Development of a Stacking Device for Medical Urine Cups by Zhao Peng, Farra A. Jumuddin

“…The study focuses on creating a stacking device for medical urine cups, integrating pressing, storing, and collecting mechanisms to streamline palletizing without manual intervention. …”
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Early Detection of Mental Health Disorders based on Sentiment using Stacking Method by Naufal Maldini, Danang Wahyu Utomo, Rahmadika Putri Tresyani

“…This study aims to predict mental health disorders through sentiment analysis using the Stacking Classifier approach, which combines Random Forest, Gradient Boosting Classifier, and Logistic Regression algorithms. …”
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Mortality prediction of inpatients with NSTEMI in a premier hospital in China based on stacking model. by Li Wang, Yu Zhang, Feng Li, Caiyun Li, Hongzeng Xu

“…Finally, a unique double-layer stacking model is designed to improve the performance of the algorithm. …”
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Enhanced early detection of dysarthric speech disabilities using stacking ensemble deep learning model by Jagat Chaitanya Prabhala, Ravi Ragoju, Venkatanareshbabu Kuppili, Christophe Chesneau

“…Early and accurate detection is crucial to enable timely intervention and improve speech therapy outcomes. This study introduces Adaptive Dysarthric Speech Disability Detection using Stacked Ensemble Deep Learning (ADSDD-SEDL), an innovative ensemble-based deep-learning framework for dysarthria detection. …”
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A stacked ensemble machine learning model for the prediction of pentavalent 3 vaccination dropout in East Africa by Meron Asmamaw Alemayehu, Shimels Derso Kebede, Agmasie Damtew Walle, Daniel Niguse Mamo, Ermias Bekele Enyew, Jibril Bashir Adem

“…The objective is to identify predictors of dropout and enhance intervention strategies.MethodsThe study utilized seven base machine learning algorithms to create a stacked ensemble model with three meta-learners: Random Forest (RF), Generalized Linear Model (GLM), and Extreme Gradient Boosting (XGBoost). …”
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Comparative Analysis of Voting and Stacking Ensemble Learning for Heart Disease Prediction: A Machine Learning Approach by Gregorius Airlangga

“…Heart disease remains a leading cause of mortality worldwide, necessitating the development of accurate predictive models for early diagnosis and intervention. This study investigates the effectiveness of ensemble learning approaches, particularly Voting and Stacking classifiers, in comparison to traditional machine learning models and deep learning architectures. …”
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ECG-based heart arrhythmia classification using feature engineering and a hybrid stacked machine learning by Raiyan Jahangir, Muhammad Nazrul Islam, Md. Shofiqul Islam, Md. Motaharul Islam

“…This research focused on developing a hybrid model with stack classifiers, which are state-of-the-art ensemble machine-learning techniques to accurately classify heart arrhythmias from ECG signals, eliminating the need for extensive human intervention. …”
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A stacked learning framework for accurate classification of polycystic ovary syndrome with advanced data balancing and feature selection techniques by Heba M. Emara, Walid El-Shafai, Naglaa F. Soliman, Abeer D. Algarni, Reem Alkanhel, Fathi E. Abd El-Samie

“…The methodology incorporates stacked learning and depends on the Adaptive Synthetic (ADASYN) algorithm, Synthetic Minority Over-sampling Technique (SMOTE), and random oversampling methods for addressing data imbalances. …”
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An ensemble machine learning framework with explainable artificial intelligence for predicting haemoglobin anaemia considering haematological markers by Dhruva Darshan B S, Punit Sharma, Krishnaraj Chadaga, Niranjana Sampathila, G. Muralidhar Bairy, Sushma Belurkar, Srikanth Prabhu, Swathi K S

“…The xgboost and the stacking classifier obtained an accuracy, precision and recall of 99% each. …”
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Evaluating soil erosion zones in the Kangsabati River basin using a stacking framework and SHAP model: a comparative study of machine learning approaches by Javed Mallick, Saeed Alqadhi, Swapan Talukdar, Md Nawaj Sarif, Tania Nasrin, Hazem Ghassan Abdo

“…Random Forest (RF), (Deep Neural Networks) DNN, Convolution Neural Network (CNN), and stacking (Meta model) models were used to map soil erosion susceptibility based on the inventory map and controlling features. …”
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Prediction of Survival in Patients With Esophageal Cancer After Immunotherapy Based on Small-Size Follow-Up Data by Yuhan Su, Chaofeng Huang, Chen Yang, Qin Lin, Zhong Chen

