Predicting Residential Energy Consumption in South Africa Using Ensemble Models by David Attipoe, Donatien Koulla Moulla, Ernest Mnkandla, Alain Abran

“…By combining the best features of individual ML models, ensemble models reduce the drawbacks of each model and improve prediction accuracy. …”
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Detection of Pear Quality Using Hyperspectral Imaging Technology and Machine Learning Analysis by Zishen Zhang, Hong Cheng, Meiyu Chen, Lixin Zhang, Yudou Cheng, Wenjuan Geng, Junfeng Guan

“…Spectral data within the 398~1004 nm wavelength range were analyzed to compare the predictive performance of the Least Squares Support Vector Machine (LS-SVM) models on various quality parameters, using different preprocessing methods and the selected feature wavelengths. …”
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Stacked LSTM Model for Contextual Correlation Detection Among Multiple Emotions by Aqsa Younas, Shazia Riaz, Saqib Ali, Rafiullah Khan, Mohib Ullah, Daehan Kwak

“…The model incorporates encoder-decoder layers to capture short- and long-term dependencies among emotions and employs a time-distributed dense layer to differentiate features associated with each emotion. …”
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Multimodal Model for Automated Pain Assessment: Leveraging Video and fNIRS by Jo Vianto, Anjitha Divakaran, Hyungjeong Yang, Soonja Yeom, Seungwon Kim, Soohyung Kim, Jieun Shin

“…Our model leverages video and fNIRS modalities as well as manually extracted statistical features. …”
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Risk prediction and effect evaluation of complicated appendicitis based on XGBoost modeling by Sunmeng Chen, Jianfu Xia, Beibei Xu, Yi Huang, Miaomiao Teng, Juyi Pan

“…A comprehensive comparison of the four algorithms was performed using model performance metrics such as the area under the receiver’s operating characteristic curve (AUC), sensitivity, specificity, accuracy, precision, negative predictive value(NPV), positive predictive value(PPV),calibration curves, and clinical decision curve analysis (DCA). …”
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Using an Improved PSO-SVM Model to Recognize and Classify the Image Signals by Ying Zhao, Guocheng Wei

“…The model first preprocesses the original image data and then uses rough set theory to select features, which reduces the input dimension of the artificial intelligence network, improves the learning and recognition speed of the artificial intelligence network, and further improves the accuracy of recognition. …”
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Insights into an Angular-Motion Electromechanical-Switching Device: Characteristics, Behavior, and Modeling by José M. Campos-Salazar, Jorge Gonzalez-Salazar

“…The results demonstrate strong consistency across the modeling hierarchy and reveal critical features—such as ripple-induced resonance and low-frequency coupling—that are essential for robust controller design. …”
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Entity Pair Relation Classification Based on Contrastive Learning and Biaffine Model by Songhua Hu, Ziming Zhang, Hengxin Wang, Lihui Jiang

“…The results validate that bidirectional entity contrastive learning and global pointer networks significantly enhance the biaffine model’s feature representation and classification performance. …”
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Predictive slope stability early warning model based on CatBoost by Yuan Cai, Ying Yuan, Aihong Zhou

“…Abstract A model for predicting slope stability is developed using Categorical Boosting (CatBoost), which incorporates 6 slope features to characterize the state of slope stability. …”
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Modeling and Forecasting Electricity Demand and Prices: A Comparison of Alternative Approaches by Ismail Shah, Hasnain Iftikhar, Sajid Ali

“…Furthermore, multivariate vector autoregressive gives superior performance compared to univariate autoregressive models.…”
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Advanced KPI framework for IVF pregnancy prediction models in IVF protocols by Sergei Sergeev, Iuliia Diakova

“…The aim of this study was to develop a novel approach to IVF laboratory data analysis, employing deep neural networks to predict the likelihood of clinical pregnancy occurrence within an individual treatment cycle, integrating both key performance indicators and clinical data. We conducted a retrospective analysis spanning 11 years, encompassing 8732 treatment cycles, to extract the most relevant features to our goal and train the model. …”
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Classification of Coronavirus Disease 2019 and Pneumonia Based on US-VM Model by Nursah Dincer, Pelin Görgel

