Marginal land identification and grain production capacity prediction of the coverage area of western route of China’s South-to-North Water Diversion Project by Heng Zhou, Jun Zhou, Jun Zhou, Kunming Lu, Minghui Niu, Chenyi Wang, Gaofeng Zhang, Jiawei Kou

“…For maize, the model yielded a root mean square error (RMSE) of 48.94, a mean absolute error (MAE) of 34.01, and a mean absolute percentage error (MAPE) of 7.65%. …”
Get full text

Regression models for predicting the effect of trash rack on flow properties at power intakes by Shuguang Li, Sultan Noman Qasem, Hojat Karami, Ely Salwana, Alireza Rezaei, Danyal Shahmirzadi, Shahab S. Band

“…Thus, the LJA-GB model has the lowest mean absolute error (MAE) (0.3344), mean squared error (MSE) (0.1784), and root mean squared error (RMSE) (0.4223) values and highest R-squared ([Formula: see text]) (0.9899) and Willmott’s index (WI) values (0.9508) in the testing stage metrics for [Formula: see text] estimation and MAE (0.0061), MSE (0.0001), RMSE (0.0073), [Formula: see text] (0.9971), WI (0.9727) for [Formula: see text] estimation. …”
Get full text

Comparison of Machine Learning and Deep Learning Models Performance in predicting wind energy by Saswati Rakshit, Anal Ranjan Sengupta

“…The assessment criteria utilized here comprised the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the R² Score. …”
Get full text

Crushing Force Prediction Method of Controlled-Release Fertilizer Based on Particle Phenotype by Linlin Sun, Xiubo Chen, Zixu Chen, Linlong Jing, Jinxing Wang, Xinpeng Cao, Shenghui Fu, Yuanmao Jiang, Hongjian Zhang

“…Comparative tests with a random forest regression, the K-nearest neighbor, a back propagation (BP) neural network, and a long short-term memory (LSTM) neural network have demonstrated that the PSO-SVM model outperforms these methods in terms of mean absolute error, root mean square error, and correlation coefficient, underscoring its effectiveness. …”
Get full text

A novel deep learning approach for investigating liquid fuel injection in combustion system by Syed Azeem Inam, Abdullah Ayub Khan, Noor Ahmed, Tehseen Mazhar, Tariq Shahzad, Sunawar Khan, Mamoon M. Saeed, Habib Hamam

“…The coupled FCNN and Extra Tree Regressor outperform the other algorithms with a Mean Square Error (MSE) of 0.0000005062, Root Mean Square Error (RMSE) of 0.00071148, Mean Absolute Error (MAE) of 0.00020672, and R-squared (R2) value of 0.99998689. …”
Get full text

Measurement of the Functional Size of Web Analytics Implementation: A COSMIC-Based Case Study Using Machine Learning by Ammar Abdallah, Alain Abran, Munthir Qasaimeh, Malik Qasaimeh, Bashar Abdallah

“…A comparison of predicted effort values with actual values indicated that Linear Regression, Extra Trees, and Random Forest ML models performed well in terms of low Root Mean Square Error (RMSE), high Testing Accuracy, and strong Standard Accuracy (SA) scores. …”
Get full text

Combining machine learning with UAV derived multispectral aerial images for wheat yield prediction, in southern Brazil by Henrique dos Santos Felipetto, Erivelto Mercante, Octavio Viana, Adão Robson Elias, Giovani Benin, Lucas Scolari, Arthur Armadori, Diandra Ganascini Donato

“…The tested supervised machine learning algorithms included Linear Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN), combined with vegetation indices from the visible spectrum (RGB), multispectral indices, and bands. …”
Get full text

Multi-Criteria Assessment of Flood Risk on Railroads Using a Machine Learning Approach: A Case Study of Railroads in Minas Gerais by Fernanda Oliveira de Sousa, Victor Andre Ariza Flores, Christhian Santana Cunha, Sandra Oda, Hostilio Xavier Ratton Neto

“…The models evaluated included linear regression, random forest, decision tree, and support vector machines. …”
Get full text

Optimizing agricultural yield: a predictive model for profitable crop harvesting based on market dynamics by Nilesh P. Sable, Nilesh P. Sable, Vinod Kumar Shukla, Parikshit N. Mahalle, Vijayshri Khedkar

“…After a thorough study using the Mean Squared Error (MSE) and R2 score, it was determined that the DT model performed the best, with an outstanding R2 score of 99%. …”
Get full text

Improving Voice Spoofing Detection Through Extensive Analysis of Multicepstral Feature Reduction by Leonardo Mendes de Souza, Rodrigo Capobianco Guido, Rodrigo Colnago Contreras, Monique Simplicio Viana, Marcelo Adriano dos Santos Bongarti

“…Empirical evaluation using the ASVSpoof 2017 v2.0 dataset measures the classification performance with the Equal Error Rate (EER) metric, achieving values of approximately 10%. …”
Get full text

