Optimizing Solar Radiation Prediction Based on The Internet of Things Platform in Photovoltaic Power Plant by Neda Ashrafi Khozani, Maryam Mahmoudi, Shabnam Nasr Esfahani

“…Managers and designers encounter economic and managerial challenges due to the uncertainty and difficulty in predicting solar radiation levels. This research introduces a highly accurate prediction method utilizing tree-based methods, enhanced by meta-heuristic algorithms to boost performance. …”
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Flow simulation algorithm for gas gathering pipelines of natural gas fields by Yuling LYU, Zhenhe JIANG, Xiaowei LI, Jiakai SHI, Huaping CAO

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Predicting Student Performance through Machine Learning Methods: Naive Bayesian Classifier by Chenyang Li, Qianzhi Zhou, Luming Du, Sihan Zhang

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Proposing Optimized Random Forest Models for Predicting Compressive Strength of Geopolymer Composites by Feng Bin, Shahab Hosseini, Jie Chen, Pijush Samui, Hadi Fattahi, Danial Jahed Armaghani

“…This paper explores advanced machine learning approaches to enhance the prediction accuracy of compressive strength (CoS) in geopolymer composites (GePC). …”
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Uncertainty prediction of wind speed based on improved multi-strategy hybrid models by Xinyi Xu, Shaojuan Ma, Cheng Huang

Subjects: “…wind speed interval prediction…”
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A Dendritic Neural Network-Based Model for Residential Electricity Consumption Prediction by Ting Jin, Rui Xu, Kunqi Su, Jinrui Gao

“…In this study, a dendritic neural network-based model (DNM), combined with the AdaMax optimization algorithm, is used to predict residential electricity consumption. …”
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Prediction of Water Quality in Agricultural Watersheds Based on VMD-GA-LSTM Model by Yuxuan Luo, Xianglan Meng, Yutong Zhai, Dongqing Zhang, Kaiping Ma

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Development of a Secured IoT-Based Flood Monitoring and Forecasting System Using Genetic-Algorithm-Based Neuro-Fuzzy Network by Hero Rafael Castillo Arante, Edwin Sybingco, Maria Antonette Roque, Leonard Ambata, Alvin Chua, Alvin Neil Gutierrez

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Model Predictive Control Using Stochastic Motion Prediction of Surrounding Vehicles in Uncontrolled Intersections by Sunyub Park, Yonghwan Jeong

“…This paper presents an autonomous driving algorithm for uncontrolled intersections based on Model Predictive Control (MPC) and Interacting Multiple Model (IMM) filters, proposing an innovative approach to addressing human driver uncertainty in mixed traffic scenarios. …”
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Leveraging diverse cell-death patterns to predict to predict prognosis and immunotherapy in hepatocellular carcinoma by Xiaoxiang Zhang, Dongxiao Ding, Dianqian Wang, Yunsheng Qin

“…The immune infiltration status and immune function of the signature were analyzed by ESTIMATE algorithm and ssGSEA algorithm. TIDE score, IPS and immune checkpoints expression and IC50 value were utilized to predict chemosensitivity and immunotherapy response. …”
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Integrating personalized shape prediction, biomechanical modeling, and wearables for bone stress prediction in runners by Liangliang Xiang, Yaodong Gu, Kaili Deng, Zixiang Gao, Vickie Shim, Alan Wang, Justin Fernandez

“…This study presents the development of a digital twin for predicting bone stress in runners. The digital twin leverages a domain adaptation-based Long Short-Term Memory (LSTM) algorithm, informed by wearable sensor data, to dynamically simulate the structural behavior of foot bones under running conditions. …”
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Predicting Live Weight for Female Rabbits of Meat Crosses From Body Measurements Using LightGBM, XGBoost and Support Vector Machine Algorithms by Hasan Önder, Cem Tirink, Taras Yakubets, Andriy Getya, Mykhalio Matvieiev, Ruslan Kononenko, Uğur Şen, Çağri Özgür Özkan, Tolga Tolun, Fahrettin Kaya

“…A highly efficient gradient‐boosting decision tree (LightGBM), eXtreme gradient‐boosting (XGBoost) and support vector machine (SVM) algorithms were evaluated and compared to the prediction of BW. …”
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Impact of surrogate model accuracy on performance and model management strategy in surrogate-assisted evolutionary algorithms by Yuki Hanawa, Tomohiro Harada, Yukiya Miura

