Optimizing CNC turning of AISI D3 tool steel using Al₂O₃/graphene nanofluid and machine learning algorithms by Leta Daba Gemechu, Dame Alemayehu Efa, Robsan Abebe

“…Machine learning helps in predicting the optimal parameters, whereas nanofluids enhance cooling efficiency while preserving both the tool and the workpiece. …”
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Artificial intelligence-driven translational medicine: a machine learning framework for predicting disease outcomes and optimizing patient-centric care by Laith Abualigah, Saleh Ali Alomari, Mohammad H. Almomani, Raed Abu Zitar, Kashif Saleem, Hazem Migdady, Vaclav Snasel, Aseel Smerat, Absalom E. Ezugwu

“…These technologies enable more accurate disease trajectory models while enhancing patient-centered care. However, challenges such as heterogeneous datasets, class imbalance, and scalability remain barriers to achieving optimal predictive performance. …”
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Printing parameters optimization assisted by machine learning and sintering behavior of binder jetting 3D printed 2024Al alloy by Yuhang Qian, Xia Luo, Qianlong Wei, Bensheng Huang, Zhou Fan, Ruo Huang, Liang Zhang, Kurapova Olga Yu, Vladimir Gennadievich Konakov

“…This study presents the results of printing parameters optimization of Binder Jetting 3D printed 2024Al alloy assisted by machine learning (ML). …”
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Multi-dimensional constraint-based coal mining machine cutting path planning technology by Shuyang SONG, Shibo WANG, Yun WANG, Lijie WANG, Dongshuai SONG

“…First, based on the operational requirements of the fully mechanized mining face, a cutting space constraint model is established with the posture of hydraulic supports, scraper conveyors, and coal mining machines as the constraint conditions. …”
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Methods of Machine-Aided Training in Small Business: Content and Management by S. A. Tishchenko, M. A. Shakhmuradian

“…The authors conducted a formal and analytical review of potential means for small business optimization. They described types of algorithms and models of machine-aided training, such as multiple regressive model, logistic regression, etc., as well as instrumental problems of their use by enterprise analysts and developers. …”
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A machine learning framework for predicting healthcare utilization and risk factors by Yead Rahman, Prerna Dua

“…Medicaid data, with its vast scale and heterogeneity, presents significant challenges in predictive modeling and healthcare analytics. This study analyzes over 6.3 million records from the Louisiana Department of Health (LDH) to identify the most effective machine learning models for predicting clinical service utilization, COVID-19 infections, and tobacco use. …”
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Optimized Clinical Feature Analysis for Improved Cardiovascular Disease Risk Screening by Sofiya Vyshnya, Rachel Epperson, Felipe Giuste, Wenqi Shi, Andrew Hornback, May D. Wang

“…<italic>Results:</italic> In this study, we propose a robust feature selection approach that identifies five key features strongly associated with CVD risk, which have been found to be consistent across various models. The machine learning model developed using this optimized feature set achieved state-of-the-art results, with an AUROC of 91.30&#x0025;, sensitivity of 89.01&#x0025;, and specificity of 85.39&#x0025;. …”
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DeepSeek-AI-enhanced virtual reality training for mass casualty management: Leveraging machine learning for personalized instructional optimization. by Zhe Li, Lei Shi, Mingyu Pei, Wan Chen, Yutao Tang, Guozheng Qiu, Xibin Xu, Liwen Lyu

“…Machine learning models were trained to predict performance outcomes, and feature importance was assessed using the Gini index. …”
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Dataset of SCAPS-1D simulated halide perovskite solar cells with SHAP and machine learning-based PCE optimizationZenodo by Ivan E. Novoselov, Alexander M. Gvozdev, Andrey A. Smirnov, Ivan S. Zhidkov

“…Its comprehensive organization makes it suitable for applications including photovoltaic device optimization, evaluation of simulation-based predictive approaches, and development of advanced data-driven models. …”
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Sinh-Cosh Optimization-Based Efficient Clustering for Big Data Applications by Lahbib Khrissi, Mohammed Es-Sabry, Nabil El Akkad, Hassan Satori, Saad Aldosary, Walid El-Shafai

“…To overcome the above-mentioned drawbacks, we have proposed in this article a new metaheuristic based on a mathematical model called Sinh Cosh Optimizer (SCHO). This optimizer is based on the geometric functions Sinh and Cosh and consists of four key stages: the exploitation and exploration phases, the limited search strategy and the implementation of a switching mechanism. …”
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Two-Level Multi-Objective Design Optimization Including Torque Ripple Minimization for Stator Excited Synchronous and Flux Switching Machines by Ali Mohammadi, Oluwaseun A. Badewa, Yaser Chulaee, Dan M. Ionel