“…Precise prognosis prediction becomes crucial for guiding appropriate interventions. This study, based on data from the First Affiliated Hospital of Xiamen University (January 2017 to May 2021), focuses on 113 EC patients undergoing immunotherapy. …”
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Development and validation of an explainable machine learning model for predicting postoperative pulmonary complications after lung cancer surgery: a machine learning studyResearch... by Shaolin Chen, Ting Deng, Qing Yang, Jin Li, Juanyan Shen, Xu Luo, Juan Tang, Xulian Zhang, Jordan Tovera Salvador, Junliang Ma

“…Further work should aim to identify high-risk patients early through clinical data analysis, enabling timely interventions and more efficient allocation of limited healthcare resources. …”
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A robot process automation based mobile application for early prediction of chronic kidney disease using machine learning by Md. Hasan Imam Bijoy, Md. Jueal Mia, Md. Mahbubur Rahman, Mohammad Shamsul Arefin, Pranab Kumar Dhar, Tetsuya Shimamura

“…Among the tested algorithms, MKR Stacking achieved the highest accuracy of 99.50%, outperforming Random Forest (98.75%) and MKR Voting (98%). …”
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THE CONCEPT OF A COMPREHENSIVE PROGNOSTIC MODEL OF THE EFFECTIVENESS OF IMPLEMENTATION OF MINIMALLY INVASIVE INTERVENTIONS IN THE TREATMENT OF CARIOUS PATHOLOGY by S.B. Kostenko, O.Ya. Bilynskyi, G.N. Nakashydze, M.O. Stetsyk, M.Yu. Goncharuk-Khomyn, I.V. Penzelyk

“… The research presents a prognostic model of the effectiveness of minimally invasive interventions in the dental patients’ treatment of carious pathology, which was developed to reduce the impact of iatrogenic interventions, increase the results of biological, biomechanical and financial feasibility of treatment. …”
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Increasing comprehensiveness and reducing workload in a systematic review of complex interventions using automated machine learning by Olalekan A Uthman, Rachel Court, Jodie Enderby, Lena Al-Khudairy, Chidozie Nduka, Hema Mistry, GJ Melendez-Torres, Sian Taylor-Phillips, Aileen Clarke

“…We evaluated the performance of the five deep-learning models [i.e. parallel convolutional neural network (CNN), stacked CNN, parallel-stacked CNN, recurrent neural network (RNN) and CNN–RNN]. …”
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Mapping HIV prevalence in Nigeria using small area estimates to develop a targeted HIV intervention strategy. by Caitlin O'Brien-Carelli, Krista Steuben, Kristen A Stafford, Rukevwe Aliogo, Matthias Alagi, Casey K Johanns, Jahun Ibrahim, Ray Shiraishi, Akipu Ehoche, Stacie Greby, Emilio Dirlikov, Dalhatu Ibrahim, Megan Bronson, Gambo Aliyu, Sani Aliyu, Laura Dwyer-Lindgren, Mahesh Swaminathan, Herbert C Duber, Man Charurat

“…<h4>Methods</h4>We used an ensemble modeling method called stacked generalization to analyze available covariates and a geostatistical model to incorporate the output from stacking as well as spatial autocorrelation in the modeled outcomes. …”
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Machine Learning-Augmented Triage for Sepsis: Real-Time ICU Mortality Prediction Using SHAP-Explained Meta-Ensemble Models by Hülya Yilmaz Başer, Turan Evran, Mehmet Akif Cifci

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BlockDroid: detection of Android malware from images using lightweight convolutional neural network models with ensemble learning and blockchain for mobile devices by Emre Şafak, İbrahim Alper Doğru, Necaattin Barışçı, İsmail Atacak

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Development of a Laser Surgical Device with Vibration Compensation: Mechanical Design and Validation of Its Compliant Mechanism by Emil Ionuț Niță, Daniel C. Comeagă, Dragos A. Apostol, Virgil-Florin Duma

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A Computational Methodology Based on Maximum Overlap Discrete Wavelet Transform and Autoencoders for Early Prediction of Sudden Cardiac Death by Manuel A. Centeno-Bautista, Andrea V. Perez-Sanchez, Juan P. Amezquita-Sanchez, David Camarena-Martinez, Martin Valtierra-Rodriguez

“…In this sense, this work proposes a novel computational methodology that combines the maximal overlap discrete wavelet packet transform (MODWPT) with stacked autoencoders (SAEs) to discover suitable features in ECG signals and associate them with SCD prediction. …”
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