AIMC Topic: Linear Models

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Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels.

Sensors (Basel, Switzerland)
Unlimited access to information and data sharing wherever and at any time for anyone and anything is a fundamental component of fifth-generation (5G) wireless communication and beyond. Therefore, it has become inevitable to exploit the super-high fre...

Metamodeling for Policy Simulations with Multivariate Outcomes.

Medical decision making : an international journal of the Society for Medical Decision Making
PURPOSE: Metamodels are simplified approximations of more complex models that can be used as surrogates for the original models. Challenges in using metamodels for policy analysis arise when there are multiple correlated outputs of interest. We devel...

Empirical analyses and simulations showed that different machine and statistical learning methods had differing performance for predicting blood pressure.

Scientific reports
Machine learning is increasingly being used to predict clinical outcomes. Most comparisons of different methods have been based on empirical analyses in specific datasets. We used Monte Carlo simulations to determine when machine learning methods per...

AutoML-ID: automated machine learning model for intrusion detection using wireless sensor network.

Scientific reports
Momentous increase in the popularity of explainable machine learning models coupled with the dramatic increase in the use of synthetic data facilitates us to develop a cost-efficient machine learning model for fast intrusion detection and prevention ...

Trivariate Linear Regression and Machine Learning Prediction of Possible Roles of Efflux Transporters in Estimated Intestinal Permeability Values of 301 Disparate Chemicals.

Biological & pharmaceutical bulletin
A system for predicting apparent bidirectional permeability (P) across Caco-2 cells of diverse chemicals has been reported. The present study aimed to investigate the relationship between in silico-generated P (from apical to basal side, P) for 301 s...

Assessment and Evaluation of Different Machine Learning Algorithms for Predicting Student Performance.

Computational intelligence and neuroscience
Student performance is crucial to the success of tertiary institutions. Especially, academic achievement is one of the metrics used in rating top-quality universities. Despite the large volume of educational data, accurately predicting student perfor...

Multi-Output Selective Ensemble Identification of Nonlinear and Nonstationary Industrial Processes.

IEEE transactions on neural networks and learning systems
A key characteristic of biological systems is the ability to update the memory by learning new knowledge and removing out-of-date knowledge so that intelligent decision can be made based on the relevant knowledge acquired in the memory. Inspired by t...

Machine learning models identify gene predictors of waggle dance behaviour in honeybees.

Molecular ecology resources
The molecular characterization of complex behaviours is a challenging task as a range of different factors are often involved to produce the observed phenotype. An established approach is to look at the overall levels of expression of brain genes-or ...

Automated quality assessment of chest radiographs based on deep learning and linear regression cascade algorithms.

European radiology
OBJECTIVES: Develop and evaluate the performance of deep learning and linear regression cascade algorithms for automated assessment of the image layout and position of chest radiographs.

Not just "big" data: Importance of sample size, measurement error, and uninformative predictors for developing prognostic models for digital interventions.

Behaviour research and therapy
There is strong interest in developing a more efficient mental health care system. Digital interventions and predictive models of treatment prognosis will likely play an important role in this endeavor. This article reviews the application of popular...