Deep Learning Techniques for Koala Activity Detection

2018 ◽  
Author(s):  
Ivan Himawan ◽  
Michael Towsey ◽  
Bradley Law ◽  
Paul Roe
Keyword(s):  
Deep Learning ◽  
Activity Detection ◽  
Learning Techniques

scholarly journals Intelligent Video Surveillance using Deep Learning

2020 ◽  
Vol 9 (3) ◽  
pp. 1314-1317
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Object Detection ◽  
Video Analysis ◽  
Research Area ◽  
Activity Detection ◽  
Big Data Applications ◽  
Learning Techniques ◽  
Area Surveillance ◽  
Human Activity Detection

Now days, Big data applications are having most of the importance and space in industry and research area. Surveillance videos are a major contribution to unstructured big data. The main objective of this paper is to give brief about video analysis using deep learning techniques in order to detect suspicious activities. Our main focus is on applications of deep learning techniques in detection the count, no of involved persons and the activity going on in a crowd considering all conditions [9]. This video analysis helps us to achieve security. Security can be defined in different terms like identification of theft, detecting violence etc. Suspicious Human Activity Detection is simply the process of detection of unusual (abnormal)l human activities . For this we need to convert the video into frames and processing these frames helps us to analyze the persons and their activities. There are two modules in this system first one Object Detection Module and Second one is Activity Detection Module .Object detection module detects whether the object is present or not. After detecting the object the next module is going to check whether the activity is suspicious or not.

Real Time Gender and Age Prediction using Deep Learning Techniques

2020 ◽  
Vol 9 (6) ◽  
pp. 797-801
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Real Time ◽  
Vital Role ◽  
Learning Techniques ◽  
Gender And Age ◽  
The Face ◽  
And Gender ◽  
Gender Detection ◽  
Age Prediction

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.

scholarly journals Deep Learning-Based Growth Prediction System: A Use Case of China Agriculture

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1551
Author(s):  
Tamoor Khan ◽  
Jiangtao Qiu ◽  
Hafiz Husnain Raza Sherazi ◽  
Mubashir Ali ◽  
Sukumar Letchmunan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Crop Production ◽  
Agricultural Development ◽  
Production Efficiency ◽  
Fruit Production ◽  
National Bureau ◽  
System A ◽  
Future Production ◽  
Learning Techniques ◽  
Agricultural Yields

Agricultural advancements have significantly impacted people’s lives and their surroundings in recent years. The insufficient knowledge of the whole agricultural production system and conventional ways of irrigation have limited agricultural yields in the past. The remote sensing innovations recently implemented in agriculture have dramatically revolutionized production efficiency by offering unparalleled opportunities for convenient, versatile, and quick collection of land images to collect critical details on the crop’s conditions. These innovations have enabled automated data collection, simulation, and interpretation based on crop analytics facilitated by deep learning techniques. This paper aims to reveal the transformative patterns of old Chinese agrarian development and fruit production by focusing on the major crop production (from 1980 to 2050) taking into account various forms of data from fruit production (e.g., apples, bananas, citrus fruits, pears, and grapes). In this study, we used production data for different fruits grown in China to predict the future production of these fruits. The study employs deep neural networks to project future fruit production based on the statistics issued by China’s National Bureau of Statistics on the total fruit growth output for this period. The proposed method exhibits encouraging results with an accuracy of 95.56% calculating by accuracy formula based on fruit production variation. Authors further provide recommendations on the AGR-DL (agricultural deep learning) method being helpful for developing countries. The results suggest that the agricultural development in China is acceptable but demands more improvement and government needs to prioritize expanding the fruit production by establishing new strategies for cultivators to boost their performance.

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