TY - JOUR
T1 - Hand gesture classification using a novel CNN-crow search algorithm
AU - Gadekallu, Thippa Reddy
AU - Alazab, Mamoun
AU - Kaluri, Rajesh
AU - Maddikunta, Praveen Kumar Reddy
AU - Bhattacharya, Sweta
AU - Lakshmanna, Kuruva
AU - Parimala, M
PY - 2021
Y1 - 2021
N2 - Human-computer interaction (HCI) and related technologies focus on the implementation of interactive computational systems. The studies in HCI emphasize on system use, creation of new techniques that support user activities, access to information, and ensures seamless communication. The use of artificial intelligence and deep learning-based models has been extensive across various domains yielding state-of-the-art results. In the present study, a crow search-based convolution neural networks model has been implemented in gesture recognition pertaining to the HCI domain. The hand gesture dataset used in the study is a publicly available one, downloaded from Kaggle. In this work, a one-hot encoding technique is used to convert the categorical data values to binary form. This is followed by the implementation of a crow search algorithm (CSA) for selecting optimal hyper-parameters for training of dataset using the convolution neural networks. The irrelevant parameters are eliminated from consideration, which contributes towards enhancement of accuracy in classifying the hand gestures. The model generates 100 percent training and testing accuracy that justifies the superiority of the model against traditional state-of-the-art models.
AB - Human-computer interaction (HCI) and related technologies focus on the implementation of interactive computational systems. The studies in HCI emphasize on system use, creation of new techniques that support user activities, access to information, and ensures seamless communication. The use of artificial intelligence and deep learning-based models has been extensive across various domains yielding state-of-the-art results. In the present study, a crow search-based convolution neural networks model has been implemented in gesture recognition pertaining to the HCI domain. The hand gesture dataset used in the study is a publicly available one, downloaded from Kaggle. In this work, a one-hot encoding technique is used to convert the categorical data values to binary form. This is followed by the implementation of a crow search algorithm (CSA) for selecting optimal hyper-parameters for training of dataset using the convolution neural networks. The irrelevant parameters are eliminated from consideration, which contributes towards enhancement of accuracy in classifying the hand gestures. The model generates 100 percent training and testing accuracy that justifies the superiority of the model against traditional state-of-the-art models.
KW - Crow search algorithm
KW - Hand gesture classification
KW - Human-computer interaction
KW - Convolution neural network
KW - Meta-heuristic algorithms
U2 - 10.1007/s40747-021-00324-x
DO - 10.1007/s40747-021-00324-x
M3 - Article
SN - 2199-4536
VL - 7
SP - 1855
EP - 1868
JO - Complex & Intelligent Systems
JF - Complex & Intelligent Systems
IS - 4
ER -