Relevant bibliographies by topics / Gradient Boosted Trees-Deep Learning (GBT-DL)

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Academic literature on the topic 'Gradient Boosted Trees-Deep Learning (GBT-DL)'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Gradient Boosted Trees-Deep Learning (GBT-DL)"

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Avram, Anca, Oliviu Matei, Camelia Pintea, and Carmen Anton. "Innovative Platform for Designing Hybrid Collaborative & Context-Aware Data Mining Scenarios." Mathematics 8, no. 5 (2020): 684. http://dx.doi.org/10.3390/math8050684.

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The process of knowledge discovery involves nowadays a major number of techniques. Context-Aware Data Mining (CADM) and Collaborative Data Mining (CDM) are some of the recent ones. the current research proposes a new hybrid and efficient tool to design prediction models called Scenarios Platform-Collaborative & Context-Aware Data Mining (SP-CCADM). Both CADM and CDM approaches are included in the new platform in a flexible manner; SP-CCADM allows the setting and testing of multiple configurable scenarios related to data mining at once. The introduced platform was successfully tested and va
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Ramya, R., and S. Panneer Arokiaraj. "Integrated Decision Support System (IDSS) for Autism Spectrum Disorder Diagnosis: A Multi-model F ramework Approach." Indian Journal Of Science And Technology 17, no. 45 (2024): 4787–97. https://doi.org/10.17485/ijst/v17i45.3348.

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Objectives: Development of an Integrated Decision Support System (IDSS) for Autism Spectrum Disorder (ASD) diagnosis using advanced Data Science techniques. Methods: A IDSS multi-model approach combining GBT-DL, VO-ADA, and CNN-RF were used. In evaluation, the following parameters were computed: accuracy, precision, recall, F1-score, and Kappa statistics. Findings: The results show that the GBT-DL model performed with excellent accuracy of 95.52% performance, while the VO-ADA model achieved an accuracy level of 98.60%, and the CNN-RF model has proven to have the highest accuracy at 99.15%. The
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Nordin, Nur Dalilla, Mohd Saiful Dzulkefly Zan, and Fairuz Abdullah. "Comparative Analysis on the Deployment of Machine Learning Algorithms in the Distributed Brillouin Optical Time Domain Analysis (BOTDA) Fiber Sensor." Photonics 7, no. 4 (2020): 79. http://dx.doi.org/10.3390/photonics7040079.

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This paper demonstrates a comparative analysis of five machine learning (ML) algorithms for improving the signal processing time and temperature prediction accuracy in Brillouin optical time domain analysis (BOTDA) fiber sensor. The algorithms analyzed were generalized linear model (GLM), deep learning (DL), random forest (RF), gradient boosted trees (GBT), and support vector machine (SVM). In this proof-of-concept experiment, the performance of each algorithm was investigated by pairing Brillouin gain spectrum (BGS) with its corresponding temperature reading in the training dataset. It was fo
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Prakash, V. Jothi, and N. K. Karthikeyan. "Dual-Layer Deep Ensemble Techniques for Classifying Heart Disease." Information Technology and Control 51, no. 1 (2022): 158–79. http://dx.doi.org/10.5755/j01.itc.51.1.30083.

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The prevalence of heart disease is increasing at a rapid rate due to changes in food habits and lifestyle of peopleall over the world. Early prediction and diagnosis of this fatal disease is a highly excruciating task. Nowadays, theensemble learning approaches are preferred owing to their effectiveness in performance when compared to theperformance of a single classification algorithm. In this work, a Dual-Layer Stacking Ensemble (DLSE) techniqueand a Deep Heterogeneous Ensemble (DHE) technique to classify heart disease are proposed. The DLSE uses several heterogeneous classifiers to form an e
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R, Ramya, and Panneer Arokiaraj S. "Integrated Decision Support System (IDSS) for Autism Spectrum Disorder Diagnosis: A Multi-model F ramework Approach." Indian Journal of Science and Technology 17, no. 45 (2024): 4787–97. https://doi.org/10.17485/IJST/v17i45.3348.

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Abstract <strong>Objectives:</strong>&nbsp;Development of an Integrated Decision Support System (IDSS) for Autism Spectrum Disorder (ASD) diagnosis using advanced Data Science techniques.&nbsp;<strong>Methods:</strong>&nbsp;A IDSS multi-model approach combining GBT-DL, VO-ADA, and CNN-RF were used. In evaluation, the following parameters were computed: accuracy, precision, recall, F1-score, and Kappa statistics.&nbsp;<strong>Findings:</strong>&nbsp;The results show that the GBT-DL model performed with excellent accuracy of 95.52% performance, while the VO-ADA model achieved an accuracy level o
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M, V. T. Ram Pavan Kumar. "Transforming Dairy Standards: Machine Learning in Milk Quality Prediction." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 1735–40. https://doi.org/10.22214/ijraset.2025.68639.

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Abstract: Accurate and interpretable milk quality prediction is critical for ensuring food safety and regulatory compliance in the dairy industry. While machine learning (ML) models like deep neural networks (DNNs) and gradient-boosted trees (GBT) achieve high predictive accuracy, their "black-box" nature limits stakeholder trust and actionable insights. This study bridges the gap between performance and interpretability by evaluating both complex and transparent ML models on a dataset of seven milk quality parameters (pH, temperature, taste, odor, fat, turbidity, color). We quantify feature c
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Kirtil, Emrah. "Universal Prediction of CO2 Adsorption on Zeolites Using Machine Learning: A Comparative Analysis with Langmuir Isotherm Models." ChemEngineering 9, no. 4 (2025): 80. https://doi.org/10.3390/chemengineering9040080.

