Modelling of spatially correlated weather-based electricity forecasting using combined frequency-based signal decomposition with optimized boosting approach by Indra A. Aditya, Didit Adytia

“…The primary contribution is a spatially correlation-driven feature selection technique to choose ideal weather input sites, coupled with the extraction of predominant frequency components from the load signal to enhance model input. Three machine learning models are evaluated: XGBoost, AdaBoost, and Multi-Layer Perceptron (MLP) on datasets from two locations in Indonesia: Bali and Jakarta-Banten. …”
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An optimized deep learning based hybrid model for prediction of daily average global solar irradiance using CNN SLSTM architecture by Yuvaraj Mariappan, Karthikeyan Ramasamy, Durgadevi Velusamy

“…The hyperparameters of the developed models are optimized using metaheuristic algorithm, a Slime Mould Optimization method. …”
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Arctic tern-optimized weighted feature regression system for predicting bridge scour depth by Jui-Sheng Chou, Asmare Molla

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Predicting CO₂ Emissions with Advanced Deep Learning Models and a Hybrid Greylag Goose Optimization Algorithm by Amel Ali Alhussan, Marwa Metwally, S. K. Towfek

“…The empirical results show that the GGBERO-optimized BIGRU model produced a Mean Squared Error (MSE) of 1.0 × 10⁻⁵, the best tested approach. …”
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Optimal Stopping Theory-Based Online Node Selection in IoT Networks for Multi-Parameter Federated Learning by Seda Dogan-Tusha, Faissal El Bouanani, Marwa Qaraqe

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RUL Prediction Method for Lithium‐Ion Batteries Based on the SOA‐ELM Algorithm by Meng Xiangdong, Zhang Haifeng, Li Dexin, Dong Yunchang, Zhang Jiajun, Cao Xinyu, Li Gang

“…This paper proposes an advanced RUL prediction model that combines the seagull optimization algorithm (SOA) with the extreme learning machine (ELM) to enhance prediction accuracy. …”
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Machine-learning-based reconstruction of long-term global terrestrial water storage anomalies from observed, satellite and land-surface model data by N. Mandal, P. Das, K. Chanda, K. Chanda

“…The most effective machine learning (ML) algorithms among convolutional neural network (CNN), support vector regression (SVR), extra trees regressor (ETR) and stacking ensemble regression (SER) models are evaluated at each grid cell to achieve optimal reproducibility. …”
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Leveraging Spectral Neighborhood Information for Corn Yield Prediction with Spatial-Lagged Machine Learning Modeling: Can Neighborhood Information Outperform Vegetation Indices? by Efrain Noa-Yarasca, Javier M. Osorio Leyton, Chad B. Hajda, Kabindra Adhikari, Douglas R. Smith

“…This study introduces an innovative approach to crop yield prediction by incorporating spatially lagged spectral data (SLSD) through the spatial-lagged machine learning (SLML) model, an enhanced version of the spatial lag X (SLX) model. …”
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Construction of Clinical Predictive Models for Heart Failure Detection Using Six Different Machine Learning Algorithms: Identification of Key Clinical Prognostic Features by Qu FZ, Ding J, An XF, Peng R, He N, Liu S, Jiang X

“…Finally, a correlation analysis was conducted to examine the relationships between these features and other significant clinical features.Results: The logistic regression (LR) model was determined to be the optimal machine learning algorithm in this study, achieving an accuracy of 0.64, a precision of 0.45, a recall of 0.72, an F1 score of 0.51, and an AUC of 0.81 in the training set and 0.91 in the testing set. …”
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A Data-Driven Approach to Improve Cocoa Crop Establishment in Colombia: Insights and Agricultural Practice Recommendations from an Ensemble Machine Learning Model by Leonardo Talero-Sarmiento, Sebastian Roa-Prada, Luz Caicedo-Chacon, Oscar Gavanzo-Cardenas

“…The fragmented nature of the existing agricultural data and the lack of targeted research hinder efforts to optimize productivity and sustainability. To bridge this gap, this research employs a data-driven approach, using advanced machine learning techniques such as supervised, unsupervised, and ensemble models, to analyze environmental datasets and provide actionable recommendations. …”
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Risk of intraoperative hemorrhage during cesarean scar ectopic pregnancy surgery: development and validation of an interpretable machine learning prediction modelResearch in contex... by Xinli Chen, Huan Zhang, Dongxia Guo, Siyuan Yang, Bao Liu, Yiping Hao, Qingqing Liu, Teng Zhang, Fanrong Meng, Longyun Sun, Xinlin Jiao, Wenjing Zhang, Yanli Ban, Yugang Chi, Guowei Tao, Baoxia Cui

