Academic Articles & Research Papers

Machine Learning Algorithms - A Review

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without being explicitly programmed. Learning algorithms in many applications that's we make use of dail...

Opportunities and Challenges for Machine Learning in Materials Science

Advances in machine learning have impacted myriad areas of materials science, such as the discovery of novel materials and the improvement of molecular simulations, with likely many more important developments to come. Given the rapid chang...

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...

Source apportionment and stochastic evaluation of soil contamination and human health risks of potentially toxic elements in Nangodi, northern Ghana

Abstract Artisanal and Small-Scale Mining (ASM) is a critical driver of soil contamination in West Africa, yet quantifying the relative contributions of natural and anthropogenic sources remains a major challenge. This study presents an int...

Accelerated Prediction of Terahertz Performance Metrics in GaN IMPATT Sources via Artificial Neural Networks

This work investigates the application of artificial neural network (ANN)-based regression models to predict the static and dynamic characteristics of GaN impact avalanche transit time (IMPATT) sources in the terahertz (THz) frequency regim...

Machine Learning in Earthquake Seismology

Machine learning (ML) is a collection of methods used to develop understanding and predictive capability by learning relationships embedded in data. ML methods are becoming the dominant approaches for many tasks in seismology. ML and data m...

Machine learning in geosciences and remote sensing

Learning incorporates a broad range of complex procedures. Machine learning (ML) is a subdivision of artificial intelligence based on the biological learning process. The ML approach deals with the design of algorithms to learn from machine...

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...

Encyclopedia of Machine Learning and Data Mining

Machine learning applications in epilepsy

Machine learning leverages statistical and computer science principles to develop algorithms capable of improving performance through interpretation of data rather than through explicit instructions. Alongside widespread use in image recogn...

Machine Learning and Law

This Article explores the application of machine learning techniques within the practice of law. Broadly speaking “machine learning” refers to computer algorithms that have the ability to “learn” or improve in performance over time on some ...

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...

Machine learning and data mining

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

Simulation-based Bayesian deep learning enables uncertainty-aware tumor fraction estimation in cell-free DNA.

Taxonomy-based data representation for data mining: an example of the magnitude of risk associated with H. pylori infection

Abstract Background The amount of available and potentially significant data describing study subjects is ever growing with the introduction and integration of different registries and data banks. The single specific attribute of these data...

Machine learning in bioinformatics

This article reviews machine learning methods for bioinformatics. It presents modelling methods, such as supervised classification, clustering and probabilistic graphical models for knowledge discovery, as well as deterministic and stochast...

Introduction to Machine Learning

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict ...

The voice of depression: speech features as biomarkers for major depressive disorder

Abstract Background Psychiatry faces a challenge due to the lack of objective biomarkers, as current assessments are based on subjective evaluations. Automated speech analysis shows promise in detecting symptom severity in depressed patient...

Defining artificial intelligence for librarians

The aim of the paper is to define Artificial Intelligence (AI) for librarians by examining general definitions of AI, analysing the umbrella of technologies that make up AI, defining types of use case by area of library operation, and then ...

Investigating the Role of Artificial Intelligence in Management of Diabetes in Iran: A Systematic Review

Background and Aim: Diabetes is one of the most common metabolic diseases in Iran and the fifth leading cause of death all over the world. Its spread around the world has created new methods in biomedical research, including artificial inte...