Academic Articles & Research Papers

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 in aerodynamic shape optimization

Machine learning (ML) has been increasingly used to aid aerodynamic shape optimization (ASO), thanks to the availability of aerodynamic data and continued developments in deep learning. We review the applications of ML in ASO to date and pr...

Machine learning and soil sciences: a review aided by machine learning tools

Abstract. The application of machine learning (ML) techniques in various fields of science has increased rapidly, especially in the last ten years. The increasing availability of soil data that can be efficiently acquired remotely and proxi...

Introduction to Machine Learning with Python: A Guide for Data Scientists

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teac...

Prognostic value of postoperative glucose-to-lymphocyte ratio and visceral fat index in non-diabetic gastric cancer patients: a mediation and modeling study.

SL-S4Wave: Self-Supervised Learning of Physiological Waveforms with Structured State Space Models.

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...

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...

On the use of aspect-based sentiment analysis of Twitter data to explore the experiences of African Americans during COVID-19

Abstract According to data from the U.S. Center for Disease Control and Prevention, as of June 2020, a significant number of African Americans had been infected with the coronavirus disease, experiencing disproportionately higher death rate...

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...

Stealing Hyperparameters in Machine Learning

Hyperparameters are critical in machine learning, as different hyperparameters often result in models with significantly different performance. Hyperparameters may be deemed confidential because of their commercial value and the confidentia...

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...

Introduction to Machine Learning

R is one of the top-rated problem-solving tools for machine learning, data science, data analytics, data mining, and statistical analysis. R ’s popularity has resulted in the development of thousands of tutorials. Unfortunately, it’s easy t...

Machine learning for advanced energy materials

The screening of advanced materials coupled with the modeling of their quantitative structural-activity relationships has recently become one of the hot and trending topics in energy materials due to the diverse challenges, including low su...