The Application of Biosignal Feedback for Reducing Cybersickness from Exposure to a Virtual Environment

2008 ◽  
Vol 17 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Young Youn Kim ◽  
Eun Nam Kim ◽  
Min Jae Park ◽  
Kwang Suk Park ◽  
Hee Dong Ko ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Virtual Environment ◽  
Detection System ◽  
Baseline Period ◽  
Field Of View ◽  
Simulator Sickness ◽  
Virtual Navigation ◽  
Electrophysiological Signals ◽  
Simulator Sickness Questionnaire

We examined the efficacy of a new method to reduce cybersickness. A real-time cybersickness detection system was constructed with an artificial neural network whose inputs were the electrophysiological signals of subjects in a virtual environment. The system was equipped with a means of feedback; it temporarily provided a narrow field of view and a message about navigation speed deceleration, both of which acted as feedback outputs whenever electrophysiological inputs signaled the occurrence of cybersickness. This system is named cybersickness relief virtual environment (CRVE). Forty-seven subjects experienced the VR for 9.5 min twice in CRVE and non-CRVE conditions. The results indicated that the frequency of cybersickness and simulator sickness questionnaire scores were lower in the CRVE condition than in the non-CRVE condition. Subjects also showed a higher net increase in tachyarrhythmia from the baseline period to the virtual navigation period in the CRVE condition compared to the non-CRVE condition. These results suggest that a CRVE condition may be a countermeasure against cybersickness.

Optimizing Error Rate in Intrusion Detection System Using Artificial Neural Network Algorithm

2018 ◽  
Vol 6 (9) ◽  
pp. 152
Author(s):  
S. Vijaya Rani ◽  
G. N. K. Suresh Babu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Error Rate ◽  
Learning Process ◽  
Nearest Neighbor ◽  
Detection System ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Artificial Neural

The illegal hackers  penetrate the servers and networks of corporate and financial institutions to gain money and extract vital information. The hacking varies from one computing system to many system. They gain access by sending malicious packets in the network through virus, worms, Trojan horses etc. The hackers scan a network through various tools and collect information of network and host. Hence it is very much essential to detect the attacks as they enter into a network. The methods  available for intrusion detection are Naive Bayes, Decision tree, Support Vector Machine, K-Nearest Neighbor, Artificial Neural Networks. A neural network consists of processing units in complex manner and able to store information and make it functional for use. It acts like human brain and takes knowledge from the environment through training and learning process. Many algorithms are available for learning process This work carry out research on analysis of malicious packets and predicting the error rate in detection of injured packets through artificial neural network algorithms.

Intrusion Detection Using Modern Techniques

2011 ◽  
pp. 259-273
Author(s):  
Tarum Bhaskar ◽  
Narasimha Kamath B.
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Set Theory ◽  
Rough Set ◽  
Intrusion Detection System ◽  
Rough Set Theory ◽  
Detection System ◽  
Mining Tools

Intrusion detection system (IDS) is now becoming an integral part of the network security infrastructure. Data mining tools are widely used for developing an IDS. However, this requires an ability to find the mapping from the input space to the output space with the help of available data. Rough sets and neural networks are the best known data mining tools to analyze data and help solve this problem. This chapter proposes a novel hybrid method to integrate rough set theory, genetic algorithm (GA), and artificial neural network. Our method consists of two stages: First, rough set theory is applied to find the reduced dataset. Second, the results are used as inputs for the neural network, where a GA-based learning approach is used to train the intrusion detection system. The method is characterized not only by using attribute reduction as a pre-processing technique of an artificial neural network but also by an improved learning algorithm. The effectiveness of the proposed method is demonstrated on the KDD cup data.

