Electrocardiogram Abnormality Detection Using Machine Learning on Summary Data and Biometric Features by Kennette James Basco, Alana Singh, Daniel Nasef, Christina Hartnett, Michael Ruane, Jason Tagliarino, Michael Nizich, Milan Toma

“…Preprocessing steps addressed class imbalance, outliers, feature scaling, and the encoding of categorical variables. Five machine learning models—Gaussian Naive Bayes, support vector machines, random forest trees, extremely randomized trees, gradient boosted trees, and an ensemble of top-performing classifiers—were trained and optimized using stratified k-fold cross-validation. …”
Get full text

Early Parkinson's Disease Detection and Classification using Machine Learning and Neutrosophic Set by Hasan H. Oudah, Ahmed A. Metwaly, Mohamed eassa, Ahmed Abdelhafeez, Ahmad M. Nagm, Ahmed S. Salama

“…The results show the support vector machine is the best ML model for the prediction of PD. …”
Get full text

Adaptive predator prey algorithm for many objective optimization by Nikunj Mashru, Kanak Kalita, Lenka Čepová, Pinank Patel, Arpita, Pradeep Jangir

Get full text

Characterization of immune microenvironment associated with medulloblastoma metastasis based on explainable machine learning by Fengmao Zhao, Xiangjun Liu, Jingang Gui, Hailang Sun, Nan Zhang, Yun Peng, Ming Ge, Wei Wang

“…Eight algorithms were evaluated, and the optimal model was selected. Lasso regression was employed for feature selection, and SHapley Additive exPlanations values were used to interpret the contribution of individual features to model predictions. …”
Get full text

Reservoir evaluation using petrophysics informed machine learning: A case study by Rongbo Shao, Hua Wang, Lizhi Xiao

“…We compare our method's performances using two datasets and evaluate the influences of multi-task learning, model structure, transfer learning, and petrophysics informed machine learning (PIML). …”
Get full text

Vibration monitoring and health status recognition technology of machine tool electric spindle by Xiaopei Tao, Yanping Zhao, Yanwei Chen

“…Abstract This paper proposes a vibration monitoring and health status recognition model for machine tool electric spindles to optimize efficiency. …”
Get full text

Machine Learning Recognizes Stages of Parkinson’s Disease Using Magnetic Resonance Imaging by Artur Chudzik

“…Because early detection is crucial for effective intervention strategies, this study investigates whether the structural analysis of selected brain regions, including volumes and their spatial relationships obtained from regular T1-weighted MRI scans (<i>N</i> = 168, PPMI database), can model stages of PD using standard machine learning (ML) techniques. …”
Get full text

Talent identification in soccer using a one-class support vector machine by Jauhiainen S., Äyrämö S., Forsman H., Kauppi J-P.

“…We trained a nonlinear one-class support vector machine (one-class SVM) on a dataset (N=951) collected from 14-year-old junior soccer players to detect potential future elite players. …”
Get full text

A Multimodal Approach of Machine and Deep Learnings to Enhance the Fall of Elderly People by Saleh Al meraikhi, Murad Al-Rajab

“…The purpose of this study is to contribute to the field of Machine Learning and Fall Detection by investigating the optimal ways to apply common machine and deep learning algorithms trained on multimodal fall data. …”
Get full text

Enhancing healthcare AI stability with edge computing and machine learning for extubation prediction by Kuo-Yang Huang, Ying-Lin Hsu, Che-Liang Chung, Huang-Chi Chen, Ming-Hwarng Horng, Ching-Hsiung Lin, Ching-Sen Liu, Jia-Lang Xu

“…Given the pivotal role of ventilators, accurately predicting extubation outcomes is essential to optimize patient care. This study presents an edge computing-based framework that incorporates machine learning algorithms to predict ventilator extubation success using real-time data collected directly from ventilators. …”
Get full text

