Quality prediction of semi-solid die casting of aluminum alloy in terms of machine learning by Zhiyuan Wang, Xiaogang Hu, Gan Li, Zhen Xu, Hongxing Lu, Qiang Zhu

“…However, the quality of these components is highly susceptible to variations in both environmental conditions and process parameters, leading to a narrow process window that restricts its widespread application in engineering. In this study, a machine learning (ML) model has been developed to identify defective products through the detection of injection pressure, thereby providing a foundation for monitoring and further optimizing the manufacturing process. …”
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Classical machine learning and artificial neural network (ANN) to predict rejection in weaving industry by Toufique Ahmed

“…Interestingly, traditional machine learning models achieved more than 95% accuracy without any data preprocessing. …”
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Modified Whale Optimization Algorithm for Multiclass Skin Cancer Classification by Abdul Majid, Masad A. Alrasheedi, Abdulmajeed Atiah Alharbi, Jeza Allohibi, Seung-Won Lee

“…The optimized features are fed into machine learning classifiers to achieve robust classification performance. …”
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Robust Fault Classification in Permanent Magnet Synchronous Machines Under Dynamic and Noisy Conditions by Mikal Laursen, Van-Van Huynh, Duy-Hung Ha, Mahmoud S. Mahmoud, van Khang Huynh

“…Two supervised learning models, namely Extra Trees (ET) and Support Vector Machine (SVM), are developed and compared using time-domain (TD) and frequency-domain (FD) features. …”
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A Unified Machine Learning Framework for Li-Ion Battery State Estimation and Prediction by Afroditi Fouka, Alexandros Bousdekis, Katerina Lepenioti, Gregoris Mentzas

“…It integrates standardized data processing pipelines with a diverse set of ML models, such as a long short-term memory neural network (LSTM), a convolutional neural network (CNN), a feedforward neural network (FFNN), automated machine learning (AutoML), and classical regressors, while accommodating heterogeneous datasets. …”
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Trends and Disparities in Parkinson’s Disease Mortality in the United States with Predictions Using Machine Learning by Henry Weresh, Kallin Hermann, Ali Al-Salahat, Amna Noor, Taylor Billion, Yu-Ting Chen, Abubakar Tauseef, Ali Bin Abdul Jabbar

“…Forecasts were obtained using the optimal Autoregressive Integrated Moving Average (ARIMA) model. …”
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Preliminary Electroencephalography-Based Assessment of Anxiety Using Machine Learning: A Pilot Study by Katarzyna Mróz, Kamil Jonak

“…<b>Conclusions</b>: By integrating ML with EEG analysis, this study highlights the potential for future healthcare applications, including neurorehabilitation, anxiety disorder therapy, and predictive clinical models. Future research should focus on optimizing ML algorithms, enhancing personalization, and addressing ethical concerns related to patient privacy.…”
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Feasibility study of texture-based machine learning approach for early detection of neonatal jaundice by Nanthida Phattraprayoon, Teerapat Ungtrakul, Patiparn Kummanee, Sunisa Tavaen, Tanin Pirunnet, Todsaporn Fuangrod

“…The best performing model, Support Vector Machine (SVM), was implemented in a web-based application (AmberSNAP) and tested using blind testing dataset. …”
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Selective Laser Sintering of Polymers: Process Parameters, Machine Learning Approaches, and Future Directions by Hossam M. Yehia, Atef Hamada, Tamer A. Sebaey, Walaa Abd-Elaziem

“…Additionally, the study explores the application of machine learning (ML) techniques—supervised, unsupervised, and reinforcement learning—in optimizing processes, detecting defects, and ensuring quality control within SLS. …”
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Shale volume estimation using machine learning methods from the southwestern fields of Iran by Parirokh Ebrahimi, Ali Ranjbar, Yousef Kazemzadeh, Ali Akbari

“…This study aims to compare the performance of several advanced ML models—namely, Artificial Neural Networks (ANNs), Bayesian Algorithm (BA), Least Squares Boosting (Lsboost), Linear Regression (LR), Random Forest (RF), and Support Vector Machine (SVM)—for shale volume estimation using well log data. …”
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Interpretable Machine Learning for Legume Yield Prediction Using Satellite Remote Sensing Data by Theodoros Petropoulos, Lefteris Benos, Remigio Berruto, Gabriele Miserendino, Vasso Marinoudi, Patrizia Busato, Chrysostomos Zisis, Dionysis Bochtis

