Three-dimensional graph reconstruction of filamentous structures from z-stack images by Oscar Sten, Emanuela Del Dottore, Marilena Ronzan, Nicola Pugno, Barbara Mazzolai

“…In this study, we propose an algorithm that, given a Z-stack image of a filamentous structure, 1) detects the filaments in each focal plane producing a binary skeleton, 2) smoothes the resulting graph into a less dense homeomorphic graph, 3) identifies the corresponding nodes from the different focal planes by solving an optimal matching problem, 4) estimates the relative depth of the filament at each node coordinate through a shape-from-focus approach, and 5) identifies shallow overlaps through a criterion based on steerable filters. …”
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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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Software Defect Prediction For Quality Evaluation Using Learning Techniques Ensemble Stacking by Muhammad Romadhona Kusuma, Windu Gata, Sigit Kurniawan, Dedi Dwi Saputra, Supriadi Panggabean

“…The results showed that the ensemble stacking approach with a combination of Gradient Boosting, Ada Boost, Decision Tree, Bayesian Ridge, and LightGBM meta learner algorithms provided high accuracy with R2 score reaching 0.97, MAE of 0.037, and MSE of 0.006. …”
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Sentiment Analysis Using Stacking Ensemble After the 2024 Indonesian Election Results by Andy Victor Pakpahan, Fahmi Reza Ferdiansyah, Robby Gustian, Muhammad Nur Faiz, Sukma Aji

“…Machine learning is commonly applied in this area, with algorithms such as Naïve Bayes, Support Vector Machine (SVM), and Random Forest being frequently used. …”
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Temporal Attention-Enhanced Stacking Networks: Revolutionizing Multi-Step Bitcoin Forecasting by Phumudzo Lloyd Seabe, Edson Pindza, Claude Rodrigue Bambe Moutsinga, Maggie Aphane

“…The proposed Temporal Attention-Enhanced Stacking Network (TAESN) integrates the complementary strengths of diverse machine learning algorithms while emphasizing critical temporal features, leading to substantial improvements in forecasting accuracy over traditional methods. …”
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Programming Languages Prediction from Stack Overflow Questions Using Deep Learning by Razowana Khan Mim, Tapu Biswas

“…In our study, queries were selected from a Stack Overflow dataset using random sampling. Then the tags were cleaned and separated the data into title, title + body, and body. …”
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An interpretable stacking ensemble model for high-entropy alloy mechanical property prediction by Songpeng Zhao, Zeyuan Li, Changshuai Yin, Zhaofu Zhang, Teng Long, Jingjing Yang, Ruyue Cao, Yuzheng Guo

“…Three machine learning algorithms-Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Gradient Boosting (Gradient Boosting)-were integrated into a multi-level stacking ensemble, with Support Vector Regression serving as the meta-learner. …”
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Improving thyroid disorder diagnosis via innovative stacking ensemble learning model by Ayesha Hassan, Shabana Ramzan, Ali Raza, Muhammad Munwar Iqbal, Aseel Smerat, Norma Latif Fitriyani, Muhammad Syafrudin, Seung Won Lee

“…Further, an innovative stacking ensemble method is proposed with the help of four applied models. …”
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HouseGanDi: A Hybrid Approach to Strike a Balance of Sampling Time and Diversity in Floorplan Generation by Azmeraw Bekele Yenew, Beakal Gizachew Assefa, Elefelious Getachew Belay

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Detection of the Pigment Distribution of Stacked Matcha During Processing Based on Hyperspectral Imaging Technology by Qinghai He, Zhiyuan Liu, Xiaoli Li, Yong He, Zhi Lin

“…Therefore, there is an urgent demand for the fast, non-destructive detection of pigments of stacked tea during processing. This paper presents the use of hyperspectral imaging technology (HSI), combined with machine learning algorithms, to detect chlorophyll a, chlorophyll b, and carotenoids in stacked matcha tea during processing. …”
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Prediction and Optimization of Bolt Parameters for Automotive Fuel Cell Stack under Random Vibration by Yao Junqi, Yin Yan, Wang Bowen, Zhang Fan, Qin Zhikun, Wang Zixuan, Guo Ting

“…By integrating XGBoost with a genetic algorithm (GA), a GA-XGBoost predictive model is developed to optimize the structural parameters of the fastening bolts. …”
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A stacked ensemble model for traffic conflict prediction using emerging sensor data by Bowen Cai, Léah Camarcat, Nicolette Formosa, Mohammed Quddus

“…The Recursive Feature Elimination (RFE) algorithm is then employed to identify the most influential SSMs for conflict prediction in each scenario. …”
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An extreme forecast index-driven runoff prediction approach using stacking ensemble learning by Zhiyuan Leng, Lu Chen, Binlin Yang, Siming Li, Bin Yi

“…EFI is introduced as an input into four machine learning models (Support Vector Regression, Multi-layer Perceptron, Gradient Boosting Decision Tree, and Ridge Regression) for runoff prediction with lead times of 24 h, 48 h, and 72 h. The stacking ensemble learning framework comprises four base-models and a meta-model, and model hyperparameters are re-optimized using the particle swarm optimization algorithm. …”
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Max-Min Fairness for Stacked Intelligent Metasurface-Assisted Multi-User MISO Systems by Nipuni Ginige, Prathapasinghe Dharmawansa, Arthur Sousa De Sena, Nurul Huda Mahmood, Nandana Rajatheva, Matti Latva-Aho

“…Stacked intelligent metasurface (SIM) is an emerging technology that uses multiple reconfigurable surface layers to enable flexible wave-based beamforming. …”
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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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Toward an Accurate Liver Disease Prediction Based on Two-Level Ensemble Stacking Model by Marghany Hassan Mohamed, Botheina Hussein Ali, Ahmed Ibrahim Taloba, Ahmad O. Aseeri, Mohamed Abd Elaziz, Shaker El-Sappagah, Nora Mahmoud El-Rashidy

“…In this study, single and ensemble machine learning (ML) algorithms are applied to the Indian Liver Patient Dataset (ILPD) dataset, and their results, without and with feature selection techniques, are compared between each other and to the existing studies. …”
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Visual impairment prevention by early detection of diabetic retinopathy based on stacked auto-encoder by Shagufta Almas, Fazli Wahid, Sikandar Ali, Ahmed Alkhyyat, Kamran Ullah, Jawad Khan, Youngmoon Lee

“…In this study, we propose a novel approach utilizing enhanced stacked auto-encoders for the detection and classification of DR stages. …”
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Predicting soil organic matter using corrected field spectra and stacking ensemble learning by Yu Wang, Xuhui Yan, Rongyanting Huo, Longcai Zhao, Jie Peng, Yongsheng Hong, Jing Liu

“…The stacking model could effectively improve the accuracy of SOM model.…”
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Optimizing machine learning methods for groundwater quality prediction: Case study in District Bagh, Azad Kashmir, Pakistan by Usman Basharat, Wenjing Zhang, Cuihong Han, Shoukat Husain Khan, Arshad Abbasi, Sehrish Mahroof, Shuxin Li

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Rolling Bearing Fault Diagnosis Based on Optimized VMD and SSAE by Baoxian Chang, Xing Zhao, Dawei Guo, Siyu Zhao, Jiyou Fei

Subjects: “…dung beetle optimization algorithm…”
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