AIMC Topic: Linear Models
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A hybrid network using transformer with modified locally linear embedding and sliding window convolution for EEG decoding.
Journal of neural engineering
Jan 6, 2025
. Brain-computer interface(BCI) is leveraged by artificial intelligence in EEG signal decoding, which makes it possible to become a new means of human-machine interaction. However, the performance of current EEG decoding methods is still insufficient...
Analyzing Secondary Cancer Risk: A Machine Learning Approach.
Asian Pacific journal of cancer prevention : APJCP
Jan 1, 2025
OBJECTIVE: Addressing the rising cancer rates through timely diagnosis and treatment is crucial. Additionally, cancer survivors need to understand the potential risk of developing secondary cancer (SC), which can be influenced by several factors incl...
Do machine learning methods solve the main pitfall of linear regression in dental age estimation?
Forensic science international
Dec 20, 2024
INTRODUCTION: Age estimation is crucial in forensic and anthropological fields. Teeth, are valued for their resilience to environmental factors and their preservation over time, making them essential for age estimation when other skeletal remains det...
Mixed-effects neural network modelling to predict longitudinal trends in fasting plasma glucose.
BMC medical research methodology
Dec 20, 2024
BACKGROUND: Accurate fasting plasma glucose (FPG) trend prediction is important for management and treatment of patients with type 2 diabetes mellitus (T2DM), a globally prevalent chronic disease. (Generalised) linear mixed-effects (LME) models and m...
A Multi-Task Deep Feature Selection Method for Brain Imaging Genetics.
IEEE/ACM transactions on computational biology and bioinformatics
Dec 10, 2024
Using brain imaging quantitative traits (QTs) for identifying genetic risk factors is an important research topic in brain imaging genetics. Many efforts have been made for this task via building linear models between imaging QTs and genetic factors ...
Optimizing critical quality attributes of fast disintegrating tablets using artificial neural networks: a scientific benchmark study.
Drug development and industrial pharmacy
Dec 8, 2024
OBJECTIVE: The objective of this study is to create predictive models utilizing machine learning algorithms, including Artificial Neural Networks (ANN), k-nearest neighbor (kNN), support vector machines (SVM), and linear regression, to predict critic...
Fatal fall from a height: is it possible to apply artificial intelligence techniques for height estimation?
International journal of legal medicine
Dec 7, 2024
Fall from a height trauma is characterized by a multiplicity of injuries, related to multiple factors. The height of the fall is the factor that most influences the kinetic energy of the body and appears to be one of the factors that most affects the...
Machine learning and regression in the management of runoff in bauxite mines under rehabilitation.
Environmental science and pollution research international
Dec 2, 2024
Accurate and reliable forecasting of monthly runoff considering several years of rehabilitation helps in planning and managing the water resources system of bauxite mining areas. A combination of linear regression models and artificial intelligence w...
A non-linear modelling approach to predict the dissolution profile of extended-release tablets.
European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
Nov 28, 2024
This study proposes a novel non-linear modelling approach to predict the dissolution profiles of extended-release tablets, by combining a full-factorial design, curve fitting to the dissolution profiles, and artificial neural networks (ANN), with lin...
Performance of artificial neural network compared to multi-linear regression in prediction of countermovement jump height.
Journal of bodywork and movement therapies
Nov 5, 2024
Previous research has used primarily linear regression models to predict jump height and establish contributors of performance. The purpose of this study was to compare the performance of artificial neural network (ANN) and multi-linear regression (M...
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