Internet of things-driven approach integrated with explainable machine learning models for ship fuel consumption prediction by Van Nhanh Nguyen, Nghia Chung, G.N. Balaji, Krzysztof Rudzki, Anh Tuan Hoang

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The Emerging Role of Implementing Machine Learning in Food Recommendation for Chronic Kidney Diseases Using Correlation Analysis by Sachin Gupta, Neeraj Garg, Divyanshu Sinha, Babita Yadav, Bhoomi Gupta, Shahajan Miah

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Evaluating waist-to-hip ratio in youth using frequency-modulated continuous wave radar and machine learning by Jun Byung Park, Jinjoo Choi, Jae Yoon Na, Seung Hyun Kim, Hyun-Kyung Park, Seung Yang, Sung Ho Cho

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Machine learning-assisted quantitative prediction of thermal decomposition temperatures of energetic materials and their thermal stability analysis by Zhi-xiang Zhang, Yi-lin Cao, Chao Chen, Lin-yuan Wen, Yi-ding Ma, Bo-zhou Wang, Ying-zhe Liu

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Machine learning-driven prediction of medical expenses in triple-vessel PCI patients using feature selection by Kuan-Yu Chen, Yen-Chun Huang, Chih-Kuang Liu, Shao-Jung Li, Mingchih Chen

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Robust Automated Harmonization of Heterogeneous Data Through Ensemble Machine Learning: Algorithm Development and Validation Study by Doris Yang, Doudou Zhou, Steven Cai, Ziming Gan, Michael Pencina, Paul Avillach, Tianxi Cai, Chuan Hong

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Analysis of damage control of thin plate with piezoelectric actuators using finite element and machine learning approach by Asraar Anjum, Abdul Aabid, Meftah Hrairi

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Machine learning prediction of combat basic training injury from 3D body shape images. by Steven Morse, Kevin Talty, Patrick Kuiper, Michael Scioletti, Steven B Heymsfield, Richard L Atkinson, Diana M Thomas

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Hybrid Plant Growth: Integrating Stochastic, Empirical, and Optimization Models with Machine Learning for Controlled Environment Agriculture by Nezha Kharraz, István Szabó

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Predicting ash content and water content in coal using full infrared spectra and machine learning models by Suprapto Suprapto, Antin Wahyuningtyas, Kartika Anoraga Madurani, Yatim Lailun Ni'mah

“…The aim of this study was to predict ash and water contents in coal samples using machine learning regression techniques, specifically LassoCV, RidgeCV, ElasticNetCV and LassoLarsCV. …”
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A novel perspective on survival prediction for AML patients: Integration of machine learning in SEER database applications by Zheng-yi Jia, Maierbiya Abulimiti, Yun Wu, Li-na Ma, Xiao-yu Li, Jie Wang

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Narrowing the gap between machine learning scoring functions and free energy perturbation using augmented data by Ísak Valsson, Matthew T. Warren, Charlotte M. Deane, Aniket Magarkar, Garrett M. Morris, Philip C. Biggin

“…Abstract Machine learning offers great promise for fast and accurate binding affinity predictions. …”
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ML-AMPSIT: Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool by D. Di Santo, C. He, F. Chen, L. Giovannini

“…This paper introduces the Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool (ML-AMPSIT), a new tool designed with the aim of providing a simple and flexible framework to estimate the sensitivity and importance of parameters in complex numerical weather prediction models. …”
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Short-Term Power Prediction of Building Integrated Photovoltaic (BIPV) System Based on Machine Learning Algorithms by R. Kabilan, V. Chandran, J. Yogapriya, Alagar Karthick, Priyesh P. Gandhi, V. Mohanavel, Robbi Rahim, S. Manoharan

“…This work is aimed at presenting a building integrated photovoltaic system power prediction concerning the building’s various orientations based on the machine learning data science tools. The proposed prediction methodology comprises a data quality stage, machine learning algorithm, weather clustering assessment, and an accuracy assessment. …”
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Robust machine learning based Intrusion detection system using simple statistical techniques in feature selection by Sunil Kaushik, Akashdeep Bhardwaj, Ahmad Almogren, Salil bharany, Ayman Altameem, Ateeq Ur Rehman, Seada Hussen, Habib Hamam

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Evaluation of Machine Learning Models for Stress Symptom Classification of Cucumber Seedlings Grown in a Controlled Environment by Kyu-Ho Lee, Samsuzzaman, Md Nasim Reza, Sumaiya Islam, Shahriar Ahmed, Yeon Jin Cho, Dong Hee Noh, Sun-Ok Chung

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Prediction of the transient coolant jet released from the nose cone at supersonic flow via machine learning by A. B. Ali, M. Yasiri, Pooya Ghodratallah, Kamal Sharma, Nimesh Raj, S. Abdul Ameer, Mohammed Yaseen Abdullah, Abbas Hameed Abdul Hussein, Mohammed Y. Al-Khuzaie, M. Ahmedi

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PHYSICS-DRIVEN FEATURE CREATION TO IMPROVE MACHINE LEARNING MODELS PERFORMANCE FOR OIL PRODUCTION RATE PREDICTION by Eghbal Motaei, Seyed Mehdi Tabatabai, Tarek Ganat, Ahmad Khanifar, Sulaiman Dzaiy, Timur Chis

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Human interpretable structure-property relationships in chemistry using explainable machine learning and large language models by Geemi P. Wellawatte, Philippe Schwaller

“…Abstract Explainable Artificial Intelligence (XAI) is an emerging field in AI that aims to address the opaque nature of machine learning models. Furthermore, it has been shown that XAI can be used to extract input-output relationships, making them a useful tool in chemistry to understand structure-property relationships. …”
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A two-step machine learning approach for predictive maintenance and anomaly detection in environmental sensor systems by Saiprasad Potharaju, Ravi Kumar Tirandasu, Swapnali N. Tambe, Devyani Bhamare Jadhav, Dudla Anil Kumar, Shanmuk Srinivas Amiripalli

“…Using Environmental Sensor Telemetry Data, this study introduces a novel methodology that combines unsupervised and supervised machine learning approaches to detect anomalies and predict sensor failures. …”
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