Multilayer Back Propagation Neural Network (MLP-BPNN)

It is hypothesized that screening mammograms obtained prior to the detection of breast cancer could contain subtle signs of early stages of breast cancer such as architectural distortion in specific. The methods are based upon Gabor filters, phase portrait analysis, Laws’ texture energy measures and Haralick’s texture features [5]. Finally the classification performance of the individual features (such as Node Value, Laws Texture and Haralick’s features) from several combinations of feature sets using Multilayer Back Propagation Neural Network as a classifier is presented.

Abstract: Image classification is a well known of the significant tools used to recognize and examine most sharp information in satellite images. In any remote sensing research, the decision-making way mainly rely on the efficiency of the classification process. There are disparate classification algorithms on the large satellite imagery: Multilayer perceptron back propagation neural network (MLP BPNN), Support vector machine (SVM), k- means, Cluster ensemble based (CEB) method, Unsupervised Deep Feature learning (UDFL), Semi- supervised Ensemble Projection (SSEP). We discussed different performance measures such as classification accuracy, root-mean-square error, kappa statistic, true positive rate, false positive rate, to know the performance of each classifier.

1) Multilayer Back propagation Neural Network For this classifier, a two layer feed forward neural network was used with topology of {10, 1} 10 neurons in hidden layer and 1 neuron in output layer. The neural network was designed to accept a 21 element input vec- tor and give a single output. The output neuron was de- signed to give 0 for baseline (relax task) and 1 for men- tal task. The five different training methods used for this classifier were Gradient Descent, Levenberg-Marquardt, Resilient Back propagation, Conjugate Gradient Descent and Gradient Descent back propagation with movemen- tum [15,16,17]. Parameter used for five training methods of neural network for classification of five mental tasks as shown in the Table 2.

Abstract— we find the problem of Privacy Preserving Back Propagation Algorithm for a Vertically Partitioned Dataset. To improve the learning, Enhanced data is more important to find the exact privacy concern of each data holder by extending the privacy preservation suggested to original learning algorithms. In this paper, we try to improve preserving the privacy in an important multilayer neural networks and learning model. We present a privacy preserving multiparty distributed algorithm of back propagation which allows a neural network to be trained without requiring either party to reveal her data to the others. We gave more correctness and security analysis of our algorithms. The effectiveness of our algorithms is checked and verified by experiments on various real world data sets. We address this open problem by incorporating the computing power of the cloud computing. The main idea of our paper can be summarized as follows: each participant first encrypts her/his private data with the system public key and then uploads the cipher texts to the cloud; cloud servers then execute most of the operations pertaining to the learning process over the cipher texts and return the encrypted results to the participants.

Perceptron approach can be extended to solve linearly non-separable classification problems, using layered structure of nodes. Such networks contain one or more layers of hidden nodes that isolate useful features of the input data. However it is not easy to train these networks. Given that the network makes an error on some sample inputs, identifying which weights in the network must be modified, and to what extent is a tough task. Hence, perceptron and other one layer networks are seriously limited in their capabilities. Feed- forward multilayer networks with non-linear node functions can overcome these limitations, and can be used for many applications. Hence a more powerful supervised learning mechanism called back-propagation is used for multi-class, multi-level discrimination [3], [5].

Abstract: Support Vector Machine (SVM) and back-propagation neural network (BPNN) has been applied successfully in many areas, for example, rule extraction, classification and evaluation. In this paper, we studied the back-propagation algorithm for training the multilayer artificial neural network and a support vector machine for data classification and image reconstruction aspects. A model focused on SVM with Gaussian RBF kernel is utilized here for data classification. Back propagation neural network is viewed as one of the most straightforward and is most general methods used for supervised training of multilayered neural network. We compared a support vector machine (SVM) with a back-propagation neural network (BPNN) for the task of data classification and image reconstruction. We made a comparison between the performances of the multi-class classification of these two learning methods. Comparing with these two methods, we can conclude that the classification accuracy of the support vector machine is better, and algorithm is much faster than the MLP with back propagation algorithm.

the patch dimensions i.e. length (L) and width (W) of the patch. The different variants of back propagation training algorithm of MLFFBP-ANN (Multilayer feed forward back propagation Artificial Neural Network) and RBF-ANN (Radial basis function Artificial Neural Network) has been used to implement the network model. The results obtained from artificial neural network when compared with simulation results, found satisfactory and also it is concluded that RBF network is more accurate and fast as compared to different variants of back propagation training algorithms of MLPFFBP. The ANNs results are more in agreement with the simulation findings. Neural network based estimation has the usual advantage of very fast and simultaneous response of all the outputs.

network systems available. In contrast to earlier work on perception, the back propagation network is a multilayer feed forward network with a different transfer function in the artificial neuron and more powerful learning rule. The learning rule is known as back propagation, which is a kind of gradient descent technique with backward error propagation. The training instance set for the network must be presented many times in order for the interconnection weights between the neurons to settle into a state for correct classification of input patterns. While the network can recognize patterns similar to these they have learned, they do not have the ability to recognize new patterns. This is true for all supervised learning networks. In order to recognize new patterns, the network needs to be retrained with these patterns along with previously known pattern. If only new patterns are provided for retrained, then old patterns may be forgotten. In this way, learning is not incremental over time. This is a major limitation of supervised learning networks.

