Relevant bibliographies by topics / Stacked Ensemble Model

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Academic literature on the topic 'Stacked Ensemble Model'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Stacked Ensemble Model"

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Altaf, Muhammad Faheem, Muhammad Waseem Iqbal, Ghulam Ali, et al. "Neural network-based ensemble approach for multi-view facial expression recognition." PLOS ONE 20, no. 3 (2025): e0316562. https://doi.org/10.1371/journal.pone.0316562.

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In this paper, we developed a pose-aware facial expression recognition technique. The proposed technique employed K nearest neighbor for pose detection and a neural network-based extended stacking ensemble model for pose-aware facial expression recognition. For pose-aware facial expression classification, we have extended the stacking ensemble technique from a two-level ensemble model to three-level ensemble model: base-level, meta-level and predictor. The base-level classifier is the binary neural network. The meta-level classifier is a pool of binary neural networks. The outputs of binary ne
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Gunasekaran, Hemalatha, Angelin Gladys, Deepa kanmani, Rex Macedo, and Wilfred Blessing N R. "Brain Stroke Prediction Using Stacked Ensemble Model." Jurnal Kejuruteraan 36, no. 4 (2024): 1759–68. http://dx.doi.org/10.17576/jkukm-2024-36(4)-38.

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Stroke is a potentially fatal illness that requires emergency care. There is a greater chance that the patient will recover and resume their regular life when they receive treatment and diagnosis as soon as feasible. Artificial Intelligence has the potential to significantly impact stroke diagnosis and facilitate prompt patient treatment for physicians. Machine learning can be utilized in stroke prediction by evaluating huge volumes of patient data and detecting patterns and risk variables that may contribute to the likelihood of a stroke. In this study, we explored a stacked ensemble model th
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AKÇAY, Selma, Selim BUYRUKOĞLU, and Ünal AKDAĞ. "Stacked Heterogeneous Ensemble Learning Model in Mixed Convection Heat Transfer from a Vertically Oscillating Flat Plate." Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 6, no. 1 (2023): 635–54. http://dx.doi.org/10.47495/okufbed.1100651.

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In this study, the effects of mixed convection heat transfer from a moving vertical flat plate with an experimental and stacked heterogeneous ensemble learning approach are analyzed. In the experimental work, the effects on both natural and forced convection of dimensionless oscillation amplitude (Ao), dimensionless oscillation frequency (Wo) and Rayleigh number (Ra) are investigated. In the experiments, the vertical movement of the plate is provided by a flywheel-motor assembly. The average Nusselt numbers (Nu) on the fixed plate and the moving plate surface were obtained. Additionally, this
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Novaes de Amorim, Arthur, Rob Deardon, and Vineet Saini. "A stacked ensemble method for forecasting influenza-like illness visit volumes at emergency departments." PLOS ONE 16, no. 3 (2021): e0241725. http://dx.doi.org/10.1371/journal.pone.0241725.

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Accurate and reliable short-term forecasts of influenza-like illness (ILI) visit volumes at emergency departments can improve staffing and resource allocation decisions within hospitals. In this paper, we developed a stacked ensemble model that averages the predictions from various competing methodologies in the current frontier for ILI-related forecasts. We also constructed a back-of-the-envelope prediction interval for the stacked ensemble, which provides a conservative characterization of the uncertainty in the stacked ensemble predictions. We assessed the accuracy and reliability of our mo
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Folake, Akinbohun, Akinbohun Ambrose, and Oyinloye Oghenerukevwe E. "Stacked Ensemble Model for Hepatitis in Healthcare System." International Journal of Computer and Organization Trends 9, no. 4 (2019): 25–29. http://dx.doi.org/10.14445/22492593/ijcot-v9i4p305.

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Faraji, Mohammad Amin, Alireza Shooshtari, and Ayman El-Hag. "Stacked Ensemble Regression Model for Prediction of Furan." Energies 16, no. 22 (2023): 7656. http://dx.doi.org/10.3390/en16227656.

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Furan tests provide a non-intrusive and cost-effective method of estimating the degradation of paper insulation, which is critical for ensuring the reliability of power grids. However, conducting routine furan tests can be expensive and challenging, highlighting the need for alternative methods, such as machine learning algorithms, to predict furan concentrations. To establish the generalizability and robustness of the furan prediction model, this study investigates two distinct datasets from different geographical locations, Utility A and Utility B. Three scenarios are proposed: in the first
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Maigari, Aminu, Zurinahni Zainol, and Chew Xinying. "Multi-modal Stacked Ensemble Model for Breast Cancer Prognosis Prediction." Statistics, Optimization & Information Computing 13, no. 3 (2024): 1013–34. https://doi.org/10.19139/soic-2310-5070-2100.

