FEPP: Advancing Software Risk Prediction in Requirements Engineering Through Innovative Rule Extraction and Multi-Class Integration by Muhammad Binsawad, Bilal Khan

“…The accuracy analysis of the models is assessed critically using the precision, F-measure (FM), and Mathew’s correlation coefficient (MCC), as well as the error rate using the Kappa Statistic (KS) and Mean Absolute Error (MAE). …”
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Early Prediction of Battery Lifetime Using Centered Isotonic Regression with Quantile-Transformed Features by Muhammad Arslan Khan, Yixing Wang, Benben Jiang

“…To validate our proposed method, we have done a comprehensive comparison among several different benchmarks, including elastic net, gradient boosting regression tree, decision tree, support vector machine, random forest, and Gaussian process regression. In contrast to existing methods, our CIR model has shown the best performance, with an average percentage error of 9.8% and a root mean square error of 149 cycles. …”
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Impact of Data Balancing and Feature Engineering on Accident Severity Models by Fayez ALANAZI, Aminu SULEIMAN

“…Evaluation metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Log Loss, Area under the Curve (AUC), and Area under the Precision-Recall Curve (AUCPR) are employed. …”
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Prediction of room temperature in Trombe solar wall systems using machine learning algorithms by Seyed Hossein Hashemi, Zahra Besharati, Seyed Abdolrasoul Hashemi, Seyed Ali Hashemi, Aziz Babapoor

“…The accuracy of the algorithms was assessed using R² and root mean squared error (RMSE) values. The results demonstrated that the k-nearest neighbors and random forest algorithms exhibited superior performance, with R² and RMSE values of 1 and 0. …”
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Improving network security using keyboard dynamics: A comparative study by Ugwunna, C.O., Chukwuogo, O.E., Alabi, O.A., Kareem, M.K., Belonwu, T.S., Oloyede, S.O.

“…Based on the comparing results, Random Forest outperforms the other models, suggesting that Random Forest can be used as the system model for Keystroke Dynamic authentication.…”
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Traffic congestion forecasting using machine learning methods by Ramil R. Zagidullin, Almaz N. Khaybullin

“…The results demonstrate the superiority of the LSTM model over ARIMA and Random Forest in terms of predictive accuracy, as confirmed by visual comparison of forecasts with test data and by the mean squared error metric. …”
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Machine learning model optimization for compressional sonic log prediction using well logs in Shahd SE field, Western Desert, Egypt by Khaled Saleh, Walid M. Mabrouk, Ahmed Metwally

“…Model performance is optimized through hyperparameter tuning and evaluated using correlation coefficients and root mean square error (RMSE) metrics. Results indicate that ensemble models (Random Forest, CatBoost, XGBoost) achieve the highest accuracy, with correlation coefficients ranging from 89 to 89.6% and RMSE between 5.85 and 6.03. …”
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An improved multiclass classification of acute lymphocytic leukemia using enhanced glowworm swarm optimization by Saranya N, Kalamani M

“…Most of the diagnostic techniques like bone marrow aspiration, imaging techniques, etc. are time consuming, error-prone, costly and depend on the skill set of experts. …”
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Comparison of spatial prediction models from Machine Learning of cholangiocarcinoma incidence in Thailand by Oraya Sahat, Supot Kamsa-ard, Apiradee Lim, Siriporn Kamsa-ard, Matias Garcia-Constantino, Idongesit Ekerete

“…Model performance was evaluated using Root Mean Square Error (RMSE) and R2 with 70:30 train-test validation. …”
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Hybrid approaches enhance hydrological model usability for local streamflow prediction by Yiheng Du, Ilias G. Pechlivanidis

“…We investigate various post-processing methods, such as random forest, long short-term memory model, quantile mapping and generalised linear model, demonstrating notable improvements in model performance, in terms of reducing errors in total volume and extremes and increasing robustness across diverse climatic and geographic conditions. …”
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SHERA: SHAP-Enhanced Resource Allocation for VM Scheduling and Efficient Cloud Computing by Ashwin Singh Slathia, Abhiram Sharma, P. B. Krishna, Saksham Anand, Ayush Rathi, Linda Joseph, Xiao-Zhi Gao

“…Three machine learning models—Random Forest, Naïve Bayes, and Support Vector Machine (SVM) were trained and assessed based on Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and an Equivalent Accuracy metric. …”
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Research on Hyperspectral Inversion of Soil Organic Carbon in Agricultural Fields of the Southern Shaanxi Mountain Area by Yunhao Han, Bin Wang, Jingyi Yang, Fang Yin, Linsen He