Shoba G. and Shobha G [1] analyzed various algorithms such as Adaptive Neuro-Fuzzy Inference System (ANFIS), ARIMA and SLIQ Decision for the forecast of rainfall. R.Sangari and M.Balamurugan [3] related data mining methods such as the K-Nearest Neighbor(KNN), Naïve Bayes, Decision Tree, Neural Networks, and Fuzzy Logic for the use of rainfall prediction. Beltrn-Castro[5] used decomposition and ensemble techniques to rainfall on daily basis. Ensemble Empirical Mode Decomposition (EEMD) is the decomposition technique adopted by Beltrn castro for dividing data into multiple segments. Few scholars like D. Nayak and A. Mahapatra [7] used different machine learning algorithms like MultiLayer Perceptron Neural Network (MLPNN), Back Propagation Algorithm(BPA), Radial Basis Function Network (RBFN), SOM (Self Organization Map) and SVM (Support Vector Machine) to predict rainfall. Their results have shown positive results on favor of back propagation algorithm. In [6] authors used different neural network models to predict rainfall. They have adopted feed forward neural network using back propagation, cascade-forward back propagation NN (CBPN), distributed time delay neural network (DTDNN) and nonlinear autoregressive exogenous network (NARX) and compared which gives best results.

STATISTICA Neural Networks software. Back propagation is the best known training algorithm for neural networks, and still one of the most useful. Back-propagation is a gradient descent on the error surface, the weights of the connections between neurons being adjusted in order to decrease the root mean squared error (rmse) between calculated and expected values for all molecules in the database. A back propagation ANN (multilayer perceptron or MLP) was selected as an empirical model.

This paper has demonstrated the usefulness of using a J48 pruned decision tree in tandem with multilayer perceptron neural network employing back propagation algorithm as a classifier in ladybird identification. The pruned decision tree indicates decision path for identification, and this is useful when coding a binary classification path in an expert system. MLP is clever due to the learning it acquires during training process. A caveat of using MLP in this domain is the network will need retraining every time new taxa are to be identified. It can be very time consuming, considering the vast amount of taxon and samples to work with. A workaround to this is the integration of an expert system into the loop, since it would acquire user inputs such as number of spots, geographical location etc. that the system could not acquire due to visual limitations. For instance, the expert system is in a better position in identifying a 2-spot ladybird among 5-spot ladybirds or 7-spot ladybirds by querying users whether they can provide the number of spots to the system. This is because the number of spots will sometimes be harder to determine from the many angles of a 3-dimensional ladybird using an image capturing device. In a way the expert system should provide considerable reasoning to the user, which is lacking in a typical ANN classifier. A possible scheme is to use J48 in the feature extractor, saving the number of features required to process by the next stage by pruning the features. Once the extracted features are obtained, they can be fed into MLP ANN for training the classifier. The expert system may interact with

Abstract: In a complex and changing a remote sensing system, which requires taking quick and informed decisions environment, connectionist methods have shown their great contribution in particular the reduction and classification of spectral data. In this context, this paper proposes to study the parameters that optimize the results of an artificial neural network ANN multilayer perceptron based, for classification of chemical agents on multi-spectral images. The mean squared error cost function remains one of the major parameters of the network convergence at its learning phase and a challenge that will face our approach to improve the gradient descent by the conjugate gradient method that seems fast and efficient.

Among females there has been an increasing trend in breast cancer for the last few years over other cancers. In the year 2006-2008 out of the total 2095 breast cancer cases in CIA the percentage was estimated as 26.5%. The average Annual Crude Incidence Rate (CIR) and, Age Standardized Rate (ASR) per 100,000 females during 2006-2008 were 30.2 and 31.6 respectively. The distribution of breast cancer by sub site revealed that the un- specified parts of breast constituted the majority (73%) followed by the upper outer quadrant (10%), upper inner quadrant (4%), Lower outer quadrant (2 and Lower Inner Quadrants (1%) of breast. The histological verification of cancer diagnosis was possible in 86%. The ductal carcinoma (79%) was the most common morphological type followed by cystosarcoma phyllodes (1%), cystic/Mucinous neoplasms (0.8%), lobular carcinoma (0.7%) and medullary carcinoma (0.5%) and Carcinoma unspecified comprised 17%. [5] proposed Association Rule Mining for classification of mammographic images. Three steps in classification are preprocessing, Mining and Organization and reported an accuracy of 80.33%. [6] proposed Feature extraction technique to detect breast masses in the early stages of cancer development. [7] proposed Computer Aided Diagnosis system to detect and classify masses on ultrasound breast images using fuzzy support vector machines. [8] proposed a hybrid clas- sifier combining unsupervised (ART) and supervised (LDA) learning method to classify malignant and benign masses. [9] proposed a Feed forward back propagation algorithm to detect and classify breast cancer. [10] pro- posed a hybrid classifier multilayer perceptron and genetic algorithm to classify the tumors based on ultrasound images.

