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

Author: Grafiati

Published: 7 June 2025

Last updated: 17 July 2025

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1

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> Development of an Integrated Decision Support System (IDSS) for Autism Spectrum Disorder (ASD) diagnosis using advanced Data Science techniques. <strong>Methods:</strong> 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. <strong>Findings:</strong> 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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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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Hasan, Mohamad, and Tania Malik. "AI-Enhanced VPN Security Framework: Integrating Open-Source Threat Intelligence and Machine Learning to Secure Digital Networks." European Conference on Cyber Warfare and Security 23, no. 1 (2024): 760–68. http://dx.doi.org/10.34190/eccws.23.1.2505.

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In today's digital age, ensuring network privacy and integrity is of utmost importance. To address this, our work proposed an advanced VPN security framework that integrates open-source threat intelligence and machine learning (ML) to enhance cyber defences. By combining Wazuh for threat detection and analysis, and pfsense for firewall capabilities, with state-of-the-art ML algorithms, we present a robust VPN security solution to the challenges presented by the evolving landscape of cyber threats, representing a significant advancement in securing digital networks. This framework is strengthen

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Piotrowski, Paweł, Dariusz Baczyński, Marcin Kopyt, and Tomasz Gulczyński. "Advanced Ensemble Methods Using Machine Learning and Deep Learning for One-Day-Ahead Forecasts of Electric Energy Production in Wind Farms." Energies 15, no. 4 (2022): 1252. http://dx.doi.org/10.3390/en15041252.

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The ability to precisely forecast power generation for large wind farms is very important, since such generation is highly unstable and creates problems for Distribution and Transmission System Operators to properly prepare the power system for operation. Forecasts for the next 24 h play an important role in this process. They are also used in energy market transactions. Even a small improvement in the quality of these forecasts translates into more security of the system and savings for the economy. Using two wind farms for statistical analyses and forecasting considerably increases credibili

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Dixon, Samuel, Ravikiran Keshavamurthy, Daniel H. Farber, Andrew Stevens, Karl T. Pazdernik, and Lauren E. Charles. "A Comparison of Infectious Disease Forecasting Methods across Locations, Diseases, and Time." Pathogens 11, no. 2 (2022): 185. http://dx.doi.org/10.3390/pathogens11020185.

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Accurate infectious disease forecasting can inform efforts to prevent outbreaks and mitigate adverse impacts. This study compares the performance of statistical, machine learning (ML), and deep learning (DL) approaches in forecasting infectious disease incidences across different countries and time intervals. We forecasted three diverse diseases: campylobacteriosis, typhoid, and Q-fever, using a wide variety of features (n = 46) from public datasets, e.g., landscape, climate, and socioeconomic factors. We compared autoregressive statistical models to two tree-based ML models (extreme gradient

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Nijhawan, Rahul, Mukul Kumar, Sahitya Arya, et al. "A Novel Artificial-Intelligence-Based Approach for Classification of Parkinson’s Disease Using Complex and Large Vocal Features." Biomimetics 8, no. 4 (2023): 351. http://dx.doi.org/10.3390/biomimetics8040351.

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Parkinson’s disease (PD) affects a large proportion of elderly people. Symptoms include tremors, slow movement, rigid muscles, and trouble speaking. With the aging of the developed world’s population, this number is expected to rise. The early detection of PD and avoiding its severe consequences require a precise and efficient system. Our goal is to create an accurate AI model that can identify PD using human voices. We developed a transformer-based method for detecting PD by retrieving dysphonia measures from a subject’s voice recording. It is uncommon to use a neural network (NN)-based solut

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Vatcharaphrueksadee, Amornvit, Rattikan Viboonpanich, and Wilairat Charoenmairungrueang. "Comparative Analysis of DNN, GBT, and KNN Models for Network Intrusion Detection." ASEAN Journal of Scientific and Technological Reports 27, no. 5 (2024): e252675. http://dx.doi.org/10.55164/ajstr.v27i5.252675.