Inspection of railway catenary systems using machine learning with domain knowledge integration by Kacper Marciniak, Paweł Majewski, Jacek Reiner

“…This paper presents innovative solutions leveraging domain knowledge to significantly improve the inference quality of machine learning models using existing training data. …”
Get full text

A hybrid machine learning approach for the personalized prognostication of aggressive skin cancers by Tom W. Andrew, Mogdad Alrawi, Ruth Plummer, Nick Reynolds, Vern Sondak, Isaac Brownell, Penny E. Lovat, Aidan Rose, Sophia Z. Shalhout

“…MCC and DeepMerkel provide the exemplar model of personalised machine learning prognostic tools in aggressive skin cancers.…”
Get full text

Review of Spot Electricity Price Prediction Studies Based on Machine Learning Methods by JIA Heping, GUO Yuchen, MA Qianxin, YANG Zhenglin, ZHENG Yaxian, ZENG Dan, LIU Dunnan

“…This study then analyzed the evaluation criteria for spot electricity price prediction models based on machine learning， and summarized the model hyperparameter training requirements and the practical application of relevant prediction methods. …”
Get full text

Cybersecurity of smart grids: Comparison of machine learning approaches training for anomaly detection by S. V. Kochergin, S. V. Artemova, A. A. Bakaev, E. S. Mityakov, Zh. G. Vegera, E. A. Maksimova

“…The relative effectiveness of such methods as multifractal analysis using wavelets, the Isolation Forest model, local outlier factor (LOF), k-means clustering, and one-class support vector machine (One-Class SVM), is analyzed.Results. …”
Get full text

A Rapid Design Method for Centrifugal Pump Impellers Based on Machine Learning by Y. Chen, W. Li, Y. Luo, L. Ji, S. Li, Y. Long

“…To reduce development time and costs, this paper proposes a rapid impeller design method focused on hydraulic performance, integrating traditional similarity design theory with machine learning. The proposed model uses neural networks to predict empirical coefficients, determine key dimensions such as the impeller’s inlet diameter, outlet diameter, outlet width, and axial distance. …”
Get full text

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

“…Graphical analyses further validated the high accuracy of the ML models, particularly CatBoost and Extra Trees. The findings underscore the effectiveness of ML approaches and optimized hyperparameter tuning in enhancing the prediction of gas adsorption capacity, thereby improving the design of gas injection and storage processes. …”
Get full text

Deep Reinforcement Learning-Based Multi-Access in Massive Machine-Type Communication by Nasim Ravi, Nuno Lourenco, Marilia Curado, and Edmundo Monteiro

“…Our model considers the Base Station (BS) as an agent navigating the landscape of machine-type communication devices. …”
Get full text

Predicting land suitability for wheat and barley crops using machine learning techniques by Bikila Abebe Ganati, Tilahun Melak Sitote

“…Then, random forest (RF), gradient boosting (GB), and K-nearest neighbour (KNN) were used to predict the land suitability of the two selected crops. To optimize the performance of the models, hyperparameters were tuned with cross-validated randomized searches. …”
Get full text

Adaptability and reproductive qualities of sows and indicators of their blood by keeping in machines with advanced elements by H. V. ТЕSАК, V. P. PUNDYК

“…Based on the selected individual structural elements of the machines for lactating sows, the improvement of which optimally ensures their biological characteristics of keeping, an experimental model of the machine for keeping lactating sows and piglets was made. …”
Get full text

Intelligent identification method of origin for Alismatis Rhizoma based on image and machine learning by Wenqi Zhao, Zongyi Zhao, Wen Zheng, Zimin Wang, Gaoting Yang, Zhiqiong Lan, Xiaoli Pan, Min Li

“…Four commonly used classification models Random Forest (RF), Extreme Learning Machine (ELM), Back Propagation (BP) neural network, and Support Vector Machines (SVM) were tested to find the optimal combination of AR fusion features and classification models. …”
Get full text