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.
Full textRamya, 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.
Full textNordin, 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.
Full textPrakash, 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.
Full textR, 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.
Full textM, 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.
Full textParreco, 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.
Full textLiu, 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.
Full textAbidi, 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.
Full textHasan, 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.
Full textPiotrowski, 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.
Full textDixon, 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.
Full textNijhawan, 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.
Full textVatcharaphrueksadee, 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.
Full textC, 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.
Full textAmmar, 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.
Full textMohamadlou, 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.
Full textMüller, 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.
Full textWelchowski, 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.
Full textNana, 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.
Full textDr., 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.
Full textAfzal, 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.
Full textChun, 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.
Full textRosa, 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.
Full textSattari, 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.
Full textKota, 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.
Full textAbdel-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.
Full textHamid, 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.
Full textPolenta, 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.
Full textOthman, 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.
Full textFriesner, 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.
Full textSingh, 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.
Full textBharathi, 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.
Full textSeetharama, 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.
Full textShin, 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.
Full textRamírez 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.
Full textSexton, 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.
Full textNeha, 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.
Full textKota, 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.
Full textFeng, 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.
Full textBharathi, 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.
Full textSeetharama, 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.
Full textYoro, 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.
Full textMuhammad 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.
Full textZeadna, 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.
Full textYILDIRIM, 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.
Full textAntony, 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.
Full textKay, 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.
Full textMiao, 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.
Full textFrndak, 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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