Offline Signature Verification and Forgery Detection Based on Computer Vision and Fuzzy Logic

2014 ◽  
Vol 3 (4) ◽  
pp. 156
Author(s):  
Gautam S. Prakash ◽  
Shanu Sharma
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Logic ◽  
Detection System ◽  
Structural Parameters ◽  
Signature Verification ◽  
Width Ratio ◽  
Forgery Detection ◽  
Offline Signature Verification ◽  
Artificial Neural

<p>Automated signature verification and forgery detection has many applications in the field of Bank-cheque processing,document  authentication, ATM access etc. Handwritten signatures have proved to be important in authenticating a person's identity, who is signing the document. In this paper a Fuzzy Logic and Artificial Neural Network Based Off-line Signature Verification and Forgery Detection System is presented. As there are unique and important variations in the feature elements of each signature, so in order to match a particular signature with the database, the structural parameters of the signatures along with the local variations in the signature characteristics are used. These characteristics have been used to train the artificial neural network. The system uses the features extracted from the signatures such as centroid, height – width ratio, total area, I<sup>st</sup> and II<sup>nd</sup> order derivatives, quadrant areas etc. After the verification of the signature the angle features are used in fuzzy logic based system for forgery detection.</p>

scholarly journals A multilayer perceptron artificial neural network approach for improving the accuracy of intrusion detection systems

2020 ◽  
Vol 9 (4) ◽  
pp. 609
Author(s):  
Abdulrahman Jassam Mohammed ◽  
Muhanad Hameed Arif ◽  
Ali Adil Ali
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Multilayer Perceptron ◽  
Detection System ◽  
Denial Of Service ◽  
Detection Accuracy ◽  
Neural Network Approach ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

<p>Massive information has been transmitted through complicated network connections around the world. Thus, providing a protected information system has fully consideration of many private and governmental institutes to prevent the attackers. The attackers block the users to access a particular network service by sending a large amount of fake traffics. Therefore, this article demonstrates two-classification models for accurate intrusion detection system (IDS). The first model develops the artificial neural network (ANN) of multilayer perceptron (MLP) with one hidden layer (MLP1) based on distributed denial of service (DDoS). The MLP1 has 38 input nodes, 11 hidden nodes, and 5 output nodes. The training of the MLP1 model is implemented with NSL-KDD dataset that has 38 features and five types of requests. The MLP1 achieves detection accuracy of 95.6%. The second model MLP2 has two hidden layers. The improved MLP2 model with the same setup achieves an accuracy of 2.2% higher than the MLP1 model. The study shows that the MLP2 model provides high classification accuracy of different request types.</p>

scholarly journals DOES IMMERSIVE VIRTUAL REALITY INDUCE CYBERSICKNESS AND IMPACT EQUILIBRIUM COORDINATION?

2021 ◽  
Vol 10 (5) ◽  
pp. 3546-3551
Author(s):  
Tamanna Nurai
Keyword(s):  
Virtual Reality ◽  
Virtual Environment ◽  
Motion Sickness ◽  
Virtual Worlds ◽  
Body Awareness ◽  
Self Report ◽  
Simulator Sickness ◽  
Cross Sectional ◽  
Head Mounted Display ◽  
Simulator Sickness Questionnaire

Cybersickness continues to become a negative consequence that degrades the interface for users of virtual worlds created for Virtual Reality (VR) users. There are various abnormalities that might cause quantifiable changes in body awareness when donning an Head Mounted Display (HMD) in a Virtual Environment (VE). VR headsets do provide VE that matches the actual world and allows users to have a range of experiences. Motion sickness and simulation sickness performance gives self-report assessments of cybersickness with VEs. In this study a simulator sickness questionnaire is being used to measure the aftereffects of the virtual environment. This research aims to answer if Immersive VR induce cybersickness and impact equilibrium coordination. The present research is formed as a cross-sectional observational analysis. According to the selection criteria, a total of 40 subjects would be recruited from AVBRH, Sawangi Meghe for the research. With intervention being used the experiment lasted 6 months. Simulator sickness questionnaire is used to evaluate the after-effects of a virtual environment. It holds a single period for measuring motion sickness and evaluation of equilibrium tests were done twice at exit and after 10 mins. Virtual reality being used in video games is still in its development. Integrating gameplay action into the VR experience will necessitate a significant amount of study and development. The study has evaluated if Immersive VR induce cybersickness and impact equilibrium coordination. To measure cybersickness, numerous scales have been developed. The essence of cybersickness has been revealed owing to work on motion sickness in a simulated system.

Sign in / Sign up

Export Citation Format

Share Document