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Network intrusion detection is critical to cybersecurity, aiming to identify and mitigate unauthorized access and attacks on computer systems and networks. This study evaluates the effectiveness of three machine learning techniques—deep neural networks (DNN), gradient boost trees (GBT), and k-nearest neighbors (KNN)—in detecting network intrusions. The performance of these models was assessed using a comprehensive dataset of 2,540,047 records encompassing 49 features across nine attack categories. The results indicate that GBT outperforms DNN and KNN in accuracy and robustness. These findings

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C, Lakshmi K., and Dr Arjun B. C. "Performance Analysis by Using the Knime Analytical Platform to Forecast Heart Failure Using Several Machine Learning Methods." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (2023): 205–11. http://dx.doi.org/10.22214/ijraset.2023.49376.

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Abstract: Using a privately available dataset from kaggle.com, this research compares the performance of six well-known machine-learning approaches for predicting heart failure. which include Logistic Regression, Gradient Boosted Trees (GBT), Naive Bayes, Random Forest (RF), and Tree Ensemble. Heart failure is a major public health problem and it is necessary to improve the treatment of heart disease patients to increase the rate of survival. Delicacy was used to assess the performance of machine learning methods. RF produced the highest performance score of 80% when compared to Decision Tree

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Ammar, M. Alqahtani, S. Elbisy Moussa, and A. Osra Faisal. "Comparing Between Support Vector Machine and Gradient Boosted Trees Models for Prediction of Wave Overtopping at Coastal Structures with Composite Slope." Indian Journal of Science and Technology 16, no. 33 (2023): 2580–88. https://doi.org/10.17485/IJST/v16i33.919.

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Abstract <strong>Background/Objectives:</strong> Predicting wave overtopping at coastal structures is a critical task in coastal engineering. The use of machine learning models can help predict wave overtopping with higher accuracy and efficiency. <strong>Methods:</strong> In this study, the accuracy of support vector machine (SVM) and gradientboosted tree (GBT) approaches for predicting wave overtopping discharge of coastal structures with composite slopes ”without a berm” was evaluated and compared. The newly developed EurOtop database was used for this study.<stro

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Mohamadlou, Hamid, Saarang Panchavati, Jacob Calvert, et al. "Multicenter validation of a machine-learning algorithm for 48-h all-cause mortality prediction." Health Informatics Journal 26, no. 3 (2019): 1912–25. http://dx.doi.org/10.1177/1460458219894494.

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In order to evaluate mortality predictions based on boosted trees, this retrospective study uses electronic medical record data from three academic health centers for inpatients 18 years or older with at least one observation of each vital sign. Predictions were made 12, 24, and 48 hours before death. Models fit to training data from each institution were evaluated using hold-out test data from the same institution, and from the other institutions. Gradient-boosted trees (GBT) were compared to regularized logistic regression (LR) predictions, support vector machine (SVM) predictions, quick Sep

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Müller, Martha-Lena, Niroshan Nadarajah, Kapil Jhalani, et al. "Employment of Machine Learning Models Yields Highly Accurate Hematological Disease Prediction from Raw Flow Cytometry Matrix Data without the Need for Visualization or Human Intervention." Blood 136, Supplement 1 (2020): 11. http://dx.doi.org/10.1182/blood-2020-140927.

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Background: Machine Learning (ML) offers automated data processing substituting various analysis steps. So far it has been applied to flow cytometry (FC) data only after visualization which may compromise data by reduction of data dimensionality. Automated analysis of FC raw matrix data has not yet been pursued. Aim: To establish as proof of concept an ML-based classifier processing FC matrix data to predict the correct lymphoma type without the need for visualization or human analysis and interpretation. Methods: A set of 6,393 uniformly analyzed samples (Navios cytometers, Kaluza software, B

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Welchowski, Thomas, Kelly O. Maloney, Richard Mitchell, and Matthias Schmid. "Techniques to Improve Ecological Interpretability of Black-Box Machine Learning Models." Journal of Agricultural, Biological and Environmental Statistics 27, no. 1 (2021): 175–97. http://dx.doi.org/10.1007/s13253-021-00479-7.

