A Make-to-Order Capacitated Lot-Sizing Model with Parallel Machines, Eligibility Constraints, Extra Shifts, and Backorders by Felipe T. Muñoz, Juan Ulloa-Navarro

“…The MILP model is solved using open-source optimization tools, specifically the HiGHS solver. …”
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A New Stator Flux Observer Based on Model-optimized Auto Disturbance Rejection Control by 胡婵娟, 刘志星, 曹国荣, 刘憾宇, 张劲松

“…In this paper, a new stator flux observer based on the model-optimized auto disturbance rejection control (ADRC) is proposed to improve the observing performance of direct torque control system at low speed. …”
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Modeling Compressive Strength of Self-Compacting Concrete (SCC) Using Novel Optimization Algorithm of AOA by Francisca Blanco, Ye Woo

“…Soft computing methods, which offer a cost-effective and highly accurate alternative to experimental techniques, have attracted interest in modeling dependent variables. This paper presents a novel approach by combining a Support Vector Machine (SVM) with advanced optimization algorithms to estimate the CS of SCC mixtures accurately. …”
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Comparison of Sugarcane Drought Stress Based on Climatology Data using Machine Learning Regression Model in East Java by Aries Suharso, Yeni Herdiyeni, Suria Darma Tarigan, Yandra Arkeman

“…Our proposed prediction model uses climatological features with additional Lag features in a machine learning regression approach and 5-fold cross-validation of the training-testing data split with the help of optimization using hyperparameters. …”
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Prediction of river dissolved oxygen (DO) based on multi-source data and various machine learning coupling models. by Yubo Zhao, Mo Chen

“…In this study, a hybrid machine learning model for river DO prediction, called DWT-KPCA-GWO-XGBoost, is proposed, which combines the discrete wavelet transform (DWT), kernel principal component analysis (KPCA), gray wolf optimization algorithm (GWO), and extreme gradient boosting (XGBoost). …”
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Construction of a model for predicting sensory attributes of cosmetic creams using instrumental parameters based on machine learning by He Jingru, Qian Xuedan, Huang Hu, Lin Bao, Zhang Jun, Zhang Chunxiao, Chen Yuyan

“…This study aims to enhance the sensory evaluation of skin creams by using machine learning to predict sensory attributes based on instrumental parameters, addressing the limitations of conventional methods. …”
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An Interpretable and Generalizable Machine Learning Model for Predicting Asthma Outcomes: Integrating AutoML and Explainable AI Techniques by Salman Mahmood, Raza Hasan, Saqib Hussain, Rochak Adhikari

“…This study develops a predictive model for asthma outcomes, leveraging automated machine learning (AutoML) and explainable AI (XAI) to balance high predictive accuracy with interpretability. …”
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An interpretable machine learning model for predicting mortality risk in adult ICU patients with acute respiratory distress syndrome by Wanyi Li, Hangyu Zhou, Yingxue Zou

“…This study used eight machine learning algorithms to construct predictive models. …”
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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

“…This study presents a comprehensive comparative analysis of Machine Learning (ML) and Deep Learning (DL) models for predicting Wind Turbine (WT) power output based on environmental variables such as temperature, humidity, wind speed, and wind direction. …”
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A Hybrid Fault Diagnosis Model for Rolling Bearing With Optimized VMD and Fuzzy Dispersion Entropy by Xin Xia, Xiaolu Wang, Weilin Chen

“…To improve the efficiency of feature extraction and fault diagnosis, a hybrid model based on optimized variational mode decomposition (VMD), fuzzy dispersion entropy (FDE), and a support vector machine (SVM) is proposed. …”
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A Hybrid Brain Stroke Prediction Framework: Integrating Feature Selection, Classification, and Hyperparameter Optimization by Mohammad Amin, Khalid M. O. Nahar, Hasan Gharaibeh, Rabia Emhamed Al Mamlook, Ahmad Nasayreh, Nesrine Atitallah, Ali Gharaibeh, Raneem Hamad, Raed Abu Zitar, Aseel Smerat, Laith Abualigah

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An interpretable stacking machine-learning model to predict the hot torsion flow characteristics of a micro-alloyed steel by Hojjat Emami, Mehdi Shaban Ghazani

“…However, existing machine learning (ML) models often lack the robustness and generalizability needed to predict flow stress across diverse processing parameters accurately. …”
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Machine Learning-Based Prediction of Resilience in Green Agricultural Supply Chains: Influencing Factors Analysis and Model Construction by Daqing Wu, Tianhao Li, Hangqi Cai, Shousong Cai

“…Secondly, by integrating configurational analysis with machine learning, it innovatively constructs a resilience level prediction model based on fsQCA-XGBoost. …”
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Spatial modeling of brine level and salinity in the Qarhan Salt Lake using GIS and automated machine learning algorithms by Dongmei Yu, Zitao Wang, Chao Yue, Jianping Wang

“…This study developed an automated machine learning (AutoML) approach to model brine levels and salinity, providing a tool for informed resource management decisions. …”
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Discrete element modeling and experimental study of biomechanical properties of cotton stalks in machine-harvested film-stalk mixtures by Jia Zhang, Jianhua Xie, Yakun Du, Yuanze Li, Yong Yue, Silin Cao

“…Abstract To address the current problems of low accuracy and poor reliability of the discrete element model of cotton stalks, as well as the difficulty of guiding the design and optimization of the equipment through simulations, the discrete element modeling and physical-mechanical tests of cotton stalks in machine harvested film-stalk mixtures are carried out. …”
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Medium- Long-Term Runoff Forecasting Using Interpretable Hybrid Machine Learning Model for Data-Scarce Regions by YOU Yu-jun, BAI Yun-gang, LU Zhen-lin, ZHANG Jiang-hui, CAO Biao, LI Wen-zhong, YU Qi-ying

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Recent Progress in Hybrid Intelligent Modeling Technology and Optimization Strategy for Industrial Energy Consumption Processes by Sheng Du, Li Jin, Zixin Huang, Xiongbo Wan

“…This editorial discusses recent progress in hybrid intelligent modeling technology and optimization strategy for industrial energy consumption processes. …”
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Design Optimization of Compliant Mechanisms for Vibration- Assisted Machining Applications Using a Hybrid Six Sigma, RSM-FEM, and NSGA-II Approach by Huy-Tuan Pham, Van-Khien Nguyen, Quang-Khoa Dang, Thi Van Anh Duong, Duc-Thong Nguyen, Thanh-Vu Phan

“…Vibration-assisted machining, a hybrid processing method, has been gaining considerable interest recently due to its advantages, such as increasing material removal rate, enhancing surface quality, reducing cutting forces and tool wear, improving tool life, or minimizing burr formation. …”
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A Hybridization of Machine Learning and NSGA-II for Multi-Objective Optimization of Surface Roughness and Cutting Force in ANSI 4340 Alloy Steel Turning by Anh-Tu Nguyen, Van-Hai Nguyen, Tien-Thinh Le, Nhu-Tung Nguyen

“…In addition, a hybridization of NSGA-II and ANN is implemented to find the optimal solutions for machining parameters, which lie on the Pareto front. …”
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Machine Learning-Assisted NIR Spectroscopy for Dynamic Monitoring of Leaf Potassium in Korla Fragrant Pear by Mingyang Yu, Weifan Fan, Junkai Zeng, Yang Li, Lanfei Wang, Hao Wang, Feng Han, Jianping Bao

“…Parameter optimization revealed that the BPNN model achieved optimal stability with 10 neurons in the hidden layer. …”
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