Academic Articles & Research Papers

Machine learning in pain research

1. Introduction Pain and pain chronification are incompletely understood and unresolved medical problems that continue to have a high prevalence.14 It has been accepted that pain is a complex phenomenon.2,32,72 Contemporary methods of compu...

Machine learning and deep learning—A review for ecologists

Abstract The popularity of machine learning (ML), deep learning (DL) and artificial intelligence (AI) has risen sharply in recent years. Despite this spike in popularity, the inner workings of ML and DL algorithms are often perceived as opa...

Machine Learning

Machine learning techniques have the potential of alleviating the complexity of knowledge acquisition. This book presents today’s state and development tendencies of machine learning. It is a multi-author book. Taking into account the large...

Identification of immune-associated biomarkers of diabetes nephropathy tubulointerstitial injury based on machine learning: a bioinformatics multi-chip integrated analysis

Abstract Background Diabetic nephropathy (DN) is a major microvascular complication of diabetes and has become the leading cause of end-stage renal disease worldwide. A considerable number of DN patients have experienced irreversible end-st...

Interpretable machine learning to detect well integrity issues

The problem of timely and accurate evaluation of well integrity is becoming increasingly relevant in the context of mature field development, high wellstream water cut, and a growing number of old wells. For production casing diagnostics, g...

Attention-based functional-group coarse-graining: a deep learning framework for molecular prediction and design

Abstract Machine learning (ML) offers considerable promise for the design of new molecules and materials. In real-world applications, the design problem is often domain-specific, and suffers from insufficient data, particularly labeled data...

A review of machine learning in building load prediction

A Survey on Distributed Machine Learning

The demand for artificial intelligence has grown significantly over the past decade, and this growth has been fueled by advances in machine learning techniques and the ability to leverage hardware acceleration. However, to increase the qual...

Artificial intelligence, machine learning and deep learning

It is increasingly recognized that artificial intelligence has been touted as a new mobile. Because of the high volume of data that being generated by devices, sensors and social media users, the machine can learn to distinguish the pattern...

Machine learning in the Chinese stock market

We add to the emerging literature on empirical asset pricing in the Chinese stock market by building and analyzing a comprehensive set of return prediction factors using various machine learning algorithms. Contrasting previous studies for ...

Corrigendum to "Prediction of Distant Metastasis in Renal Cell Carcinoma Using Machine Learning Algorithms: A Multicenter Cohort Study".

Benchmarking Supervised Classifiers for Concurrent Multidomain Dropout-Intention Attributions in Higher Education: Evidence from a Colombian Public University

Student retention analytics often treats withdrawal as a single outcome, although students may attribute dropout intention to personal, socioeconomic, and academic pressures simultaneously. We benchmarked nine supervised classifiers for ide...

Interpretable machine learning for predicting compression index of clays using SHAP and gradient boosting models

Abstract This study introduces a novel, interpretable machine learning framework for predicting the compression index (Cc) of clay soils by integrating three advanced gradient boosting algorithms—XGBoost, CatBoost, and LightGBM—with SHapley...

Improving Intrusion Detection Model Prediction by Threshold Adaptation

Network traffic exhibits a high level of variability over short periods of time. This variability impacts negatively on the accuracy of anomaly-based network intrusion detection systems (IDS) that are built using predictive models in a batc...

Integrating BIM, Machine Learning, and PMBOK for Green Project Management in Saudi Arabia: A Framework for Energy Efficiency and Environmental Impact Reduction

This study introduces a comprehensive framework combining building information modeling (BIM), project management body of knowledge (PMBOK), and machine learning (ML) to optimize energy efficiency and reduce environmental impacts in Riyadh’...

A First Course in Machine Learning

"A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations ...

Machine Learning Approaches for Clinical Psychology and Psychiatry

Machine learning approaches for clinical psychology and psychiatry explicitly focus on learning statistical functions from multidimensional data sets to make generalizable predictions about individuals. The goal of this review is to provide...

Introduction to machine learning: k-nearest neighbors

Machine learning techniques have been widely used in many scientific fields, but its use in medical literature is limited partly because of technical difficulties. k-nearest neighbors (kNN) is a simple method of machine learning. The articl...

Development of Machine Learning Models to Predict Compressed Sward Height in Walloon Pastures Based on Sentinel-1, Sentinel-2 and Meteorological Data Using Multiple Data Transformations

Accurate information about the available standing biomass on pastures is critical for the adequate management of grazing and its promotion to farmers. In this paper, machine learning models are developed to predict available biomass express...

Automated speech-fluency explanations for schizophrenia diagnosis

Abstract Schizophrenia is a chronic and severe mental disorder that still relies on time-intensive, clinician-administered assessments. Although several automated approaches have been proposed to support diagnosis, these systems often lack ...