Explainable Machine Learning for Efficient Diabetes Prediction Using Hyperparameter Tuning, SHAP Analysis, Partial Dependency, and LIME by Md. Manowarul Islam, Habibur Rahman Rifat, Md. Shamim Bin Shahid, Arnisha Akhter, Md Ashraf Uddin, Khandaker Mohammad Mohi Uddin

“…This research focused on classifying type 2 diabetes using machine learning and providing an explanation for the outcomes derived from the model predictions. …”
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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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Study on the Dynamic Evolution of Transverse Collusive Bidding Behavior and Regulation Countermeasures Under the “Machine-Managed Bidding” System by Zongyuan Zhang, Jincan Liu, Zhitian Zhang, Bin Chen

“…Results indicate that: (1) The MMB model significantly mitigates vertical collusive bidding behavior but lacks measures for governing transverse collusive bidding; (2) The game model has five evolutionarily stable strategies, with the one where the collusion initiator adopting the “non-collude” strategy, the free bidder adopting the “bid” strategy, and the regulator adopting the “negative regulate” strategy being the optimal evolutionary stable strategy; (3) Decreasing the costs associated with preparing bid documents, enhancing supervision costs, increasing the technical complexity of collusive bidding, and expanding the total number of construction enterprises with high-credit and low-credit ratings can expedite the evolution of the three participants toward the optimal evolutionarily stable strategy. …”
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Antihypertensive Drug Recommendations for Reducing Arterial Stiffness in Patients With Hypertension: Machine Learning–Based Multicohort (RIGIPREV) Study by Iván Cavero-Redondo, Arturo Martinez-Rodrigo, Alicia Saz-Lara, Nerea Moreno-Herraiz, Veronica Casado-Vicente, Leticia Gomez-Sanchez, Luis Garcia-Ortiz, Manuel A Gomez-Marcos

“…Model performance was evaluated using the coefficient of determination (R2) and mean squared error. …”
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Exploring factors affecting patient satisfaction in online healthcare: A machine learning approach grounded in empathy theory by Junbai Chen, Guoping Wu, Tong Zhang, Butian Zhao, Ruojia Wang, Xing Zhai, Fengying Guo

“…The optimal model was then chosen to investigate the essential factors impacting patients’ satisfaction with online healthcare. …”
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Pre Hoc and Co Hoc Explainability: Frameworks for Integrating Interpretability into Machine Learning Training for Enhanced Transparency and Performance by Cagla Acun, Olfa Nasraoui

“…Post hoc explanations for black-box machine learning models have been criticized for potentially inaccurate surrogate models and computational burden at prediction time. …”
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Pit Collapse Risk Fusion Early-Warning Method Based on Machine Learning and Improved Cloud Dempster–Shafer by Jiajia Zeng, Bo Wu, Cong Liu

“…The main contributions include (1) presenting a new input to the fusion model by optimizing the machine learning model through a multi-step rolling method, and then using the basic probability assignment values obtained from the cloud model as input to the fusion model and (2) developing an improved methodology to address the paradoxical results of the fusion of traditional Dempster–Shafer evidence theory when there is a high level of conflict in multi-source risk prediction data. …”
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Production monitoring and machine tracking in underground mines based on a collision avoidance system: A case study by Artur Skoczylas, Natalia Duda-Mróz, Wioletta Koperska, Paweł Stefaniak, Paweł Śliwiński

“…As part of this study, several analytical models (enhanced by machine learning techniques) were developed to identify movement patterns and cooperation among wheeled transport machinery, as well as the entire course of ore logistics within the mining area. …”
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Measurement of Suspended Sediment Concentration at the Outlet of the Yellow River Canyon: Using Sentinel-2 Images and Machine Learning by Genxin Song, Youjing Jiang, Xinyu Lei, Shiyan Zhai

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Scalable Detection of Underground Water Leaks in Dense Urban Environments Using L-Band SAR and Machine Learning by E. Ali, E. Ali, L. Xie, A. Sani-Mohammed, W. Xu, T. Zayed

“…Features extracted via Gray-Level Co-occurrence Matrix (GLCM) metrics and backscattering coefficients were used to train various machine learning, deep learning, and ensemble learning models, with hyperparameter optimization performed using a grid search algorithm. …”
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Typhoon and Storm Surge Hazard Analysis Along the Coast of Zhejiang Province in China Using TCRM and Machine Learning by Yong Fang, Xiangyu Li, Yanhua Sun, Ailian Li, Yunxia Guo

