Interpretable material descriptors for critical pitting temperature in austenitic stainless steel via machine learning by Faguo Hou, Hong-Hui Wu, Dexin Zhu, Jinyong Zhang, Liudong Hou, Shuize Wang, Guilin Wu, Junheng Gao, Jing Ma, Xinping Mao

“…Utilizing interpretable machine learning techniques, a predictive model for CPT is developed and confirmed via cross-validation, demonstrating superior predictive accuracy. …”
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Machine learning approaches for mapping and predicting landslide-prone areas in São Sebastião (Southeast Brazil) by Enner Alcântara, Cheila Flávia Baião, Yasmim Carvalho Guimarães, José Roberto Mantovani, José Antonio Marengo

“…This research demonstrates the effectiveness of machine learning in landslide susceptibility mapping and offers valuable insights for disaster risk reduction and urban planning in coastal mountainous regions.…”
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Machine learning techniques in monitoring and controlling friction stir welding process: a critical review by Bhardwaj Kulkarni, Saurabh Tayde, Yashwant Chapke, Swapnil Vyavahare, Avinash Badadhe

“…By employing machine learning techniques, the FSW process can become more cost-effective through optimizing process parameters, early detection of defects and tool failures, reduction of waste, and attainment of superior joint properties, all while minimizing the need for extensive trial and error. …”
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Enhanced Plant Leaf Classification over a Large Number of Classes Using Machine Learning by Ersin Elbasi, Ahmet E. Topcu, Elda Cina, Aymen I. Zreikat, Ahmed Shdefat, Chamseddine Zaki, Wiem Abdelbaki

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An FPGA Prototype for Parkinson’s Disease Detection Using Machine Learning on Voice Signal by Mujeev Khan, Abdul Moiz, Gani Nawaz Khan, Mohd Wajid, Mohammed Usman, Jabir Ali

“…This paper proposes an efficient machine learning model for PD detection using voice-based features, which offer a non-invasive, cost-effective, and accessible alternative to complex imaging methods. …”
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Multi-Index Assessment and Machine Learning Integration for Drought Monitoring Using Google Earth Engine by Xulong Duan, Rana Waqar Aslam, Syed Ali Asad Naqvi, Dmitry E. Kucher, Zohaib Afzal, Danish Raza, Rana Muhammad Zulqarnain, Yahia Said

“…This study advances multisensor remote sensing data fusion integrating optical (Sentinel-2, MODIS), thermal (LST), and hydrological (SMAP) sensors with climate datasets to evaluate soil moisture dynamics at five depths (0–50 cm) across nine agricultural zones (October 2021–September 2023), leveraging AI and machine learning to address data quality challenges in heterogeneous sensor inputs. …”
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Machine learning-based pseudo-continuous pedotransfer function for predicting soil freezing characteristic curve by Sangyeong Park, Yongjoon Choe, Hangseok Choi, Khanh Pham

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Optimizing ML models for cybercrime detection: balancing performance, energy consumption, and carbon footprint through multi-objective optimization by Romil Rawat

“…Abstract This study aims to enhance computational performance while minimizing environmental impact in AI (Artificial Intelligence) and ML (Machine Learning) applications, especially in cybersecurity, by developing energy-efficient models using a multi-objective optimization approach. …”
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The Estimation of the Remaining Useful Life of Ceramic Plates Used in Iron Ore Filtration Through a Reliability Model and Machine Learning Methods Applied to Industrial Process Var... by Robert Bento Florentino, Luiz Gustavo Lourenço Moura

“…Through the use of the CRISP-DM data analysis methodology, the fault logs of ceramic plates applied in an iron ore filtration process are coupled with sensor readings of the process variables over the time of operation to create exponential survival models via two techniques: a multiple linear regression model with averaged data and a random forest regression machine learning model with individual instant data. …”
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Differential gray matter correlates and machine learning prediction of abuse and internalizing psychopathology in adolescent females by Sara A. Heyn, Taylor J. Keding, Josh Cisler, Katie McLaughlin, Ryan J. Herringa

