Advancing Mn-based electrocatalysts: Evolving from Mn-centered octahedral entities to bulk forms by Huan Li, Jinchao Xu, Liyuan Yang, Wanying Wang, Bin Shao, Fangyi Cheng, Chunning Zhao, Weichao Wang

“…According to the catalytic requirements of an individual entity and its stacking modes, we further developed a search algorithm to identify three-dimensional (3D) structures from 154,718 candidates, pinpointing CaMnO3 as the most effective one among the screened candidates. …”
Get full text

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

“…Based on a heterogeneous dataset of blood markars, we investigate the performance of many machine learning techniques such as Logistic Regression, CatBoost, XgBoost Decision Trees, KNN and others. The algorithms are further ensembled using a customized stacking approach. …”
Get full text

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

“…The study considered 17 factors in four primary categories: topographic, climatic, soil, and land use/land cover (LULC). The Boruta algorithm assessed the importance of these variables. …”
Get full text

A Pilot Study: Sleep and Activity Monitoring of Newborn Infants by GRU-Stack-Based Model Using Video Actigraphy and Pulse Rate Variability Features by Ádám Nagy, Zita Lilla Róka, Imre Jánoki, Máté Siket, Péter Földesy, Judit Varga, Miklós Szabó, Ákos Zarándy

“…However, we developed a system that automatizes the preceding evaluations in a non-contact way using deep learning algorithms. In this work, we provide a Gated Recurrent Unit (GRU)-stack-based solution that works on a dynamic feature set generated by computer vision methods from the cameras’ video feed and patient monitor to classify the activity phases of infants adapted from the NIDCAP (Newborn Individualized Developmental Care Program) scale. …”
Get full text

M3S-GRPred: a novel ensemble learning approach for the interpretable prediction of glucocorticoid receptor antagonists using a multi-step stacking strategy by Nalini Schaduangrat, Hathaichanok Chuntakaruk, Thanyada Rungrotmongkol, Pakpoom Mookdarsanit, Watshara Shoombuatong

“…Here, we develop a new ensemble learning approach using a multi-step stacking strategy (M3S), termed M3S-GRPred, aimed at rapidly and accurately discovering novel GR antagonists. …”
Get full text

Multimodal MRI radiomics-based stacking ensemble learning model with automatic segmentation for prognostic prediction of HIFU ablation of uterine fibroids: a multicenter study by Bing Wen, Chengwei Li, Qiuyi Cai, Dan Shen, Xinyi Bu, Fuqiang Zhou

“…It was followed by the SVM, LightGBM, and RF, which obtained AUC values of 0.841 (95% CI: 0.737–0.946), 0.823 (95% CI: 0.711–0.934), and 0.750 (95% CI: 0.619–0.881), respectively. The stacking ensemble learning model, which integrated these five algorithms, demonstrated a notable enhancement in performance, with an AUC of 0.897 (95% CI: 0.818–0.977) in the internal test set and 0.854 (95% CI: 0.759–0.948) in the external test set.ConclusionThe DL based automatic segmentation MRI radiomics stacking ensemble learning model demonstrated high accuracy in predicting the prognosis of HIFU ablation of uterine fibroids.…”
Get full text

Explainable MRI-Based Ensemble Learnable Architecture for Alzheimer’s Disease Detection by Opeyemi Taiwo Adeniran, Blessing Ojeme, Temitope Ezekiel Ajibola, Ojonugwa Oluwafemi Ejiga Peter, Abiola Olayinka Ajala, Md Mahmudur Rahman, Fahmi Khalifa

“…With the advancements in deep learning methods, AI systems now perform at the same or higher level than human intelligence in many complex real-world problems. The data and algorithmic opacity of deep learning models, however, make the task of comprehending the input data information, the model, and model’s decisions quite challenging. …”
Get full text

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

“…Feature selection involved univariate analysis, collinearity analysis, nine ML algorithms, and expert consensus. Twelve independent ML models and 26 stacking ensemble models were developed. …”
Get full text

Enhanced hunger games search algorithm that incorporates the marine predator optimization algorithm for optimal extraction of parameters in PEM fuel cells by Mohamed Issa, Mohamed Abd Elaziz, Sameh I. Selem

“…The proposed method, HGS-MPA, enhances the Hunger Games Search (HGS) algorithm by integrating Marine Predator Algorithm (MPA) operators, significantly boosting its exploitation capabilities and convergence rate. …”
Get full text

