Development and evaluation of machine learning models for premixed flame classification in different hydrogen-natural gas proportions using images and audio by Pedro Narvaez, Alejandro Lopez, Jousef E. Karam, Alejandro Restrepo, Andrés A. Amell

“…The feature extraction process employed convolutional neural networks (CNNs) for both data types, with a segmentation stage applied to the audio signals. Various machine learning models, including Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Random Forests, and CNNs, were tested for classification purposes. …”
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Statistical Modelling to Study the Implications of Coated Tools for Machining AA 2014 Using Grey Taguchi-Based Response Surface Methodology by Madhanagopal Manoharan, Arul Kulandaivel, Adinarayanan Arunagiri, Mohamad Reda A. Refaai, Simon Yishak, Gowthaman Buddharsamy

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Framework and Key Technologies of Human-machine Hybrid-augmented Intelligence System for Large-scale Power Grid Dispatching and Control by Shixiong Fan, Jianbo Guo, Shicong Ma, Lixin Li, Guozheng Wang, Haotian Xu, Jin Yang, Zening Zhao

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Bioinformatic Analysis of Complex In Vitro Fertilization Data and Predictive Model Design Based on Machine Learning: The Age Paradox in Reproductive Health by Myrto A. Lantzi, Eleni Papakonstantinou, Dimitrios Vlachakis

“…Concurrently, the utilization of machine learning algorithms has facilitated the development and implementation of predictive models. …”
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CONTACT STIFFNESS MEASUREMENT AND GRINDING PARAMETERS OPTIMIZATION OF CYLINDRICAL PLUNGE GRINDING (MT) by WANG JiaLe, LI HaoLin, SUN ShiYu, WANG NengYang, CAO WenJie, CUI Yi

“…According to the experimental results, the variation of the system time constant and the grinding force with the grinding parameters are analyzed. The relationship model between the contact stiffness and the grinding parameters is established by regression analysis, and the optimal values of the wheel feed speed and the workpiece speed in the specific grinding process are determined under the condition of meeting the machining requirements.…”
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Optimizing Bioleaching for Printed Circuit Board Copper Recovery: An AI-Driven RGB-Based Approach by Jordi Vives Pons, Albert Comerma, Teresa Escobet, Antonio D. Dorado, Marta I. Tarrés-Puertas

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RU-OLD: A Comprehensive Analysis of Offensive Language Detection in Roman Urdu Using Hybrid Machine Learning, Deep Learning, and Transformer Models by Muhammad Zain, Nisar Hussain, Amna Qasim, Gull Mehak, Fiaz Ahmad, Grigori Sidorov, Alexander Gelbukh

“…Extracted features use TF-IDF and Count Vectorizer for unigrams, bigrams, and trigrams. Of all the ML models—Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Naïve Bayes (NB)—the best performance was achieved by the same SVM. …”
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A hybrid CFD and machine learning study of energy performance of photovoltaic systems with a porous collector: Model development and validation by Yinling Wang, Lei Yu, Mazhar Ali, Imran Ali Khan, Tahir Maqsood, Haining Gao, Qi Wang, Xiaolei Guo

“…Three advanced machine learning models, i.e., Gradient Boosting (GB), Extreme Gradient Boosting (XGB), and Histogram-based Gradient Boosting (HGB) were applied to analyze and predict T in system. …”
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Early Fault Detection in Electro-Pneumatic Actuators using Mathematical Modelling and Machine Learning: A Bottling Company Case Study by Samuel Olufemi Amudipe, Adeyinka Moses Adeoye, Aderonke Oluwaseunfunmi Akinwumi, Rotimi Adedayo Ibikunle, Segun Adebayo

“…Real-time measurement points were validated through a baseline reference and machine learning models based on support vector machines received training data from labelled sets. …”
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Estimation of the Time of Occurrence of the Maximum Electrical Demand by Selecting the Optimal Classification Model and Making Use of Unbalanced Data by César Aristóteles Yajure, Valesca M. Fuenzalida Sánchez

“…The most important conclusion drawn by the study is that the model obtained with the support vector machine algorithm turned out to be optimal, and successfully predicted the time of maximum demand on 15 of the 17 test days. …”
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Proactive detection of anomalous behavior in Ethereum accounts using XAI-enabled ensemble stacking with Bayesian optimization by Vasavi Chithanuru, Mangayarkarasi Ramaiah

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Deep Learning-Based Step Size Determination for Hill Climbing Metaheuristics by Sándor Szénási, Gábor Légrádi, Gábor Kovács

“…This paper proposes a hybrid variation of the Hill Climbing method using a Machine Learning model to learn this domain-specific knowledge in advance to help determine the optimal step size of each iteration. …”
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ASPECTS REGARDING PRODUCTION SCHEDULING BY USING ORDERING AND LOTTING MODELS by Grațian-Daniel GOLOȘIE

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An Interpretable Model for Salinity Inversion Assessment of the South Bank of the Yellow River Based on Optuna Hyperparameter Optimization and XGBoost by Xia Liu, Yu Hu, Xiang Li, Ruiqi Du, Youzhen Xiang, Fucang Zhang

“…However, machine learning model training requires many samples and hyper-parameter optimization and lacks solvability. …”
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Optimizing data-driven arctic marine forecasting: a comparative analysis of MariNet, FourCastNet, and PhyDNet by Aleksei V. Buinyi, Aleksei V. Buinyi, Dias A. Irishev, Edvard E. Nikulin, Aleksandr A. Evdokimov, Polina G. Ilyushina, Natalia A. Sukhikh, Natalia A. Sukhikh

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Classification Prediction of Jujube Variety Based on Hyperspectral Imaging: A Comparative Study of Intelligent Optimization Algorithms by Quancheng Liu, Jun Zhou, Zhaoyi Wu, Didi Ma, Yuxuan Ma, Shuxiang Fan, Lei Yan

“…This study integrates hyperspectral imaging with intelligent optimization algorithms—Zebra Optimization Algorithm (ZOA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO)—and a Support Vector Machine (SVM) model to classify jujube varieties. …”
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Machine learning for predicting device-associated infection and 30-day survival outcomes after invasive device procedure in intensive care unit patients by Xiang Su, Ling Sun, Xiaogang Sun, Quanguo Zhao

“…Abstract This study aimed to preliminarily develop machine learning (ML) models capable of predicting the risk of device-associated infection and 30-day outcomes following invasive device procedures in intensive care unit (ICU) patients. …”
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Research on Interval Probability Prediction and Optimization of Vegetation Productivity in Hetao Irrigation District Based on Improved TCLA Model by Jie Ren, Delong Tian, Hexiang Zheng, Guoshuai Wang, Zekun Li

“…We propose a multimodal regression prediction model utilizing the TCLA framework—comprising the Transient Trigonometric Harris Hawks Optimizer (TTHHO), Convolutional Neural Networks (CNN), Least Squares Support Vector Machine (LSSVM), and Adaptive Bandwidth Kernel Density Estimation (ABKDE)—with the Hetao Irrigation District, a vast irrigation basin in China, serving as the study area. …”
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Comparison of CatBoost and LightGBM Models for Air Humidity Prediction by Tangkas Surya Wibawa, Novita Kurnia Ningrum, Ahmad Syahreza

“…This study uses historical weather data from the Badan Meteorologi, Klimatologi, dan Geofisika (BMKG) to evaluate the performance of two combination machine learning models, LightGBM and CatBoost, in predicting air humidity. …”
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Detecting Online Sexism: Integrating Sentiment Analysis with Contextual Language Models by Faiza Belbachir, Thomas Roustan, Assia Soukane

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