Traditional systems of handwriting recognition have relied on handcrafted features and a large amount prior knowledge. Training an Optical character (OCR) system based these prerequisites is challenging task. Research in the field focused around deep learning techniques has achieved breakthrough performance last few years. Still, rapid growth handwritten data availability massive processing power demands improvement accuracy deserves further investigation. Convolutional neural networks...

The aim of this paper is to develop a hybrid model powerful Convolutional Neural Networks (CNN) and Support Vector Machine (SVM) for recognition handwritten digit from MNIST dataset. proposed combines the key properties both classifiers. In model, CNN works as an automatic feature extractor SVM binary classifier. dataset digits used training testing algorithm adopted in model. consists images which are diverse highly distorted. receptive field helps automatically extracting most...

This study addresses the problem of automatic detection disease states retina. In order to solve abovementioned problem, this develops an artificially intelligent model. The model is based on a customized 19-layer deep convolutional neural network called VGG-19 architecture. (VGG-19 architecture) empowered by transfer learning. designed so that it can learn from large set images taken with optical coherence tomography (OCT) and classify them into four conditions retina: (1) choroidal...

The choice of pattern classifier and the technique used to extract features are main factors judge recognition accuracy capability an Optical Character Recognition (OCR) system. focus this work is obtained by binarization for handwritten characters English language. character images have been done using multi-layered feed forward artificial neural network as a classifier. Some preprocessing techniques such thinning, foreground background noise removal, cropping size normalization etc. also...

Character Segmentation is the most crucial step for any OCR (Optical Recognition) System. The selection of segmentation algorithm being used key factor in deciding accuracy system. If there a good characters, recognition will also be high. words into characters becomes very difficult due to cursive and unconstrained nature handwritten script. This paper proposes new vertical which points are located after thinning word image get stroke width single pixel. knowledge shape geometry English...

Characters extraction is the most critical pre-processing step for any off-line text recognition system because characters are smallest unit of language script. The paper proposes an approach to segment character images from containing and computer printed or handwritten words. This segmentation app roach based on a set properties each connected component (object) in whole binary image machine some other images. These words which along with different lengths by cursive fonts sizes. technique...

This work is focused on improving the character recognition capability of feed-forward back-propagation neural network by using one, two and three hidden layers modified additional momentum term.182 English letters were collected for this equivalent binary matrix form these characters was applied to as training patterns.While getting trained, connection weights at each epoch learning.For sample, error surface examined minima computing gradient descent.We started experiment one layer number...

Intrusion Detection System is based on the belief that an intruder's behavior will be noticeably different from of a legitimate user and would exploit security vulnerabilities. This paper proposes neural network approach to improve alert throughput making it attack prohibitive using IDS. For evolving testing intrusion KDD CUP 99 dataset are used. The result proposed found more efficient in area promises good scope for further research.

Aim of this paper is to analyze the performance back-propagation feed-forward algorithm using various different activation functions for neurons hidden and output layers. For sample creation, 250 numerals were gathered form 35 people. After binarization, these clubbed together training patterns neural network. Network was trained learn its behavior by adjusting connection strengths at every iteration. The conjugate gradient descent each presented pattern calculated identify minima on error...

This article describes how predicting change-prone classes is essential for effective development of software. Evaluating changes from one release software to the next can enhance quality. proposes an efficient novel-based approach early in object-oriented Earlier researchers have calculated change prone using static characteristics such as source line code e.g. added, deleted and modified. research work use dynamic metrics execution duration, run time information, regularity, class...

In today's world, the heart of modern technology is software. order to compete with pace new technology, changes in software are inevitable. This article aims at association between and object-oriented metrics using different versions open source Change prediction models can detect probability change a class earlier life cycle which would result better effort allocation, more rigorous testing easier maintenance any Earlier, researchers have used various techniques such as statistical methods...

The traditional method of doing business has been disrupted by social media. In order to develop the enterprise, it is essential forecast level interaction that a new post would receive from media users. It possible for user’s interest in any one be impacted external factors or dwindle as result changes his behaviour. popularity detection strategies are user-based population-based unable keep up with these shifts, which leads inaccurate forecasts. This work makes prediction about how popular...

Software systems changes constantly with time. Changing the software affects all classes associated it. For effective project management it becomes important to predict change impact in earlier phases of life cycle. This paper aims develop a novel model using dynamic metrics and several behavioural dependencies. Using code analyser trace events 30 different are analysed which further used for refining degree feature class. Further is validated K-means clustering technique, naïve Bayes...