Development and Validation of a Machine Learning Prediction Model for Textbook Outcome in Liver Surgery: Results From a Multicenter, International Cohort by Jane Wang, MD, Amir Ashraf Ganjouei, MD, MPH, Taizo Hibi, MD, PhD, Nuria Lluis, MD, PhD, Camilla Gomes, MD, Fernanda Romero-Hernandez, MD, Han Yin, BA, Lucia Calthorpe, MD, Yukiyasu Okamura, MD, PhD, Yuta Abe, MD, PhD, Shogo Tanaka, MD, PhD, Minoru Tanabe, MD, PhD, Zeniche Morise, MD, PhD, Horacio Asbun, MD, PhD, David Geller, MD, Mohammed Abu Hilal, MD, PhD, Mohamed Adam, MD, Adnan Alseidi, MD, EdM, International Hepatectomy Study Group, Alison Baskin, Annie Wong-On-Wing, Annie Yang, Devesh Sharma, Taisuke Imamura, Masanori Nakamura, Yuya Miura, Koki Hayashi, Masatsugu Ishii, Keita Shimata, Kazuya Hirukawa, Hiroki Ueda, June S. Peng, Lucas Thornblade, Kenzo Hirose, Kimberly Kirkwood, Eric Nakakura, Carlos Corvera, Ajay Maker

“…Following model optimization, the performance of the models was evaluated using receiver operating characteristic curves, and a web-based calculator was developed. …”
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Development of a Machine Learning–Based Predictive Model for Postoperative Delirium in Older Adult Intensive Care Unit Patients: Retrospective Study by Houfeng Li, Qinglai Zang, Qi Li, Yanchen Lin, Jintao Duan, Jing Huang, Huixiu Hu, Ying Zhang, Dengyun Xia, Miao Zhou

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Predictive Modeling of Yoga's Impact on Venous Clinical Severity Scoring Using Gaussian Process Classification and Advanced Optimization Algorithms by Yazdan Ashgevari, Faranak Kazemi

“…This research explores the impact of yoga on Venous Clinical Severity Score (VCSS) using machine learning techniques. The study employs the Adaptive Opposition Slime Mould Algorithm (AOSM) and Mountain Gazelle Optimizer (MGO) to enhance the predictive capabilities of a Gaussian Process Classification (GPC) model. …”
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Optimization of Bayesian Neural Networks using hybrid PSO and fuzzy logic approach for time series forecasting by Farideh Sobhanifard

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Hybrid Paddy disease classification using optimized statistical feature based transformation technique with explainable AI by M Amudha, Brindha K

“…Using SHAP analysis and the Cat Boost classifier, this study delves deeper into the model’s classification process and the effects of each feature on the model’s operation. …”
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Multi-stage Alzheimer's disease diagnosis using Diffusion Tensor Imaging and Cost-Sensitive Machine Learning by Ali KHAZAEE, Abdolreza MOHAMMADI

“…The proposed method, which incorporates various machine learning models, Bayesian hyperparameter optimization, 10-fold cross-validation, and cost-sensitive learning, achieved a high test accuracy of 90.4%. …”
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An ensemble-based enhanced short and medium term load forecasting using optimized missing value imputation by Tania Gupta, Richa Bhatia, Sachin Sharma

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Predicting Energy Consumption in Building Heating Systems Using Model Identification Methods by Qu Minglu, Du Shanghe, Zhang Xinlin, Yu Zhen, Li Huai

“…The final identification model was determined after optimizing three machine learning methods, including polynomial regression, artificial neural networks, and extreme gradient boosting. …”
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Unit-level Digital Twin Model Construction Technology for Part Manufacturing Processes by LI Dong, LIN Zhiwen, CHEN Chuanhai, ZHAO Yongsheng, LIU Zhifeng

“…The application of digital twin technology in manufacturing is becoming increasingly widespread, particularly in the machining process of parts. Constructing high-fidelity digital twin models of manufacturing units is crucial for optimizing quality control and decision-making in machining processes. …”
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OPTICALS: A Novel Framework for Optimizing Predictive Trading Indicators in Cryptocurrency Using Advanced Learning Simulations by Hasib Shamshad, Fasee Ullah, Syed Adeel Ali Shah, Muhammad Faheem, Beena Shamshad