Improving Cardiovascular Disease Prediction through Stratified Machine Learning Models and Combined Datasets by Tara Yousif Mawlood, Alla Ahmad Hassan, Rebwar Khalid Muhammed, Aso M. Aladdin, Tarik A. Rashid, Bryar A. Hassan

“…Seven classification algorithms – logistic regression, random forest (RF), support vector machine (SVM), Gaussian naive Bayes (GNB), gradient boosting (GB), K-nearest neighbors, and decision tree (DT) – were employed. …”
Get full text

A Machine Learning-Based Intelligent Framework for Predicting Energy Efficiency in Next-Generation Residential Buildings by Hafiz Muhammad Shakeel, Shamaila Iram, Richard Hill, Hafiz Muhammad Athar Farid, Akbar Sheikh-Akbari, Farrukh Saleem

“…Further, machine learning models revealed that Random Forest, Gradient Boosting, XGBoost, and LightGBM deliver the lowest mean square error scores of 6.305, 6.023, 7.733, 5.477, and 5.575, respectively, and demonstrated the effectiveness of advanced algorithms in forecasting energy performance. …”
Get full text

Near-Infrared Spectroscopy and Machine Learning for Fast Quality Prediction of Bottle Gourd by Xiao Guo, Hongyu Huang, Haiyan Wang, Chang Cai, Ying Wang, Xiaohua Wu, Jian Wang, Baogen Wang, Biao Zhu, Yun Xiang

“…The models for protein and FAAs estimation were developed using support vector regression (SVR), ridge regression (RR), random forest regression (RFR), and fully connected neural networks (FCNNs). …”
Get full text

Genetic structure analysis and core germplasm construction of Robinia pseudoacacia and its closely related species based on SNP by Haoran Wang, Yan Ma, Ruixue Wang, Dekui Zang, Xiaoyan Yu, Jingtao Li, Qichao Wu, Fengqi Zang

“…Abstract Robinia pseudoacacia is a forest biomass energy tree species with substantial potential for development and utilization. …”
Get full text

Vehicle Fuel Consumption Prediction Method Based on Driving Behavior Data Collected from Smartphones by Ying Yao, Xiaohua Zhao, Chang Liu, Jian Rong, Yunlong Zhang, Zhenning Dong, Yuelong Su

“…All three models could predict fuel consumption accurately, with an absolute relative error less than 10%. The random forest model is proved to have the highest accuracy and runs faster, making it suitable for wide application. …”
Get full text

Operational Performance Assessment of PV-Powered Street Lighting: A Comparative Study of Different Machine Learning Prediction Models by Safwan Nadweh, Nabil Mohammed, Charalambos Konstantinou, Shehab Ahmed

“…The results indicate that DNNs and DBNs algorithms achieve the lowest error rate (2.5%) and highest accuracy (97%) with high-quality data. …”
Get full text

Evaluation of the biodiversity of arbuscular mycorrhizal fungi during regenerative succession in quarries by A. A. Kryukov, A. P. Yurkov, A. O. Gorbunova, T. R. Kudriashova, A. I. Gorenkova, Y. V. Kosulnikov, Y. V. Laktionov

“…The results showed maximum AMF biodiversity at the initial stages of overgrowth – pioneer and grass stages – with minimum diversity observed at the shrub stage, where it decreased by five times. At the forest stage, the biodiversity of AMF was almost restored to the level seen at the grass stage. …”
Get full text

Bias correction and application of labeled smartphone pressure data for evaluating the best track of landfalling tropical cyclones by G. Qiao, Y. Cao, Q. Zhang, J. Sun, H. Yu, L. Bai

“…We propose a quality control procedure utilizing random forest machine learning models. By applying this quality control approach to the selected TCs, we discovered that the performance of the method for labeled data significantly surpassed that for unlabeled data developed in a previous study, reducing the mean absolute error from 3.105 to 0.904 <span class="inline-formula">hPa</span>. …”
Get full text

Prediction of retention time in larger antisense oligonucleotide datasets using machine learning by Manal Rahal, Bestoun S. Ahmed, Christoph A. Bauer, Johan Ulander, Jörgen Samuelsson

“…Four ML models—Gradient Boosting, Random Forest, Decision Tree, and Support Vector Regression — were evaluated on three large ASOs datasets with different gradient times. …”
Get full text

Vortex-Induced Vibration Performance Prediction of Double-Deck Steel Truss Bridge Based on Improved Machine Learning Algorithm by Yang Yang, Huiwen Hou, Gang Yao, Bo Wu

“…For the prediction of VIV parameters, the Random Forest model is the most effective. The RMSE values of the improved optimal algorithm are 0.017, 0.026, and 0.295, and the R² values are 0.9421, 0.8875, and 0.9462. …”
Get full text