scholarly journals Phishing Website Detection using Neural Network and PCA based on Feature Selection

2020 ◽  
Vol 8 (6) ◽  
pp. 1150-1152
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Feature Selection ◽  
Computing Time ◽  
Back Propagation ◽  
Principal Component ◽  
Criminal Activity ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
Network Method

Phishing is a criminal activity that tries to steal user account password or other confidential information by tricking user into believing they are on the actual website. In order to phishing, they must get user to go from an email to a website. User can also land on phishing site by mistyping a URL (web address). However, the numbers of phishing attacks have been growing and need the protection technique. Neural network and Principal Component Analysis (PCA) can be combined to detect phishing website. This study uses back-propagation algorithm based on neural network method and PCA based on feature selection to reduce large attributes into small attributes. Neural network without using PCA will be compared with neural network using PCA. The result shows that neural network using PCA has better accuracy in 55.67% and neural network without using PCA only reaches 54.43% accuracy. However neural network without using PCA has faster computing time than neural network using PCA. This study can be used as a phishing protection technique.

Forecasting Crude Oil price using Artificial Neural Network model

2021 ◽  
pp. 321-326
Author(s):  
Sivaprakash J. ◽  
Manu K. S.
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Crude Oil ◽  
Back Propagation ◽  
Oil Prices ◽  
Oil Price ◽  
Back Propagation Algorithm ◽  
Crude Oil Price ◽  
Propagation Algorithm ◽  
Crude Oil Prices

In the advanced global economy, crude oil is a commodity that plays a major role in every economy. As Crude oil is highly traded commodity it is essential for the investors, analysts, economists to forecast the future spot price of the crude oil appropriately. In the last year the crude oil faced a historic fall during the pandemic and reached all time low, but will this situation last? There was analysis such as fundamental analysis, technical analysis and time series analyses which were carried out for predicting the movement of the oil prices but the accuracy in such prediction is still a question. Thus, it is necessary to identify better methods to forecast the crude oil prices. This study is an empirical study to forecast crude oil prices using the neural networks. This study consists of 13 input variables with one target variable. The data are divided in the ratio 70:30. The 70% data is used for training the network and 30% is used for testing. The feed forward and back propagation algorithm are used to predict the crude oil price. The neural network proved to be efficient in forecasting in the modern era. A simple neural network performs better than the time series models. The study found that back propagation algorithm performs better while predicting the crude oil price. Hence, ANN can be used by the investors, forecasters and for future researchers.

Emotion Recognition From Speech Using Perceptual Filter and Neural Network

2020 ◽  
pp. 78-91 ◽  
Author(s):  
Revathi A. ◽  
Sasikaladevi N.
Keyword(s):  
Neural Network ◽  
Emotion Recognition ◽  
Vector Quantization ◽  
Group Performance ◽  
Back Propagation ◽  
Critical Band ◽  
Emotion Classification ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
Speaker Independent

This chapter on multi speaker independent emotion recognition encompasses the use of perceptual features with filters spaced in Equivalent rectangular bandwidth (ERB) and BARK scale and vector quantization (VQ) classifier for classifying groups and artificial neural network with back propagation algorithm for emotion classification in a group. Performance can be improved by using the large amount of data in a pertinent emotion to adequately train the system. With the limited set of data, this proposed system has provided consistently better accuracy for the perceptual feature with critical band analysis done in ERB scale.

Identification and Recognition of Speaker Voice Using a Neural Network-Based Algorithm

2021 ◽  
pp. 278-289
Author(s):  
Neeraja Koppula ◽  
K. Sarada ◽  
Ibrahim Patel ◽  
R. Aamani ◽  
K. Saikumar
Keyword(s):  
Neural Network ◽  
Speech Signal ◽  
Moving Objects ◽  
Back Propagation ◽  
Data Sets ◽  
Back Propagation Algorithm ◽  
Neural Organization ◽  
Propagation Algorithm ◽  
Individual Voice

This chapter explains the speech signal in moving objects depending on the recognition field by retrieving the name of individual voice speech and speaker personality. The adequacy of precisely distinguishing a speaker is centred exclusively on vocal features, as voice contact with machines is getting more pervasive in errands like phone, banking exchanges, and the change of information from discourse data sets. This audit shows the location of text-subordinate speakers, which distinguishes a solitary speaker from a known populace. The highlights are eliminated; the discourse signal is enrolled for six speakers. Extraction of the capacity is accomplished utilizing LPC coefficients, AMDF computation, and DFT. By adding certain highlights as information, the neural organization is prepared. For additional correlation, the attributes are put away in models. The qualities that should be characterized for the speakers were acquired and dissected utilizing back propagation algorithm to a format picture.

scholarly journals Neural network design and feature selection using principal component analysis and Taguchi method for identifying wood veneer defects

2014 ◽  
Vol 2 (1) ◽  
pp. 291-308 ◽  
Author(s):  
Baris Yuce ◽  
Ernesto Mastrocinque ◽  
Michael Sylvester Packianather ◽  
Duc Pham ◽  
Alfredo Lambiase ◽  
...  
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Feature Selection ◽  
Taguchi Method ◽  
Network Design ◽  
Principal Component ◽  
Component Analysis ◽  
Wood Veneer

scholarly journals Development of combined back propagation algorithm and radial basis function for diagnosing depression patients

2015 ◽  
Vol 4 (1) ◽  
pp. 244
Author(s):  
Bhuvana R. ◽  
Purushothaman S. ◽  
Rajeswari R. ◽  
Balaji R.G.
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Mean Squared Error ◽  
Back Propagation ◽  
Optimum Number ◽  
Back Propagation Algorithm ◽  
Efficient System ◽  
Propagation Algorithm ◽  
Radial Basis

Depression is a severe and well-known public health challenge. Depression is one of the most common psychological problems affecting nearly everyone either personally or through a family member. This paper proposes neural network algorithm for faster learning of depression data and classifying the depression. Implementation of neural networks methods for depression data mining using Back Propagation Algorithm (BPA) and Radial Basis Function (RBF) are presented. Experimental data were collected with 21 depression variables used as inputs for artificial neural network (ANN) and one desired category of depression as the output variable for training and testing proposed BPA/RBF algorithms. Using the data collected, the training patterns, and test patterns are obtained. The input patterns are pre-processed and presented to the input layer of BPA/RBF. The optimum number of nodes required in the hidden layer of BPA/RBF is obtained, based on the change in the mean squared error dynamically, during the successive sets of iterations. The output of BPA is given as input to RBF. Through the combined topology, the work proves to be an efficient system for diagnosis of depression.

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