AIMC Topic: Forecasting

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Machine learning to predict unintended pregnancy among reproductive-age women in Ethiopia: evidence from EDHS 2016.

BMC women's health
BACKGROUND: An unintended pregnancy is a pregnancy that is either unwanted or mistimed, such as when it occurs earlier than desired. It is one of the most important issues the public health system is currently facing, and it comes at a significant co...

An enhanced drought forecasting in coastal arid regions using deep learning approach with evaporation index.

Environmental research
Coastal arid regions are similar to deserts, where it receives significantly less rainfall, less than 10 cm. Perhaps the world's worst natural disaster, coastal area droughts, can only be detected using reliable monitoring systems. Creating a reliabl...

Forecasting stock prices changes using long-short term memory neural network with symbolic genetic programming.

Scientific reports
This study introduces an augmented Long-Short Term Memory (LSTM) neural network architecture, integrating Symbolic Genetic Programming (SGP), with the objective of forecasting cross-sectional price returns across a comprehensive dataset comprising 45...

Forecasting emergent risks in advanced AI systems: an analysis of a future road transport management system.

Ergonomics
Artificial Intelligence (AI) is being increasingly implemented within road transport systems worldwide. Next generation of AI, Artificial General Intelligence (AGI) is imminent, and is anticipated to be more powerful than current AI. AGI systems will...

Meteorological factors cannot be ignored in machine learning-based methods for predicting dengue, a systematic review.

International journal of biometeorology
In recent years, there has been a rapid increase in the application of machine learning methods about predicting the incidence of dengue fever. However, the predictive factors and models employed in different studies vary greatly. Hence, we conducted...

Prediction of early-onset colorectal cancer mortality rates in the United States using machine learning.

Cancer medicine
INTRODUCTION: The current study, focusing on a significant US (United States) colorectal cancer (CRC) burden, employs machine learning for predicting future rates among young population.

The rise of artificial intelligence and the future of scientific publication.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie

BOO-ST and CBCEC: two novel hybrid machine learning methods aim to reduce the mortality of heart failure patients.

Scientific reports
Heart failure (HF) is a leading cause of mortality worldwide. Machine learning (ML) approaches have shown potential as an early detection tool for improving patient outcomes. Enhancing the effectiveness and clinical applicability of the ML model nece...

Explainable hierarchical clustering for patient subtyping and risk prediction.

Experimental biology and medicine (Maywood, N.J.)
We present a pipeline in which machine learning techniques are used to automatically identify and evaluate subtypes of hospital patients admitted between 2017 and 2021 in a large UK teaching hospital. Patient clusters are determined using routinely c...

Beyond multilayer perceptrons: Investigating complex topologies in neural networks.

Neural networks : the official journal of the International Neural Network Society
This study delves into the crucial aspect of network topology in artificial neural networks (NNs) and its impact on model performance. Addressing the need to comprehend how network structures influence learning capabilities, the research contrasts tr...