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AbstractStatistical modeling of ecological data is often faced with a large number of variables as well as possible nonlinear relationships and higher-order interaction effects. Gradient boosted trees (GBT) have been successful in addressing these issues and have shown a good predictive performance in modeling nonlinear relationships, in particular in classification settings with a categorical response variable. They also tend to be robust against outliers. However, their black-box nature makes it difficult to interpret these models. We introduce several recently developed statistical tools to

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Nana, Dr Gunjal Sanjay, Dr D. B. Kshirsagar, Dr B. J. Dange, Dr H. E. Khodke, and Dr C. S. Kulkarni. "Machine Learning Approach for Big-Mart Sales Prediction Framework." International Journal of Innovative Technology and Exploring Engineering 11, no. 6 (2022): 69–75. http://dx.doi.org/10.35940/ijitee.f9916.0511622.

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The amounts of data predicted to increase at an exponential rate in the future. The modifications are essential to meet transaction speeds as well as the anticipated growth in data and customer behaviors. The information derived from prior data is extensively relied upon by the majority of companies. One of the primary goals of the suggested system is to identify a reliable sales trend prediction mechanism that is executed using machine learning techniques in order to maximize income. Sales forecasting advises managers about how to manage a company's employees, working capital and assets. It's

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Dr., Gunjal Sanjay Nana, D.B Kshirsagar Dr., B.J Dange Dr., H.E Khodke Dr., and C.S Kulkarni Dr. "Machine Learning Approach for Big-Mart Sales Prediction Framework." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 11, no. 6 (2022): 69–75. https://doi.org/10.35940/ijitee.F9916.0511622.

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<strong>Abstract: </strong>The amounts of data predicted to increase at an exponential rate in the future. The modifications are essential to meet transaction speeds as well as the anticipated growth in data and customer behaviors. The information derived from prior data is extensively relied upon by the majority of companies. One of the primary goals of the suggested system is to identify a reliable sales trend prediction mechanism that is executed using machine learning techniques in order to maximize income. Sales forecasting advises managers about how to manage a company's employees, w

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Afzal, Muhammad, Beom Joo Park, Maqbool Hussain, and Sungyoung Lee. "Deep Learning Based Biomedical Literature Classification Using Criteria of Scientific Rigor." Electronics 9, no. 8 (2020): 1253. http://dx.doi.org/10.3390/electronics9081253.

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A major blockade to support the evidence-based clinical decision-making is accurately and efficiently recognizing appropriate and scientifically rigorous studies in the biomedical literature. We trained a multi-layer perceptron (MLP) model on a dataset with two textual features, title and abstract. The dataset consisting of 7958 PubMed citations classified in two classes: scientific rigor and non-rigor, is used to train the proposed model. We compare our model with other promising machine learning models such as Support Vector Machine (SVM), Decision Tree, Random Forest, and Gradient Boosted T

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Chun, Matthew, Robert Clarke, Benjamin J. Cairns, et al. "Stroke risk prediction using machine learning: a prospective cohort study of 0.5 million Chinese adults." Journal of the American Medical Informatics Association 28, no. 8 (2021): 1719–27. http://dx.doi.org/10.1093/jamia/ocab068.

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Abstract Objective To compare Cox models, machine learning (ML), and ensemble models combining both approaches, for prediction of stroke risk in a prospective study of Chinese adults. Materials and Methods We evaluated models for stroke risk at varying intervals of follow-up (&lt;9 years, 0–3 years, 3–6 years, 6–9 years) in 503 842 adults without prior history of stroke recruited from 10 areas in China in 2004–2008. Inputs included sociodemographic factors, diet, medical history, physical activity, and physical measurements. We compared discrimination and calibration of Cox regression, log

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Rosa, Angelo, and Alessandro Massaro. "Process Mining Organization (PMO) Based on Machine Learning Decision Making for Prevention of Chronic Diseases." Eng 5, no. 1 (2024): 282–300. http://dx.doi.org/10.3390/eng5010015.

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This paper discusses a methodology to improve the prevention processes of chronic diseases such as diabetes and strokes. The research motivation is to find a new methodological approach to design advanced Diagnostic and Therapeutic Care Pathways (PDTAs) based on the prediction of chronic disease using telemedicine technologies and machine learning (ML) data processing techniques. The aim is to decrease health risk and avoid hospitalizations through prevention. The proposed method defines a Process Mining Organization (PMO) model, managing risks using a PDTA structured to prevent chronic risk.