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Mitigating malicious denial of wallet attack using attribute reduction with deep learning approach for serverless computing on next generation applications by Amal K. Alkhalifa, Mohammed Aljebreen, Rakan Alanazi, Nazir Ahmad, Sultan Alahmari, Othman Alrusaini, Ali Alqazzaz, Hassan Alkhiri

“…Serverless computing, Function-as-a-Service (FaaS), is a cloud computing (CC) system that permits developers to construct and run applications without a conventional server substructure. The deep learning (DL) model, a part of the machine learning (ML) technique, has developed as an effectual device in cybersecurity, permitting more effectual recognition of anomalous behaviour and classifying patterns indicative of threats. …”
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Optimized Ensemble Methods for Classifying Imbalanced Water Quality Index Data by Zaharaddeen Karami Lawal, Ali Aldrees, Hayati Yassin, Salisu Dan'azumi, Sujay Raghavendra Naganna, Sani I. Abba, Saad Sh. Sammen

“…This study aimed to develop an effective ensemble model for classifying river water as drinkable or polluted using advanced machine learning. …”
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Predicting the outcome of psychological treatments for borderline personality disorder and posttraumatic stress disorder: a machine learning approach to predict long-term outcome o... by Jakob Blaß, Benjamin Iffland, Philipp Herzog, Tim Kaiser, Thomas Elbert, Carolin Steuwe

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Clinical and economic effectiveness of Schroth therapy in adolescent idiopathic scoliosis: insights from a machine learning- and active learning-based real-world study by Erdal Ayvaz, Merve Uca, Ednan Ayvaz, Zafer Yıldız

“…Recent advancements in active learning (AL) and machine learning (ML) techniques offer the potential to optimize treatment protocols by uncovering hidden predictors and enhancing model efficiency. …”
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Automated methane emission monitoring systems based on satellite data: Radiation transfer model analysis by K. Aghayeva, G. Krauklit

“…The findings highlight the need to integrate satellite data with ground-based measurements and radiative transfer models to improve monitoring accuracy and develop emission reduction strategies…”
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Ensemble modeling of the climate-energy nexus for renewable energy generation across multiple US states by Joy Adul, Rohini Kumar, Renee Obringer

“…This study aims to leverage machine learning models to predict renewable energy generation based on the surrounding climate. …”
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Advanced Handover Optimization (AHO) using deep reinforcement learning in 5G Networks by T. Senthil Kumar, Mardeni Roslee, J. Jayapradha, Yasir Ullah, Chilakala Sudhamani, Sufian Mousa Ibrahim Mitani, Anwar Faizd Osman, Fatimah Zaharah Ali

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Development of an electronic health record-based Human Immunodeficiency Virus (HIV) risk prediction model for women, incorporating social determinants of health by Yiyang Liu, Aokun Chen, Hwayoung Cho, Khairul A. Siddiqi, Robert L. Cook, Mattia Prosperi

“…Contextual-level SDoH were linked to EHR/claim data. Various machine learning (ML) methods were tested, and Shapley Additive Explanations (SHAP) values were used to interpret the model. …”
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Causality-Driven Feature Selection for Calibrating Low-Cost Airborne Particulate Sensors Using Machine Learning by Vinu Sooriyaarachchi, David J. Lary, Lakitha O. H. Wijeratne, John Waczak

“…However, traditional machine learning models often lack interpretability and generalizability when applied to complex, dynamic environmental data. …”
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Predicting the compressive strength of concrete incorporating waste powders exposed to elevated temperatures utilizing machine learning by Islam N. Fathy, Hany A. Dahish, Mohammed K. Alkharisi, Alaa A. Mahmoud, Hala Emad Elden Fouad

“…In this study, three machine learning approaches, extreme gradient boosting (XGBoost), random forest (RF), and M5P, were used for constructing the prediction model for the impact of elevated temperatures on the compressive strength of concrete modified by marble and granite construction waste powders as partial cement replacements in concrete. …”
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