“…Accurate crop yield prediction is vital towards optimizing agricultural productivity. Machine Learning (ML) has shown promise in this field; however, its application to legume crops, especially to lupin, remains limited, while many models lack interpretability, hindering real-world adoption. …”
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Solutions for Lithium Battery Materials Data Issues in Machine Learning: Overview and Future Outlook by Pengcheng Xue, Rui Qiu, Chuchuan Peng, Zehang Peng, Kui Ding, Rui Long, Liang Ma, Qifeng Zheng

“…Abstract The application of machine learning (ML) techniques in the lithium battery field is relatively new and holds great potential for discovering new materials, optimizing electrochemical processes, and predicting battery life. …”
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Machine Learning-Based Predictive Maintenance at Smart Ports Using IoT Sensor Data by Sheraz Aslam, Alejandro Navarro, Andreas Aristotelous, Eduardo Garro Crevillen, Alvaro Martınez-Romero, Álvaro Martínez-Ceballos, Alessandro Cassera, Kyriacos Orphanides, Herodotos Herodotou, Michalis P. Michaelides

“…Firstly, a statistical model was developed to check the status and health of the hydraulic system, as it is crucial for the operation of the machines. …”
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A Comparison of Machine Learning Algorithms for Predicting Alzheimer’s Disease Using Neuropsychological Data by Zakaria Mokadem, Mohamed Djerioui, Bilal Attallah, Youcef Brik

“…Hyperparameter tuning was performed to optimize model performance. The results indicate that ensemble learning models, particularly Gradient Boosting (GB) and Random Forest (RF), exhibited superior accuracy compared to other algorithms. …”
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IoT Based Health Monitoring with Diet, Exercise and Calories recommendation Using Machine Learning by Muhammad Hassaan Naveed, Omar Bin Samin, Muhammad Bilal, Mustehsum Waseem

“…The study’s method ology includes utilizing a comprehensive dataset from Kaggle, separated into sets for testing and training, to develop and evaluate machine learning models. The Random Forest model demonstrated superior performance in precision, recall, F1-score, and R2 score metrics, making it the optimal choice for the recommender system. …”
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Smart Irrigation System with IoT, Machine Learning, and Solar Power for Efficient Plant Care by Justin M. A. Capcha-Ochoa, Jefferson A. Chahua-Benito, Miguel A. Serafin-Cayllahua, Sebastian E. Mamani-Martinez, Jesus G. Mendivil-Imbertis, Jordan I. Mendoza-Fernandez, Roberto J. M. Casas-Miranda, Maritza Cabana-Cáceres, Cristian Castro-Vargas

“…This study aims to develop an intelligent irrigation system based on the Internet of Things (IoT) and machine learning to optimize water use, improve plant monitoring, and enhance security. …”
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Analysis of Driving Factors of Cropland Productivity in Northeast China Using OPGD-SHAP Framework by Runzhao Gao, Hongyan Cai, Xinliang Xu

“…Various machine learning models were fine-tuned and compared, and optimal models were selected for subsequent SHAP analysis. …”
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An Optimized Design of New XYθ Mobile Positioning Microrobotic Platform for Polishing Robot Application Using Artificial Neural Network and Teaching-Learning Based Optimization by Minh Phung Dang, Hieu Giang Le, Ngoc Le Chau, Thanh-Phong Dao

“…The leaf hinges were employed due to their large rotation, and the right circular hinges were adopted because of their high accuracy. In modeling the behaviors of the developed platform, the artificial neural network is formulated in combination with the teaching-learning-based optimization (TLBO) method. …”
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Digital mapping of peat thickness and extent in Finland using remote sensing and machine learning by Jonne Pohjankukka, Timo A. Räsänen, Timo P. Pitkänen, Arttu Kivimäki, Ville Mäkinen, Tapio Väänänen, Jouni Lerssi, Aura Salmivaara, Maarit Middleton

“…Our exceptionally comprehensive approach is broadly applicable, offering new insights into optimizing machine learning-based digital peatland mapping, particularly through refining feature selection to account for local conditions and enhance prediction accuracy.…”
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A synthetic data-driven machine learning approach for athlete performance attenuation prediction by Mauricio C. Cordeiro, Ciaran O. Cathain, Ciaran O. Cathain, Lorcan Daly, Lorcan Daly, David T. Kelly, David T. Kelly, Thiago B. Rodrigues

“…IntroductionAthlete performance monitoring is effective for optimizing training strategies and preventing injuries. …”
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