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Model Data Mining sebagai Prediksi Penyakit Hipertensi Kehamilan dengan Teknik Decision Tree by Ari Muzakir, Rika Anisa Wulandari

Subjects: “…Data mining, Decision tree, C4.5 algorithms, Prediction, Pregnancy…”
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Prediction of cold region dew volume based on an ECOA-BiTCN-BiLSTM hybrid model by Yi Zhang, Pengtao Liu, Yingying Xu, Meng Zhang

Subjects: “…Swarm intelligence optimization algorithm…”
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Study on Short Term Temperature Forecast Model in Jiangxi Province based on LightGBM Machine Learning Algorithm by Kanghui SUN, An XIAO, Houjie XIA

“…In order to achieve further improvement in the forecast accuracy of station temperatures and enhance the forecast capability for extreme temperatures， this study establishes a 24-hour national station daily maximum （minimum） temperature forecast model for Jiangxi Province based on the LightGBM machine-learning algorithm and the MOS forecast framework by using the surface observation data of 91 national stations in Jiangxi Province and the upper-air and surface forecast data of the ECMWF model from 2017 to 2019.The results of the 2020 evaluation show that the LightGBM model daily maximum （minimum） temperature forecast is consistent with the observed trend， and the annual average forecast is better than that of three numerical models， ECMWF， CMA-SH9 and CMA-GFS， two machine learning products， RF and SVM， and subjective revision products.In terms of the spatial and temporal distribution of forecast errors， the model's daily maximum （minimum） temperature forecast errors in winter and spring are slightly larger than those in summer and autumn； the daily maximum temperature forecast errors show the spatial distribution characteristics of "larger in the south and smaller in the north， and larger in the periphery than in the centre"， while the opposite is true for the daily minimum temperatures.In terms of important weather processes， the LightGBM model has the best prediction effect among the seven products in the high temperature process； in the strong cold air process， the LightGBM model is still better than the three numerical model products and the other two machine-learning models， but the prediction effect of the daily minimum temperature is not as good as that of the subjective revision products.After a simple empirical correction for the low-temperature forecast error in the strong cold air process， the model low-temperature forecast effect is close to that of the subjective revision product.The model significance analysis shows that the recent surface observation features also contribute to the model construction， and the results can be used as a reference for model improvement and temperature forecast product development.At present， the LightGBM model temperature forecast products have been applied to meteorological operations in Jiangxi Province.…”
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A Data-Driven Strategy for Long-Term Agrarian Sustainability using the Application of Machine Learning Algorithms to Predictive Models for Pest and Disease Management by Almusawi Muntather, Ameer S. Abdul, Lalitha Yaragudipati Sri

“…PDM-MLA based on predictive modeling predicts infestations with high accuracy by analyzing weather, parameters of soil, history of outbreaks of pests, and crop health data. …”
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Establishment of predictive models for postoperative delirium in elderly patients after knee/hip surgery based on total bilirubin concentration: machine learning algorithms by Shuhui Hua, Chuan Li, Yuanlong Wang, YiZhi Liang, Shanling Xu, Jian Kong, Hongyan Gong, Rui Dong, Yanan Lin, Xu Lin, Yanlin Bi, Bin Wang

“…Subsequently, we employed ten machine learning algorithms to train and develop the predictive models: Logistic Regression (LR), Support Vector Machine (SVM), Gradient Boosting Model (GBM), Neural Network (NN), Random Forest (RF), Xgboost, K-Nearest Neighbors (KNN), AdaBoost, LightGBM, and CatBoost. …”
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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

“…Following the elimination of features with significant missing values, the remaining features were utilized to construct predictive models employing six machine learning algorithms. …”
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GLOPS: A Hybrid Approach for Enhanced Scheduling in Cloud Computing Environments via Machine Learning-Based Process Prediction by Syed Abu Ammar Muhammad Zarif, Layla Kader Suma, Md. Sadat Rahman, Md. Motaharul Islam, Phuoc Hung Pham, Phong Dung Thi Nguyen, Nguyen Thanh Binh

Subjects: “…Process prediction…”
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