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Breast cancer (BC) is a global health challenge that affects millions of women worldwide and leads to significant mortality. Recent advancements in next-generation sequencing technology have enabled comprehensive diagnosis and prognosis determination using multiple data modalities. Deep learning methods have shown promise in utilizing these multimodal data sources, outperforming single-modal models. However, integrating these heterogeneous data sources poses significant challenges in clinical decision-making. This study proposes an optimized multimodal CNN for a stacked ensemble model (OMCNNSE
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Adetunji, Olusogo Julius. "Flood Area Prediction using a Stacked Ensemble of Tree-Based Algorithms." Sakarya University Journal of Computer and Information Sciences 8, no. 2 (2025): 322–44. https://doi.org/10.35377/saucis...1626057.

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Floods cause significant loss of life, property damage, and long-term socioeconomic disruptions, with over 100 annual deaths globally. This research addresses the drawbacks of the existing models, such as overfitting effects, inadequate dataset and limited study areas through the adoption of a stacked ensemble-based model. The model contained five different tree - based models namely hoeffding tree, decision tree, functional tree, reduced error pruning (REP) tree and decision stump algorithms. The model was implemented as a system using MATLAB Simulink, version 2020a on laptop with 4GB Memory.
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Sung, Chih-Wei, Joshua Ho, Cheng-Yi Fan, et al. "Prediction of high-risk emergency department revisits from a machine-learning algorithm: a proof-of-concept study." BMJ Health & Care Informatics 31, no. 1 (2024): e100859. http://dx.doi.org/10.1136/bmjhci-2023-100859.

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BackgroundHigh-risk emergency department (ED) revisit is considered an important quality indicator that may reflect an increase in complications and medical burden. However, because of its multidimensional and highly complex nature, this factor has not been comprehensively investigated. This study aimed to predict high-risk ED revisit with a machine-learning (ML) approach.MethodsThis 3-year retrospective cohort study assessed adult patients between January 2019 and December 2021 from National Taiwan University Hospital Hsin-Chu Branch with high-risk ED revisit, defined as hospital or intensive
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Mahajan, Asmita, Nonita Sharma, Silvia Aparicio-Obregon, et al. "A Novel Stacking-Based Deterministic Ensemble Model for Infectious Disease Prediction." Mathematics 10, no. 10 (2022): 1714. http://dx.doi.org/10.3390/math10101714.

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Infectious Disease Prediction aims to anticipate the aspects of both seasonal epidemics and future pandemics. However, a single model will most likely not capture all the dataset’s patterns and qualities. Ensemble learning combines multiple models to obtain a single prediction that uses the qualities of each model. This study aims to develop a stacked ensemble model to accurately predict the future occurrences of infectious diseases viewed at some point in time as epidemics, namely, dengue, influenza, and tuberculosis. The main objective is to enhance the prediction performance of the proposed
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Book chapters on the topic "Stacked Ensemble Model"

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Zulfiker, Md Sabab, Nasrin Kabir, Al Amin Biswas, and Partha Chakraborty. "Predicting Insomnia Using Multilayer Stacked Ensemble Model." In Communications in Computer and Information Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81462-5_31.

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Menon, Vishnu, and Lakshmi Harika Palivela. "A Stacked Ensemble Learning Model for Enhanced Network Intrusion Detection." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-0924-6_25.

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Verma, Pratibha, Vineet Kumar Awasthi, A. K. Shrivas, and Sanat Kumar Sahu. "Stacked Generalization Based Ensemble Model for Classification of Coronary Artery Disease." In Internet of Things and Connected Technologies. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94507-7_6.

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Sahu, Priyabrata, and Jibendu Kumar Mantri. "Stacked Generalization Ensemble-Based Hybrid Gradient Boosted Model for Predicting Diabetes." In Advances in Engineering Research. Atlantis Press International BV, 2024. http://dx.doi.org/10.2991/978-94-6463-529-4_23.

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Xiong, Shilong, Qibo Sun, and Ao Zhou. "Improve the House Price Prediction Accuracy with a Stacked Generalization Ensemble Model." In Internet of Vehicles. Technologies and Services Toward Smart Cities. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38651-1_32.

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Rajaraman, Sivaramakrishnan, Sema Cemir, Zhiyun Xue, Philip Alderson, George Thoma, and Sameer Antani. "A Novel Stacked Model Ensemble for Improved TB Detection in Chest Radiographs." In Medical Imaging. CRC Press, 2019. http://dx.doi.org/10.1201/9780429029417-1.

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Tasnim, Nowshin, Md Musfique Anwar, and Iqbal H. Sarker. "A Stacked Ensemble Spyware Detection Model Using Hyper-Parameter Tuned Tree Based Classifiers." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-34622-4_32.

