Advanced evaluation of performance of machine learning models for soapstock splitting optimisation under uncertainty by Bartosz Szeląg, Krzysztof Barbusiński, Michał Stachura, Przemysław Kowal, Adam Kiczko, Eldon R. Rene

“…Machine learning algorithms—Extreme Gradient Boosting (XGBoost) and Support Vector Machines (SVM)—were assessed in comparison with Response Surface Methodology (RSM). …”
Get full text

Data-driven modeling for evaluating deformation of a deep excavation near existing tunnels by Fengwen Lai, Songyu Liu, Jim Shiau, Mingpeng Liu, Guojun Cai, Ming Huang

Get full text

Method and application of stability prediction model for rock slope by Yun Qi, Chenhao Bai, Xuping Li, Hongfei Duan, Wei Wang, Qingjie Qi

“…Secondly, the XGBoost model is optimized by fine-tuning parameters such as maximum depth (max_depth), learning rate (learning_rate), subsample rate, column sampling rate (colsample-bytree), and minimum loss (gamma) through NRBO. …”
Get full text

Evaluating the impact of waste marble on the compressive strength of traditional concrete using machine learning by Kennedy C. Onyelowe, Viroon Kamchoom, Ahmed M. Ebid, Shadi Hanandeh, Susana Monserrat Zurita Polo, Rolando Fabián Zabala Vizuete, Rodney Orlando Santillán Murillo, Rolando Marcel Torres Castillo, Siva Avudaiappan

“…Error indices such as the sum of squared error (SSE), mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and Error (%), and performance metrics such as Accuracy % and the R2 between predicted and calculated compressive strength parameters were used to evaluate the overall behavior of the models. …”
Get full text

Inversion of Water Quality Parameters from UAV Hyperspectral Data Based on Intelligent Algorithm Optimized Backpropagation Neural Networks of a Small Rural River by Manqi Wang, Caili Zhou, Jiaqi Shi, Fei Lin, Yucheng Li, Yimin Hu, Xuesheng Zhang

“…Again, based on the screened features, a back-propagation neural network (BPNN) model optimized using a mixture of the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm was established as a means of estimating water quality parameter concentrations. To intuitively evaluate the performance of the hybrid optimization algorithm, its prediction accuracy is compared with that of conventional machine learning algorithms (Random Forest, CatBoost, XGBoost, BPNN, GA–BPNN and PSO–BPNN). …”
Get full text

State ownership and firm performance: A performance evaluation of disinvested public sector enterprises by I. Chhabra, S. Gupta, V. K. Gupta

Get full text

Machine learning for detection of diffusion abnormalities-related respiratory changes among normal, overweight, and obese individuals based on BMI and pulmonary ventilation paramet... by Xin-Yue Song, Xin-Peng Xie, Wen-Jing Xu, Yu-Jia Cao, Bin-Miao Liang

“…Additionally, we performed feature importance analysis using shapley additive explanations (SHAP) and permutation importance to evaluate the contribution of individual parameters to the classification process. …”
Get full text

Comprehensive evaluation of machine learning models for predicting the cognitive status of Alzheimer's disease subjects and susceptible by Lucien Gnegne Meteumba, Vaghawan Prasad Ojha, Shantia Yarahmadian

Get full text

Evaluation of mesoporous silica synthesized for green adsorption by modeling via machine learning and mass transfer by Minge Yang, Qiqing Yue, Junyi He

“…Mass transfer and machine learning evaluations were carried out to obtain separation efficiency. …”
Get full text

Residential Building Energy Usage Prediction Using Bayesian-Based Optimized XGBoost Algorithm by Nabaa Riyadh Baqer, Parviz Rashidi-Khazaee

Get full text

Mechanisms of some Lactobacillus sp. for enhancing the growth of mulberry silkworm, Bombyx mori larvae, and their role as biocontrol agents against diseases by Radwa G. Attia, Marwa N. Moustafa, Shireen A. M. Ma’moun, Hayam, A. E. Sayed, Doaa R. Abdel-Haleem

Get full text

Evaluation and Mapping of Snow Characteristics Using Remote Sensing Data in Astore River Basin, Pakistan by Ihsan Ullah Khan, Mudassar Iqbal, Zeshan Ali, Abu Bakar Arshed, Mo Wang, Rana Muhammad Adnan

“…The meteorological parameters and basin characteristics affect the SWE and can determine the SD values.…”
Get full text

Large vessel vasculitis evaluation by CTA: impact of deep-learning reconstruction and “dark blood” technique by Ning Ding, Xi-Ao Yang, Min Xu, Yun Wang, Zhengyu Jin, Yining Wang, Huadan Xue, Lingyan Kong, Zhiwei Wang, Daming Zhang

“…HIR or DLR DB image sets were generated using corresponding arterial and delayed-phase image sets based on a “contrast-enhancement-boost” technique. Quantitative parameters of aortic wall image quality were evaluated. …”
Get full text

Flood risk mapping and performance efficiency evaluation of machine learning algorithms: Best practice in northern Iran by M. Shirmohammadi, M. Shirmohammadi, S. Pirasteh, W. Li, D. Mafi-Gholami

Get full text

Machine Learning-Based Recommender System for Pulsed Laser Ablation in Liquid: Recommendation of Optimal Processing Parameters for Targeted Nanoparticle Size and Concentration Usin... by Anesu Nyabadza, Dermot Brabazon

“…Multiple ML models were evaluated, including K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), Random Forest, and Decision trees. …”
Get full text

Evaluating Machine Learning and Deep Learning models for predicting Wind Turbine power output from environmental factors. by Montaser Abdelsattar, Mohamed A Ismeil, Karim Menoufi, Ahmed AbdelMoety, Ahmed Emad-Eldeen

Get full text

Personalized prediction of breast cancer candidates for Anti-HER2 therapy using 18F-FDG PET/CT parameters and machine learning: a dual-center study by Zhenguo Sun, Jianxiong Gao, Wenji Yu, Xiaoshuai Yuan, Peng Du, Peng Chen, Yuetao Wang

“…BackgroundAccurately evaluating human epidermal growth factor receptor (HER2) expression status in breast cancer enables clinicians to develop individualized treatment plans and improve patient prognosis. …”
Get full text

Predictive Models Using Machine Learning to Identify Fetal Growth Restriction in Patients With Preeclampsia: Development and Evaluation Study by Qing Hua, Fengchun Yang, Yadan Zhou, Fenglian Shi, Xiaoyan You, Jing Guo, Li Li

“…ML models were constructed to evaluate the predictive value of maternal parameter changes on preeclampsia combined with FGR. …”
Get full text

Evaluation of a double-lens dielectric radome using a microstrip patch antenna for electromagnetic applications by Taehoon Kim, Sathish Kumar, C.V. Ravikumar, Shafiq Ahmad, Karuna Yepuganti, P. Srinivasa Varma

“…Because the presence of the radome affects the performance parameters of the antenna, such as the radiation pattern, reflected power, and side lobe level, its design should not be done independently of the antenna analysis. …”
Get full text

Development and validation of an interpretable machine learning model for predicting Philadelphia chromosome-positive acute lymphoblastic leukaemia using clinical and laboratory pa... by Jing Zhang, Cheng Zhang, Xi Zhang, Wuchen Yang, Jingya Liu, Yang Gou, Xingqin Huang, Maoshan Chen, Dezhi Huang, Shengwang Wu, Shuiqing Liu, Xiangui Peng

“…The interpretability of the model was evaluated by using SHapley Additive Interpretation (SHAP), and external validation was conducted on an independent test cohort.Results 10 parameters were selected to construct multiple ML models. …”
Get full text