Academic Articles & Research Papers

A flexible analytic wavelet transform and ensemble bagged tree model for electroencephalogram-based meditative mind-wandering detection

Mind-wandering (MW) is when an individual’s concentration drifts away from the task or activity. Researchers found a greater variability in electroencephalogram (EEG) signals due to MW. Collecting more nuanced information from raw EEG data ...

Interpretable Intersection Control by Reinforcement Learning Agent With Linear Function Approximator

ABSTRACT Reinforcement learning (RL) is a promising machine‐learning solution to traffic signal control problems, which have been extensively studied. However, variants of non‐linear, deep artificial neural network (ANN) function approximat...

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...

Automatic Differentiation in Machine Learning: a Survey

Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning. Automatic differentiation (AD) is a technique for calculating derivatives of numeric functions expressed as computer programs efficiently and acc...

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...

Hybrid machine learning and 3D numerical modeling-based multi-objective optimization of geosynthetic-reinforced pile-supported embankments

Efficient and reliable design of Geosynthetic Reinforced Pile-Supported (GRPS) embankments on soft ground remains challenging due to the complex nonlinear soil–structure interaction and the high computational cost of repeated numerical anal...

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...

Enhancing host-pathogen phenotyping dynamics: early detection of tomato bacterial diseases using hyperspectral point measurement and predictive modeling

Early diagnosis of plant diseases is needed to promote sustainable plant protection strategies. Applied predictive modeling over hyperspectral spectroscopy (HS) data can be an effective, fast, cost-effective approach for improving plant dis...

Learning-Aided Deep Path Prediction for Sphere Decoding in Large MIMO Systems

In this paper, we propose a novel learning-aided sphere decoding (SD) scheme for large multiple-input-multiple-output systems, namely, deep path prediction-based sphere decoding (DPP-SD). In this scheme, we employ a neural network (NN) to p...

Machine Learning–Based Wear Prediction of Recycled Magnesium Matrix Composites Reinforced With Ceramic Fibers

ABSTRACT This study deals with an integrated experimental‐machine learning framework for wear estimation in functionally graded composites made from recycled magnesium machining chips, using low‐cost ceramic fibers as reinforcement with the...

Readings in Machine Learning

From the Publisher: The ability to learn is a fundamental characteristic of intelligent behavior. Consequently, machine learning has been a focus of artificial intelligence since the beginnings of AI in the 1950s. The 1980s saw tremendous ...

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...

Context Aware Evapotranspiration (ETs) for Saline Soils Reclamation

Accurate Evapotranspiration for saline soils (ETs) is important as well as challenging for the reclamation of saline soils through an effective leaching process. Evapotranspiration (ET) by FAO-56 Penman-Monteith standard method is complex, ...

Machine Learning Inversion of Runway Base-Course Parameters Using Multi-Domain Vibration Features Under Moving Aircraft Loads

Accurate identification of runway base-course parameters is an important basis for structural condition assessment, yet separating base-course elastic modulus, thickness, and local damage from runway responses under moving aircraft loads re...

Automated, economical, and environmentally-friendly asphalt mix design based on machine learning and multi-objective grey wolf optimization

The increasing impact of the greenhouse effect on ecosystems is prompting transportation agencies to seek methods for reducing CO2 emissions during pavement construction and maintenance. Additionally, the laboratory mix design process, whic...

Deep Learning for Automatic Vision-Based Recognition of Industrial Surface Defects: A Survey

Automatic vision-based inspection systems have played a key role in product quality assessment for decades through the segmentation, detection, and classification of defects. Historically, machine learning frameworks, based on hand-crafted ...

Evaluating Model Resilience to Data Poisoning Attacks: A Comparative Study

Machine learning (ML) has become a cornerstone of critical applications, but its vulnerability to data poisoning attacks threatens system reliability and trustworthiness. Prior studies have begun to investigate the impact of data poisoning ...

Introduction to Machine Learning