This paper describes the identification of events for Primary Sodium Circuit in PFBR using Artificial Neural Network model. The steady state and transient data have been collected from the thermo hydraulics model of PFBR simulator. The effective SCRAM parameters which cause the events are used as inputs to the neural network. Two separate ANN models have been developed to detect PSP trip and PSP seizure events. Multilayer feed forward network using back propagation algorithm is implemented and training has been carried out to achieve least mean square error with optimal network parameters. The occurrence of events has been successfully detected during the validation of our ANN model. The network once trained properly, is ready to identify the transient in much less time compared to identification of events detected manually. Hence these models can be very helpful in quick detection and corrective decision making during the operation of nuclear reactors. Our future aim is to identify the all associated events by integrating individual subsystem models into a single neural network model.

The back propagation algorithm for training multilayer artificial neural network is studied and successfully implemented on FPGA. This can help in achieving online training of neural networks on FPGA than training in computer system and make a trainable neural network chip. In terms of hardware efficiency, the design can be optimized to reduce the number of multipliers. The training time for the network to converge to the expected output can be reduced by implementing the adaptive learning rate back-propagation algorithm. By doing this there is an area tradeoff for speed. Also the timing performance of the network can be improved by designing a larger neural network with more number of neurons. This will allow the network to process more number of pixels and reduce the time of operation. With all the optimization and on-line training we would be able to build a more complex neural network chip, which can be trained for realizing complex functions.

Abstract: In this paper we present Document analysis and classification system to segment and classify contents of Arabic document images. This system includes preprocessing, document segmentation, feature extraction and document classification. A document image is enhanced in the preprocessing by removing noise, binarization, and detecting and correcting image skew. In document segmentation, an algorithm is proposed to segment a document image into homogenous regions. In document classification, Neural Network (Multilayer Perceptron- Back propagation) classifier is applied to classify each region to text or non text based on a number of features extracted in feature extraction. These features are collected from different other researchers’ works. Experiments were conducted on 398 document images selected randomly from printed Arabic text database (PATDB) which was selected from various printing forms which are advertisements, book chapters, magazines, newspapers, letters and reports documents. As results, the proposed segmentation algorithm achieved only 0.814% as ratio of the overlapping areas of the merged zones to the total size of zones and 1.938% as the ratio of missed areas to total size of zones. The features, that show the best accuracy individually, are Background Vertical Run Length (RL) Mean, and Standard Deviation of foreground.

Back propagation calculation is utilized as a part of feed forward ANNs. First term, “feed forward” defines how this neural network works and recalls patterns and the term “back propagation” defines how this kind of neural networks are accomplished. The network obtains inputs by neurons in the input layer, and the output of the network is given by the neurons on an output layer. There may be one or additional intermediate hidden layers.

with significant effect in various fields, including target detection [8], activity recognition [3, 9], and natural lan- guage processing [10]. For example, Ji et al. [11] pro- posed a convolutional neural network model for video analysis on the basis of the traditional convolutional neural network. This model can decompose the video data into frames and then hard-coded them. After that, multiple convolutional kernel operations are done to ex- tract low-dimensional feature information at the same position in consecutive input frames, and the mean- pooling method will downsample the output features of the convolutions to reduce feature dimensions. This method has got good results in video-based human body recognition. Another method, an unsupervised one, was put forward by Le et al. [12] to directly learn human be- havior characteristics from surveillance videos. They de- signed an independent subspace analysis algorithm (ISA) with the adoption of cascading and convolutional strat- egies to learn the characteristic information from the data. It has been testified by experiments that the algo- rithm can obtain higher recognition rates in human

As a popular and most widely used algorithm, BPNN is known to be able to train Artificial Neural Networks (ANN) successfully. However, BPNN algorithms have some limitations; such as getting stuck in local minima and slow convergence rate. In this paper, Bat-BP algorithm is proposed to train BPNN in order to achieve fast convergence rate and enhanced accuracy. The performance of Bat-BP algorithm is then compared with the ABC-CP, ABC-LM, and also BPNN algorithms. The performance of the proposed Bat-BP is verified by means of simulations on Breast Cancer (Wisconsin) Classification, Diabetes, and Glass datasets are used respectively. The simulation results show that the proposed Bat-BP shows high accuracy for all dataset.

§ the number of neurons in each of the layers. The selection of the number of epochs is done following the selection of these parameters and starts the network training. The obtained output value for the defined synaptic weights and input values is brought to the network entry in order to make a potential correction of the mentioned coefficients and obtain the final output with a certain accuracy in the framework of the defined number of epochs. Obtaining the output once for all the input values and one set of placed weight coefficients represents an iteration, which is known as an epoch in the terminology of neural networks.