Unboxing Tree ensembles for interpretability: A hierarchical visualization tool and a multivariate optimal re-built tree by Giulia Di Teodoro, Marta Monaci, Laura Palagi

“…However, while combining multiple trees may provide higher prediction quality than a single one, it sacrifices the interpretability property resulting in “black-box” models. …”
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Research on the Molding Design and Optimization of the Molding Process Parameters of the Automobile Trunk Trim Panel by Youmin Wang, Zhaozhe Zhu, Lingfeng Tang, Qinshuai Jiang

“…In order to put forward the theoretical calculation formula for the compression force of the compression mold of the trunk trim panel, obtain the influence trend of the process parameters on the molding quality of the trunk trim panel, and obtain the optimal process parameters combination for the compression molding of the trunk trim panel, four process parameters, the heating temperature, time, compression pressure, and holding time, which affected the compression molding, were selected as the level factors; the maximum thinning rate, maximum thickening rate, and shrinkage rate of the trunk trim panel were selected as evaluation indicators and orthogonal experiments were designed and completed; the comprehensive weighted scoring method was used to obtain the comprehensive score results and obtain the comprehensive evaluation indicators of the best combination of process parameters of trunk trim panel; BP neural network and genetic algorithm were used to study the change trend of the evaluation indicators of trunk trim panel with the changes of process parameters; based on the optimal process parameter combination and the established neural network’s prediction function, the maximum thinning rate, maximum thickening rate, and shrinkage rate under a single process parameter change could be predicted, and the influence of a single process parameter on the maximum thinning rate, maximum thickening rate, and shrinkage rate could be obtained; the process parameters were optimized, and a maximum thinning rate of 28%, a maximum thickening rate of 4.3%, and a shrinkage rate of 0.8% were obtained; the optimal molding process parameters of the trunk trim panel were heating temperature of 209°C, heating time of 62 s, molding pressure of 14 kPa, and holding pressure time of 49 s; after optimization, the maximum shrinkage rate was 28.0880%, the maximum thickening rate was 44.3264%, and the shrinkage rate was 0.8901%; according to the optimal process parameters, the quality of the trunk trim panel was very good, which met the production quality requirements.…”
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Comparative analysis of lumped and semi-distributed hydrological models for an upland watershed in Ethiopia by Gebiaw T. Ayele, Bofu Yu

“…The study aimed at evaluating model performance and sensitivity of parameters in predicting streamflow for tropical watersheds. Calibration and uncertainty analysis (UA) for SWAT was performed using four UA techniques available in the SWAT (SWAT-CUP) and the genetic algorithm was used for parameter estimation for conceptual models. …”
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Inversion Method for Permitting Loadings of Pollutant from Lateral Effluents Based on Adjoint Equations by SHI Xiaoyan, ZHANG Hong, TAO Chunhua, LU Lingjiang, WAN Xin, LIU Zhaowei

“…However, optimization objectives that rely on discrepancies between predicted and observed concentrations cannot be directly applied to determine the permissible loadings, limiting the application of the adjoint equation method to this issue. …”
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Numerical analysis method of stress wave transmission attenuation of coal and rock structural plane by Wenlong SHEN, Renren ZHU, Ziqiang CHEN, Guocang SHI

“…This study demonstrates that the machine learning prediction model based on BP artificial neural network technology has well-applicability, which can quickly determine the model parameters under the current inclination angle and axial static load of the coal rock structural plane, provide an efficient data-driven correction method for the parameters of the Barton-Bandis intrinsic model of the coal rock structural plane and also predict the parameters of numerical simulation of the coal rock structural plane under the larger inclination angle and axial static load ranges other than the given training samples.…”
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Inversion Method for Permitting Loadings of Pollutant from Lateral Effluents Based on Adjoint Equations by SHI Xiaoyan, ZHANG Hong, TAO Chunhua, LU Lingjiang, WAN Xin, LIU Zhaowei

