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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Explainable Machine Learning and Predictive Statistics for Sustainable Photovoltaic Power Prediction on Areal Meteorological Variables by Sajjad Nematzadeh, Vedat Esen

“…This study proposes an explainable machine-learning framework that simultaneously ranks the most informative weather parameters and reveals their physical relevance to PV generation. …”
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Prediction of additional hospital days in patients undergoing cervical spine surgery with machine learning methods by Bin Zhang, Shengsheng Huang, Chenxing Zhou, Jichong Zhu, Tianyou Chen, Sitan Feng, Chengqian Huang, Zequn Wang, Shaofeng Wu, Chong Liu, Xinli Zhan

“…Background Machine learning (ML), a subset of artificial intelligence (AI), uses algorithms to analyze data and predict outcomes without extensive human intervention. …”
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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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Adaptive and Scalable Database Management with Machine Learning Integration: A PostgreSQL Case Study by Maryam Abbasi, Marco V. Bernardo, Paulo Váz, José Silva, Pedro Martins

“…This paper proposes a comprehensive framework for integrating advanced machine learning (ML) models within the architecture of a database management system (DBMS), with a specific focus on PostgreSQL. …”
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Optimization and predictive performance of fly ash-based sustainable concrete using integrated multitask deep learning framework with interpretable machine learning techniques by Bhupesh P. Nandurkar, Jayant M. Raut, Pawan K. Hinge, Boskey V. Bahoria, Tejas R. Patil, Sachin Upadhye, Vikrant S. Vairagade, Sagar D. Shelare

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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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Electrical discharge machining: Recent advances and future trends in modeling, optimization, and sustainability by Muhamad Taufik Ulhakim, Sukarman, Khoirudin, Dodi Mulyadi, Hendri Susilo, Rohman, Muji Setiyo

“…Future studies should focus on the effects of AI-driven approaches on environmentally friendly EDM practices by prioritizing green dielectrics, energy-efficient machining, and waste reduction strategies. This review highlights the interconnected roles of modeling, optimization, and sustainability in advancing EDM and outlines key research directions to address the remaining challenges.…”
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Spatial analysis of air pollutant exposure and its association with metabolic diseases using machine learning by Jingjing Liu, Chang Liu, Zhangdaihong Liu, Yibin Zhou, Xiaoguang Li, Yang Yang

“…(iii) AP exposure is adjusted by demographic and lifestyle confounders to predict MDs using machine learning models (e.g., eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Decision Tree (DT), LightGBM, and Multi-Layer Perceptron (MLP)). …”
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Combination of machine learning and Raman spectroscopy for prediction of drug release in targeted drug delivery formulations by Wael A. Mahdi, Adel Alhowyan, Ahmad J. Obaidullah

“…The considered drug is 5-aminosalicylic acid for colonic drug delivery, and its release was estimated using Raman data as inputs along with other categorical parameters. The models, including Kernel Ridge Regression (KRR), Kernel-based Extreme Learning Machine (K-ELM), and Quantile Regression (QR) incorporate sophisticated approaches like the Sailfish Optimizer (SFO) for hyperparameter optimization and K-fold cross-validation to enhance predictive accuracy. …”
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LEVERAGING MACHINE LEARNING METHODS IN PREDICTING AND ANALYZING THE ASSOCIATION BETWEEN DIETARY INFLAMMATORY INDEX AND ALOPECIA by Mohammed Sarwat M Salih, Hawal Lateef Fateh, Soran Abdulkarim Pasha, Hassan M Tawfiq

“…Three machine learning models were developed: K-Nearest Neighbors (KNN) with dimensionality reduction to prevent overfitting, Logistic Regression with L2 regularization, and Random Forest enhanced through grid search for hyperparameter tuning. …”
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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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A Portable Real-Time Electronic Nose for Evaluating Seafood Freshness Using Machine Learning by Muhammad Rafi Mahfuz Setyagraha, Hurul Aini Nurqamaradillah, Laksamana Mikhail Hermawan, Nyoman Raflly Pratama, Ledya Novamizanti, Dedy Rahman Wijaya

“…This study presents an electronic nose (e-nose) system designed to assess seafood freshness using gas sensors and machine learning (ML) algorithms. The system detects volatile organic compounds (VOCs) released during spoilage and employs hyperparameter-optimized ML models for both classification (fresh vs. not fresh) and regression (shelf-life prediction). …”
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Experimental and machine learning based analysis of pervious concrete enhanced with fly ash and silica fume by Siva Shanmukha Anjaneya Babu Padavala, Siva Avudaiappan, Venkatesh Noolu

“…Machine learning (ML) models were also created in order to predict compressive strength based on mix composition and curing age using Orange Data Mining software version 3.36. …”
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A step forward in the diagnosis of urinary tract infections: from machine learning to clinical practice by Emilio Flores, Laura Martínez-Racaj, Álvaro Blasco, Elena Diaz, Patricia Esteban, Maite López-Garrigós, María Salinas

“…The aim of this study was to improve UTI diagnostics in clinical practice by application of machine learning (ML) models for real-time UTI prediction. …”
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Versatile machine learning algorithms for FTIR spectroscopy: differentiating crosslinked and non-crosslinked gelatin samples by Juliana Rincón-López, Eliana Álvarez-Valdés, Daniela Velez-Arango, Estefanía Rojas Zuleta, Leidy Yuliana Vargas Soto, Liliana Lellesch, Victor Alonso García Londoño, Milton Rosero-Moreano, Gonzalo Taborda-Ocampo

“…For the FTIR results, machine learning classification models developed in the Python language were used to distinguish between cross-linked and non-cross-linked gelatin samples and two dimensionality reduction techniques (PCA, PLS) and four classification models (NCA-KNN, SVM, LDA, DT) were incorporated, all effectively classifying spectra across gelatin types in adjustment, training, test stages, and predictions, with higher precision observed for gelatins A, C, and D. …”
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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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Learning the factors controlling mineral dissolution in three-dimensional fracture networks: applications in geologic carbon sequestration by Aleksandra A. Pachalieva, Aleksandra A. Pachalieva, Jeffrey D. Hyman, Daniel O’Malley, Gowri Srinivasan, Hari Viswanathan

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