An Efficient Action Detection Model Using Deep Belief Networks

2019 ◽  
Vol 16 (8) ◽  
pp. 3232-3236
Author(s):  
L. K. Joshila Grace ◽  
K. Rahul ◽  
P. S. Sidharth
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Action Recognition ◽  
Machine Learning Techniques ◽  
Action Detection ◽  
Detection Model ◽  
Research Areas ◽  
Learning Techniques ◽  
Bounding Boxes ◽  
Living Being

Computer Vision and image processing have gained an enormous advance in the field of machine learning techniques. Some of the major research areas within machine learning are Action detection and Pattern Recognition. Action recognition is a new advancement of pattern recognition approaches where the actions performed by any action or living being is tracked and monitored. Action recognition still encounters some challenges that needs to be looked upon and perform recognize the actions is a very minimal time. Networks like SVM and Neural Networks are used to train the network in such a way they are able to detect a pattern of an action when a new frame is given. In this paper, we have proposed a model which detects patterns of actions from a video or an image. Bounding boxes are used to detect the actions and localize it. Deep Belief Network is used to train the model where numerous images having actions are given as the training set. The performance evaluation was done on the model and it is observed that it detects the actions very accurately when a new image is given to the network.

Overview of Machine Learning Approaches for Wireless Communication

2021 ◽  
pp. 1579-1597
Author(s):  
Tolga Ensari ◽  
Melike Günay ◽  
Yağız Nalçakan ◽  
Eyyüp Yildiz
Keyword(s):  
Machine Learning ◽  
Wireless Communications ◽  
Smart Phones ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Research Areas ◽  
Learning Techniques ◽  
Recent Developments ◽  
Security And Reliability ◽  
Day By Day

Machine learning is one of the most popular research areas, and it is commonly used in wireless communications and networks. Security and fast communication are among of the key requirements for next generation wireless networks. Machine learning techniques are getting more important day-by-day since the types, amount, and structure of data is continuously changing. Recent developments in smart phones and other devices like drones, wearable devices, machines with sensors need reliable communication within internet of things (IoT) systems. For this purpose, artificial intelligence can increase the security and reliability and manage the data that is generated by the wireless systems. In this chapter, the authors investigate several machine learning techniques for wireless communications including deep learning, which represents a branch of artificial neural networks.

scholarly journals Animal species classification using machine learning techniques

2019 ◽  
Vol 277 ◽  
pp. 02033
Author(s):  
Fahad Alharbi ◽  
Abrar Alharbi ◽  
Eiji Kamioka
Keyword(s):  
Machine Learning ◽  
Feature Detection ◽  
Animal Species ◽  
Machine Learning Techniques ◽  
Species Classification ◽  
Research Areas ◽  
Learning Techniques ◽  
Multiple Feature ◽  
The Face ◽  
Great Danger

Animals recognition is one of the research areas in which few effective technologies have been proposed, especially in the predator animals' domain. Predator animals present a great danger to people who are camping or staying in outdoor areas and they are also a menace to livestock. In this paper, a multiple feature detection of predator animals is proposed. This method focuses on the face of the animal, explicitly the eyes and the ears. A database was created by collecting the features of ears and eyes from 10 animals and an experiment was conducted using machine learning techniques such as SVM and MLP to classify them as predators or pets. The evaluation results achieved the classification accuracies of 82% for MLP and 78% for SVM, which justify its effectiveness for the proposed method.

scholarly journals Detecting Website Defacements Based on Machine Learning Techniques and Attack Signatures

Computers ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 35 ◽  
Author(s):  
Xuan Dau Hoang ◽  
Ngoc Tuong Nguyen
Keyword(s):  
Machine Learning ◽  
Web Applications ◽  
False Positive Rate ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Web Pages ◽  
Government Organizations ◽  
Detection Model ◽  
Learning Techniques ◽  
Positive Rate

Defacement attacks have long been considered one of prime threats to websites and web applications of companies, enterprises, and government organizations. Defacement attacks can bring serious consequences to owners of websites, including immediate interruption of website operations and damage of the owner reputation, which may result in huge financial losses. Many solutions have been researched and deployed for monitoring and detection of website defacement attacks, such as those based on checksum comparison, diff comparison, DOM tree analysis, and complicated algorithms. However, some solutions only work on static websites and others demand extensive computing resources. This paper proposes a hybrid defacement detection model based on the combination of the machine learning-based detection and the signature-based detection. The machine learning-based detection first constructs a detection profile using training data of both normal and defaced web pages. Then, it uses the profile to classify monitored web pages into either normal or attacked. The machine learning-based component can effectively detect defacements for both static pages and dynamic pages. On the other hand, the signature-based detection is used to boost the model’s processing performance for common types of defacements. Extensive experiments show that our model produces an overall accuracy of more than 99.26% and a false positive rate of about 0.27%. Moreover, our model is suitable for implementation of a real-time website defacement monitoring system because it does not demand extensive computing resources.

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