“…However, optimization objectives based on the discrepancies between predicted and observed concentrations cannot be straightforwardly employed for determining the permissible loadings, thus restricting the application of the adjoint equation method to this issue. …”
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Utilization of Neural Network in the Diagnosis of Pes Planus and Pes Cavus with a Smartphone Camera by Samir Ghandour MD, Anton Lebedev BS, Wei Shao Tung BS, Konstantin Semianov BS, Artem Semyanov MS, Daniel Guss MD, MBA, Gregory R. Waryasz MD, John Y. Kwon MD, Christopher W. DiGiovanni MD, Soheil Ashkani-Esfahani MD, Lorena Bejarano-Pineda MD

“…Conclusion: Our smartphone-based CNN model is highly accurate as a decision-support tool, and it is reliable and accessible for predicting Pes planus and Pes cavus deformities. This tool may be very useful in underserved healthcare settings and for patients with limited access to expert clinical assessment. …”
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Chemometric-assisted UV spectrophotometric methods for determination of miconazole nitrate and lidocaine hydrochloride along with potential impurity and dosage from preservatives by Esraa S. Ashour, Ghada M. El-Sayed, Maha A. Hegazy, Nermine S. Ghoniem

“…The obtained results revealed that PLS algorithm was superior to PCR depending on the lowest root mean square error of prediction (RMSEP) and correlation coefficient values (r). …”
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Joint classification and regression with deep multi task learning model using conventional based patch extraction for brain disease diagnosis by Padmapriya K., Ezhumalai Periyathambi

“…Magnetic resonance imaging (MRI) is increasingly used in clinical score prediction and computer-aided brain disease (BD) diagnosis due to its outstanding correlation. …”
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Securing the economic management and service infrastructure of banks via the use of artificial intelligence (MO-ILSTM) by Xintong Wu

“…The platform can also effectively forecast financial risks, with a prediction accuracy of 75.6 % due to information exchange and interaction. …”
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Development of an MRI based artificial intelligence model for the identification of underlying atrial fibrillation after ischemic stroke: a multicenter proof-of-concept analysisRes... by Zijie Zhang, Yang Ding, Kaibin Lin, Wenli Ban, Luyue Ding, Yudong Sun, Chuanliang Fu, Yihang Ren, Can Han, Xue Zhang, Xiaoer Wei, Shundong Hu, Yuwu Zhao, Li Cao, Jun Wang, Saman Nazarian, Ying Cao, Lan Zheng, Min Zhang, Jianliang Fu, Jingbo Li, Xiang Han, Dahong Qian, Dong Huang

“…Area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were calculated for model evaluation. …”
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Unmanned Aerial Vehicle Remote Sensing for Monitoring Fractional Vegetation Cover in Creeping Plants: A Case Study of <i>Thymus mongolicus</i> Ronniger by Hao Zheng, Wentao Mi, Kaiyan Cao, Weibo Ren, Yuan Chi, Feng Yuan, Yaling Liu

“…FVC growth rates exhibited distinct variations across phenological stages, indicating high consistency between predicted and actual growth trends. This study highlights the feasibility of UAV-based FVC monitoring for <i>T. mongolicus</i> and indicates its potential for tracking creeping plants.…”
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Dynamic Evacuation Shelter Allocation in Response to Human Mobility: A Case Study of Taipei City by Chang-Hung Shih, Cheng-Yun Wu, Shu-Ping Tseng, Yi-Lin Huang, Rong-Pu Jhuang, Yi-Chung Chen, Tien-Yi Yang, Wei-Ting Chen

“…This study developed a system for the targeted assignment of evacuation sites during air raids. The DBSCAN algorithm was used to group data based on pedestrian flow patterns and an LSTM model was used to enhance the prediction speed and accuracy. …”
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Study on the Key Technology of Image Transmission Mechanism Based on Channel Coding Ghost Imaging by Leihong Zhang, Ye Hualong, Dawei Zhang

