2 research outputs found
A Quantum Convolutional Neural Network Approach for Object Detection and Classification
This paper presents a comprehensive evaluation of the potential of Quantum
Convolutional Neural Networks (QCNNs) in comparison to classical Convolutional
Neural Networks (CNNs) and Artificial / Classical Neural Network (ANN) models.
With the increasing amount of data, utilizing computing methods like CNN in
real-time has become challenging. QCNNs overcome this challenge by utilizing
qubits to represent data in a quantum environment and applying CNN structures
to quantum computers. The time and accuracy of QCNNs are compared with
classical CNNs and ANN models under different conditions such as batch size and
input size. The maximum complexity level that QCNNs can handle in terms of
these parameters is also investigated. The analysis shows that QCNNs have the
potential to outperform both classical CNNs and ANN models in terms of accuracy
and efficiency for certain applications, demonstrating their promise as a
powerful tool in the field of machine learning
Noise removal methods on ambulatory EEG: A Survey
Over many decades, research is being attempted for the removal of noise in
the ambulatory EEG. In this respect, an enormous number of research papers is
published for identification of noise removal, It is difficult to present a
detailed review of all these literature. Therefore, in this paper, an attempt
has been made to review the detection and removal of an noise. More than 100
research papers have been discussed to discern the techniques for detecting and
removal the ambulatory EEG. Further, the literature survey shows that the
pattern recognition required to detect ambulatory method, eye open and close,
varies with different conditions of EEG datasets. This is mainly due to the
fact that EEG detected under different conditions has different
characteristics. This is, in turn, necessitates the identification of pattern
recognition technique to effectively distinguish EEG noise data from a various
condition of EEG data