“…The model achieved a coefficient of determination (R²) of 0.81, a root mean square error of prediction (RMSEP) of 1.54 g kg⁻¹, and a mean absolute error (MAE) of 1.37 g kg⁻¹. …”
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Construction and application of a TCN-LSTM-SVM-based time series prediction model for water inflow in coal seam roofs by Xuan LIU, Yadong JI, Kaipeng ZHU, Chunhu ZHAO, Kai LI, Chaofeng LI, Chenhan YUAN, Panpan LI, Pengzhen YAN

“…This model exhibited more accurate prediction results compared to the commonly used prediction models like backpropagation neural network (BPNN), random forest (RF), and Transformer while avoiding excessive errors produced by most of these models on the validation and test sets. …”
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Design of Chinese traditional Jiaoyi (Folding chair) based on Kansei Engineering and CNN-GRU-attention by Xinyan Yang, Nan Zhang, Jiufang Lv, Jiufang Lv, Jiufang Lv

“…Based on the top-ranked preference encoding, we designed a new Jiaoyi chair prototype, achieving significantly reduced prediction errors in final user testing (RMSE = 0.0034127, 0.0026915, 0.0035955).ConclusionThis research establishes a quantifiable intelligent design paradigm for modernizing cultural heritage through computational design.…”
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Quantitative assessment of the scale conversion from instantaneous to daily GPP under various sky conditions based on MODIS local overpassing time by Junghee Lee, Jungho Im

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Predicting Weaning Weight of Romanov Lambs From Biometric Measurements Before Weaning Age Using Machine Learning Algorithms by Mehmet Eroğlu, Ali Osman Turgut, Mürsel Küçük, Muhammed Furkan Önen

“…In contrast, the random forest and CatBoost models showed lower predictive performance, with higher errors in the test data. …”
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Machine learning-based technique for directivity prediction of a compact and highly efficient 4-port MIMO antenna for 5G millimeter wave applications by Md Ashraful Haque, Kamal Hossain Nahin, Jamal Hossain Nirob, Md Kawsar Ahmed, Narinderjit Singh Sawaran Singh, Liton Chandra Paul, Abeer D. Algarni, Mohammed ElAffendi, Abdelhamied A. Ateya

“…Out of the five ML models, the one that has the greatest accuracy and has a low margin of error when predicting directivity is the Random Forest Regression model. …”
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Multi-Objective Optimal Scheduling of Water Transmission and Distribution Channel Gate Groups Based on Machine Learning by Yiying Du, Chaoyue Zhang, Rong Wei, Li Cao, Tiantian Zhao, Wene Wang, Xiaotao Hu

“…An example analysis demonstrates that the optimal feedforward time of the open channel gate group is negatively connected with the flow condition and that the method can manage the water distribution error within 13.97% and the water level error within 13%. …”
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Mountain flood forecasting in small watershed based on loop multi-step machine learning regression model by Songsong Wang, Bo Peng, Ouguan Xu, Yuntao Zhang, Jun Wang

“…The traditional hydrodynamic and manual forecasting methods have high error rates for hourly forecasting. In order to improve the accuracy and real-time of water level forecasting in small watershed, we extract effective disaster-causing information, integrate multi-dimensional disaster-causing factors (such as hydrology, meteorology, geography, etc.), use a short-term prediction window and loop multi-step input method to improve the Machine Learning (ML) regression models’ accuracy, which can reduce the ML model’s process error. …”
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TPE-LCE-SHAP: A Hybrid Framework for Assessing Vehicle-Related PM2.5 Concentrations by Hamad Almujibah, Abdulrazak H. Almaliki, Caroline Mongina Matara, Adil Abdallah Mohammed Elhassan, Khalaf Alla Adam Mohamed, Mudthir Bakri, Afaq Khattak

“…The TPE-tuned LCE model outperformed benchmark algorithms including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost), and Multiple Linear Regression (MLR) achieved the lowest Mean Absolute Error (MAE) of 1.94, Mean Squared Error (MSE) of 21.50, Root Mean Squared Error (RMSE) of 4.64, Residual Standard Ratio (RSR) of 0.38, and the highest Coefficient of Determination (R2) of 0.87. …”
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