Designing metal halide-based road illumination systems in developing countries using regression and neural networksThe dataset is available through this link by Sourin Bhattacharya, Khondekar Lutful Hassan, Pallav Dutta

“…Through extensive photometric simulation and data analysis, this study provides useful insights for the commissioning of road lighting projects especially pertaining to the usefulness of ANN models for the planning and optimization of public road illumination systems in developing countries.…”
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Study of linear and nonlinear isotherm and kinetic parameters of hexavalent chromium adsorption onto reduced graphene oxide coated iron oxide by Selim Gürsoy, Nagehan Kübra Zeytinci, Buse Tuğba Zaman, Sezgin Bakırdere, Elif Öztürk Er

“…Furthermore, multiple error functions were employed to assess the validity of models. …”
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Assessing the impact of artifact correction and artifact rejection on the performance of SVM- and LDA-based decoding of EEG signals by Guanghui Zhang, Steven J. Luck

“…Independent component analysis (ICA) was used to correct ocular artifacts, and artifact rejection was used to discard trials with large voltage deflections from other sources (e.g., muscle artifacts). …”
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Prediction of China’s Polysilicon Prices: A Combination Model Based on Variational Mode Decomposition, Sparrow Search Algorithm and Long Short-Term Memory by Jining Wang, Lin Jiang, Lei Wang

“…Compared to the traditional LSTM model and VMD–LSTM model, the VMD–SSA–LSTM model exhibits the smallest error and the highest goodness-of-fit on the polysilicon dataset, demonstrating higher predictive accuracy for polysilicon prices, which provides more accurate reference data for market analysis and pricing decisions of the polysilicon industry.…”
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Estimation of Cement Asphalt Mortar Disengagement Degree Using Vehicle Dynamic Response by Hui Shi, Liqiang Zhu, Hongmei Shi, Zujun Yu

“…In this paper, a novel method called CA mortar disengagement degree estimation algorithm (CMDEA) is proposed through an analysis of wheel acceleration of a passing vehicle. …”
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Autonomous surgical planning of mandibular angle reduction based on anatomical landmarks and osteotomy plane detection by Zhenggang Cao, Yichi Zhang, Junlei Hu, Gang Chai, Li Lin, Le Xie

“…The analysis of the preoperative CT images of 100 patients with mandibular osteotomy showed that the average RMS error of the automatic identification algorithm was 1.87 ± 0.33 mm. …”
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Evaluation of ground based spectral imaging for real time maize biomass monitoring by Andrea Szabó, Andrea Szabó, Nxumalo Gift Siphiwe, Nxumalo Gift Siphiwe, Erika Buday-Bódi, Erika Buday-Bódi, Blessing Ademola, János Tamás, János Tamás, Attila Nagy, Attila Nagy

“…The carotenoid prediction model, with a moderate R² (0.54), exhibited a slight overestimation, characterized by a Mean Bias Error (MBE) of 0.02 µg/g and a Normalized Root Mean Square Error (NRMSE) of 17%. …”
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Combination of machine learning and Raman spectroscopy for prediction of drug release in targeted drug delivery formulations by Wael A. Mahdi, Adel Alhowyan, Ahmad J. Obaidullah

“…The models, including Kernel Ridge Regression (KRR), Kernel-based Extreme Learning Machine (K-ELM), and Quantile Regression (QR) incorporate sophisticated approaches like the Sailfish Optimizer (SFO) for hyperparameter optimization and K-fold cross-validation to enhance predictive accuracy. …”
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Pioneering machine learning techniques to estimate thermal conductivity of carbon-based phase change materials: A comprehensive modeling framework by Raouf Hassan, Alireza Baghban

“…Among them, CatBoost achieved the highest predictive performance with an R2 of 0.979 and the lowest mean squared error (MSE) of 0.006 on the test set. SHAP analysis revealed that nanoparticle concentration was the most influential feature. …”
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Power Supply Management for an Electric Vehicle Using Fuzzy Logic by Yolanda Pérez-Pimentel, Ismael Osuna-Galán, Carlos Avilés-Cruz, Juan Villegas-Cortez

“…Speed signals acquired show improvements in some dynamic, such as overshoot, settling time, and steady-state error parameters. It is shown that this fuzzy controller increases the overall energy efficiency of the vehicle.…”
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Heterogeneous Hesitant Fuzzy Preference Relation and Its Application in Group Decision-making by FENG Xue, GENG Shengling

