Modified tree-based selection in hierarchical mixed-effect models with trees: A simulation study and real-data application by Asrirawan, Khairil Anwar Notodiputro, Budi Susetyo, Sachnaz Desta Oktarina

“…These methods utilize the classification and regression trees (CART) algorithm to select the best tree through a backfitting algorithm. …”
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Autoregressive Neural Network for Cloud Concentration Forecast from Hemispheric Sky Images by Cristian Crisosto

“…The results are quantified in terms of the root mean square error (RMSE) and the mean absolute error (MAE). The new algorithm reduced both the RMSE and the MAE of the prediction by approximately 30% compared to the reference persistence model under diverse cloud conditions. …”
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Analysis of Energy Consumption in a Federated Learning-Based Zero-Touch Network by Urooj Yousuf Khan, Musharaf Ali Talpur, Umme Laila, Samar Raza Talpur

“… The current world revolves around data. Internet predicts that there are currently 2.8 million devices connected to the Internet. …”
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CatBoost Optimization Using Recursive Feature Elimination by Agus Hadianto, Wiranto Herry Utomo

“…CatBoost is a powerful machine learning algorithm capable of classification and regression application. …”
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Forecasting Ultrafine Dust Concentrations in Seoul: A Machine Learning Approach by Sophia Park, Myeong Jun Kim

“…Using daily data from 1 January 2018 to 30 June 2023, this study employed the Boruta algorithm, a variable selection technique based on the random forest model, to identify the most influential predictors for predicting PM2.5 concentrations. …”
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A self-learning method with domain knowledge integration for intelligent welding sequence planning by Weidong Shen, Xuewen Wang, Juanli Li, Yong Wang, Xiaojun Qiao

“…With FEA as the benchmark, the maximum relative error of the welding deformation predicted by the algorithm designed to predict the welding deformation was 8%. …”
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Modeling of the Power Station Boiler Combustion Efficiency Considering Multiple Work Condition with Feature Selection by TANG Zhenhao, WU Xiaoyan, CAO Shengxian

“…It is difficult for power station boiler efficiency to measure precisely A datadriven modeling method is proposed to establish the boiler combustion efficiency model, according to the machine learning theories A classification and regression trees (CART) algorithm provides correlated variables which have significant relation with the boiler combustion efficiency by data analysis Then, a KNearest Neighbor (KNN) classifies the samples to distinguish the data from different work conditions Based on the classified data, a least square support vector machine (LSSVM) optimized by differential evolution (DE) algorithm is proposed to establish a datadriven model (DDMMF) The parameters of LSSVM are optimized dynamically by DE to improve the model accuracy Finally, the prediction model is corrected dynamically for further improvement of the prediction accuracy The experimental results based on actual production data illustrate that the proposed approach can predict the boiler combustion efficiency accurately, which meets the requirements of boiler control and optimization…”
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Multivariate Load Forecasting of Integrated Energy System Based on CEEMDAN-CSO-LSTM-MTL by WANG Yongli, LIU Zeqiang, DONG Huanran, LI Dexin, CHEN Xin, GUO Lu, WANG Jiarui

“…Firstly，preprocess the collected raw load data and calculate the actual load value considering system energy loss； Secondly，the maximum information coefficient （MIC） is used to analyze the correlation between multiple loads and between multiple loads and weather factors，and to extract strongly correlated variables of multiple loads； Once again，the strongly correlated variables of multiple loads are substituted into CEEMDAN，and the load data is decomposed into stationary subsequences； Then，the feature sequence is substituted into the LSTM-MTL shared layer and the CSO algorithm is used to optimize the prediction model，achieving collaborative prediction of multiple loads； Finally，the performance of the constructed model was validated using a multivariate load dataset from a chemical park in Jilin City，Jilin Province，China. …”
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A Novel Dynamic Weight Neural Network Ensemble Model by Kewen Li, Wenying Liu, Kang Zhao, Mingwen Shao, Lu Liu

