Artificial Neural Network Approach for Predicting Enzymatic Hydrolysis of Steam Exploded Pine Wood Chip in Mild Alkaline Pretreatment by Hyeon Cheol Kim, Si Young Ha, Jae-Kyung Yang

“…Although the application of steam explosion and alkaline pretreatment has gained widespread popularity for enhancing digestibility, the optimization of process parameters remains a formidable challenge due to the nonlinear interactions among variables. …”
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Enhancing Sentiment Analysis with a CNN-Stacked LSTM Hybrid Model by Shao Shuaijie

“…This paper focuses on developing a new hybrid model to solve sentiment analysis problems in Natural language processing. Sentiment analysis is a key branch of Natural language processing (NLP) and new models with better performance can boost the development of machine learning. …”
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MLA-Machine Learning Approach for Dependable Battery Condition Monitoring in Electric Vehicles by Tirgar Pravin, Priya R Karpaga, Sampath Kumar Vankadara, Lakhanpal Sorabh, Raj R Gowtham, N K Rayaguru

“…By focusing on relevant data at each moment, the monitoring process enhances the model’s ability to track long- term changes in battery life. …”
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Intelligent System for Reducing Waste and Enhancing Efficiency in Copper Production Using Machine Learning by Bagdaulet Kenzhaliyev, Timur Imankulov, Aksultan Mukhanbet, Sergey Kvyatkovskiy, Maral Dyussebekova, Nurdaulet Tasmurzayev

“…Five ML algorithms were evaluated, with Gradient Boosting and Support Vector Regression demonstrating superior performance in capturing complex, non-linear relationships. …”
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Design and Application of an Energy Management System Based on Artificial Intelligence Technology by Hongye Lin, Xuanying Bai, Chun Li, Shenghan Xu, Haibin Xu, Zne-Jung Lee, Yun Lin, Qunshan Zhou, Jingxun Cai

“…The system is functionally complete, completing the process from data collection to visualization, the cloud platform’s construction, and finally a full energy management platform. …”
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Preparation of land subsidence susceptibility map using machine learning methods based on decision tree (case study: Isfahan–Borkhar) by Negar Ghasemi, Iman Khosravi, Ali Bahrami

“…Advanced machine learning methods, namely Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) are employed to develop a susceptibility map divided into five probability classes: very high, high, medium, low, and very low. …”
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Physical exercise as a key to activating fat burning through the activation of uncoupling protein 1 (ucp1) in adipose tissue: a scoping review by Dany Pramuno Putra, Junian Cahyanto Wibawa, Baskoro Nugroho Putro

“…Conclusions: It has been demonstrated that physical activity increases UCP1 expression. The process of boosting metabolism and thermogenesis will be triggered by this rise. in order for the energy expenditure generated by adipose tissue to increase. …”
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An Innovative Approach for Calibrating Hydrological Surrogate Deep Learning Models by Amir Aieb, Antonio Liotta, Alexander Jacob, Iacopo Federico Ferrario, Muhammad Azfar Yaqub

“…The proposed framework aims to enhance the parameter-calibration quality. The process begins by mapping the statistical characteristics of DAE and DSM across the whole region using an unsupervised fusion technique. …”
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Machine learning frameworks to accurately estimate the adsorption of organic materials onto resin and biochar by Raouf Hassan, Mohammad Reza Kazemi

“…Various machine learning methods were evaluated, including Linear Regression, Ridge Regression, Lasso Regression, Elastic Net, Support Vector Regression (SVR), k-Nearest Neighbors (KNN), Decision Trees, Random Forests, Gradient Boosting Machines, Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Gaussian Processes, as well as ensemble algorithms such as XGBoost, LightGBM, and CatBoost. …”
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Towards precision oncology: a multi-level cancer classification system integrating liquid biopsy and machine learning by Amr Eledkawy, Taher Hamza, Sara El-Metwally

“…Following the feature selection process, classifiers—including eXtreme Gradient Boosting, Random Forest, Extra Tree, and Quadratic Discriminant Analysis—are customized for each cancer type individually or in an ensemble soft voting setup to optimize predictive accuracy. …”
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Optimizing protein-ligand docking through machine learning: algorithm selection with AutoDock Vina by Ala’ Omar Hasan Zayed

“…The feature selection process was optimized using Gini importance metrics, with model performance evaluated through mean squared errors and mean absolute errors.…”
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Development of a low‐dimensional model to predict admissions from triage at a pediatric emergency department by Fiona Leonard, John Gilligan, Michael J. Barrett

“…The Cross Industry Standard Process for Data Mining methodology was followed. …”
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Efficient Dynamic Performance Prediction of Railway Bridges Situated on Small-Radius Reverse Curves by Yumin Song, Bin Hu, Xiaoliang Meng

“…Through supervised training with dynamic performance labels, this process empowers the SVM model to predict the dynamic performance of the bridge. …”
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Utilization of Ensemble Techniques in Machine Learning to Predict the Porosity and Hardness of Plasma-Sprayed Ceramic Coating by N. Radhika, M. Sabarinathan, S. Sivaraman

“…To address this challenge, the present study employs advanced machine learning ensemble techniques, including bagging, boosting, stacking, weighted averaging, voting, and hybrid methods, to accurately predict the porosity and hardness of plasma-sprayed ceramic coatings based on key process parameters. …”
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Analysis and Prediction of Wear in Interchangeable Milling Insert Tools Using Artificial Intelligence Techniques by Sonia Val, María Pilar Lambán, Javier Lucia, Jesús Royo

“…It compares three distinct modeling approaches for predicting tool lifespan using algorithms: traditional ensemble methods (Random Forest, Gradient Boosting) and a deep learning-based LSTM network. Each model is evaluated independently, and this comparative analysis aims to determine which modeling strategy best captures the intricate interactions between various process variables affecting tool wear. …”
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Optimizing Power Consumption in Aquaculture Cooling Systems: A Bayesian Optimization and XGBoost Approach Under Limited Data by Sina Ghaemi, Hessam Gholmohamadi, Amjad Anvari-Moghaddam, Birgitte Bak-Jensen

“…However, the absence of sufficient measured data in industrial processes limits the ability to fully harness this flexibility. …”
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The Influence of Music on Prefrontal Cortex during Episodic Encoding and Retrieval of Verbal Information: A Multichannel fNIRS Study by Laura Ferreri, Emmanuel Bigand, Patrick Bard, Aurélia Bugaiska

“…Music can be thought of as a complex stimulus able to enrich the encoding of an event thus boosting its subsequent retrieval. However, several findings suggest that music can also interfere with memory performance. …”
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International activity of the innovative enterprises – experience and recommendations by Zofia GRÓDEK-SZOSTAK, Karolina KOTULEWICZ-WISIŃSKA, Małgorzata LUC

“…There is also the significant element like public support for companies in the whole technology transfer process.…”
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Modeling energy consumption indexes of an industrial cement ball mill for sustainable production by Saeed Chehreh Chelgani, Rasoul Fatahi, Ali Pournazari, Hamid Nasiri

“…However, grinding in tumbling mills is a random process, and a maximum of 5% of this energy would be directly devoted to particle size reduction. …”
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Presenting an Innovative Method Based on Ensemble Learning for a Credit Approval System by Eshonkulov Uchkun, Elmurodov Tulkin, Ravshanov Zavqiddin, Каramanov Asqar

“…A credit approval system is a framework or process that financial institutions use to assess the creditworthiness of individuals or businesses applying for loans or credit lines. …”
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