Automated epileptic seizure detection using improved correlation-based feature selection with random forest classifier
M Mursalin, Y Zhang, Y Chen, NV Chawla - Neurocomputing, 2017 - Elsevier
Abstract Analysis of electroencephalogram (EEG) signal is crucial due to its non-stationary
characteristics, which could lead the way to proper detection method for the treatment of …
characteristics, which could lead the way to proper detection method for the treatment of …
Integrated animal monitoring system with animal detection and classification capabilities: a review on image modality, techniques, applications, and challenges
N Sundaram, SD Meena - Artificial Intelligence Review, 2023 - Springer
The continuous monitoring of animals is crucial for the well-being of both humans and
animals. A comprehensive animal monitoring system must incorporate animal detection …
animals. A comprehensive animal monitoring system must incorporate animal detection …
Animal classification using facial images with score‐level fusion
A real‐world animal biometric system that detects and describes animal life in image and
video data is an emerging subject in machine vision. These systems develop computer …
video data is an emerging subject in machine vision. These systems develop computer …
Discriminative analysis-synthesis dictionary learning for image classification
Dictionary learning has played an important role in the success of sparse representation.
Although discriminative synthesis dictionary learning for sparse representation with a high …
Although discriminative synthesis dictionary learning for sparse representation with a high …
Deep learning of smartphone sensor data for personal health assistance
H Li, M Trocan - Microelectronics Journal, 2019 - Elsevier
This paper strives to construct the relevance between smartphone sensor data and personal
health via a deep learning method. Firstly, the data captured by smartphone sensors is …
health via a deep learning method. Firstly, the data captured by smartphone sensors is …
[HTML][HTML] Improved deep learning based method for molecular similarity searching using stack of deep belief networks
Virtual screening (VS) is a computational practice applied in drug discovery research. VS is
popularly applied in a computer-based search for new lead molecules based on molecular …
popularly applied in a computer-based search for new lead molecules based on molecular …
Group ensemble: Learning an ensemble of convnets in a single convnet
H Chen, A Shrivastava - arXiv preprint arXiv:2007.00649, 2020 - arxiv.org
Ensemble learning is a general technique to improve accuracy in machine learning.
However, the heavy computation of a ConvNets ensemble limits its usage in deep learning …
However, the heavy computation of a ConvNets ensemble limits its usage in deep learning …
Towards effective codebookless model for image classification
The bag-of-features (BoF) model for image classification has been thoroughly studied over
the last decade. Different from the widely used BoF methods which model images with a pre …
the last decade. Different from the widely used BoF methods which model images with a pre …
Epileptic seizure classification using statistical sampling and a novel feature selection algorithm
M Mursalin, SS Islam, MK Noman… - arXiv preprint arXiv …, 2019 - arxiv.org
Epilepsy is a well-known neuronal disorder that can be identified by interpretation of the
electroencephalogram (EEG) signal. Usually, the length of an EEG signal is quite long which …
electroencephalogram (EEG) signal. Usually, the length of an EEG signal is quite long which …
Deep Belief Network for Molecular Feature Selection in Ligand-Based Virtual Screening
It became obvious that it is required to reduce the high-dimensional of data in many data
mining researches and applications. Virtual Screening (VS) is a set of computational …
mining researches and applications. Virtual Screening (VS) is a set of computational …