“…Firstly, the consistency of three hesitant preference relationships is defined, and three optimization models are established to obtain the weight vector of alternatives. …”
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A state-of-the-art novel approach to predict potato crop coefficient (Kc) by integrating advanced machine learning tools by Saad Javed Cheema, Masoud Karbasi, Gurjit S. Randhawa, Suqi Liu, Travis J. Esau, Kuljeet Singh Grewal, Farhat Abbas, Qamar Uz Zaman, Aitazaz A. Farooque

“…Different performance metrics such as correlation coefficient (R), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) were used to compare different model's performance. …”
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Four New Patient-Reported Outcome Measures Examining Health-Seeking Behavior in Persons With Type 2 Diabetes Mellitus (REDD-CAT): Instrument Development Study by Suzanne E Mitchell, Michael A Kallen, Jonathan P Troost, Barbara A De La Cruz, Alexa Bragg, Jessica Martin-Howard, Ioana Moldovan, Jennifer A Miner, Brian W Jack, Noelle E Carlozzi

“…Classical Test Theory and Item Response Theory were used for measurement development. Exploratory factor analysis (EFA; criterion ratio of eigenvalue 1 to eigenvalue 2 being >4; variance for eigenvalue 1 ≥40%) and confirmatory factor analysis (CFA; criterion 1-factor CFA loading <.50; 1-factor CFA residual correlation >.20; comparative fit index ≥0.90; Tucker-Lewis index ≥0.90; root mean square error of approximation <0.15) were used to determine unidimensional sets of items. …”
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Enhanced Single-Diode Solar Cell Model: Analytical Solutions Using Lambert W Function and Circuit Innovations by Martin Calasan, Snezana Vujosevic, Kristina Bakic

“…Results demonstrated the models&#x2019; accuracy and robustness, with Root Mean Square Error (RMSE) analysis showing superior alignment between simulated and experimental I-V curves compared to existing single-, double-, and triple-diode solar cell models from the literature. …”
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Effect Mechanism of Splitter Blade Length on the Energy Performance of Centrifugal Pump by Q. Ma, Z. Liu, S. Jiang

“…The results demonstrate that numerical simulations closely align with experimental data in terms of head and efficiency, with an error margin of less than 5%, underscoring the accuracy and reliability of the simulations. …”
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Dynamic Discrete GM (1,1) Model and Its Application in the Prediction of Urbanization Conflict Events by Ersi Liu, Qiangqiang Wang, Xinran Ge, Wei Zhou

“…Based on the dynamic average values, we further develop two dynamic discrete GM (1,1) models and provide the gradual heuristics method to draw the initial equal division number and the dichotomy approach to optimize the equal division number. Finally, based on an empirical analysis of the number of conflict events in the urbanization process in China, we verify that the dynamic discrete GM (1,1) model has higher fitting and prediction accuracy than the GM (1,1) model and the discrete GM (1,1) model, and its prediction result is beneficial to the government for prevention and solution of the urbanization conflict events.…”
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Long short-term memory (LSTM) networks for precision prediction of Schottky barrier photodiode behavior at different illumination levels by Gökalp Tulum, Sajjad Nematzadeh, İlke Taşçıoğlu, Şemsettin Altındal, Fahrettin Yakuphanoğlu

“…At the same time, the remaining dataset was divided into 80% for training and 20% for validation. The optimization algorithm was selected as Adaptive Moment Estimation (Adam), and the root mean squared error (RMSE) served as the loss function. …”
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Machine learning-selected minimal features drive high-accuracy rule-based antibiotic susceptibility predictions for Staphylococcus aureus via metagenomic sequencing by Xuefeng Jia, Yongfen Xiong, Yanping Xu, Fangyuan Chen, Peng Han, Jieming Qu, Quanli He, Guanhua Rao

“…The model demonstrated an overall sensitivity of 97.43% and specificity of 99.02%, respectively, with a very major error (VME) rate of 2.57% and a major error (ME) rate of 0.98% for isolate-level testing. …”
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A Data and Machine Learning-Based Approach for the Conversion of the Encounter Wave Frequency Spectrum to the Original Wave Spectrum by JeongYong Park, MooHyun Kim

“…The hyperparameters of the ANN model were subsequently tested and optimized. The results demonstrate that the ANN model can effectively predict the original wave spectrum with high accuracy, as evidenced by a favorable R2 value and error distribution analysis. …”
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Data-Driven Pavement Performance: Machine Learning-Based Predictive Models by Mohammad Fahad, Nurullah Bektas

“…A k-fold cross-validation technique was employed to optimize hyperparameters. Results indicate that LightGBM and CatBoost outperform other models, achieving the lowest mean squared error and highest R² values. …”
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