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An overview on the advancements of support vector machine models in healthcare applications: a review
Support vector machines (SVMs) are well-known machine learning algorithms for
classification and regression applications. In the healthcare domain, they have been used …
classification and regression applications. In the healthcare domain, they have been used …
A rapid method to predict type and adulteration of coconut milk by near-infrared spectroscopy combined with machine learning and chemometric tools
Coconut milk is a soft target for adulterators owing to its simplicity of chemical composition.
Professionals and consumers want to control the originalitas of coconut milk, while sellers …
Professionals and consumers want to control the originalitas of coconut milk, while sellers …
A hyper-parameter tuning approach for cost-sensitive support vector machine classifiers
In machine learning, hyperparameter tuning is strongly useful to improve model
performance. In our research, we concentrate our attention on classifying imbalanced data …
performance. In our research, we concentrate our attention on classifying imbalanced data …
An integrated system of multifaceted machine learning models to predict if and when hospital-acquired pressure injuries (bedsores) occur
Hospital-Acquired Pressure Injury (HAPI), known as bedsore or decubitus ulcer, is one of the
most common health conditions in the United States. Machine learning has been used to …
most common health conditions in the United States. Machine learning has been used to …
A hybrid system of Braden scale and machine learning to predict hospital-acquired pressure injuries (bedsores): a retrospective observational cohort study
Background: The Braden Scale is commonly used to determine Hospital-Acquired Pressure
Injuries (HAPI). However, the volume of patients who are identified as being at risk stretches …
Injuries (HAPI). However, the volume of patients who are identified as being at risk stretches …
[HTML][HTML] Hyper-parameter optimization of stacked asymmetric auto-encoders for automatic personality traits perception
In this work, a method for automatic hyper-parameter tuning of the stacked asymmetric auto-
encoder is proposed. In previous work, the deep learning ability to extract personality …
encoder is proposed. In previous work, the deep learning ability to extract personality …
[HTML][HTML] Cost-sensitive models to predict risk of cardiovascular events in patients with chronic heart failure
Chronic heart failure (CHF) is a clinical syndrome characterised by symptoms and signs due
to structural and/or functional abnormalities of the heart. CHF confers risk for cardiovascular …
to structural and/or functional abnormalities of the heart. CHF confers risk for cardiovascular …
A new approach data processing: density-based spatial clustering of applications with noise (DBSCAN) clustering using game-theory
Due to the unpredictable growth of data in various fields, rapid clustering of big data is
seriously needed in order to identify the hidden structure of data and discover the …
seriously needed in order to identify the hidden structure of data and discover the …
Support Vector Machines: Unveiling the Power and Versatility of SVMs in Modern Machine Learning
Support vector machines, or SVMs, have become a really big deal in machine learning
because of how good they are at classification and regression problems. This article …
because of how good they are at classification and regression problems. This article …
Hybrid neural network and evolutionary model for detection of rice plant disease
Rice production is still the most important thing for food needs in Indonesia. However, rice
plants cannot be separated from pests and diseases, especially diseases in rice plants. Rice …
plants cannot be separated from pests and diseases, especially diseases in rice plants. Rice …