Exploring Machine Learning Models for Vault Safety in ICL Implantation: A Comparative Analysis of Regression and Classification Models by Qing Zhang, Qi Li, Zhilong Yu, Ruibo Yang, Emmanuel Eric Pazo, Yue Huang, Hui Liu, Chen Zhang, Salissou Moutari, Shaozhen Zhao

“…Abstract Introduction Accurate prediction of postoperative vault height following implantable collamer lens (ICL) V4c implantation is critical for minimizing complications and achieving optimal surgical outcomes. This study aims to evaluate the performance of machine learning models in predicting postoperative vault height, focusing on both regression and classification approaches. …”
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Active learning concerning sampling cost for enhancing AI-enabled building energy system modeling by Ao Li, Fu Xiao, Ziwei Xiao, Rui Yan, Anbang Li, Yan Lv, Bing Su

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Energy storage efficiency modeling of high-entropy dielectric capacitors using extreme learning machine and swarm-based hybrid support vector regression computational methods by Yas Al-Hadeethi, Taoreed O. Owolabi, Mouftahou B. Latif, Bahaaudin M. Raffah, Ahmad H. Milyani, Saheed A. Tijani

“…This work employs single hidden layer extreme learning machine (ELM) algorithm and hybrid particle swarm optimization-based support vector regression (PS-SVR) for determining energy storage efficiency of high-entropy ceramics. …”
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Modeling Portfolio Optimization based on behavioral Preferences and Investor’s Memory by vahideh mousavi kakhki, Sanaz Khatabi

“…Traditional optimization models, such as Markowitz's Mean-Variance model, aim to maximize expected returns while minimizing risk. …”
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Forecasting returns with machine learning and optimizing global portfolios: evidence from the Korean and U.S. stock markets by Dohyun Chun, Jongho Kang, Jihun Kim

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Novel machine learning driven design strategy for high strength Zn Alloys optimization with multiple constraints by Chenfeng Pan, Wenwen Lin, Jianxing Zhou, Wei Jian, Ka Chun Chan, Yuk Lun Chan, Lu Ren

“…Interpretability analysis of the models was performed using the SHAP method with particle swarm optimization (PSO). …”
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Optimization of EEG-based wheelchair control: machine learning, feature selection, outlier management, and explainable AI by Amr M. Hamed, Abdel-Fattah Attia, Heba El-Behery

“…This study proposes an optimized classification framework that evaluates ten machine learning (ML) models, emphasizing ensemble methods, feature selection (FS), and outlier utilization. …”
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Elegante+: A Machine Learning-Based Optimization Framework for Sparse Matrix–Vector Computations on the CPU Architecture by Muhammad Ahmad, Sardar Usman, Ameer Hamza, Muhammad Muzamil, Ildar Batyrshin

“…After creating a comprehensive dataset, we trained various machine learning models to predict the optimal scheduling policy, significantly enhancing the computational efficiency and reducing the overhead in high-performance computing environments. …”
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Optimizing water purification in double slope solar stills using abc algorithm and machine learning techniques by Mcluret, S. Joe Patrick Gnanaraj

“…In this era of intelligent optimization, this study integrates metaheuristic algorithms, computational intelligence, and statistical modeling to enhance performance. …”
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Optimizing visual data retrieval using deep learning driven CBIR for improved human machine interaction by Arulmozhi P, Gopi R

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Efficient Hyperparameter Optimization Using Metaheuristics for Machine Learning in Truss Steel Structure Cross-Section Prediction by Donwoo Lee, Seunghyeon Noh, Jeonghyun Kim, Seungjae Lee

“…However, the design problem of truss structures poses substantial challenges for machine learning models due to the highly diverse and nonlinear characteristics of the optimal cross-sectional distributions, which may hinder effective learning. …”
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A Machine Learning-Assisted Automation System for Optimizing Session Preparation Time in Digital Audio Workstations by Bogdan Moroșanu, Marian Negru, Georgian Nicolae, Horia Sebastian Ioniță, Constantin Paleologu

“…The proposed approach highlights how practical automation, combined with lightweight Neural Network (NN) models, can optimize workflow efficiency in real-world music production environments.…”
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A Hybrid Automata-Driven Machine Learning Framework for Real-Time Energy Optimization in Smart Buildings by Rikame Rajashri, Ranjan Mritunjay Kr., Jadhav Monali, Wankhede Shrushti, Bankar Sakshi, Bachhav Pranjal

“…These inputs train the predictive models like Random Forest, XGBoost, LightGBM, and Neural Networks to predict energy requirements. …”
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Submarine Cable Vibration Signal Recognition Based on Sparrow Search Algorithm Optimized Support Vector Machine by GUO Jiaxing, QIAN Junxia, LIU Rui, MA Hongjuan

“…The results show that the EEMD-SSA-SVM algorithm exhibits higher accuracy and superior optimization ability. Specifically, the accuracy of the test set reaches 95%, surpassing that achieved by other algorithm models.…”
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Interpretable Machine Learning Reveals the Crucial Role of Water Availability in Regulating Thermal Optimality of Terrestrial Ecosystems by Arman Ahmadi, Kanishka Mallick, Koong Yi, Dennis Baldocchi

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Optimizing casting process using a combination of small data machine learning and phase-field simulations by Xiaolong Pei, Jiaqi Pei, Hua Hou, Yuhong Zhao

“…Abstract It has been a challenge to employ machine learning (ML) to optimize casting processes due to the scarcity of data and difficulty in feature expansion. …”
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Explainable AI-Based Skin Cancer Detection Using CNN, Particle Swarm Optimization and Machine Learning by Syed Adil Hussain Shah, Syed Taimoor Hussain Shah, Roa’a Khaled, Andrea Buccoliero, Syed Baqir Hussain Shah, Angelo Di Terlizzi, Giacomo Di Benedetto, Marco Agostino Deriu

“…Multiple pretrained CNN models were evaluated, with Xception emerging as the optimal choice for its balance of computational efficiency and performance. …”
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A Hybrid MCDM-Grey Wolf Optimizer Approach for Multi-Objective Parametric Optimization of μ-EDM Process by Partha Protim Das

“…The polynomial regression (PR) meta-models are observed as the inputs to these hybrid optimizers. …”
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Building Fire Location Predictions Based on FDS and Hybrid Modelling by Yanxi Cao, Hongyan Ma, Shun Wang, Yingda Zhang

“…Combining convolutional neural networks (CNNs) and support vector machines (SVMs) for prediction, the fire-source location prediction model with temperature, smoke, and CO concentration as feature quantities was constructed, and the hyperparameters affecting the model accuracy and generalisation were optimised by the Crested Porcupine Optimizer (CPO) algorithm. …”
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An adaptive hierarchical hybrid kernel ELM optimized by aquila optimizer algorithm for bearing fault diagnosis by Hao Yan, Liangliang Shang, Wan Chen, Mengyao Jiang, Tianqi lu, Fei Li

“…The AO algorithm is further enhanced by incorporating chaos mapping, implementing a refined balanced search strategy, and fine-tuning parameter $$G_2$$ , which collectively improve its ability to escape local optima and conduct global searches, thus strengthening the robustness of the model during parameter optimization. Experimental results on the CWRU , MFPT and JNU datasets demonstrate that stacked denoising autoencoder-adaptive hierarchical hybrid kernel extreme learning machine (SDAE-AHHKELM) has better fault classification accuracy, robustness, and generalization than KELM and other methods.…”
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