“…This study introduces OPTICALS, a novel framework for daily cryptocurrency price forecasting, focusing on transparency, robust performance assessment, and interpretability in machine and deep learning models. Unlike existing methods, OPTICALS provides detailed insights into model predictions by optimizing hyperparameters and identifying each model’s strengths and limitations. …”
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A multi-objective supply chain optimization model for reliable remanufacturing problems with M/M/m/k queues by Vahid Hajipour, Shermineh Hadad Kaveh, Fatih Yiğit, Ali Gharaei

“…This study presents a model that optimizes the remanufacturing process using in-house workstations and outsourcing to maximize supply chain profitability, reduce queue lengths, and ensure machine reliability. …”
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Cloud federation three-layer architecture: game-theoretic QoS modeling by MA Kun, HU Chuangyue, ZHANG Yuzhi, MA Fangchao, WANG Xiaodong, DANG Zhonghua, XIAO Wenhong, CHEN Shuangxi

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A Multi-Objective Optimization Design Method for High-Aspect-Ratio Wing Structures Based on Mind Evolution Algorithm Backpropagation Surrogate Model by Jin Nan, Junhua Zheng, Bochuan Jiang, Yuhang Li, Jiayun Chen, Xuanqing Fan

“…The wing structure optimization model was established using the multi-objective grey wolf optimizer (MOGWO) based on the surrogate model for search and optimization. …”
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Opinion mining using ensemble model for restaurant feedback analysis by Rohan S. Kamath, M. R. Kaushik, M. Ramakrishna

“…The main contribution of this work is the integration of models with optimized hyperparameters into an ensemble framework, which greatly improves model performance. …”
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A MaxEnt-TRIGRS hybrid model with dynamic safety factor mapping for enhanced debris flow susceptibility assessment in rainfall-triggered terrains by Xinlong Xu, Yue Qiang, Li Li, Siyu Liang, Tao Chen, Wenjun Yang, Xinyi Tan, Xi Wang, He Yang

“…Its susceptibility map corrects 34.7% of the overpredicted areas from the statistical model and enlarges stable zones by 1.8 times. Additionally, to determine the optimal weighting between machine learning and the physical model, we tested three weight combinations and found that a 0.55:0.45 ratio (MaxEnt: TRIGRS) yields the best performance. …”
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An artificial intelligence model for predicting an appropriate mAs with target exposure indicator for chest digital radiography by Jia-Ru Lin, Tai-Yuan Chen, Yu-Syuan Liang, Jyun-Jie Li, Ming-Chung Chou

“…Thus, there is an unmet need to establish a model to predict appropriate mAs for optimizing image quality before radiography. …”
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Predicting COVID-19 severity in pediatric patients using machine learning: a comparative analysis of algorithms and ensemble methods by Babak Pourakbari, Setareh Mamishi, Sepideh Keshavarz Valian, Shima Mahmoudi, Reihaneh Hosseinpour Sadeghi, Mohammad Reza Abdolsalehi, Mahmoud Khodabandeh, Mohammad Farahmand

“…Integrating these predictive models into clinical practice could support early identification of high-risk patients and optimize clinical decision-making.…”
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Ensemble machine learning prediction accuracy: local vs. global precision and recall for multiclass grade performance of engineering students by Yagyanath Rimal, Yagyanath Rimal, Navneet Sharma

“…This study examines the prediction accuracy of ensemble machine learning models by comparing local and global precision, recall, and accuracy for multiclass grading of engineering students. …”
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Integrated strategies for biosurfactant production and scale-up: Advances in fermentation engineering and computational modelling by Alice Angel Solomon, Dhanya Vishnu

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Blind Parameter Identification of MAR Model and Mutation Hybrid GWO-SCA Optimized SVM for Fault Diagnosis of Rotating Machinery by Wenlong Fu, Jiawen Tan, Xiaoyuan Zhang, Tie Chen, Kai Wang

“…The optimal SVM model was later employed to classify different fault samples. …”
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