“…This study integrates the Tropical Cyclone Risk Model (TCRM) with a machine learning-based storm surge forecasting model to analyze typhoon hazards and storm surge risks at four representative coastal sites in Zhejiang Province: Haimen, Ruian, Wenzhou, and Zhapu. …”
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A New Approach Based on the Learning Effect for Sequence-Dependent Parallel Machine Scheduling Problem under Uncertainty by Maryam Ebrahimi, Parviz Dinari, Mohamad Samaei, Rouhollah Sohrabi, Soheil Sherafatianfini

“…In this research, a mathematical model for scheduling the parallel machines in terms of job degradation and operator learning is presented. …”
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The relative data hungriness of unpenalized and penalized logistic regression and ensemble-based machine learning methods: the case of calibration by Peter C. Austin, Douglas S. Lee, Bo Wang

“…Methods We used Monte Carlo simulations to assess the effect of number of events per variable (EPV) on the optimism of six learning methods when assessing model calibration: unpenalized logistic regression, ridge regression, lasso regression, bagged classification trees, random forests, and stochastic gradient boosting machines using trees as the base learners. …”
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Metabolic profiles and prediction of failure to thrive of citrin deficiency with normal liver function based on metabolomics and machine learning by Peiyao Wang, Duo Zhou, Lingwei Hu, Pingping Ge, Ziyan Cen, Zhenzhen Hu, Qimin He, Kejun Zhou, Benqing Wu, Xinwen Huang

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Evaluation of vascular cognitive impairment and identification of imaging markers using machine learning: a multimodal MRI study by Haoying He, Dongwei Lu, Sisi Peng, Jiu Jiang, Fan Fan, Dong Sun, Tianqi Sun, Zhipeng Xu, Ping Zhang, Xiaoxiang Peng, Ming Lei, Junjian Zhang

“…After imaging processing and preliminary model selection, optimal models using various data modalities were constructed. …”
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Driver Steering Intention Prediction for Human-Machine Shared Systems of Intelligent Vehicles Based on CNN-GRU Network by Chen Zhou, Fan Zhang, Edric John Cruz Nacpil, Zheng Wang, Fei-Xiang Xu

“…In order to mitigate human-machine conflicts and optimize shared control strategy in advance, it is essential for the shared control system to understand and predict driver behavior. …”
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Nanotechnology and LSTM machine learning algorithms in advanced fuel spray dynamics in CI engines with different bowl geometries by Harish Venu, Manzoore Elahi M. Soudagar, Tiong Sieh Kiong, N. M. Razali, Hua-Rong Wei, Armin Rajabi, V. Dhana Raju, T. M. Yunus Khan, Naif Almakayeel, Erdem Cuce, Huseyin Seker

“…Abstract This study explores the integration of nanotechnology and Long Short-Term Memory (LSTM) machine learning algorithms to enhance the understanding and optimization of fuel spray dynamics in compression ignition (CI) engines with varying bowl geometries. …”
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Research on a machine vision-based electro-killing pheromone-baited intelligent agricultural pest monitoring method by Guozhi Li, Zhengbo Liu, Zhengbo Liu, Zelin Feng, Jun Lyu, Bin Li, Guo Chen, Qing Yao

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Intelligent Fault Diagnosis for Rotating Mechanical Systems: An Improved Multiscale Fuzzy Entropy and Support Vector Machine Algorithm by Yuxin Pan, Yinsheng Chen, Xihong Fei, Kang Wang, Tian Fang, Jing Wang

“…Finally, a support vector machine (SVM) model is utilized to construct the optimal hyperplane for the diagnosis of fault types. …”
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Early Yield Prediction of Oilseed Rape Using UAV-Based Hyperspectral Imaging Combined with Machine Learning Algorithms by Hongyan Zhu, Chengzhi Lin, Zhihao Dong, Jun-Li Xu, Yong He

“…The optimal yield estimation models based on EWs and VIs were established, respectively, by using multiple linear regression (MLR), partial least squares regression (PLSR), extreme learning machine (ELM), and a least squares support vector machine (LS-SVM). …”
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