Solving multiple linear regression problem using artificial neural network

Multiple linear regressions are an important tool used to find the relationship between a set of variables used in various scientific experiments. In this article we are going to introduce a simple method of solving a multiple rectilinear regressions (MLR) problem that uses an artificial neural network to find the accurate and expected output from MLR problem. Different artificial neural network (ANN) types with different architecture will be tested, the error between the target outputs and the calculated ANN outputs will be investigated. A recommendation of using a certain type of ANN based on the experimental results will be raised

Analysis of color image features extraction using texture methods

A digital color images are the most important types of data currently being traded; they are used in many vital and important applications. Hence, the need for a small data representation of the image is an important issue. This paper will focus on analyzing different methods used to extract texture features for a color image. These features can be used as a primary key to identify and recognize the image. The proposed discrete wave equation DWE method of generating color image key will be presented, implemented and tested. This method showed that the percentage of reduction in the key size is 85% compared with other methods

Wave File Features Extraction using Reduced LBP

In this work, we present a novel approach for extracting features of a digital wave file. This approach will be presented, implemented and tested. A signature or a key to any wave file will be created.  This signature will be reduced to minimize the efforts of digital signal processing applications. Hence, the features array can be used as key to recover a wave file from a database consisting of several wave files using reduced Local binary patterns (RLBP). Experimental results are presented and show that The proposed RLBP method is at least 3 times faster than CSLBP method, which mean that the proposed method is more efficient

A new method for voice signal features creation

Digital audio is one of the most important types of data at present. It is used in several applications, such as human knowledge and many security and banking applications. A digital voice signal is usually of a large size where the acoustic signal consists of a set of values distributed in one column (one channel) (mono signal) or distributed in two columns (two channels) (stereo signal), these values usually are the results of sampling and quantization of the original analogue voice signal. In this paper we will introduce a method which can be used to create a signature or key, which can be used later to identify or recognize the wave file. The proposed method will be implemented and tested to show the accuracy and flexibility of this method

Analysis Of Kmeans Clustering Method Used For Audio Signal Features Extraction

The digital audio file is used in many vital applications, especially applications that require the identification of a person through the use of a word or a sentence pronounced by the person. In this research paper, we will deal with the representation of the audio file using audio file histogram, a method based on LBP operator will be intoduced to calculate the histogram, this histogram will be used as an input data set to kmeans clustering method to generate audio file features vector. The extracted audio files features will be examined to insue the abilty of using them in a classification system. Several audio files will be processd, the obtained results will be analyzed to show the expected enhancement provided by using audio file histogram