scholarly journals Artificial Neural Networks for Airport Runway Safety Systems

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Denis Bekasov
Keyword(s):  
Neural Networks ◽  
Moving Objects ◽  
Subject Area ◽  
Original Method ◽  
Surveillance Systems ◽  
Security Threats ◽  
Safety Systems ◽  
Airport Runway ◽  
Runway Safety

This paper presents the analysis of the existing approaches to ensuring the safety of aircraft`s takeoff and landing at airport runways using video surveillance systems. The subject area is formalized, and security threats and measures to prevent them are assessed. Optional architecture of the system designed for detection and classification of moving objects in the airport runway area is presented. The architecture is based on Neural Networks with AI elements. Also the original method of runway objects’ trajectory tracking is proposed. And finally, the research results of the applicability of the proposed architecture are presented.

Detection and Classification of Public Security Threats in the Philippines Using Neural Networks

2020 ◽  
Author(s):  
Marielet Guillermo ◽  
Rogelio Ruzcko Tobias ◽  
Luigi Carlo De Jesus ◽  
Robert Kerwin Billones ◽  
Edwin Sybingco ◽  
...  
Keyword(s):  
Neural Networks ◽  
The Philippines ◽  
Public Security ◽  
Security Threats

scholarly journals Classification of Rigid and Non-Rigid Objects Using CNN

2021 ◽  
Vol 35 (4) ◽  
pp. 341-347
Author(s):  
Aparna Gullapelly ◽  
Barnali Gupta Banik
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Moving Objects ◽  
Binary Classification ◽  
High Accuracy ◽  
Learning System ◽  
Data Set ◽  
Rigid Objects ◽  
Soft Objects

Classifying moving objects in video surveillance can be difficult, and it is challenging to classify hard and soft objects with high Accuracy. Here rigid and non-rigid objects are limited to vehicles and people. CNN is used for the binary classification of rigid and non-rigid objects. A deep-learning system using convolutional neural networks was trained using python and categorized according to their appearance. The classification is supplemented by the use of a data set, which contains two classes of images that are both rigid and not rigid that differ by illuminations.

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

Technique of scientific and technical forecasting using neural networks

2018 ◽  
pp. 103-110
Author(s):  
V. V. Nefedev
Keyword(s):  
Mathematical Modeling ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Original Method ◽  
Forecasting Methods ◽  
Expert Assessments ◽  
Artificial Neural

For the definition and implementation of breakthrough technologies the most important is the role of scientific and technical forecasting. Well-known forecasting methods based on extrapolation, expert assessments and mathematical modeling are not universal and have a number of significant disadvantages. The article proposes an original method of scientific and technical forecasting based on the use of the methodology of artificial neural networks. 

CLASSIFICATION OF SOUNDS OF ASTHMATIC BREATHING USING NEURAL NETWORKS

2018 ◽  
Author(s):  
G. К. Berdibaeva ◽  
◽  
O. N. Bodin ◽  
D. S. Firsov ◽  
◽  
...  
Keyword(s):  
Neural Networks

Classification of Cloud Systems Cyber-security Threats and Solutions Directives

2017 ◽  
Vol 2 (3) ◽  
pp. 1
Author(s):  
Hanane Bennasar ◽  
Mohammad Essaaidi ◽  
Ahmed Bendahmane ◽  
Jalel Benothmane
Keyword(s):  
Cloud Computing ◽  
Cyber Security ◽  
Data Security ◽  
Denial Of Service ◽  
Security Threats ◽  
Huge Number ◽  
Open Problems ◽  
Long Period ◽  
Near Future

Cloud computing cyber security is a subject that has been in top flight for a long period and even in near future. However, cloud computing permit to stock up a huge number of data in the cloud stockage, and allow the user to pay per utilization from anywhere via any terminal equipment. Among the major issues related to Cloud Computing security, we can mention data security, denial of service attacks, confidentiality, availability, and data integrity. This paper is dedicated to a taxonomic classification study of cloud computing cyber-security. With the main objective to identify the main challenges and issues in this field, the different approaches and solutions proposed to address them and the open problems that need to be addressed.

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