“…The optimization process is implemented on two distinct designs: a 20-pole inner rotor PM-excited stator machine and a 28-pole outer rotor DC-excited stator machine. …”
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Towards Machine Learning-Driven Catalyst Design and Optimization of Operating Conditions for the Production of Jet Fuel Via Fischer-Tropsch Synthesis by Parisa Shafiee, Bogdan Dorneanu, Harvey Arellano-Garcia

“…Fischer-Tropsch synthesis (FTS) offers a promising route for producing sustainable jet fuels from syngas. However, optimizing the catalyst design and operating conditions to maximize the desired C8-C16 jet fuel range is a challenging task. …”
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Prediction Model of Water Demand for Scouring Siltation in Coastal River Networks Based on APSO and SVM: A Case Study of Doulong Port by MA Zhutong, XIANG Long, YAN Ke

“…By analyzing the in-situ monitoring data for the downstream channel of Doulong Port from 2001 to 2018, the paper extracted key variables such as initial riverbed volume, siltation amount, time duration, rainfall volume, and the frequency of sluice openings. A predictive model of water demand for scouring siltation was constructed, which combined adaptive particle swarm optimization (APSO) algorithm with support vector machine (SVM) and optimized the model parameters of the SVM through the APSO algorithm, enhancing the prediction accuracy of the APSO-SVM model. …”
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Prediction Model of Water Demand for Scouring Siltation in Coastal River Networks Based on APSO and SVM: A Case Study of Doulong Port by MA Zhutong, XIANG Long, YAN Ke

“…By analyzing the in-situ monitoring data for the downstream channel of Doulong Port from 2001 to 2018, the paper extracted key variables such as initial riverbed volume, siltation amount, time duration, rainfall volume, and the frequency of sluice openings. A predictive model of water demand for scouring siltation was constructed, which combined adaptive particle swarm optimization (APSO) algorithm with support vector machine (SVM) and optimized the model parameters of the SVM through the APSO algorithm, enhancing the prediction accuracy of the APSO-SVM model. …”
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Development of predictive models for differential diagnosis of hypertrophic cardiomyopathy by V. V. Zaitsev, K. S. Safronov, K. S. Konasov, T. R. Bavshin, K. A. Manokhin, L. A. Obraztsova, O. M. Moiseeva

“…The original dataset contains 74 parameters. Machine learning models of the following classes were created and optimized: logistic regression, support vector machine, decision tree, and gradient boosting decision trees.Results. …”
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Artificial intelligence in the tourism business: a systematic review by Alexandra Lorena López-Naranjo, Mariana Isabel Puente-Riofrio, Verónica Adriana Carrasco-Salazar, Juan Diego Erazo-Rodríguez, Pamela Alexandra Buñay-Guisñan

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Proposed Model to Minimize Machining Time by Chip Removal Under Structural Constraint Taking into Consideration Machine Power, Surface Finish, and Cutting Speed by Using Sorting Al... by Abraham Manilla-García, Néstor F. Guerrero-Rodriguez, Ivan Rivas-Cambero

“…This article proposes a model to estimate the optimal cutting speed and depth of cut used in the machining process by chip removal during the turning operation, considering the structural integrity of the workpiece to be machined. …”
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Machine learning-driven insights into phase prediction for high entropy alloys by Reliance Jain, Sandeep Jain, Sheetal Kumar Dewangan, Lokesh Kumar Boriwal, Sumanta Samal

“…After assessing the accuracy and tuning of each model, an random forest classifier (accuracy = 0.914. precision = 0.916, ROC-AUC score = 0.97) model showed the best predictive capabilities for phase prediction. …”
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A Machine Learning Approach to Differentiate Cold and Hot Syndrome in Viral Pneumonia Integrating Traditional Chinese Medicine and Modern Medicine: Machine Learning Model Developme... by Xiaojie Jin, Yanru Wang, Jiarui Wang, Qian Gao, Yuhan Huang, Lingyu Shao, Jiali Zhao, Jintian Li, Ling Li, Zhiming Zhang, Shuyan Li, Yongqi Liu

“…ObjectiveThis study aims to construct a diagnostic model for differentiating cold and hot syndromes in viral pneumonia by integrating TCM and modern medical features using machine learning methods. …”
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A statistical study on factors influencing piezoelectric properties of upside-down composites towards machine learning-driven development for recycling by Sivagnana Sundaram Anandakrishnan, Suhas Yadav, Mohadeseh Tabeshfar, Mikko Nelo, Jani Peräntie, Yang Bai

“…The approach lays down a foundation for scaling up the optimization of the recycled materials by providing training and/or testing datasets for possible machine learning algorithms via potential high-throughput manufacturing routes.…”
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