Epidemiology (Cambridge, Mass.)
Oct 25, 2023
Causal inference from observational data requires untestable identification assumptions. If these assumptions apply, machine learning methods can be used to study complex forms of causal effect heterogeneity. Recently, several machine learning method...
Sensors (Basel, Switzerland)
Oct 24, 2023
The assessment of food and industrial crops during harvesting is important to determine the quality and downstream processing requirements, which in turn affect their market value. While machine learning models have been developed for this purpose, t...
IEEE transactions on neural networks and learning systems
Oct 5, 2023
Data production has followed an increased growth in the last years, to the point that traditional or batch machine-learning (ML) algorithms cannot cope with the sheer volume of generated data. Stream or online ML presents itself as a viable solution ...
Micron (Oxford, England : 1993)
Oct 1, 2023
The Golgi body is a critical organelle in eukaryotic cells responsible for processing and modifying proteins and lipids. Under certain conditions, such as stress, disease, or ageing, the Golgi structure alters. Therefore, understanding the mechanisms...
Neural networks : the official journal of the International Neural Network Society
Sep 25, 2023
This article presents a learning algorithm for dendrite morphological neurons (DMN) based on stochastic gradient descent (SGD). In particular, we focus on a DMN topology that comprises spherical dendrites, smooth maximum activation function nodes, an...
PloS one
Sep 21, 2023
Stock market forecasting is one of the most challenging problems in today's financial markets. According to the efficient market hypothesis, it is almost impossible to predict the stock market with 100% accuracy. However, Machine Learning (ML) method...
Tropical animal health and production
Sep 19, 2023
This study aimed to predict Blackbelly sheep carcass tissue composition using ultrasound measurements and machine learning models. The models evaluated were decision trees, random forests, support vector machines, and multi-layer perceptrons and were...
BMC neuroscience
Sep 15, 2023
Previous studies have demonstrated the potential of machine learning (ML) in classifying physical pain from non-pain states using electroencephalographic (EEG) data. However, the application of ML to EEG data to categorise the observation of pain ver...
BMC medical informatics and decision making
Sep 9, 2023
BACKGROUND: Food frequency questionnaires (FFQs) are one of the most useful tools for studying and understanding diet-disease relationships. However, because FFQs are self-reported data, they are susceptible to response bias, social desirability bias...
Journal of environmental management
Sep 7, 2023
As a non-linear phenomenon that varies along with agro-climatic conditions alongside many other factors, Evapotranspiration (ET) process represents a complexity when be assessed especially if there is a data scarcity in the weather data. However, eve...