Academic Articles & Research Papers

Perspectives in machine learning for wildlife conservation

Inexpensive and accessible sensors are accelerating data acquisition in animal ecology. These technologies hold great potential for large-scale ecological understanding, but are limited by current processing approaches which inefficiently d...

High-throughput laboratory evolution reveals evolutionary constraints in Escherichia coli

Understanding evolutionary constraints in antibiotic resistance is crucial for prediction and control. Here, the authors use high-throughput laboratory evolution of Escherichia coli alongside machine learning to identify trade-off relations...

A Topical Review of Graph Embedding in Graph Neural Networks

Graph embeddings map graph-structured data into vector spaces for machine learning tasks. In Graph Neural Networks (GNNs), these embeddings are computed through message passing and support tasks such as node classification, link prediction ...

Feature-driven static analysis for learning-based android malware detection: A review

The extensive embrace of Android has amplified malware risks, resulting in a need for better detection methods. This article investigates the area of static analysis, which analyses applications without execution by examining code and manif...

Determinants of Tourism Demand Using Machine Learning Techniques

The purpose of the current study was to determine factors affecting tourism demand using machine learning techniques. The results of different linear regression and random forest models on both the train and test sets were compared using RM...

An intelligent machine learning paradigm for gyrotactic micro-organism-induced squeezing flow of Eyring–Powell hybrid nanofluids with entropy generation

A novel unsteady squeezing flow of a non-Newtonian ternary hybrid nanofluid is investigated by focusing Eyring–Powell fluid rheology in an infinite channel. The nanofluid consists of AA1072, AA1075, and ZrO2 nanoparticles suspended in blood...

Predicting crude oil returns and trading position: evidence from news sentiment

We study the effectiveness of textual information in predicting the returns of crude oil futures and understanding the behavior of market participants. Using a machine learning method to extract oil market sentiment from news articles, we f...

Evaluation of PCA with variable selection for cluster typological domains

Abstract The modeling of mineral deposits has been improved over the years with the incorporation of mineralogical and metallurgical information obtained from drilling samples that make up the pillars for the construction of resource models...

Unsupervised Machine Learning for Improved Delaunay Triangulation

Physical oceanography models rely heavily on grid discretization. It is known that unstructured grids perform well in dealing with boundary fitting problems in complex nearshore regions. However, it is time-consuming to find a set of unstru...

Aerosol Optical Depth Retrieval from MODIS Using a Physically Informed Machine Learning Framework

Retrieving aerosol optical depth (AOD) over land remains challenging because the relatively weak aerosol signal in top-of-atmosphere (TOA) observations must be separated from strong and spatially heterogeneous surface reflectance. Here, we ...

Machine Learning in Antenna Design: An Overview on Machine Learning Concept and Algorithms

With the growth and wide variety of available data, advanced processing, and affordable data storage, machine learning is witnessing great attention in finding optimized solutions in various fields. Machine learning techniques are currently...

The security of machine learning

Machine learning’s ability to rapidly evolve to changing and complex situations has helped it become a fundamental tool for computer security. That adaptability is also a vulnerability: attackers can exploit machine learning systems. We pre...

Machine Learning into Metaheuristics

During the past few years, research in applying machine learning (ML) to design efficient, effective, and robust metaheuristics has become increasingly popular. Many of those machine learning-supported metaheuristics have generated high-qua...

Machine Learning in Medicine

Interview with Dr. Isaac Kohane on machine learning in medicine. (16:31)Download In this view of the future of medicine, patient–provider interactions are informed and supported by massive amounts of data from interactions with similar pati...

How Machine Learning Will Transform Biomedicine

A survey on machine learning for data fusion

Machine Learning in Medicine

Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular va...

Machine Learning, a Probabilistic Perspective

Kevin P. MurphyHardcover: 1104 pagesYear: 2012Publisher: The MIT PressISBN-13: 978-0262018029I have to admit the rather embarrassing fact that Machine Learning, a Probabilistic Perspective is the f......

Introduction to Machine Learning (Adaptive Computation and Machine Learning)

Automated Machine Learning

This open access book gives the first comprehensive overview of general methods in Automatic Machine Learning, AutoML, collects descriptions of existing AutoML systems based on these methods, and discusses the first international challenge ...