UAV Spiral Maneuvering Trajectory Intelligent Generation Method Based on Virtual Trajectory by Tao Chen, Shaopeng Li, Yong Xian, Leliang Ren, Zhenyu Liu

“…This paper addresses the challenge of ineffective coordination between terminal maneuvering and precision strike capabilities in hypersonic unmanned aerial vehicles (UAVs). …”
Get full text

Improved Prediction of Hourly PM_2.5 Concentrations with a Long Short-Term Memory Optimized by Stacking Ensemble Learning and Ant Colony Optimization by Zuhan Liu, Xianping Hong

“…To address the performance degradation in existing PM_2.5 prediction models caused by excessive complexity, poor spatiotemporal efficiency, and suboptimal parameter optimization, we employ stacking ensemble learning for feature weighting analysis and integrate the ant colony optimization (ACO) algorithm for model parameter optimization. …”
Get full text

SpatioTemporal Random Forest and SpatioTemporal Stacking Tree: A novel spatially explicit ensemble learning approach to modeling non-linearity in spatiotemporal non-stationarity by Yun Luo, Shiliang Su

“…We specifically develop the SpatioTemporal Random Forest (STRF) and SpatioTemporal Stacking Tree (STST) algorithms11 Python package link: https://github.com/46319943/GeoRegression., which capture and interpret the non-linearity in the spatial and temporal context more effectively. …”
Get full text

ANALISIS KINERJA MODEL STACKING BERBASIS RANDOM FOREST DAN SVM DALAM KLASIFIKASI RUMAH TANGGA BERDASARKAN GARIS KEMISKINAN MAKANAN DI PROVINSI JAWA BARAT by Ghardapaty Ghaly Ghiffary, Nabila Tri Amanda, Rizky Ardhani, Bagus Sartono, Aulia Rizki Firdawanti

“…This research applies the stacking method with two machine learning algorithms, namely Random Forest and Support Vector Machine (SVM) as base learners and logistic regression as a meta learner. …”
Get full text

Detection of Welding Defects Tracked by YOLOv4 Algorithm by Yunxia Chen, Yan Wu

Get full text

Comparative evaluation of slide scanners, scan settings, and cytopreparations for digital urine cytology by Jen-Fan Hang, Yen-Chuan Ou, Wei-Lei Yang, Tang-Yi Tsao, Cheng-Hung Yeh, Chi-Bin Li, En-Yu Hsu, Po-Yen Hung, Yi-Ting Hwang, Tien-Jen Liu, Min-Che Tung

“…These advanced focus methods do not impact the number of atypical cells or their coverage rate. While Z-stacking enhances the AI algorithm's inferred quantity and coverage rates of atypical cells, it simultaneously results in longer scanning times and larger image file sizes.…”
Get full text

Analysis of the Effectiveness of Traditional and Ensemble Machine Learning Models for Mushroom Classification by Neny Sulistianingsih, Galih Hendro Martono

“…This research employs the UCI Mushroom Dataset to evaluate and compare the effectiveness of several machine learning models, including traditional algorithms like Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors and Naïve Bayes, as well as advanced ensemble techniques such as Stacking and Voting Classifier. …”
Get full text

The analysis of fraud detection in financial market under machine learning by Jing Jin, Yongqing Zhang

“…Therefore, this paper proposes a financial fraud detection model based on Stacking ensemble learning algorithm, which integrates many basic learners such as logical regression (LR), decision tree (DT), random forest (RF), Gradient Boosting Tree (GBT), support vector machine (SVM) and neural network (NN), and introduces feature importance weighting and dynamic weight adjustment mechanism to improve the model performance. …”
Get full text

Taking into account the lens geometric distortion during accumulation of blurred star images in an astro-inertial attitude sensor by S. N. Vasilyuk

Get full text

Evaluating ensemble models for fair and interpretable prediction in higher education using multimodal data by Felipe Emiliano Arévalo-Cordovilla, Marta Peña

“…The methodology involved a comparative evaluation of seven base learners, including traditional algorithms, Random Forest, and gradient boosting ensembles (XGBoost, LightGBM), and a final stacking model, all validated using a 5-fold stratified cross-validation. …”
Get full text

Optimization of multi-objective feature regression models for designing performance assessment methods in college and university educational reform by Fengjun Qi, Zhenping Liu, Wenzheng Zhang, Zhenjie Sun

“…By extending the single-objective stacking framework, the proposed method learns label-specific features for each target and employs cluster analysis on binned samples to uncover underlying correlations among objectives. …”
Get full text