Machine learning and optimization in hybrid energy storage systems: Integrating MPC, PSO, MILP, and ANN for grid stability and efficiency by Rajesh M. Bhagat, Dilendra B. Jasutkar, Amitkumar B. Ranit, Vikrant S. Vairagade, Balram Yelamasetti, Sagar D. Shelare, Shubham Sharma, Abinash Mahapatro, V.K. Bupesh Raja, Quadri Noorulhasan Naveed, Naoufel Kraiem, Saiful Islam, Jasmina Lozanovic

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Unveiling geological complexity in the Serra Dourada Granite using self-organizing maps and hierarchical clustering: Insights for REE prospecting in the Goiás Tin Province, Brasíli... by Guilherme Ferreira da Silva, Marcos Vinícius Ferreira, Lucy Takehara Chemale, Igor Vasconcelos Santana, Nilson Francisquini Botelho

“…Our findings underscore the value of machine learning techniques, particularly SOM, in geoscientific data analysis. …”
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A Novel Approach to Continual Knowledge Transfer in Multilingual Neural Machine Translation Using Autoregressive and Non-Autoregressive Models for Indic Languages by Shailashree K. Sheshadri, Deepa Gupta, Biswajit Paul, J. Siva Bhavani

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Multi-model ensemble machine learning-based downscaling and projection of GRACE data reveals groundwater decline in Saudi Arabia throughout the 21st century by Arfan Arshad, Muhammad Shafeeque, Thanh Nhan Duc Tran, Ali Mirchi, Zaichen Xiang, Cenlin He, Amir AghaKouchak, Jessica Besnier, Md Masudur Rahman

“…This was accomplished by using multi-model ensemble machine learning (ML) approach leveraging Random Forest, CART, and Gradient Tree Boosting algorithms within Google Earth Engine (GEE). …”
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Chemometric and computational modeling of polysaccharide coated drugs for colonic drug delivery by Ahmad Khaleel AlOmari, Khaled Almansour

“…The Raman method was used for collection of spectral data which were then used as inputs to the ML models for estimation of drug release. For ML modeling, we examined the predictive accuracy of three machine learning models—Elastic Net (EN), Group Ridge Regression (GRR), and Multilayer Perceptron (MLP)—for forecasting the release behavior of samples. …”
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Safety Status Prediction Model of Transmission Tower Based on Improved Coati Optimization-Based Support Vector Machine by Xinxi Gong, Yaozhong Zhu, Yanhai Wang, Enyang Li, Yuhao Zhang, Zilong Zhang

“…The predictive outcomes indicate that the proposed ICOA-SVM model exhibits rapid convergence and high prediction accuracy, with a 62.5% reduction in root mean square error, a 59.6% decrease in average relative error, and a 75.0% decline in average absolute error compared to the conventional support vector machine. …”
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Development and validation of a quick screening tool for predicting neck pain patients benefiting from spinal manipulation: a machine learning study by Changxiao Han, Guangyi Yang, Haibao Wen, Minrui Fu, Bochen Peng, Bo Xu, Xunlu Yin, Ping Wang, Liguo Zhu, Minshan Feng

“…This study aims to develop and validate a machine learning-based prediction model to identify NP patients most likely to benefit from spinal manipulation. …”
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Engineering a multi model fallback system for edge devices by Gaurav Kadve, Abishi Chowdhury, Vishal Krishna Singh, Amrit Pal

“…Machine learning (ML) is an effective way to extract information from data and perform decision making on it. …”
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Interpretable Machine Learning for Multi-Energy Supply Station Revenue Forecasting: A SHAP-Driven Framework to Accelerate Urban Carbon Neutrality by Zhihui Zhao, Minjuan Wang, Jin Wei, Xiao Cen, Shengnan Du, Ziwen Wu, Huanying Liu, Weiqiang Wang

“…By leveraging real-world consumption data from Hangzhou West Lake Tanghe Station, we constructed a dataset with nine critical parameters, including energy types, transaction frequency, and temporal features. Four machine learning models—decision tree regression, random forest (RF), support vector regression, and multilayer perceptron—were evaluated using MAE, MSE, and R² metrics. …”
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Predicting Oil Price Trends During Conflict With Hybrid Machine Learning Techniques by Hicham Boussatta, Marouane Chihab, Mohamed Chiny, Younes Chihab

“…Using advanced machine learning techniques, we developed a hybrid system combining Random Forest, ElasticNet, K-Nearest Neighbors, Gradient Boosting, and Support Vector Regressor models. …”
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Predicting Ship Waiting Times Using Machine Learning for Enhanced Port Operations by Min-Hwa Choi, Woongchang Yoon

