AIMC Topic: Linear Models
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Integrating traditional QSAR and read-across-based regression models for predicting potential anti-leishmanial azole compounds.
Molecular diversity
Dec 10, 2024
Leishmaniasis, a neglected tropical disease caused by various Leishmania species, poses a significant global health challenge, especially in resource-limited regions. Visceral Leishmaniasis (VL) stands out among its severe manifestations, and current...
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...
Application of machine-learning methods in age-at-death estimation from 3D surface scans of the adult acetabulum.
Forensic science international
Oct 28, 2024
OBJECTIVE: Age-at-death estimation is usually done manually by experts. As such, manual estimation is subjective and greatly depends on the past experience and proficiency of the expert. This becomes even more critical if experts need to evaluate ind...
Prognostic insights after surgery for advances in understanding signet ring cell gastric cancer: a machine learning approach.
Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract
Oct 3, 2024
BACKGROUND: Signet ring cell (SRC) gastric carcinoma is traditionally associated with a poor prognosis. However, the literature has presented contradictory results. Linear models are the standard statistical tools typically used to study these condit...
scCrab: A Reference-Guided Cancer Cell Identification Method based on Bayesian Neural Networks.
Interdisciplinary sciences, computational life sciences
Sep 30, 2024
Cancer is a significant global public health concern, where early detection can greatly enhance curative outcomes. Therefore, the identification of cancer cells holds significant importance as the primary method for cancer diagnosis. The advancement ...
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