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Verma, Saurabh, Renu Dhir, Mohit Kumar, and Mansi Gupta. "Digital Healthcare System Using Stacked Ensemble Machine Learning Model to Predict Heart Diseases." In Signals and Communication Technology. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-56818-3_7.

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Joseph, Amal, Bansi Pambhar, Allen George, Sujatha Arun Kokatnoor, and Sandeep Kumar. "Prediction of Next-Day Stock Price Using Stacked Ensemble Learning Techniques—An Exploration of Model Compatibility." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-1188-1_19.

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Kumar, Deepika, Varun Srivastava, Shilpa Gupta, and Akhtar Jamil. "Classification of Brain-MRI Images Using a Stacked-Deep-Network Ensemble Model into Multiple Region-Based Classes." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-82377-0_40.

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Conference papers on the topic "Stacked Ensemble Model"

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Akbas, Ayhan, Gonca Buyrukoglu, and Selim Buyrukoglu. "Robust Stacked Ensemble Model for Lung Cancer Diagnosis." In 2024 9th International Conference on Computer Science and Engineering (UBMK). IEEE, 2024. https://doi.org/10.1109/ubmk63289.2024.10773466.

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Hasan, Md Nahid, Raiyan Azim, Mahmudul Hasan, and Md Monarul Islam. "A Stacked Ensemble Model to Identify Bangla Religious Hate Comments." In 2024 IEEE 3rd Conference on Information Technology and Data Science (CITDS). IEEE, 2024. https://doi.org/10.1109/citds62610.2024.10791360.

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Tabassum, Tasniah, and Mostafa Zaman Chowdhury. "Hybrid Stacked Ensemble Model for Improved Predictions in Cellular Traffic Analysis." In 2024 International Conference on Recent Progresses in Science, Engineering and Technology (ICRPSET). IEEE, 2024. https://doi.org/10.1109/icrpset64863.2024.10955961.

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Kannan, Nandini, Erugu Krishna, P. Joel Josephson, Ravi Kant, M. V. Rathnamma, and A. Bharathi. "A Machine Learning-based Smart Water Management Framework for Agricultural Irrigation using a Stacked Ensemble Model." In 2025 5th International Conference on Trends in Material Science and Inventive Materials (ICTMIM). IEEE, 2025. https://doi.org/10.1109/ictmim65579.2025.10987969.

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Wabi, Hussaini Abdullahi, Joseph A. Ojeniyi, Ismaila Idris, and Sikiru Olanrewaju Subairu. "Stack Ensemble Model For Detection Of Phishing Website." In 2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG). IEEE, 2024. http://dx.doi.org/10.1109/seb4sdg60871.2024.10629721.

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Mathivanan, Norsyela Muhammad Noor, Debbie Foo Yong Xi, Gerard Chong Soon Lee, Cindy De Vosse, Siti Fazilah Shamsudin, and Mahayaudin M. Mansor. "Optimizing Depression Prediction Models: A Stacked Ensemble Approach with Weighted Feature Importance." In 2025 IEEE 15th Symposium on Computer Applications & Industrial Electronics (ISCAIE). IEEE, 2025. https://doi.org/10.1109/iscaie64985.2025.11080844.

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Jayabalan, Anandhi, R. Uma, and S. Padmakala. "Comprehensive Heart Attack Prediction Model Using Stacked Ensembles and Clinical Feature Engineering." In 2025 International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI). IEEE, 2025. https://doi.org/10.1109/icmsci62561.2025.10894120.

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Yang, Zihan, Yufeng Zhang, Bingbing He, Zhiyao Li, and Jingsong Guo. "Classification of Erythrocyte Aggregation in Sparse B-mode Ultrasound Image Samples via Stacked Ensemble Deep Learning Models." In 2024 4th International Conference on Electronic Information Engineering and Computer Science (EIECS). IEEE, 2024. https://doi.org/10.1109/eiecs63941.2024.10800233.

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Zeain, Abdulrahman, and Abdullahi Abdu Ibrahim. "Enhancing Content-Based Image Retrieval with a Stacked Ensemble of Deep Learning Models." In 2024 8th International Symposium on Innovative Approaches in Smart Technologies (ISAS). IEEE, 2024. https://doi.org/10.1109/isas64331.2024.10845555.

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Zaman, Zahura, Tabassum Majumdar Nova, Laboni Sultana Riya, et al. "Machine Learning Based Depression Prediction: Comparative Analysis of Models and Stacked Ensemble Approach." In 2024 27th International Conference on Computer and Information Technology (ICCIT). IEEE, 2024. https://doi.org/10.1109/iccit64611.2024.11021746.

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