“…&#x00A0;It solves problems such as insufficient feasibility, low reduction degree, long imaging time of current imaging algorithm, and so on.…”
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Phase-Controlled Closing Strategy for UHV Circuit Breakers with Arc-Chamber Insulation Deterioration Consideration by Hao Li, Qi Long, Xu Yang, Xiang Ju, Haitao Li, Zhongming Liu, Dehua Xiong, Xiongying Duan, Minfu Liao

“…Compared with the least squares fitting, this algorithm achieves a reasonable balance between goodness of fit and complexity, with prediction deviations tending to be randomly distributed, no obvious systematic offset, and low dispersion degree. …”
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Mathematical Modeling of Tuberculosis Transmission Dynamics With Reinfection and Optimal Control by Francis Oketch Ochieng

“…Existing TB models often neglect seasonal variations, optimal control, and reinfection, limiting their accuracy in predicting disease dynamics. This study presents a novel data‐driven SVEITRS mathematical model incorporating these factors to analyze TB transmission dynamics. …”
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Unlocking potent anti-tuberculosis natural products through structure–activity relationship analysis by Delfly Booby Abdjul, Fitri Budiyanto, Joko Tri Wibowo, Tutik Murniasih, Siti Irma Rahmawati, Dwi Wahyu Indriani, Masteria Yunovilsa Putra, Asep Bayu

“…Significant characteristics and relevant biological properties of each compound were analysed using a Random Forest, machine learning algorithm, to explore SAR. Using molecular docking, AutoDock Vina was utilised to assess molecular interactions with protein targets, and predictive accuracy was enhanced using the XGBoost machine learning model. …”
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Optimizing Automatic Voltage Control Collaborative Responses in Chain-Structured Cascade Hydroelectric Power Plants Using Sensitivity Analysis by Li Zhang, Jie Yang, Jun Wang, Lening Wang, Haiming Niu, Xiaobing Liu, Simon X. Yang, Kun Yang

“…Furthermore, they provide a practical foundation for future advancements in multi-scenario hydropower regulation, enhanced coordination strategies, and predictive control capabilities within clean energy systems.…”
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Coordinated Design of Power System Stabilizer and Virtual Inertia Control Using Modified Harris Hawk Optimization for Improving Power System Stability by Mohamad Almas Prakasa, Imam Robandi, Alberto Borghetti, Muhammad Ruswandi Djalal, Waseda Himawari

“…Based on the obtained results, HHO-MSS is 1.44% to 9.28% more accurate and 34.63% to 53.94% more consistent than Electric Eel Foraging Optimization (EEFO), and Puma Optimizer (PO), Evolutionary Mating Algorithm (EMA). With the optimal damping ratios of 9.94% to 9.96% achieved by HHO-MSS, the overall power system stability improvements, including both local and interarea responses across 38 simulations involving sudden load changes, varying inertia, and different RES levels, are as follows: 41.17% to 70.89% frequency nadir improvement, 25.9% to 67.38% power angle deviation improvement, 84.83% to 85.26% settling time reduction, and 51.57% to 89.73% average error reduction calculated with performance indices. …”
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Optimal design of high‐performance rare‐earth‐free wrought magnesium alloys using machine learning by Shaojie Li, Zaixing Dong, Jianfeng Jin, Hucheng Pan, Zongqing Hu, Rui Hou, Gaowu Qin

“…Abstract In this study, a small dataset of 370 datapoints of Mg alloys are selected for machine learning (ML), in which each datapoint includes five rare‐earth‐free alloying elements (Ca, Zn, Al, Mn and Sn), three extrusion parameters (extrusion speed, temperature and ratio), and three mechanical properties (yield strength [YS], ultimate tensile strength [UTS] and elongation [EL]). The ML algorithms, including support vector machine regression (SVR), artificial neural network, and other three methods, are employed, and the SVR has the best performance in predicting mechanical properties based on the components, and process parameters, with the mean absolute percentage error of YS, UTS, and EL being 6.34%, 4.19%, and 13.64% in the test set, respectively. …”
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