Object tracking: A survey
The goal of this article is to review the state-of-the-art tracking methods, classify them into
different categories, and identify new trends. Object tracking, in general, is a challenging …
different categories, and identify new trends. Object tracking, in general, is a challenging …
Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm
The Gravitational Search Algorithm (GSA) is a novel heuristic optimization method based on
the law of gravity and mass interactions. It has been proven that this algorithm has good …
the law of gravity and mass interactions. It has been proven that this algorithm has good …
Application of K-means and genetic algorithms for dimension reduction by integrating SVM for diabetes diagnosis
T Santhanam, MS Padmavathi - Procedia Computer Science, 2015 - Elsevier
Vast amount of data available in health care industry is difficult to handle, hence mining is
necessary to find the necessary pattern and relationship among the features available …
necessary to find the necessary pattern and relationship among the features available …
A deep learning approach for efficiently and accurately evaluating the flow field of supercritical airfoils
The efficient and accurate access to the aerodynamic performance is important for the
design and optimization of supercritical airfoils. The aerodynamic performance is usually …
design and optimization of supercritical airfoils. The aerodynamic performance is usually …
Assessment of computational intelligence and conventional dissolved gas analysis methods for transformer fault diagnosis
Transformers are vital components of power systems as they are situated between energy
generation and consumers and their failure disrupts the use of electrical energy. Therefore …
generation and consumers and their failure disrupts the use of electrical energy. Therefore …
[HTML][HTML] Self-organized operational neural networks with generative neurons
Abstract Operational Neural Networks (ONNs) have recently been proposed to address the
well-known limitations and drawbacks of conventional Convolutional Neural Networks …
well-known limitations and drawbacks of conventional Convolutional Neural Networks …
Operational neural networks
Feed-forward, fully connected artificial neural networks or the so-called multi-layer
perceptrons are well-known universal approximators. However, their learning performance …
perceptrons are well-known universal approximators. However, their learning performance …
Feature selection method based on artificial bee colony algorithm and support vector machines for medical datasets classification
This paper offers a hybrid approach that uses the artificial bee colony (ABC) algorithm for
feature selection and support vector machines for classification. The purpose of this paper is …
feature selection and support vector machines for classification. The purpose of this paper is …
[HTML][HTML] Rule extraction using Recursive-Rule extraction algorithm with J48graft combined with sampling selection techniques for the diagnosis of type 2 diabetes …
Y Hayashi, S Yukita - Informatics in Medicine Unlocked, 2016 - Elsevier
Diabetes is a complex disease that is increasing in prevalence around the world. Type 2
diabetes mellitus (T2DM) accounts for about 90–95% of all diagnosed adult cases of …
diabetes mellitus (T2DM) accounts for about 90–95% of all diagnosed adult cases of …
FFT based ensembled model to predict ranks of higher educational institutions
Predicting international rankings has always been a demanding area for Universities and
Higher Educational Institutions (HEIs) all over the world in the recent decade. In this …
Higher Educational Institutions (HEIs) all over the world in the recent decade. In this …