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Sattari, Mohammad Taghi, Anca Avram, Halit Apaydin, and Oliviu Matei. "Soil Temperature Estimation with Meteorological Parameters by Using Tree-Based Hybrid Data Mining Models." Mathematics 8, no. 9 (2020): 1407. http://dx.doi.org/10.3390/math8091407.

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The temperature of the soil at different depths is one of the most important factors used in different disciplines, such as hydrology, soil science, civil engineering, construction, geotechnology, ecology, meteorology, agriculture, and environmental studies. In addition to physical and spatial variables, meteorological elements are also effective in changing soil temperatures at different depths. The use of machine-learning models is increasing day by day in many complex and nonlinear branches of science. These data-driven models seek solutions to complex and nonlinear problems using data obse

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Kota, Navyaja, Raju Kamaraj, S. Murugaanandam, Mohan Bharathi, and T. Sudheer Kumar. "A data-driven approach utilizing a raw material database and machine learning tools to predict the disintegration time of orally fast-disintegrating tablet formulations." Pharmacia 71 (June 19, 2024): 1–12. https://doi.org/10.3897/pharmacia.71.e122507.

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Orally fast-disintegrating tablets (OFDTs) have seen a significant increase in popularity over the past decade, becoming a rapidly expanding sector in the pharmaceutical market. The aim of the current study is to use machine learning (ML) methods to predict the disintegration time (DT) of OFDTs. In this study, we have developed seven ML models using the TPOT AutoML platform to predict the DT of OFDTs. These models include the decision tree regressor (DTR), gradient boost regressor (GBR), random forest regressor (RFR), extra tree regressor (ETR), least absolute shrinkage and selection operator

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Abdel-Fattah, Manal A., Nermin Abdelhakim Othman, and Nagwa Goher. "Predicting Chronic Kidney Disease Using Hybrid Machine Learning Based on Apache Spark." Computational Intelligence and Neuroscience 2022 (February 23, 2022): 1–12. http://dx.doi.org/10.1155/2022/9898831.

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Chronic kidney disease (CKD) has become a widespread disease among people. It is related to various serious risks like cardiovascular disease, heightened risk, and end-stage renal disease, which can be feasibly avoidable by early detection and treatment of people in danger of this disease. The machine learning algorithm is a source of significant assistance for medical scientists to diagnose the disease accurately in its outset stage. Recently, Big Data platforms are integrated with machine learning algorithms to add value to healthcare. Therefore, this paper proposes hybrid machine learning t

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Hamid, Danish, Syed Sajid Ullah, Jawaid Iqbal, Saddam Hussain, Ch Anwar ul Hassan, and Fazlullah Umar. "A Machine Learning in Binary and Multiclassification Results on Imbalanced Heart Disease Data Stream." Journal of Sensors 2022 (September 20, 2022): 1–13. http://dx.doi.org/10.1155/2022/8400622.

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In medical filed, predicting the occurrence of heart diseases is a significant piece of work. Millions of healthcare-related complexities that have remained unsolved up until now can be greatly simplified with the help of machine learning. The proposed study is concerned with the cardiac disease diagnosis decision support system. An OpenML repository data stream with 1 million instances of heart disease and 14 features is used for this study. After applying to preprocess and feature engineering techniques, machine learning approaches like random forest, decision trees, gradient boosted trees,

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Polenta, Andrea, Selene Tomassini, Nicola Falcionelli, Paolo Contardo, Aldo Franco Dragoni, and Paolo Sernani. "A Comparison of Machine Learning Techniques for the Quality Classification of Molded Products." Information 13, no. 6 (2022): 272. http://dx.doi.org/10.3390/info13060272.

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The developments in the internet of things (IoT), artificial intelligence (AI), and cyber-physical systems (CPS) are paving the way to the implementation of smart factories in what is commonly recognized as the fourth industrial revolution. In the manufacturing sector, these technological advancements are making Industry 4.0 a reality, with data-driven methodologies based on machine learning (ML) that are capable of extracting knowledge from the data collected by sensors placed on production machines. This is particularly relevant in plastic injection molding, with the objective of monitoring

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Othman, Berrada Chakour, Ettaoufik Abdelaziz, Aissaoui Khalid, and Maizate Abderrahim. "Artificial intelligence algorithms to predict customer satisfaction: a comparative study." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 2 (2025): 1654–62. https://doi.org/10.11591/ijai.v14.i2.pp1654-1662.

