Academic Articles & Research Papers

Attractor reconstruction by machine learning

A machine-learning approach called "reservoir computing" has been used successfully for short-term prediction and attractor reconstruction of chaotic dynamical systems from time series data. We present a theoretical framework that describes...

Machine learning in chemoinformatics and drug discovery

Chemoinformatics is an established discipline focusing on extracting, processing and extrapolating meaningful data from chemical structures. With the rapid explosion of chemical 'big' data from HTS and combinatorial synthesis, machine learn...

Machine Learning Mastery

This chapter delves into the core principles of machine learning, offering practical insights for effective data processing. From foundational concepts to advanced techniques, the narrative unfolds as a comprehensive guide for harnessing th...

Deciphering the regulatory code of RNA inosine through enzymatic precision mapping and an explainable deep learning model.

Community-centric modeling of citation dynamics explains collective citation patterns across different knowledge systems.

The WorkReach model for urban work location choices through economic complexity and informality.

Improving the Robustness of Odour Recognition with Odour-Image Data Fusion in Open-Air Settings

Odour recognition with low-cost gas sensors is challenging in open-air settings due to the non-specificity of the sensors and environmental variability. This can be mitigated by incorporating additional information into the classification p...

Explainable machine learning for predicting clinical outcomes in HIV/TB co-infection: a comparative retrospective study

Abstract Background HIV/TB co-infection presents substantial public-health challenges, showing greater treatment-failure and mortality rates than tuberculosis alone. Recent advances in machine learning (ML) provide a robust means of identif...

Operational Digital Shadow for Onshore Wind Energy Systems

Accurate, uncertainty-aware estimation of instantaneous wind turbine output is a prerequisite for integrating onshore assets into low-emission energy systems, where operational monitoring, energy-performance verification, and cooperative as...

IoT Device Identification Using Directional Packet Length Sequences and 1D-CNN

With the large-scale application of the Internet of Things (IoT), security issues have become increasingly prominent. Device identification is an effective way to secure IoT environment by quickly identifying the category or model of device...

Using Soft Voting Classifier of Machine Learning Algorithms for Earthquake Prediction

Earthquakes are among the most dangerous natural disasters that can cause major losses to buildings and threaten human lives. The research community is very interested in the topic of earthquakes because they occur suddenly and predicting t...

The association of Coronavirus Disease-19 mortality and prior bacille Calmette-Guerin vaccination: a robust ecological analysis using unsupervised machine learning

Abstract Population-level data have suggested that bacille Calmette-Guerin (BCG) vaccination may lessen the severity of Coronavirus Disease-19 (COVID-19) prompting clinical trials in this area. Some reports have demonstrated conflicting res...

Machine Learning Model Coupled with Graphical User Interface for Predicting Mechanical Properties of Flax Fiber

Machine learning model coupled with graphical user interface was developed to predict mechanical properties of flax fiber. The experiment was conducted using test setup which applies constant rate of loading (CRL). Flax fiber was tested und...

Machine learning for neuroimaging with scikit-learn

Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g., multivariate analysis of activation images or resting-state time series....

Bayesian Reasoning and Machine Learning

Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot ...

Machine learning methods in chemoinformatics

Machine learning algorithms are generally developed in computer science or adjacent disciplines and find their way into chemical modeling by a process of diffusion. Though particular machine learning methods are popular in chemoinformatics ...

Machine learning for combustion

Combustion science is an interdisciplinary study that involves nonlinear physical and chemical phenomena in time and length scales, including complex chemical reactions and fluid flows. Combustion widely supplies energy for powering vehicle...

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...

Multi-omics reveals the DR5-FXR-CHPT1 axis governs hepatocyte pyroptosis via glycerophospholipid remodeling after major partial hepatectomy in male mice.

Development of a Data-Based Machine Learning Model for Classifying and Predicting Property Damage Caused by Fire

Large fires in factories cause severe human casualties and property damage. Thus, preparing more economical and efficient management strategies for fire prevention can significantly improve fire safety. This study deals with property damage...