athletic skills during the game, and then compares the LGBM, SVC, MLP and LR models, analyzes the accuracy and regression rate of each model, and then selects the optimal LGBM model, and then calculates th

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Fernandes, Pedro Henrique Evangelista, Giovanni Corsetti Silva, Diogo Berta Pitz, et al. "Data-Driven, Physics-Based, or Both: Fatigue Prediction of Structural Adhesive Joints by Artificial Intelligence." Applied Mechanics 4, no. 1 (2023): 334–55. http://dx.doi.org/10.3390/applmech4010019.

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Here, a comparative investigation of data-driven, physics-based, and hybrid models for the fatigue lifetime prediction of structural adhesive joints in terms of complexity of implementation, sensitivity to data size, and prediction accuracy is presented. Four data-driven models (DDM) are constructed using extremely randomized trees (ERT), eXtreme gradient boosting (XGB), LightGBM (LGBM) and histogram-based gradient boosting (HGB). The physics-based model (PBM) relies on the Findley’s critical plane approach. Two hybrid models (HM) were developed by combining data-driven and physics-based appro

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Yockey, Robert Andrew, and Tracey E. Barnett. "Past-Year Blunt Smoking among Youth: Differences by LGBT and Non-LGBT Identity." International Journal of Environmental Research and Public Health 20, no. 7 (2023): 5304. http://dx.doi.org/10.3390/ijerph20075304.

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Blunt use (co-use of tobacco and marijuana) is a growing phenomenon among youth and disproportionately affects minority populations. LGBT+ populations are significantly more likely to use marijuana and tobacco, but this relationship has yet to be examined among LGBT+ adolescents. This analysis aimed to investigate past-year blunt use among a national sample of youth and delineate the differences between non-LGBT and LGBT+ youth. We used Wave 2 of the Population and Tobacco Health (PATH) study. We analyzed data from 7518 youth, comparing past-year blunt use between LGBT+ and non-LGBT youth, con

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Wang, Changyang, Kegen Yu, Fangyu Qu, Jinwei Bu, Shuai Han, and Kefei Zhang. "Spaceborne GNSS-R Wind Speed Retrieval Using Machine Learning Methods." Remote Sensing 14, no. 14 (2022): 3507. http://dx.doi.org/10.3390/rs14143507.

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This paper focuses on sea surface wind speed estimation using L1B level v3.1 data of reflected GNSS signals from the Cyclone GNSS (CYGNSS) mission and European Centre for Medium-range Weather Forecast Reanalysis (ECMWF) wind speed data. Seven machine learning methods are applied for wind speed retrieval, i.e., Regression trees (Binary Tree (BT), Ensembles of Trees (ET), XGBoost (XGB), LightGBM (LGBM)), ANN (Artificial neural network), Stepwise Linear Regression (SLR), and Gaussian Support Vector Machine (GSVM), and a comparison of their performance is made. The wind speed is divided into two d

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