Academic Articles & Research Papers

Upper Limb Movement Prediction Based on Segmented sEMG Signals

Motion intent recognition research is one of the key challenges in achieving human-robot collaboration in rehabilitation robots. In the traditional method, intention recognition is performed based on the complete sEMG, however, due to the m...

A novel framework for spatio-temporal prediction of environmental data using deep learning

Abstract As the role played by statistical and computational sciences in climate and environmental modelling and prediction becomes more important, Machine Learning researchers are becoming more aware of the relevance of their work to help ...

Machine Learning Methods in the Environmental Sciences

Machine learning methods originated from artificial intelligence and are now used in various fields in environmental sciences today. This is the first single-authored textbook providing a unified treatment of machine learning methods and th...

Machine Learning and AI for Risk Management

We explore how machine learning and artificial intelligence (AI) solutions are transforming risk management. A non-technical overview is first given of the main machine learning and AI techniques of benefit to risk managementRisk management...

Prediction of overall survival in acute myeloid leukemia using supervised machine learning.

Identification and Predictors for Cardiovascular Disease Risk Equivalents Among Adults With Diabetes.

Predicting Stock Price Movements with Combined Deep Learning Models and Two-Tier Metaheuristic Optimization Algorithm

Predictions on stock market prices are a noble task owing to huge complex, dynamic, and chaotic surroundings. Fast ups and downs arise in the stock market due to influences from foreign merchandise, such as sensitive political, stockholder,...

Can Geopolitical Risk Improve the Forecasting of Thai Stock-Market Returns? Evidence from Econometrics and Machine-Learning Models

Geopolitical uncertainty may affect financial markets, but its incremental value for forecasting emerging-market stock returns remains unclear. Using monthly data from January 1990 to July 2026, this study compares ARIMA-GARCH and ARIMAX-GA...

Using Network-Based Machine Learning to Predict Transcription Factors Involved in Drought Resistance

Gene regulatory networks underpin stress response pathways in plants. However, parsing these networks to prioritize key genes underlying a particular trait is challenging. Here, we have built the Gene Regulation and Association Network (GRA...

Learning metal microstructural heterogeneity through spatial mapping of diffraction latent space features

Abstract To leverage advancements in machine learning for metallic materials design and property prediction, it is crucial to develop a data-reduced representation of metal microstructures that surpasses the limitations of current physics-b...

Data-driven performance prediction of polymer solar cells using multi-layer perceptron with Bayesian hyperparameter tuning

Polymer solar cells (PSCs) rely on labor-intensive trial-and-error experiments to optimize power conversion efficiency (PCE), due to complex coupling of molecular electronic properties, blend morphology and fabrication parameters. This work...

Machine learning insight in quality control and maintenance management of drilling mud pumps

Abstract This study proposes a machine learning-based framework for predictive maintenance and quality control of drilling mud pumps, targeting critical operational challenges such as equipment failure, downtime, and high maintenance costs....

Malware Detection via Extended Label Propagation Through Graph Inference

In this paper, we model the malware detection problem as a graph inference problem, and develop a novel belief propagation approach within a semi-supervised learning scheme that fully makes use of files' and hosts' connections to detect mal...

A machine-learning approach for predicting the effect of carnitine supplementation on body weight in patients with polycystic ovary syndrome

The present study aimed to explore the effect of carnitine supplementation on body weight in patients with polycystic ovary syndrome (PCOS) and predict an appropriate dosage schedule using a machine-learning approach. Data were obtained fro...

Individual-Tree DBH Estimation from Airborne LiDAR Data Using MSFS–XGBoost

Diameter at breast height (DBH) is a fundamental structural parameter for forest inventory and ecological analysis. However, field-based measurements (e.g., diameter tape surveys) are labor-intensive and inefficient for large-scale applicat...

Machine Learning and Data Mining Methods in Diabetes Research

The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, ap...

COVID-19 Outbreak Prediction with Machine Learning

Abstract Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simpl...

Machine Learning for Hackers

If you're an experienced programmer interested in crunching data, this book will get you started with machine learning-a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John...

Outcome Prediction in Older Adults With Head and Neck Cancer Undergoing Chemoradiation.

In vivo prediction of carcass and meat quality in lambs using multi-site bioelectrical impedance combined with body morphometrics.