<span>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.</span>
Color digital images are widely circulated in various means of communication, and these images are used in multiple and vital applications, which forces us to search for easy and effective ways to represent the digital image with a set of unique values that facilitate the process of retrieving or recognizing the digital image. A digital image is mostly made up of a group of objects that can be used to generate a features vector for an image that can be used as an image identifier. In this paper, we will present a set of easy procedures through which it is possible to retrieve objects in a digital image and how to use the information of these objects toform the properties of the image. We will also demonstrate the flexibility of the presented procedures in using a wide range of object information to formulate unique image values that can be used as properties for digital image retrieval or recognition.
The process of digital image features extraction is very important and it is required in many applications such as classification, prediction and regression. The extracted features for each image must be unique and capable to be used as an image identifier. In this paper we will introduce a method of image features extraction; it will be shown that this method will enhance the efficiency of the features extraction process. The proposed method will be experimentally tested using various images; the obtained experimental results will be compared with other existing methods of feature extraction to show the advantages of the proposed method and to show how to increase the speed up of the method.
Regression analysis, in statistic a modelling, is a set of statical processes that can be used to estimate the relationship between a dependent variable, commonly known as the outcome or response, and more independent variables generally called predictors of covariant. On the other hand, autoregression, which is based on regression equations, is a sequential model that uses time to predict the next step data from the previous step. Given the importance of accurate modelling and reliable predictions. in this paper we have analyzed the most popular methods used for data prediction. Nonlinear autoregressive methods were introduced, and then the machine deep learning approach was used to apply prediction based on a selected input data set. The mean square error was calculated for various artificial neural networks architecture to reach the optimal architecture, which minimized the error. Different artificial neural network (ANN) architectures were trained, tested, and validated using various regressive models, a recommendation was raised according to the obtained and analyzed experimental results. It was shown that using the concepts of machine deep learning will enhance the response of the prediction model.
The fingerprint is used in many vital applications important to humans, which requires searching for an effective way to extract the characteristics of the fingerprint. In this paper we will study some of the most popular methods used to extract fingerprints features. For each method the efficiency, accuracy, flexibility and sensitivity for image rotation will be experimentally tested, measured, analyzed in order to give good recommendations of how and when to use a certain method of features extraction. A detailed comparison analysis between MLBP, K_means, WPT, Minutiae methods will be done using several color images in various rotation modes to insure the stability of image features.
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