“…By using a dataset of 121,401 voyage records, we evaluated nine regression models, including conventional, ensemble-based, and deep learning models. …”
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Prediction of Monthly Temperature Over China Based on a Machine Learning Method by Ping Mei, Zixin Yin, Haoyu Wang, Changzheng Liu, Yaoming Liao, Qiang Zhang, Liping Yin

“…These characteristics limit both traditional empirical forecasting and machine learning methods. This paper proposes a novel method called dynamically modeled machine learning to predict monthly temperature anomalies over China. …”
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Machine learning and multicriteria analysis for prediction of compressive strength and sustainability of cementitious materials by Khuram Rashid, Fatima Rafique, Zunaira Naseem, Fahad K. Alqahtani, Idrees Zafar, Minkwan Ju

“…In the initial phase, three machine learning models—Decision Tree, Random Forest, and Multi-layer Perceptron—were developed and trained on a dataset of 1030 records to predict sustainable concrete's compressive strength accurately. …”
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Machine learning analysis of CO2 and methane adsorption in tight reservoir rocks by Mehdi Maleki, Mohammad Rasool Dehghani, Moein Kafi, Ali Akbari, Yousef Kazemzadeh, Ali Ranjbar

“…In this study, the adsorption behavior of CO2 and CH4 in tight reservoirs is examined using experimental data and advanced machine learning (ML) techniques. The dataset incorporates key variables such as temperature, pressure, rock type, total organic carbon (TOC), moisture content, and the CO2 fraction in the injected gas. …”
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Enhancing Business Success Prediction: A Data-Driven Machine Learning Mode by Deo Arpit, Korde Manish, Tiwari Anant, Jain Anant, Choudhary Akash

“…This study presents a machine learning model for predicting company failure, utilizing logistic regression, random forest, and neural networks. …”
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Machine learning in CTEPH: predicting the efficacy of BPA based on clinical and echocardiographic features by Qiumeng Xi, Juanni Gong, Jianfeng Wang, Xiaojuan Guo, Yuanhua Yang, Xiuzhang lv, Suqiao Yang, Yidan Li

“…Abstract Background This study aims to develop a machine learning (ML)-based predictive model for evaluating the efficacy of percutaneous pulmonary balloon angioplasty (BPA) in patients with chronic thromboembolic pulmonary hypertension (CTEPH) by integrating clinical and echocardiographic parameters. …”
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Evaluation of Time-Domain Acoustic Signature in TIG Welding of 5083 Aluminum Alloy: A Methodological Comparison of Feature Reduction Approaches by V M Gautham, A Sumesh, E V Jithin, K Rameshkumar, Dinu Thomas Thekkuden

“…In the present study, a machine learning model was developed to identify weld conditions such as good weld, porosity, and burn-through in TIG welding of aluminium alloy. …”
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Enhancing structural health monitoring with machine learning for accurate prediction of retrofitting effects by A. Presno Vélez, M. Z. Fernández Muñiz, J. L. Fernández Martínez

“…Structural health monitoring (SHM) systems used sensors to detect damage indicators such as vibrations and cracks, which were crucial for predicting service life and planning maintenance. Machine learning (ML) enhanced SHM by analyzing sensor data to identify damage patterns often missed by human analysts. …”
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Development and Feasibility Study of HOPE Model for Prediction of Depression Among Older Adults Using Wi-Fi-based Motion Sensor Data: Machine Learning Study by Shayan Nejadshamsi, Vania Karami, Negar Ghourchian, Narges Armanfard, Howard Bergman, Roland Grad, Machelle Wilchesky, Vladimir Khanassov, Isabelle Vedel, Samira Abbasgholizadeh Rahimi

“…For depression classification, we proposed a HOPE (Home-Based Older Adults’ Depression Prediction) machine learning model with feature selection, dimensionality reduction, and classification stages, evaluating various model combinations using accuracy, sensitivity, precision, and F1-score. …”
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Equitable hospital length of stay prediction for patients with learning disabilities and multiple long-term conditions using machine learning by Emeka Abakasanga, Rania Kousovista, Georgina Cosma, Ashley Akbari, Francesco Zaccardi, Navjot Kaur, Navjot Kaur, Danielle Fitt, Gyuchan Thomas Jun, Reza Kiani, Satheesh Gangadharan

“…However, there is limited research on the application of machine learning (ML) models to this population. Furthermore, approaches designed for the general population often lack generalisability and fairness, particularly when applied across sensitive groups within their cohort.MethodThis study analyses hospitalisations of 9,618 patients with LD in Wales using electronic health records (EHR) from the SAIL Databank. …”
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