Academic Articles & Research Papers

UAV-Embedded Sensors and Deep Learning for Pathology Identification in Building Façades: A Review

The use of geotechnologies in the field of diagnostic engineering has become ever more present in the identification of pathological manifestations in buildings. The implementation of Unmanned Aerial Vehicles (UAVs) and embedded sensors has...

Machine Learning in Nutrition Research

Data currently generated in the field of nutrition are becoming increasingly complex and high-dimensional, bringing with them new methods of data analysis. The characteristics of machine learning (ML) make it suitable for such analysis and ...

Machine learning for biochemical engineering: A review

The field of machine learning is comprised of techniques, which have proven powerful approaches to knowledge discovery and construction of ‘digital twins’ in the highly dimensional, nonlinear and stochastic domains common to biochemical eng...

Context-Aware LLM-Guided Neighbourhood Search for Simulation-Based Production Scheduling

The paper presents a novel large language model-based neighbourhood search method for production scheduling using discrete-event simulation (DES) as the modelling and evaluation engine. The combination of large language models (LLMs) and DE...

Machine Learning Refined

With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuiti...

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 in Chemical Engineering: A Perspective

Abstract The transformation of the chemical industry to renewable energy and feedstock supply requires new paradigms for the design of flexible plants, (bio‐)catalysts, and functional materials. Recent breakthroughs in machine learning (ML)...

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...

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...

CViewer: a Java-based statistical framework for integration of shotgun metagenomics with other omics datasets

Abstract Background Shotgun metagenomics for microbial community survey recovers enormous amount of information for microbial genomes that include their abundances, taxonomic, and phylogenetic information, as well as their genomic makeup, t...

Student Modeling and Machine Learning

After identifying essential student modeling issues and machine learning approaches, this paper examines how machine learning techniques have been used to automate the construction of student models as well as the background knowledge neces...

Machine learning algorithms review

Machine learning is a field of study where the computer can learn for itself without a human explicitly hardcoding the knowledge for it. These algorithms make up the backbone of machine learning. This paper aims to study the field of machin...

A few useful things to know about machine learning

Tapping into the "folk knowledge" needed to advance machine learning applications....

C4.5: Programs for Machine Learning

Classifier systems play a major role in machine learning and knowledge-based systems, and Ross Quinlan's work on ID3 and C4.5 is widely acknowledged to have made some of the most significant contributions to their development. This book is ...

Introduction to Machine Learning

1 Why Machine Learning and AI: The Contributions of AI to Learning Techniques 2 Theoretical Foundations for Machine Learning 3 Representation of Complex Knowledge by Clauses 4 Representation of Knowledge about Actions and the Addition of Ne...

Machine Learning: An Algorithmic Perspective

Traditional books on machine learning can be divided into two groups - those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is r...

Machine learning for chemical discovery

Discovering chemicals with desired attributes is a long and painstaking process. Curated datasets containing reliable quantum-mechanical properties for millions of molecules are becoming increasingly available. The development of novel mach...

Data Analysis, Machine Learning and Applications

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 for Detection and Diagnosis of Disease

Machine learning offers a principled approach for developing sophisticated, automatic, and objective algorithms for analysis of high-dimensional and multimodal biomedical data. This review focuses on several advances in the state of the art...