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Customer satisfaction is the key for every business successful. Therefore, keeping the current customer portfolio and expanding it over time is the main goal for any business. Hence, we need first to satisfy these clients. The customer satisfaction helps to retain consumers of its products, increase the life value of the customer, also make known its brand through positive word of mouth to get a better reputation and thus increase turnover. For this reason, several studies have been conducted on this subject to explore all tools and technologies that will help retain customers and reduce their

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Friesner, Isabel D., Kevin Miao, Justice Dahle, et al. "Prospective validation of machine learning-based approaches to predict potentially preventable emergency visits and hospitalizations." JCO Oncology Practice 19, no. 11_suppl (2023): 404. http://dx.doi.org/10.1200/op.2023.19.11_suppl.404.

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404 Background: Patients undergoing cancer treatment are at risk for unplanned acute care. Early identification of at-risk patients could enable preventative interventions, reducing costs and treatment delays. To address this, the Centers for Medicare & Medicaid Services developed the Chemotherapy Measure (OP-35) to monitor potentially preventable acute care utilization during outpatient treatment. We previously developed machine-learning (ML) models using three approaches: least absolute shrinkage selection operator (LASSO), random forest (RF), and gradient boosted trees (GBT). The models

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Singh, Balraj, and Vijay K. Minocha. "Clear Water Scour Depth Prediction using Gradient Boosting Machine and Deep Learning." IOP Conference Series: Earth and Environmental Science 1327, no. 1 (2024): 012030. http://dx.doi.org/10.1088/1755-1315/1327/1/012030.

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Abstract The scouring process in adjacent to spur dikes has the potential for compromising the stability of riverbanks. Hence, it is necessary for river engineering to conduct precise measurement of maximum scour depth in the vicinity of spur dikes. Nevertheless, the determination of the maximum scour depth has proven to be a challenging task, primarily due to the complex nature of the scour phenomena associated with these structures. In this study, two data-driven models, namely the Gradient Boost Machine (GBM) and Deep Learning (DL), were developed to predict the clear water scour depth near

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Bharathi, M., Raju Kamaraj, S. Murugaanandam, Kota Navyaja, and T. Sudheer Kumar. "A data-driven approach to predict the in vitro dissolution time of sustained-release tablets using raw material databases and machine learning algorithms." Pharmacia 71 (August 26, 2024): 1–7. https://doi.org/10.3897/pharmacia.71.e122772.

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Tablets are the most typical dosage forms of pharmaceutical inventions. Sustained-release (SR) tablet formulations are designed to release the drug gradually in the bloodstream and often require less frequent dosing. Current strategies to optimize sustained-release tablet dissolution time still rely on the traditional approach, which is time-consuming and expensive. In the present context, we have demonstrated alternate machine learning and deep learning models through the TPOT AutoML platform. Six machine learning (ML) models were compared to improve the methodology for dissolution time predi

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Seetharama, Pavithra Durganivas, and Shrishail Math. "Ataxic person prediction using feature optimized based on machine learning model." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 2 (2024): 2100–2109. https://doi.org/10.11591/ijece.v14i2.pp2100-2109.

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Ataxic gait monitoring and assessment of neurological disorders belong to important areas that are supported by digital signal processing methods and artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) techniques. This paper uses spatio-temporal data from Kinect sensor to optimize machine learning model to distinguish between ataxic and normal gait. Existing ML-based methodologies fails to establish feature correlation between different gait parameters; thus, exhibit very poor performance. Further, when data is imbalanced in nature the existing ML-based

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Shin, Taehwan, Jonghan Ko, Seungtaek Jeong, Jiwoo Kang, Kyungdo Lee, and Sangin Shim. "Assimilation of Deep Learning and Machine Learning Schemes into a Remote Sensing-Incorporated Crop Model to Simulate Barley and Wheat Productivities." Remote Sensing 14, no. 21 (2022): 5443. http://dx.doi.org/10.3390/rs14215443.

