Optimization of a photovoltaic/wind/battery energy-based microgrid in distribution network using machine learning and fuzzy multi-objective improved Kepler optimizer algorithms by Fude Duan, Mahdiyeh Eslami, Mohammad Khajehzadeh, Ali Basem, Dheyaa J. Jasim, Sivaprakasam Palani

“…The variables are microgrid optimal location and capacity of the HMG components in the network which are determined through a multi-objective improved Kepler optimization algorithm (MOIKOA) modeled by Kepler’s laws of planetary motion, piecewise linear chaotic map and using the FDMT. …”
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PREDICTIVE MODELS FOR EARLY DETECTION OF PARKINSON’S DISEASE: A MACHINE LEARNING APPROACH by S. Jeyantha Jafna Juliet, D. Jasmine David, J. S. Raj Kumar, Angelin Jeba P., R. Golden Nancy, M. Selvarathi, T. Jemima Jebaseeli

“…These methods involve the analysis of various types of data, including clinical assessments, imaging scans, and genetic markers, to develop accurate predictive models. Even in the initial stages of the conditions, machine learning techniques can discriminate between patients who have and do not have PD by identifying minor variations and traits from such multivariate data. …”
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Enhanced Fault Diagnosis in Milling Machines Using CWT Image Augmentation and Ant Colony Optimized AlexNet by Niamat Ullah, Muhammad Umar, Jae-Young Kim, Jong-Myon Kim

“…These optimized features are then classified using a support vector machine, effectively distinguishing between fault types and normal conditions with high accuracy. …”
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A machine learning-based risk prediction model for diabetic oral ulceration by Wang Xiaoling, Wang BingQian, Zhu Zhenqi, Li Wen, Gu Shuyan, Chen Hanbei, Xin Feng, Chenglong Yang, Jutang li, Guoyao Tang, Jie Wei

“…Four prediction models, Support Vector Machine Classifier (SVC), Multi-layer Perceptron (MLP), Logistic Regression Classifier (LogReg), and Perceptron, were established and evaluated. …”
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Physics-informed machine learning for building performance simulation-A review of a nascent field by Zixin Jiang, Xuezheng Wang, Han Li, Tianzhen Hong, Fengqi You, Ján Drgoňa, Draguna Vrabie, Bing Dong

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A novel unified Inception-U-Net hybrid gravitational optimization model (UIGO) incorporating automated medical image segmentation and feature selection for liver tumor detection by Tathagat Banerjee, Davinder Paul Singh, Pawandeep Kour, Debabrata Swain, Shubham Mahajan, Seifedine Kadry, Jungeun Kim

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Evapotranspiration Partitioning in Selected Subtropical Fruit Tree Orchards Based on Sentinel 2 Data Using a Light Gradient-Boosting Machine (LightGBM) Learning Model in Malelane,... by Prince Dangare, Zama E. Mashimbye, Paul J. R. Cronje, Joseph N. Masanganise, Shaeden Gokool, Zanele Ntshidi, Vivek Naiken, Tendai Sawunyama, Sebinasi Dzikiti

“…The accurate estimation of evapotranspiration (<inline-formula><math display="inline"><semantics><mrow><mi>E</mi><mi>T</mi></mrow></semantics></math></inline-formula>) and its components are vital for water resource management and irrigation planning. This study models tree transpiration (<inline-formula><math display="inline"><semantics><mrow><mi>T</mi></mrow></semantics></math></inline-formula>) and <inline-formula><math display="inline"><semantics><mrow><mi>E</mi><mi>T</mi></mrow></semantics></math></inline-formula> for grapefruit, litchi, and mango orchards using light gradient-boosting machine (LightGBM) optimized using the Bayesian hyperparameter optimization. …”
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Average Corrosion Rate Prediction Model for Buried Oil and Gas Pipelines Based on SSA-LightGBM by Weigang Fu, Haitao Wang, Kuankuan Zhang, Xia Wang, Kunlun Chen, Chunmei Sun, Zhengwei Wang, Liuyang Song, Niannian Wang