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The global atmospheric concentration of carbon dioxide (CO2) has exceeded 420 ppm. Adsorption-based carbon capture technologies, offer energy-efficient, sustainable solutions. Relying on classical adsorption models like Langmuir to predict CO2 uptake presents limitations due to the need for case-specific parameter fitting. To address this, the present study introduces a universal machine learning (ML) framework using multiple algorithms—Generalized Linear Model (GLM), Feed-forward Multilayer Perceptron (DL), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Gradient Boo
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Parreco, Joshua, Hahn Soe-Lin, Jonathan J. Parks, et al. "Comparing Machine Learning Algorithms for Predicting Acute Kidney Injury." American Surgeon 85, no. 7 (2019): 725–29. http://dx.doi.org/10.1177/000313481908500731.

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Prior studies have used vital signs and laboratory measurements with conventional modeling techniques to predict acute kidney injury (AKI). The purpose of this study was to use the trend in vital signs and laboratory measurements with machine learning algorithms for predicting AKI in ICU patients. The eICU Collaborative Research Database was queried for five consecutive days of laboratory measurements per patient. Patients with AKI were identified and trends in vital signs and laboratory values were determined by calculating the slope of the least-squares-fit linear equation using three days f
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Liu, Rencheng, Saqib Ali, Syed Fakhar Bilal, et al. "An Intelligent Hybrid Scheme for Customer Churn Prediction Integrating Clustering and Classification Algorithms." Applied Sciences 12, no. 18 (2022): 9355. http://dx.doi.org/10.3390/app12189355.

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Nowadays, customer churn has been reflected as one of the main concerns in the processes of the telecom sector, as it affects the revenue directly. Telecom companies are looking to design novel methods to identify the potential customer to churn. Hence, it requires suitable systems to overcome the growing churn challenge. Recently, integrating different clustering and classification models to develop hybrid learners (ensembles) has gained wide acceptance. Ensembles are getting better approval in the domain of big data since they have supposedly achieved excellent predictions as compared to sin
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Abidi, Syed, Mushtaq Hussain, Yonglin Xu, and Wu Zhang. "Prediction of Confusion Attempting Algebra Homework in an Intelligent Tutoring System through Machine Learning Techniques for Educational Sustainable Development." Sustainability 11, no. 1 (2018): 105. http://dx.doi.org/10.3390/su11010105.

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Incorporating substantial, sustainable development issues into teaching and learning is the ultimate task of Education for Sustainable Development (ESD). The purpose of our study was to identify the confused students who had failed to master the skill(s) given by the tutors as homework using the Intelligent Tutoring System (ITS). We have focused ASSISTments, an ITS in this study, and scrutinized the skill-builder data using machine learning techniques and methods. We used seven candidate models including: Naïve Bayes (NB), Generalized Linear Model (GLM), Logistic Regression (LR), Deep Learning
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Book chapters on the topic "Gradient Boosted Trees-Deep Learning (GBT-DL)"

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Vanithamani, R., K. Keerthana, and Pavithra Suchindran. "Kidney Stone Detection Using Machine Learning Approaches." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-7352-1.ch004.

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Kidney stones, formed from urine-derived molecules like uric acid and calcium oxalate, are a growing global health issue, affecting 11% of men and 9% of women. Diseases like high blood pressure, diabetes, and obesity increase the risk. This study aims to detect kidney stones in CT images using Machine Learning (ML) and Deep Learning (DL) methods. A dataset of 12,446 CT images from Kaggle was used. Pre-processing involved median filtering and erosion, followed by segmentation using gradient vector flow. Features like mean, contrast, and entropy were selected using the Butterfly Optimization Alg
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Conference papers on the topic "Gradient Boosted Trees-Deep Learning (GBT-DL)"

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Abu-Farsakh, Murad. "Develop Machine Learning Models to Establish the Load-Settlement Curve of Piles from Cone Penetration Test Data." In Deep Foundations Institute 49th Annual Conference. Deep Foundations Institute, 2024. https://doi.org/10.37308/dfi49.2024970301.

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The evaluation of load-settlement behavior of piles is very crucial in meeting the serviceability criteria for pile analysis and design. The most reliable approach for estimating this behavior can be achieved by conducting pile load tests. However, due to the considerable expense and time requirement of such in-situ testing, the load-transfer methods have been used routinely in practice. In this paper, an alternative tree-based machine learning (ML) modeling is explored to predict the load-settlement behavior of axially loaded single piles from cone penetration test (CPT) data. Two variants of
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Jawthari, Moohanad, and Veronika Stoffova. "EFFECT OF ENCODING CATEGORICAL DATA ON STUDENT'S ACADEMIC PERFORMANCE USING DATA MINING METHODS." In eLSE 2020. University Publishing House, 2020. http://dx.doi.org/10.12753/2066-026x-20-068.

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Educational data mining (EDM) is the techniques used to discover the knowledge from student's data .it is used to improve the students' performance and teachers' performances as well. Since Machine learning (ML) models deals with numeric data, preprocessing of the categorical data is a must step to transform such data into accepted types by ML models. Categorical data is further divided into nominal and ordinal attributes in the dataset. The used data set was collected by using a learner activity tracker tool, which called experience API (xAPI). The purposed was to monitor the behaviors of stu
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