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Deep learning (DL) and machine learning (ML) procedures are prevailing data-driven schemes capable of advancing crop-modelling practices that assimilate these techniques into a mathematical crop model. A DL or ML modelling scheme can effectively represent complicated algorithms. This study reports on an advanced fusion methodology for evaluating the leaf area index (LAI) of barley and wheat that employs remotely sensed information based on deep neural network (DNN) and ML regression approaches. We investigated the most appropriate ML regressors for exploring LAI estimations of barley and wheat

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Ramírez Molina, Abel Andrés, Igor Leščešen, Glenn Tootle, Jiaqi Gong, and Milan Josić. "Hydrological Dynamics and Climate Variability in the Sava River Basin: Streamflow Reconstructions Using Tree-Ring-Based Paleo Proxies." Water 17, no. 3 (2025): 417. https://doi.org/10.3390/w17030417.

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This study reconstructs historical streamflow in the Sava River Basin (SRB), focusing on hydrological variability over extended timescales. Using a combination of Machine Learning (ML) and Deep Learning (DL) models, streamflow patterns were reconstructed from self-calibrated Palmer Drought Severity Index (scPDSI) proxies. The analysis included nine ML models and two DL architectures, with a post-prediction bias correction applied uniformly using the RQUANT method. Results indicate that ensemble methods, such as Random Forest and Gradient Boosted Tree, along with a six-layer DL model, effective

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Sexton, Justin, Yvette Everingham, David Donald, Steve Staunton, and Ronald White. "A comparison of non-linear regression methods for improved on-line near infrared spectroscopic analysis of a sugarcane quality measure." Journal of Near Infrared Spectroscopy 26, no. 5 (2018): 297–310. http://dx.doi.org/10.1177/0967033518802448.

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On-line near infrared (NIR) spectroscopic analysis systems play an important role in assessing the quality of sugarcane in Australia. As quality measures are used to calculate the payment made to growers, it is imperative that NIR models are both accurate and robust. Machine learning and non-linear modelling approaches have been explored as methods for developing improved NIR models in a variety of industrial settings, yet there has been little research into their application to cane quality measures. The objective of this paper was to compare chemometric models of commercial cane sugar (CCS)

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Neha, Neha, and Abhishek Kajal. "Implementing Comparative Analysis on Feature Engineering Techniques and Multi-Model Evaluation Framework for IDS." Journal of Cybersecurity and Information Management 16, no. 1 (2025): 53–67. https://doi.org/10.54216/jcim.160105.

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In recent years, most of the current intrusion detection methods run for critical information infrastructure are tested for IDS datasets, but does not provide desired protection against emerging cyber- threats. Most machine and deep learning-based intrusion detection methods are inefficient on networks due to their high imbalanced or noisy IDS datasets. Therefore, in this paper, our proposed work implements a comprehensive framework, using multiple models of machine learning and deep learning by taking advantage of advanced feature engineering approaches. Our research explores the impacts of a

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Kota, Navyaja, Raju Kamaraj, S. Murugaanandam, Mohan Bharathi, and T. Sudheer Kumar. "A data-driven approach utilizing a raw material database and machine learning tools to predict the disintegration time of orally fast-disintegrating tablet formulations." Pharmacia 71 (June 19, 2024): 1–12. http://dx.doi.org/10.3897/pharmacia.71.e122507.

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Orally fast-disintegrating tablets (OFDTs) have seen a significant increase in popularity over the past decade, becoming a rapidly expanding sector in the pharmaceutical market. The aim of the current study is to use machine learning (ML) methods to predict the disintegration time (DT) of OFDTs. In this study, we have developed seven ML models using the TPOT AutoML platform to predict the DT of OFDTs. These models include the decision tree regressor (DTR), gradient boost regressor (GBR), random forest regressor (RFR), extra tree regressor (ETR), least absolute shrinkage and selection operator

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Feng, Jin, Yanjie Li, Yulu Qiu, and Fuxin Zhu. "Capturing synoptic-scale variations in surface aerosol pollution using deep learning with meteorological data." Atmospheric Chemistry and Physics 23, no. 1 (2023): 375–88. http://dx.doi.org/10.5194/acp-23-375-2023.

