Automated Violence Detection in Video Crowd Using Spider Monkey-Grasshopper Optimization Oriented Optimal Feature Selection and Deep Neural Network

2022 ◽  
Author(s):  
Anuja Jana Naik ◽  
M. T. Gopalakrishna
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Deep Neural Network ◽  
Spider Monkey ◽  
Violence Detection ◽  
Optimal Feature Selection ◽  
Grasshopper Optimization ◽  
Optimal Feature

scholarly journals OFS-NN: An Effective Phishing Websites Detection Model Based on Optimal Feature Selection and Neural Network

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 73271-73284 ◽  
Author(s):  
Erzhou Zhu ◽  
Yuyang Chen ◽  
Chengcheng Ye ◽  
Xuejun Li ◽  
Feng Liu
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Detection Model ◽  
Model Based ◽  
Optimal Feature Selection ◽  
Optimal Feature

scholarly journals Automated Human Activity Recognition by Colliding Bodies Optimization-based Optimal Feature Selection with Recurrent Neural Network (RNN)

2020 ◽  
Author(s):  
Pankaj Khatiwada ◽  
Matrika Subedi ◽  
Ayan Chatterjee ◽  
Martin Wulf Gerdes
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Activity Recognition ◽  
Human Activity ◽  
Recurrent Neural Network ◽  
Human Activity Recognition ◽  
Colliding Bodies Optimization ◽  
Proposed Model ◽  
Optimal Feature Selection ◽  
Optimal Feature

— In a smart healthcare system," Human Activity Recognition (HAR)" is considered as an efficient approach in pervasive computing from activity sensor readings. The "Ambient Assisted Living (AAL)" in the home or community helps the people to provide independent care and enhanced living quality. However, many AAL models are restricted to multiple factors that include both the computational cost and system complexity. Moreover, the HAR concept has more relevance because of its applications, such as content-based video search, sports play analysis, crowd behavior prediction systems, patient monitoring systems, and surveillance systems. This paper attempts to implement the HAR system using a popular deep learning algorithm, namely "Recurrent Neural Network (RNN)" with the activity data collected from smart activity sensors over time, and it is publicly available in the "UC Irvine Machine Learning Repository (UCI)". The proposed model involves three processes: (1) data collection, (b) optimal feature learning, and (c) activity recognition. The data gathered from the benchmark repository was initially subjected to optimal feature selection that helped to select the most significant features. The proposed optimal feature selection method is based on a new meta-heuristic algorithm called "Colliding Bodies Optimization (CBO)". An objective function derived from the recognition accuracy has been used for accomplishing the optimal feature selection. The proposed model on the concerned benchmark dataset outperformed the conventional models with enhanced performance.

Sign in / Sign up

Export Citation Format

Share Document