Random Forest-Based Prediction of the Optimal Solid Ink Density in Offset Lithography by Laihu Peng, Hao Fan, Yubao Qi, Jianqiang Li

“…An optimal solid ink density prediction model for lithographic offset printing is established, and the L*a*b* colorimetric values of CMY three-color prints are used as inputs for training through hyperparameter optimization of the model. …”
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Research on Tongue Image Segmentation and Classification Methods Based on Deep Learning and Machine Learning by Bin Liu, Zeya Wang, Kang Yu, Yunfeng Wang, Haiying Zhang, Tingting Song, Hao Yang

“…In this study, we propose a tongue image segmentation method based on deep learning and a pixel-level tongue color classification method utilizing machine learning techniques such as support vector machine (SVM) and ridge regression. …”
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Utilizing an Innovative Gaussian Process Regression Machine Learning Algorithm for Estimating Unconfined Compressive Strength Predictions by Hazel Abraham

“…Embedding two meta-heuristic algorithms, namely Adaptive Opposition Slime Mould Algorithm and Ebola Optimization Search, will further ensure accuracy from the models. …”
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Predicting the glass transition temperature of polymer based on generative adversarial networks and automated machine learning by Zhanjie Liu, Yixuan Huo, Qionghai Chen, Siqi Zhan, Qian Li, Qingsong Zhao, Lihong Cui, Jun Liu

“…The TPOT is then applied to automatically find the best model and parameter combinations, creating an optimal predictive model for the mixed dataset. …”
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Modeling and design of architected structures and metamaterials assisted with artificial intelligence by Angel Mora, Gustavo Herrera-Ramos, Diana L Ramirez-Gutierrez, Gildardo Sanchez-Ante, Rafael Batres, Armando Roman-Flores, Rita Q Fuentes-Aguilar, Arturo Molina, Enrique Cuan-Urquizo

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Optimized DenseNet Architectures for Precise Classification of Edible and Poisonous Mushrooms by Jay Prakash Singh, Debolina Ghosh, Jagannath Singh, Anurag Bhattacharjee, Mahendra Kumar Gourisaria

“…Traditional methods often result in errors which led to misclassifications and conventional machine learning models often struggle in feature extraction due to subtle differences in mushroom species. …”
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A Machine-Learning-Based Approach to Informing Student Admission Decisions by Tuo Liu, Cosima Schenk, Stephan Braun, Andreas Frey

“…In this illustration, first, several machine learning models were trained and compared. …”
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Bridging prediction and decision: Advances and challenges in data-driven optimization by Yanzhi Wang, Jianxiao Wang, Haoran Zhang, Jie Song

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An Ensemble Approach for Detection of Malicious URLs Using SOM and Tabu Search Optimization by Simar Preet Singh, Abhilash Maroju, Mohammad Kamrul Hasan, Karan Tejpal

“…For feature extraction, we provide a Self-Organizing Map based Radial Movement Optimization (SOM-RMO); for classification, we present an Ensemble Radial Basis Function Network (ERBFN) optimized by Tabu Search. …”
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Conceptual framework for managing quality costs at machine-building enterprises by М.О. Skliar

“…The quality cost models are divided into four groups according to the basic principles. …”
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Increasing the energy efficiency of auxiliary machines of AC electric locomotive by Yu. M. Kulinich, S. A. Shukharev, A. V. Gulyaev

“…As a result of simulation modeling, it was found that the extreme control system with a variable step allows for each fixed value of the electromagnetic moment of the motor in the minimum time to find the optimal (extreme) value of the magnetic flux of the motor rotor, which corresponds to the minimum value of the stator current. …”
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Robust prediction of chlorophyll-A from nitrogen and phosphorus content in Philippine and global lakes using fine-tuned, explainable machine learning by Karl Ezra Pilario, Eric Jan Escober, Aurelio de los Reyes V, Maria Pythias Espino

“…This paper presents a methodology using 8 popular machine learning (ML) models for estimating Chl-a concentration from nutrient content in lakes. …”
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Application and feasibility analysis of knowledge-based machine learning in predicting fatigue performance of stainless steel by Jia Wang, Dongkui Fan, C.S. Cai

“…Finally, the results predicted by the optimal model were compared with multiple design standards to verify the feasibility and effectiveness of the model in predicting the S-N curves of stainless steel materials. …”
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Predicting metabolic dysfunction associated steatotic liver disease using explainable machine learning methods by Yihao Yu, Yuqi Yang, Qian Li, Jing Yuan, Yan Zha

“…We aimed to develop and validate an explainable prediction model based on machine learning (ML) approaches for MASLD among the adult population. …”
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Optimizing Federated Learning With Aggregation Strategies: A Comprehensive Survey by Naeem Khan, Shibli Nisar, Muhammad Asghar Khan, Yasar Abbas Ur Rehman, Fazal Noor, Gordana Barb

“…This article provides a comprehensive survey of aggregation strategies in federated learning (FL). This decentralized machine learning (ML) paradigm enables multiple clients to collaboratively train models without sharing their local datasets. …”
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Simulation of Flow Field and Experimental Study on the Electric Discharge Machining of Small Holes in Renewable Dielectrics by Ruili Wang, Yangjing Zhao, Binghui Dong, Shuo Sun, Na Xiao, Wuyi Ming

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Scalable earthquake magnitude prediction using spatio-temporal data and model versioning by Rahul Singh, Bholanath Roy

“…This novel integration ensures adaptability to evolving datasets and facilitates dynamic model selection for optimal performance. Multiple machine learning algorithms, including Gradient Boosting, Light Gradient Boosting Machine (LightGBM), XGBoost, and Random Forest, are evaluated on dataset sizes of 20%, 35%, 65%, and 100%, with performance metrics such as Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, and R 2. …”
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NUMERICAL CONTROL MILLING PARAMETER OPTIMIZATION ON THE BASIS OF IMPROVED GENETIC ALGORITHM by YAN ShengLi, FU Hui, LI Hao

“…Firstly, a constrained multi-objective optimization function is constructed as a parameter optimization model. …”
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Comparative analysis of machine learning techniques in metabolomic-based preterm birth prediction by Ying-Chieh Han, Jane Shearer, Chunlong Mu, Donna M. Slater, Suzanne C. Tough, Gavin E. Duggan

“…Conclusions: Our results highlight the complexity of metabolomics-based modelling for preterm birth and support an iterative, model-driven approach for optimizing predictive accuracy in small-scale clinical datasets.…”
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Unlocking the link: predicting cardiovascular disease risk with a focus on airflow obstruction using machine learning by Xiyu Cao, Jianli Ma, Xiaoyi He, Yufei Liu, Yang Yang, Yaqi Wang, Chuantao Zhang

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