“…To test the proposed model, an original dataset was created by collecting images from different open-source data sets with COVID-19, normal, and pneumonia features. …”
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Understanding the flowering process of litchi through machine learning predictive models by SU Zuanxian, NING Zhenchen, WANG Qing, CHEN Houbin

“…The meteorological data were downloaded from the website “https://tianqi.911cha.com/” and recorded at a frequency of one hour, with meteorological factors including atmospheric temperature, atmospheric relative humidity, wind scale and rainfall. Feature engineering of the data, which involved removing irrelevant or redundant features and ensuring that there was no high correlation between the retained features, was used to improve the performance and generalization of the model. …”
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An Explainable Machine Learning Model for Predicting Macroseismic Intensity for Emergency Management by Federico Mori, Giuseppe Naso

“…The model achieves strong predictive performance (RMSE = 0.73, R² = 0.76), corresponding to a 33% reduction in residual standard deviation compared to traditional GMICE-based regression methods. …”
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Low-Cost Experimental Methodology for the Dynamic Model Approximation of Multirotor Actuators by Walter A. Mayorga-Macías, Luis E. González-Jiménez, Marco A. Meza-Aguilar, Luis F. Luque-Vega

“…However, during the control design procedure for the multirotor, it is important to account with a model of the actuators as its dynamical features define the closed-loop performance of the overall aircraft. …”
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ALTERNATIVE (NON-MILITARY) SERVICE MODEL IN FINLAND: STRUCTURE, MECHANISMS AND CHALLENGES by Yaroslav Kotylko

“…The aim of the article is a comprehensive study of the system of alternative (non-military) service in Finland, its legal regulation, appointment procedures, features of passage and forms of performance of duty in case of emergency situations and mobilization. …”
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Performance of radiomics analysis in ultrasound imaging for differentiating benign from malignant adnexal masses: A systematic review and meta‐analysis by Francesca Moro, Marianna Ciancia, Maria Sciuto, Giulia Baldassari, Huong Elena Tran, Antonella Carcagnì, Anna Fagotti, Antonia Carla Testa

“…All studies that developed and internally or externally validated ML models using only radiomics features extracted from ultrasound images were included. …”
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Artificial Intelligence model to predict resistances in Gram-negative bloodstream infections by Cecilia Bonazzetti, Ettore Rocchi, Alice Toschi, Nicolas Riccardo Derus, Claudia Sala, Renato Pascale, Matteo Rinaldi, Caterina Campoli, Zeno Adrien Igor Pasquini, Beatrice Tazza, Armando Amicucci, Milo Gatti, Simone Ambretti, Pierluigi Viale, Gastone Castellani, Maddalena Giannella

“…Distribution of resistance was FQ-R 48.6%, 3GC-R 40.1%, BL/BLI-R 29.9%, and C-R 16.9%. Models’ validation showed good performance predicting antibiotic resistance for all four resistance classes, with the best performance for C-R (AUC-ROC 0.921 ± 0.013). …”
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New Model for Weather Stations Integrated to Intelligent Meteorological Forecasts in Brasilia by Thomas Alexandre da Silva, Andre L. M. Serrano, Erick R. C. Figueiredo, Geraldo P. Rocha Filho, Fábio L. L. de Mendonça, Rodolfo I. Meneguette, Vinícius P. Gonçalves

“…This paper presents a new model for low-cost solar-powered Automatic Weather Stations based on the ESP-32 microcontroller, modern sensors, and intelligent forecasts for Brasilia. …”
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An Ensemble Learning Based Intrusion Detection Model for Industrial IoT Security by Mouaad Mohy-Eddine, Azidine Guezzaz, Said Benkirane, Mourade Azrour, Yousef Farhaoui

“…Results prove that our designed model has several advantages and higher performance than related models.…”
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