“…This study establishes an average corrosion rate prediction model using the Sparrow Search Algorithm-optimized Light Gradient Boosting Machine (SSA-LightGBM). …”
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A Comparative Study of Machine Learning Models for Accurate E-Waste Prediction by Mohammed Algafri, Mohammed Sayad, Mohammad A.M. Abdel-Aal, Ahmed M. Attia

“…Traditional linear production models fail to optimize resource recovery, while circular economy (CE) strategies remain underutilized due to inadequate forecasting methods. …”
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A machine learning-based model for predicting survival in patients with Rectosigmoid Cancer. by Yifei Wang, Bingbing Chen, Jinhai Yu

“…It also had the lowest Brier scores at all time points, and decision curve analysis (DCA) demonstrated the best clinical decision benefits compared to other models.<h4>Conclusion</h4>We developed a prediction model based on the optimal machine learning, XGBoost, which can assist clinical decision-making and potentially extend the survival of patients with rectosigmoid junction cancer.…”
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Permanent Magnet Axial Length Optimization for Transverse Magnetic Flux Generator with Disk Rotor by Duniev O., Yehorov A., Masliennikov A., Stamann M., Dobzhanskyi O.

“…Based on the analysis of the transverse flux machine designs, they were found to have a relative design simplicity and a high-power density. …”
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Effectiveness of machine learning models in diagnosis of heart disease: a comparative study by Waleed Alsabhan, Abdullah Alfadhly

“…An extensive array of preprocessing techniques is thoroughly examined in order to optimize the predictive models’ quality and performance. …”
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Evaluation of machine learning models for predicting performance metrics of aero-engine combustors by Huan Yang, Shu Guo, Haolin Xie, Jian Wen, Jiarui Wang

“…The findings highlight the promise of machine learning in optimizing combustor design and improving the reliability of the aero-engine.…”
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Data-Driven Machine Learning-Informed Framework for Model Predictive Control in Vehicles by Edgar Amalyan, Shahram Latifi

“…A machine learning framework is developed to interpret vehicle subsystem status from sensor data, providing actionable insights for adaptive control systems. …”
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Application Analysis of Credit Scoring of Financial Institutions Based on Machine Learning Model by Yi Wu, Yuwen Pan

“…On this basis, WOE coding was carried out on the dataset, which was applied to random forest, support vector machine, and logistic regression models, and the performance was compared. …”
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Enhanced Detection of Leishmania Parasites in Microscopic Images Using Machine Learning Models by Michael Contreras-Ramírez, Jhonathan Sora-Cardenas, Claudia Colorado-Salamanca, Clemencia Ovalle-Bracho, Daniel R. Suárez

“…The phenotypic features of the parasites were extracted, focusing on morphology, texture, and color. Machine learning models (ANN, SVM, and RF) optimized through Grid Search were applied for classification. …”
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Machine learning-based e-commerce platform repurchase customer prediction model. by Cheng-Ju Liu, Tien-Shou Huang, Ping-Tsan Ho, Ping-Tsan Ho, Jui-Chan Huang, Ching-Tang Hsieh

“…After optimizing the model, it is found that the nonlinear model can make better use of these features and get better prediction results. …”
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Research progress on modeling of heavy metal adsorption by biochar based on machine learning by FENG Ding, LIU Jingjing, MA Wendan, LIU Yuxue, YANG Chen, ZHANG Mengmeng

“…This paper described the workflow and advantages of machine learning modeling. The application of machine learning in heavy metal adsorption across three aspects of predicting adsorption efficiency, aiding in optimizing experiments, and gaining insights into adsorption mechanisms was summarized. …”
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Increasing Minority Recall Support Vector Machine Model for Imbalanced Data Classification by Chunye Wu, Nan Wang, Yu Wang

“…Imbalanced data classification is gaining importance in data mining and machine learning. The minority class recall rate requires special treatment in fields such as medical diagnosis, information security, industry, and computer vision. …”
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Physics-informed machine learning for automatic model reduction in chemical reaction networks by Joseph Pateras, Colin Zhang, Shriya Majumdar, Ayush Pal, Preetam Ghosh

“…Abstract Physics-informed machine learning bridges the gap between the high fidelity of mechanistic models and the adaptive insights of artificial intelligence. …”
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