Introduction Most of telecommunication companies consider the customer as the most important asset for them. For that reason, nowadays, a challenging problem that encounters telecommunication companies is when the customer leaves the company to another service provider for a reason or another [1]. In most cases, this churn can happen in rates which seriously affect the profitability of the companies since it is easy for the customers to switch companies. In market, where the competition between the
wavelet transform is discussed. For the next chapter the combination of PCA and DWT, which can be useful in de-noising, come about. In this study, we have examined the neural network structure and modeling that is most of usage these days. The backpropagation is one of the common methods of training neural networks and for the last model, we discussed autoregressive model and the strategies to choose a model order.
1 INTRODUCTION Heart is the basic part of the body. Heart-failure is a serious medical situation in which this master organ doesn’t operate properly. The malfunctioning of the heart may impact the whole body organs since it is affiliated to all the body parts through its arteries and veins. One of the most dangerous and insidious heart diseases is the heart attack. It can be delineated as a chest pain aligned with a constriction in the coronary vessels which is called atherosclerosis. The two main
Neural Network 1.1.2 Backpropagation Learning is the way we acquire knowledge about the world around us, and it is through this process of knowledge acquisition, that the environment alters our behavioural responses. Similarly, in arti cial neural networks, learning rules are used, to modify the behaviour of the network in response to the external stimuli (inputs). For multilayered feedforward networks, a commonly used algorithm for weight adjustment is the backpropagation algorithm. There is
Analysis (PCA), Automatic Feature Extraction (AFE) and Independent Component Analysis (ICA) are employed to extract the iris feature from a pattern named IrisPattern based on the iris image. The IrisPatterns are classified using a Feedforward Backpropagation Neural Network (BPNN) and Support Vector Machines (SVM) with Radial Basis Function (RBF) kernel with different dimensions and a comparative study is carried out. From the experimental result, it is observed that ICA is the most appropriate feature
— The use of digital instruments in industries and laboratories is rapidly increasing as they are simple to calibrate and have relatively high precision. In this paper, an automatic data acquisition system is proposed using OCR technique from digital multi-meter and other similar digital display devices. The input image is taken from a digital multi-meter having LCD seven segment display using a web cam. The image is then processed to extract numeric digits which are recognized using a feedforward
Data mining with agricultural soil databases is a relatively young research area. In agricultural field, the determination of soil category mainly depends on the atmospheric conditions and different soil characteristics. Classification as an essential data mining technique used to develop models describing different soil classes. Such analysis can present us with a complete understanding of various soil databases at large. In our study, we proposed a novel Neuro-fuzzy classification based technique
The breast cancer is a life-threatening disease observed among females all over the world. Detection and analysis of the disease is a significant part of data mining research. Classification as an essential data mining procedure also helps in clinical diagnosis and analysis of this disease. In our study, we proposed a novel Neuro-fuzzy classification based method. We applied our method to three benchmark data sets from the UCI machine learning repository for detection of breast cancer; they were
deep learning while emphasizing their ability to learn from enormous amounts of data which in turn allows them to perform great tasks such as language processing and image recognition. Hinton is responsible for key breakthroughs in AI, including backpropagation, which transformed the training of neural networks and created the ability for these systems to have a deep belief complex. Geoffrey says he imagines a future where machines can interact with and emotionally understand humans in natural ways.
INFOrmatics and Systems Computational Intelligence and Multimedia Computing Track , 2012 ,pp mm72-mm77 [7] Mohammed Alwakeel , Zyad Shaaban ,”Face Recognition Based on Haar Wavelet Transform and Principal Component Analysis via Levenberg-Marquardt Backpropagation Neural Network” in European Journal of Scientific Research,2010 pp. 25-31 [8] Kyungnam Kim, “Face Recognition using Principle Component”, Analysis” www.google.com [9] Kyungnam Kim, “Face Recognition using Principle Component”, Analysis” www