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Abstract. The estimation of daily variations in aerosol concentrations using meteorological data is meaningful and challenging, given the need for accurate air quality forecasts and assessments. In this study, a 3×50-layer spatiotemporal deep learning (DL) model is proposed to link synoptic variations in aerosol concentrations and meteorology, thereby building a “deep” Weather Index for Aerosols (deepWIA). The model was trained and validated using 7 years of data and tested in January–April 2022. The index successfully reproduced the variation in daily PM2.5 observations in China. The coeffici

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Bharathi, M., Raju Kamaraj, S. Murugaanandam, Kota Navyaja, and T. Sudheer Kumar. "A data-driven approach to predict the in vitro dissolution time of sustained-release tablets using raw material databases and machine learning algorithms." Pharmacia 71 (August 26, 2024): 1–7. http://dx.doi.org/10.3897/pharmacia.71.e122772.

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Tablets are the most typical dosage forms of pharmaceutical inventions. Sustained-release (SR) tablet formulations are designed to release the drug gradually in the bloodstream and often require less frequent dosing. Current strategies to optimize sustained-release tablet dissolution time still rely on the traditional approach, which is time-consuming and expensive. In the present context, we have demonstrated alternate machine learning and deep learning models through the TPOT AutoML platform. Six machine learning (ML) models were compared to improve the methodology for dissolution time predi

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Seetharama, Pavithra Durganivas, and Shrishail Math. "Ataxic person prediction using feature optimized based on machine learning model." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 2 (2024): 2100. http://dx.doi.org/10.11591/ijece.v14i2.pp2100-2109.

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Ataxic gait monitoring and assessment of neurological disorders belong to important areas that are supported by digital signal processing methods and artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) techniques. This paper uses spatio-temporal data from Kinect sensor to optimize machine learning model to distinguish between ataxic and normal gait. Existing ML-based methodologies fails to establish feature correlation between different gait parameters; thus, exhibit very poor performance. Further, when data is imbalanced in nature the existing ML-based

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Yoro, Rume Elizabeth, Margaret Dumebi Okpor, Maureen Ifeanyi Akazue, et al. "Adaptive DDoS detection mode in software-defined SIP-VoIP using transfer learning with boosted meta-learner." PLOS One 20, no. 6 (2025): e0326571. https://doi.org/10.1371/journal.pone.0326571.

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The Internet has continued to provision its infrastructure as a platform for competitive marketing, enhanced productivity, and monetization efficacy. However, it has become a means for adversaries to exploit unsuspecting users and, in turn, compromise network resources. The utilization of filters, gateways, firewalls, and intrusion detection systems has only minimized the effects of adversaries. Thus, with the constant evolution of exploitation and penetrative techniques in network security, security experts are required to also evolve their mitigation and defensive measures by using advanced

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Muhammad Fawwaz Narendra. "Forecasting Manpower Planning Using the CRISP-DM Method and Machine Learning Algorithm: A Case Study of Tiki Jalur Nugraha Ekakurir (JNE) Company." Journal of Information Systems Engineering and Management 10, no. 19s (2025): 371–78. https://doi.org/10.52783/jisem.v10i19s.3040.

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Introduction: The logistics industry faces significant fluctuations in demand, particularly during peak seasons, making effective manpower planning essential to maintaining service quality and controlling operational costs. Poor workforce planning can lead to inefficiencies such as overstaffing, which increases costs, or understaffing, which negatively impacts delivery performance. To address these challenges, this research focuses on developing a predictive analytics model for workforce planning at Tiki Jalur Nugraha Ekakurir (JNE) Company, a leading logistics company in Indonesia. By leverag

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Zeadna, A., N. Khateeb, L. Rokach, et al. "Prediction of sperm extraction in non-obstructive azoospermia patients: a machine-learning perspective." Human Reproduction 35, no. 7 (2020): 1505–14. http://dx.doi.org/10.1093/humrep/deaa109.

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Abstract STUDY QUESTION Can a machine-learning-based model trained in clinical and biological variables support the prediction of the presence or absence of sperm in testicular biopsy in non-obstructive azoospermia (NOA) patients? SUMMARY ANSWER Our machine-learning model was able to accurately predict (AUC of 0.8) the presence or absence of spermatozoa in patients with NOA. WHAT IS KNOWN ALREADY Patients with NOA can conceive with their own biological gametes using ICSI in combination with successful testicular sperm extraction (TESE). Testicular sperm retrieval is successful in up to 50% of

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YILDIRIM, Rıfat. "Machine Learning Applications in Biogas and Methane Production: A Bibliometric Analysis." Energy, Environment and Storage 5, no. 2 (2025): 67–77. https://doi.org/10.52924/uscm8798.