“…The paper proposes a novel dynamic weight neural network ensemble model (DW-NNE). The Bagging algorithm generates certain neural network individuals which then are selected by the K -means clustering algorithm. …”
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Hyperspectral estimation of chlorophyll content in grapevine based on feature selection and GA-BP by YaFeng Li, XinGang Xu, WenBiao Wu, Yaohui Zhu, LuTao Gao, XiangTai Jiang, Yang Meng, GuiJun Yang, HanYu Xue

“…Comparison of the prediction ability of Random Forest Regression (RFR) algorithm, Support Vector Machine Regression (SVR) model, and Genetic Algorithm-Based Neural Network (GA-BP) on grape LCC based on sensitive features. …”
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Tree Species Classification at the Pixel Level Using Deep Learning and Multispectral Time Series in an Imbalanced Context by Florian Mouret, David Morin, Milena Planells, Cécile Vincent-Barbaroux

“…Validation on independent in situ data shows that all models struggle to predict in areas not well covered by training data, but even in this situation, the RF algorithm is largely outperformed by deep learning models for minority classes. …”
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Online monitoring data processing methods for railway slopes and its application: A case study of the Shuohuang Railway by Mu GU

“…Subsequently, the CLEAN algorithm, introduced to the field of deformation monitoring, is utilized to suppress noise, minimizing its impact on subsequent deformation trend predictions. …”
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Handling method for GPS outages based on PSO-LSTM and fading adaptive Kalman filtering by Xiaoming Li, Xianchen Wang, Can Pei

“…Considering that the predicted pseudo-position may contain outliers or accumulated errors, a robust algorithm is employed to mitigate its impact on correcting INS errors. …”
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A monitoring method of semiconductor manufacturing processes using Internet of Things–based big data analysis by Seok-Woo Jang, Gye-Young Kim

“…We modify the Line, Buzo, and Gray algorithm for classifying the time-series patterns. …”
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Enhanced Convolutional Neural Network for Accurate Crop Recommendation System on Climate Data by Adnan Myasar M., AI_Sadi Hafidh l., Abhilash Pideka Kundil

“…The chosen features are classified and Improved Convolutional Neural Network (ICNN) algorithm predicts crops accurately. Our model, combining the CS-ICNN framework, offers enhanced recommendations by considering both soil-specific characteristics and environmental factors. …”
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Crowd navigation monitoring during emergencies by Oksana Tymoshchuk, Maksym Tishkov, Victor Bondarenko

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INFO-RF-based fault diagnosis and analysis method for busbars by Chen Xue, Jian Zhu, Haiou Cao, Yan Gu, Siyu Chen

“…The RF model is then used to predict fault types and fault resistance, with the INFO algorithm iteratively optimizing the hyperparameters of the RF model to further improve prediction accuracy. …”
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A Wi-Fi Indoor Localization Strategy Using Particle Swarm Optimization Based Artificial Neural Networks by Nan Li, Jiabin Chen, Yan Yuan, Xiaochun Tian, Yongqiang Han, Mingzhe Xia

“…Thus, with the combined strategy, we can reduce the positioning error and shorten the prediction time. We implement the proposed system on a mobile phone and the positioning results show that our algorithm can provide a higher localization accuracy and significantly improves the prediction speed.…”
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Finite Element Model Updating in Bridge Structures Using Kriging Model and Latin Hypercube Sampling Method by Jie Wu, Quansheng Yan, Shiping Huang, Chao Zou, Jintu Zhong, Weifeng Wang

“…Compared to the predicted results obtained by using a genetic algorithm, the proposed method can reduce the computational time without losing the accuracy.…”
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A Personalized Energy Expenditure Estimation Method Using Modified MET and Heart Rate-Based DQN by Min-Seo Kim, Ju-Hyeon Seong

“…Therefore, the proposed algorithm can be applied to various heart rate-based energy consumption prediction methods.…”
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