scholarly journals Artificial Neural Network in Virtual Reality : A Survey

2016 ◽  
Vol 15 (2) ◽  
pp. 44-52
Author(s):  
G. Sharma ◽  
Sushil Chandra ◽  
Saraynya Venkatraman ◽  
Alok Mittal ◽  
Vijander Singh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Virtual Reality ◽  
Learning Algorithm ◽  
Primary Objective ◽  
Mathematical Representation ◽  
3 Dimensional ◽  
Simulated Environment ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Virtual reality (VR) is defined as a 3-dimensional (3D), artificially simulated environment which allows the user to immerse himself/herself in it. From rehabilitation to data visualization, VR has been found to have many profound applications over the past decade or so. The addition of a suitable interface (e.g: haptics) is necessary in order to improve the quality of interaction with VR. Artificial Neural Network (ANN), a learning algorithm [i.e., a mathematical representation of any form of biological activity], which is one of the most widely adopted algorithms, is used for maintaining the properties of virtual reality (i.e., Immersivity and Interactivity). The primary objective of this review is to explore the limitless possibilities through the integration of ANN and VR. In addition to this, it also highlights the fact that an incumbent association of VR and ANN can lead to the construction of a highly interactive and immersive module in virtual reality.

Study on the Effectiveness Evaluation of the Weapon Equipment Based on Artificial Neural Network

2014 ◽  
Vol 926-930 ◽  
pp. 3262-3265
Author(s):  
Feng Gao ◽  
Fei Song ◽  
Guo Qing Huang ◽  
Mao Yang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bp Neural Network ◽  
Learning Algorithm ◽  
Effectiveness Evaluation ◽  
Matlab Toolbox ◽  
New Approach ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Better Than

A new approach to weapons and equipment effectiveness evaluation based on artificial neural network (ANN) performs better than traditional method, which is in view of the complex relationship between the effectiveness and many factors that influence the evaluation. The structure and learning algorithm of BP neural network is evaluated the fighters’ air-to-air combat capability. The evaluation is accomplished by a two-layer BP neural network and MATLAB toolbox. The simulation results show that the artificial neural network have better generalization ability and approximation performance for continuous function, which is valuable in weapons and equipment effectiveness evaluation application.

Monitoring-System Development of Gillnet Using Artificial Neural Network

2020 ◽  
Author(s):  
Chungkuk Jin ◽  
HanSung Kim ◽  
JeongYong Park ◽  
MooHyun Kim ◽  
Kiseon Kim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Big Data ◽  
Learning Algorithm ◽  
System Development ◽  
Ann Model ◽  
Ann Models ◽  
Artificial Neural ◽  
Input Variables ◽  
Artificial Neural Network Ann

Abstract This paper presents a method for detecting damage to a gillnet based on sensor fusion and the Artificial Neural Network (ANN) model. Time-domain numerical simulations of a slender gillnet were performed under various wave conditions and failure and non-failure scenarios to collect big data used in the ANN model. In training, based on the results of global performance analyses, sea states, accelerations of the net assembly, and displacements of the location buoy were selected as the input variables. The backpropagation learning algorithm was employed in training to maximize damage-detection performance. The output of the ANN model was the identification of the particular location of the damaged net. In testing, big data, which were not used in training, were utilized. Well-trained ANN models detected damage to the net even at sea states that were not included in training with high accuracy.

scholarly journals Controlling Electronic Devices with Brain Rhythms/Electrical Activity Using Artificial Neural Network (ANN)

2019 ◽  
Vol 6 (2) ◽  
pp. 46 ◽  
Author(s):  
Yar Muhammad ◽  
Daniil Vaino
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Learning Algorithm ◽  
Brain Activity ◽  
Electronic Devices ◽  
Testing Dataset ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The purpose of this research study was to explore the possibility to develop a brain-computer interface (BCI). The main objective was that the BCI should be able to recognize brain activity. BCI is an emerging technology which focuses on communication between software and hardware and permitting the use of brain activity to control electronic devices, such as wheelchairs, computers and robots. The interface was developed, and consists of EEG Bitronics, Arduino and a computer; moreover, two versions of the BCIANNET software were developed to be used with this hardware. This BCI used artificial neural network (ANN) as a main processing method, with the Butterworth filter used as the data pre-processing algorithm for ANN. Twelve subjects were measured to collect the datasets. Tasks were given to subjects to stimulate brain activity. The purpose of the experiments was to test and confirm the performance of the developed software. The aim of the software was to separate important rhythms such as alpha, beta, gamma and delta from other EEG signals. As a result, this study showed that the Levenberg–Marquardt algorithm is the best compared with the backpropagation, resilient backpropagation, and error correction algorithms. The final developed version of the software is an effective tool for research in the field of BCI. The study showed that using the Levenberg–Marquardt learning algorithm gave an accuracy of prediction around 60% on the testing dataset.

scholarly journals White whole (WW) grades cashew kernel’s classification using artificial neural network (ANN)

2020 ◽  
Vol 9 (1) ◽  
pp. 187
Author(s):  
Narendra VG ◽  
Dasharathraj K Shetty
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Closed Form Solution ◽  
Form Solution ◽  
Least Square ◽  
Least Square Approach ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper, we introduce an algorithm for the fitting of bounding rectangle to a closed region of cashew kernel in a given image. We propose an algorithm to automatically compute the coordinates of the vertices closed form solution. Which is based on coordinate geometry and uses the boundary points of regions. The algorithm also computes directions of major and minor axis using least-square approach to compute the orientation of the given cashew kernel. More promising results were obtained by extracting shape features of a cashew kernel, it is proved that these features may predominantly use to make the better distinction of cashew kernels of different grades. The intelligent model was designed using Artificial Neural Network (ANN). The model was trained and tested using Back-Propagation learning algorithm and obtained classification accuracy of 89.74%. 

Development of Quran Reciter Identification System Using MFCC and Neural Network

2016 ◽  
Vol 1 (1) ◽  
pp. 168 ◽  
Author(s):  
Tayseer Mohammed Hasan Asda ◽  
Teddy Surya Gunawan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Identification System ◽  
Successful Match ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Mel Frequency Cepstral Coefficient

Currently, the Quran is recited by so many reciters with different ways and voices.  Some people like to listen to this reciter and others like to listen to other reciters. Sometimes we hear a very nice recitation of al-Quran and want to know who the reciter is. Therefore, this paper is about  the development of Quran reciter recognition and identification system based on Mel Frequency Cepstral Coefficient (MFCC) feature extraction and artificial neural network (ANN). From every speech, characteristics from the utterances will be extracted through neural network model. In this paper a database of five Quran reciters is created and used in training and testing. The feature vector will be fed into Neural Network back propagation learning algorithm for training and identification processes of different speakers. Consequently,  91.2%  of the successful match between targets and input occurred with certain number of hidden layers  which shows how efficient are Mel Frequency Cepstral Coefficient (MFCC) feature extraction  and artificial neural network (ANN) in identifying the reciter voice perfectly.

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

2020 ◽  
Vol 38 (2A) ◽  
pp. 255-264
Author(s):  
Hanan A. R. Akkar ◽  
Sameem A. Salman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Average Rate ◽  
Mean Square ◽  
Performance Measurements ◽  
Artificial Intelligent ◽  
Average Mean Square Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.

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