“…Accuracy and decision curve analysis (DCA) were used to assess model performance for selection of the optimal model. …”
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Advanced long-term actual evapotranspiration estimation in humid climates for 1958–2021 based on machine learning models enhanced by the RReliefF algorithm by Ahmed Elbeltagi, Salim Heddam, Okan Mert Katipoğlu, Abdullah A. Alsumaiei, Mustafa Al-Mukhtar

“…To address this issue and guarantee more accurate ET predictions, this study attempts the following: i) to assess the performance of five machine learning (ML) models optimized by the RReliefF algorithm in estimating actual ET values for each month in four Chinese provinces under various agroclimatic conditions; and ii) to select the optimal model based on statistical metrics while minimizing discrepancies between the estimated and actual ET values. …”
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Development and validation of an early predictive model for hemiplegic shoulder pain: a comparative study of logistic regression, support vector machine, and random forest by Qiang Wu, Qiang Wu, Fang Zhang, Yuchang Fei, Zhenfen Sima, Shanshan Gong, Qifeng Tong, Qingchuan Jiao, Hao Wu, Jianqiu Gong, Jianqiu Gong

“…ObjectiveIn this study, we aim to identify the predictive variables for hemiplegic shoulder pain (HSP) through machine learning algorithms, select the optimal model and predict the occurrence of HSP.MethodsData of 332 stroke patients admitted to a tertiary hospital in Zhejiang Province from January 2022 to January 2023 were collected. …”
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Development and validation of an interpretable machine learning model for predicting left atrial thrombus or spontaneous echo contrast in non-valvular atrial fibrillation patients. by Chaoqun Huang, Shangzhi Shu, Miaomiao Zhou, Zhenming Sun, Shuyan Li

“…This study aimed to construct and validate an interpretable predictive model of LAT/SEC risk in NVAF patients using machine learning (ML) methods.…”
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ZERO-DEFECT ITEM MACHINING by O.A. POLUSHKIN

“…Original non-Gaussian distribution of item size values, derived by the qualified tool machining, might be used as a model of such a machining process. …”
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Short-Term Power Load Forecasting Based on DPSO-LSSVM Model by Shujun Ji, Linhao Zhang, Jinteng Wang, Tao Wei, Jiadong Li, Bu Ling, Jinglong Xu, Zuoping Wu

“…A short-term load forecasting model based on least squares support vector machine is constructed, and the optimal parameters of the model are established. …”
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Prediction of Ki-67 Expression in HIV-Associated Lung Adenocarcinoma Patients Using Multiple Machine Learning Models Based on CT Imaging Radiomics by Song C, Chen J, Zhao C, Song S, Yang T, Huang A, Liu R, Pan Y, Xu C, Chen C, Zhu Q

“…The Support Vector Machine (SVM) model demonstrated the most balanced and optimal performance among the seven developed models. …”
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Machine learning-based seismic response forecasting using feature mapping algorithms and scientometric analysis of nailed vertical excavation in a soil mass by Surya Muthukumar, Dhanya Sathyan, Premjith B, Sanjay Kumar Shukla

“…The research gap between the accuracy of observed and predicted values can be bridged by employing artificial intelligence-based machine learning (ML) models. The seismic displacement of the nailed soil wall obtained from experimental studies were assessed using suitable ML approaches. …”
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Development of a Machine Learning Natural Ventilation Rate Model by Studying the Wind Field Inside and Around Multiple-Row Chinese Solar Greenhouses by Ran Liu, Yunyan Shi, Pierre-Emmanuel Bournet, Kaige Liu

“…This paper experimented with a methodology of machine learning modelling using virtual samples generated by fast CFD (Computational Fluid Dynamics) simulations in order to predict the greenhouse natural ventilation. …”
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A Novel MILP Model for the Production, Lot Sizing, and Scheduling of Automotive Plastic Components on Parallel Flexible Injection Machines with Setup Common Operators by Beatriz Andres, Eduardo Guzman, Raul Poler

“…In this article, a mixed integer linear program (MILP) model is proposed for the production, lot sizing, and scheduling of automotive plastic components to minimize the setup, inventory, stockout, and backorder costs, by taking into account injection molds as the main index to schedule on parallel flexible injection machines. …”
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