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Biogas processes play an important role in the disposal of organic waste. However, these processes are difficult to control because they are highly sensitive and variable. A lot of work has been done to date in order to eliminate this problem. With the development of technology and artificial intelligence, the spread of “Autonomous” systems has become widespread in the control of anaerobic processes as in many other fields. The Anaerobic Digestion Model No. 1 (ADM1) developed by the International Water Association (IWA) has been adopted as the standard model for the AD process since 2002. With

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Antony, Veena, and Nainan Thangarasu. "Chaotic crow search enhanced CRNN: a next-gen approach for IoT botnet attack detection." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 3 (2025): 1745. https://doi.org/10.11591/ijeecs.v38.i3.pp1745-1754.

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Internet of things (IoT) botnet attack detection is crucial for reducing and identifying hostile threats in networks. To create efficient threat detection systems, deep learning (DL) and machine learning (ML) are currently being used in many sectors, mostly in information security. The botnet attack categorization problem is difficult as data dimensionality increases. By combining convolutional and recurrent neural layers, our work effectively addressed the vanishing and expanding gradient difficulties, improving the ability to capture spatial and temporal connections. The problem of weight de

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Kay, Fernando U., Cynthia Lumby, Yuki Tanabe, Suhny Abbara, and Prabhakar Rajiah. "Detection of Low Blood Hemoglobin Levels on Pulmonary CT Angiography: A Feasibility Study Combining Dual-Energy CT and Machine Learning." Tomography 9, no. 4 (2023): 1538–50. http://dx.doi.org/10.3390/tomography9040123.

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Objectives: To evaluate if dual-energy CT (DECT) pulmonary angiography (CTPA) can detect anemia with the aid of machine learning. Methods: Inclusion of 100 patients (mean age ± SD, 51.3 ± 14.8 years; male-to-female ratio, 42/58) who underwent DECT CTPA and hemoglobin (Hb) analysis within 24 h, including 50 cases with Hb below and 50 controls with Hb ≥ 12 g/dL. Blood pool attenuation was assessed on virtual noncontrast (VNC) images at eight locations. A classification model using extreme gradient-boosted trees was developed on a training set (n = 76) for differentiating cases from controls. The

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Miao, Kevin, Justice Dahle, Sasha Yousefi, et al. "Machine learning-based approach to the risk assessment of potentially preventable outpatient cancer treatment-related emergency care and hospitalizations." Journal of Clinical Oncology 39, no. 28_suppl (2021): 333. http://dx.doi.org/10.1200/jco.2020.39.28_suppl.333.

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333 Background: Patients undergoing outpatient infusion chemotherapy for cancer are at risk for potentially preventable, unplanned acute care in the form of emergency department (ED) visits and hospital admissions. This can impact outcomes, patient decisions, and costs to the patient and healthcare system. To address this need, the Centers for Medicare & Medicaid Services developed the Chemotherapy Measure (OP-35). Recent randomized controlled data indicate that electronic health record (EHR)-based machine learning (ML) approaches accurately direct supportive care to reduce acute care duri

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Frndak, Seth, Fengxia Yan, Mike Edelson, et al. "Predicting Low-Level Childhood Lead Exposure in Metro Atlanta Using Ensemble Machine Learning of High-Resolution Raster Cells." International Journal of Environmental Research and Public Health 20, no. 5 (2023): 4477. http://dx.doi.org/10.3390/ijerph20054477.

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Low-level lead exposure in children is a major public health issue. Higher-resolution spatial targeting would significantly improve county and state-wide policies and programs for lead exposure prevention that generally intervene across large geographic areas. We use stack-ensemble machine learning, including an elastic net generalized linear model, gradient-boosted machine, and deep neural network, to predict the number of children with venous blood lead levels (BLLs) ≥2 to <5 µg/dL and ≥5 µg/dL in ~1 km2 raster cells in the metro Atlanta region using a sample of 92,792 children ≤5 years o

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