Mechanism-learning prediction model for pitting depth of buried pipeline based on HMOGWO-RF by Fulin SONG, Hong ZHAO, Xingyuan MIAO

“…This feature selection approach was integrated with the multiobjective optimization process, considering three comprehensive optimization objectives: the number of features, prediction accuracy, and model stability. …”
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Unveiling the Power of Large Language Models: A Comparative Study of Retrieval-Augmented Generation, Fine-Tuning, and Their Synergistic Fusion for Enhanced Performance by Gulsum Budakoglu, Hakan Emekci

“…Large-language model optimization for a particular application is crucial and challenging in natural language processing. …”
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TO THE SYNTHESIS OF A LAYOUT OF MACHINE TOOL FOR BATCH PROCESSING by B. M. Rozin, V. E. Zdanovich

“…The problems of choosing the optimal standard size of the basic units and assembly components of machine tools for batch processing of parts are considered. …”
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Emerging Applications of Machine Learning in 3D Printing by Izabela Rojek, Dariusz Mikołajewski, Marcin Kempiński, Krzysztof Galas, Adrianna Piszcz

“…New emerging opportunities are provided by the ability of machine learning (ML) to analyze complex data sets and learn from previous (historical) experience and predictions to dynamically optimize and individuate products and processes. …”
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Machine Learning and Neural Networks for IT-Diagnostics of Neurological Diseases by U. A. Vishniakou, Y. W. Xia, Ch. Y. Yu

“…Models of information about disease features (including frequency, jitter, mel-cepstral coefficients, etc.) extracted from voice data are presented. …”
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Synthesis and characterization of machine learning designed TADF molecules by Weimei Shi, Yan Li, Ziying Zhang, Zheng Tan, Shiqing Yang

“…Recognizing the imperative for high-efficiency, low-cost emissive materials, we integrated ML driven models with experimental characterization to expedite the discovery of TADF compounds. …”
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Faithful novel machine learning for predicting quantum properties by Gavin Nop, Micah Mundy, Jonathan D. H. Smith, Durga Paudyal

“…We show that faithful representations, which directly represent crystal structure and symmetry, both refine current ML and effectively implement advanced deep networks to accurately predict these materials and optimize their properties. Our new models reveal the previously hidden power of novel convolutional and pure attentional approaches to represent atomic connectivity and achieve strong performance in predicting topological properties, magnetic properties, and formation energies. …”
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Machine learning and microfluidic integration for oocyte quality prediction by Hassan Saffari, Davood Fathi, Peyman Palay, Hamid Gourabi, Rouhollah Fathi

“…A dataset of 54 oocytes was labeled based on maturation, fertilization, and cleavage outcomes. Supervised learning models (Random Forest, Decision Tree, K-Nearest Neighbors, eXtreme Gradient Boosting, Logistic Regression, Naive Bayes, Support Vector Machines, and Light Gradient Boosting Machine were evaluated. …”
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Machine Learning-Based Network Detection Research for SDNs by Lai Jiayue

“…Reasserting the idea of the critically important role of machine learning for securing SDNs against possible intrusions, these results point not only to the highly beneficial applications of machine learning for protecting SDNs against malicious intrusions but also its indispensable role in preserving network stability and optimizing performance. …”
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Machine learning in additive manufacturing: A comprehensive insight by Md Asif Equbal, Azhar Equbal, Zahid A. Khan, Irfan Anjum Badruddin

“…The barrier can be minimized by proper monitoring of the AM process and precise measurement of AM materials and components, which is difficult to achieve through analytical and numerical models. Current research demonstrates machine learning (ML) and its techniques as a novel way to reduce costs. …”
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Effective Dose Estimation in Computed Tomography by Machine Learning by Matteo Ferrante, Paolo De Marco, Osvaldo Rampado, Laura Gianusso, Daniela Origgi

“…E calculated by DTS was chosen as the target value for prediction. Different machine learning algorithms were selected, optimizing parameters to achieve the best performance for each algorithm. …”
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Opinion mining in e-commerce: Evaluating machine learning approaches for sentiment analysis by L. Lakshmi, Ali B.M. Ali, K Dhana Sree Devi, Muhammad Rafiq, Iskandar Shernazarov, Nashwan Adnan Othman, M. Ijaz Khan

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Diagnosis and classification of neuromuscular disorders using Bi-LSTM optimized with grey Wolf optimizer for EMG signals by Ruth Keziah Mani, Balakrishnan Nagaraj

“…Existing approaches range from deep learning techniques such as Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) to conventional machine learning methods like Support Vector Machines (SVM) and Random Forest. …”
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Implementation of XGBoost Models for Predicting CO₂ Emission and Specific Tractor Fuel Consumption by Nebojša Balać, Zoran Mileusnić, Aleksandra Dragičević, Mihailo Milanović, Andrija Rajković, Rajko Miodragović, Olivera Ećim-Đurić

“…Although not optimized for high precision, these models offer a valuable basis for preliminary assessments and highlight the potential of data-driven approaches for improving energy efficiency and environmental sustainability in agricultural mechanization.…”
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CNC machining data repository: Geometry, NC code & high-frequency energy consumption data for aluminum and plastic machiningMendeley Data by Markus Brillinger, Muaaz Abdul Hadi, Stefan Trabesinger, Johannes Schmid, Florian Lackner

“…Potential use cases include optimizing machining parameters for energy reduction based on power consumption patterns, and enhancing digital twin models with real-world machining data. …”
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A review of machine learning techniques for urban resilience research: The application and progress of different machine learning techniques in assessing and enhancing urban resili... by Yu Chen, Wenxing You, Lu Ou, Hui Tang

“…Reinforcement learning optimizes rewards through trial-and-error interactions. …”
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Developing an Hourly Water Level Prediction Model for Small- and Medium-Sized Agricultural Reservoirs Using AutoML: Case Study of Baekhak Reservoir, South Korea by Jeongho Han, Joo Hyun Bae

“…This study focuses on developing an hourly water level prediction model for small- and medium-sized agricultural reservoirs using the Tree-based Pipeline Optimization Tool (TPOT), an automated machine learning (AutoML) technique. …”
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Deep learning model for grading carcinoma with Gini-based feature selection and linear production-inspired feature fusion by Shreyan Kundu, Souradeep Mukhopadhyay, Rahul Talukdar, Dmitrii Kaplun, Alexander Voznesensky, Ram Sarkar

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Predictive modeling and interpretative analysis of risks of instability in patients with Myasthenia Gravis requiring intensive care unit admission by Chao-Yang Kuo, Emily Chia-Yu Su, Hsu-Ling Yeh, Jiann-Horng Yeh, Hou-Chang Chiu, Chen-Chih Chung

“…This study aimed to develop and validate machine-learning models for predicting intensive care unit (ICU) admission risk among patients with MG-related disease instability. …”
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Predicting Thermal Performance of Aquifer Thermal Energy Storage Systems in Depleted Clastic Hydrocarbon Reservoirs via Machine Learning: Case Study from Hungary by Hawkar Ali Abdulhaq, János Geiger, István Vass, Tivadar M. Tóth, Tamás Medgyes, Gábor Bozsó, Balázs Kóbor, Éva Kun, János Szanyi

“…This study presents an innovative approach for repurposing depleted clastic hydrocarbon reservoirs in Hungary as High-Temperature Aquifer Thermal Energy Storage (HT-ATES) systems, integrating numerical heat transport modeling and machine learning optimization. A detailed hydrogeological model of the Békési Formation was built using historical well logs, core analyses, and production data. …”
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