Data Research Papers

Machine Learning

Machine learning, one of the top emerging sciences, has an extremely broad range of applications. However, many books on the subject provide only a theoretical approach, making it difficult for a newcomer to grasp the subject material. This...

Machine Learning

Hyperparameter Search in Machine Learning

We introduce the hyperparameter search problem in the field of machine learning and discuss its main challenges from an optimization perspective. Machine learning methods attempt to build models that capture some element of interest based o...

Development of a Data-Based Machine Learning Model for Classifying and Predicting Property Damage Caused by Fire

Large fires in factories cause severe human casualties and property damage. Thus, preparing more economical and efficient management strategies for fire prevention can significantly improve fire safety. This study deals with property damage...

A Prediction Model for Spot LNG Prices Based on Machine Learning Algorithms to Reduce Fluctuation Risks in Purchasing Prices

The ongoing Russia–Ukraine conflict has exacerbated the global crisis of natural gas supply, particularly in Europe. During the winter season, major importers of liquefied natural gas (LNG), such as South Korea and Japan, were directly affe...

Machine learning approach for the estimation of missing precipitation data: a case study of South Korea

Precipitation is one of the driving forces in water cycles, and it is vital for understanding the water cycle, such as surface runoff, soil moisture, and evapotranspiration. However, missing precipitation data at the observatory becomes an ...

Comparing Clinical Metadata Fusion Strategies for Diabetic and Hypertensive Retinopathy Classification

Structured clinical variables may complement fundus photographs, but the most appropriate point at which to integrate them into a retinal foundation model remains uncertain. This study compares an image-only baseline with three strategies f...

Machine Learning with R

Perhaps you already know a bit about machine learning but have never used R, or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of...

Machine learning: a primer

Hands-On Machine Learning with R

Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process a...

Machine learning and data mining

Machine learning algorithms enable discovery of important "regularities" in large data sets....

Overfitting and undercomputing in machine learning

suggests a reasonable line of research: find algorithms that can search the hypothesis class better. Hence, there is been extensive research in applying second-order methods to fit neural networks and in conducting much more thorough search...

An Introduction to Machine Learning

Improved railway track faults detection using Mel-frequency cepstral coefficient and constant-Q transform features

Abstract Regular inspection of the health of railway tracks is crucial to maintaining reliable and safe train operations. Some factors including cracks, rail discontinuity, ballast issues, burn wheels, super-elevation, loose nuts and bolts,...

Predicting Urban Quality of Life Using Machine Learning on Street Images and Personal Information with Statistical Feature Selection: A Comparative Study of Bangkok and London

This study proposes a machine learning framework for predicting perceived urban quality of life (QoL) by integrating visual features from street-level imagery with personal attributes, including demographic, socioeconomic, and travel behavi...

lab2clean: a novel algorithm for automated cleaning of retrospective clinical laboratory results data for secondary uses

Abstract Background The integrity of clinical research and machine learning models in healthcare heavily relies on the quality of underlying clinical laboratory data. However, the preprocessing of this data to ensure its reliability and acc...

Elements of Machine Learning

Elements of Machine Learning by Pat Langley Preface 1. An overview of machine learning 1.1 The science of machine learning 1.2 Nature of the environment 1.3 Nature of representation and performance 1.4 Nature of the learning component 1.5 F...

Pattern Recognition and Machine Learning

(2007). Pattern Recognition and Machine Learning. Technometrics: Vol. 49, No. 3, pp. 366-366....

Machine Learning in Action

SummaryMachine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to bui...

A Transformer-Based Approach for Efficient Geometric Feature Extraction from Vector Shape Data

The extraction of shape features from vector elements is essential in cartography and geographic information science, supporting a range of intelligent processing tasks. Traditional methods rely on different machine learning algorithms tail...