Abnormal Events and Behavior Detection in Crowd Scenes Based on Deep Learning and Neighborhood Component Analysis Feature Selection

2020 ◽  
pp. 258-267
Author(s):  
Alaa Atallah Almazroey ◽  
Salma Kammoun Jarraya
Keyword(s):  
Feature Selection ◽  
Deep Learning ◽  
Component Analysis ◽  
Behavior Detection ◽  
And Behavior ◽  
Abnormal Events

Motion-shape-based deep learning approach for divergence behavior detection in high-density crowd

2021 ◽  
Author(s):  
Muhammad Umer Farooq ◽  
Mohamad Naufal M. Saad ◽  
Sultan Daud Khan
Keyword(s):  
Deep Learning ◽  
High Density ◽  
Learning Approach ◽  
Behavior Detection

Feature Selection for Classification using Principal Component Analysis and Information Gain

2021 ◽  
Vol 174 ◽  
pp. 114765 ◽  
Author(s):  
Erick Odhiambo Omuya ◽  
George Onyango Okeyo ◽  
Michael Waema Kimwele
Keyword(s):  
Principal Component Analysis ◽  
Feature Selection ◽  
Information Gain ◽  
Principal Component ◽  
Component Analysis ◽  
Selection For

scholarly journals Deep Learning‐based Classification of Resting‐state fMRI Independent‐component Analysis

2021 ◽  
Author(s):  
Victor Nozais ◽  
Philippe Boutinaud ◽  
Violaine Verrecchia ◽  
Marie-Fateye Gueye ◽  
Pierre-Yves Hervé ◽  
...  
Keyword(s):  
Deep Learning ◽  
Independent Component Analysis ◽  
Resting State ◽  
Resting State Fmri ◽  
Component Analysis ◽  
Independent Component

Enhancement of email spam detection using improved deep learning algorithms for cyber security

2021 ◽  
pp. 1-34
Author(s):  
Kadam Vikas Samarthrao ◽  
Vandana M. Rohokale
Keyword(s):  
Feature Selection ◽  
Deep Learning ◽  
Visual Features ◽  
Spam Detection ◽  
Learning Approaches ◽  
Learning Technique ◽  
Text Features ◽  
Optimal Feature Selection ◽  
Optimal Feature ◽  
Email Spam

Email has sustained to be an essential part of our lives and as a means for better communication on the internet. The challenge pertains to the spam emails residing a large amount of space and bandwidth. The defect of state-of-the-art spam filtering methods like misclassification of genuine emails as spam (false positives) is the rising challenge to the internet world. Depending on the classification techniques, literature provides various algorithms for the classification of email spam. This paper tactics to develop a novel spam detection model for improved cybersecurity. The proposed model involves several phases like dataset acquisition, feature extraction, optimal feature selection, and detection. Initially, the benchmark dataset of email is collected that involves both text and image datasets. Next, the feature extraction is performed using two sets of features like text features and visual features. In the text features, Term Frequency-Inverse Document Frequency (TF-IDF) is extracted. For the visual features, color correlogram and Gray-Level Co-occurrence Matrix (GLCM) are determined. Since the length of the extracted feature vector seems to the long, the optimal feature selection process is done. The optimal feature selection is performed by a new meta-heuristic algorithm called Fitness Oriented Levy Improvement-based Dragonfly Algorithm (FLI-DA). Once the optimal features are selected, the detection is performed by the hybrid learning technique that is composed of two deep learning approaches named Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN). For improving the performance of existing deep learning approaches, the number of hidden neurons of RNN and CNN is optimized by the same FLI-DA. Finally, the optimized hybrid learning technique having CNN and RNN classifies the data into spam and ham. The experimental outcomes show the ability of the proposed method to perform the spam email classification based on improved deep learning.

Driver drowsiness and behavior detection in prolonged conditionally automated drives

2016 ◽  
Author(s):  
Jurgen Schmidt ◽  
Christian Braunagel ◽  
Wolfgang Stolzmann ◽  
Katja Karrer-Gauss
Keyword(s):  
Behavior Detection ◽  
Driver Drowsiness ◽  
And Behavior
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