Rapid urbanization influences green infrastructure (GI) development in cities. The government plans to optimize GI urban areas, which requires understanding spatiotemporal trends areas and driving forces influencing their pattern. Traditional GIS-based methods, used determine the greening potential of vacant land are incapable predicting future scenarios based on past trend. Therefore, we propo...

Remote sensing sensors-based image processing techniques have been widely applied in non-destructive quality inspection systems of agricultural crops. Image and analysis were performed with computer vision external grading by general standard steps, such as acquisition, pre-processing segmentation, extraction classification characteristics. This paper describes the design implementation a real-...

Hindi is the most widely spoken language in India, with more than 300 million speakers. As there is no separation between the characters of texts written in Hindi as there is in English, the Optical Character Recognition (OCR) systems developed for the Hindi language carry a very poor recognition rate. In this paper we propose an OCR for printed Hindi text in Devanagari script, using Artificial...

brain mr images tissue segmentation is one of the most important parts of the clinical diagnostic tools. pixel classification methods have been frequently used in the image segmentation with two supervised and unsupervised approaches up to now. supervised segmentation methods lead to high accuracy but they need a large amount of labeled data, which is hard, expensive and slow to obtain. moreove...

Automatic diagnosis of diseases in the medical field using image processing techniques has evolved tremendously recent times. Sickle cell anemia (SCA) is a kind disease connected with red blood cells (RBCs) present human body which deformation take place. The purpose this work to propose an automatic technique for detection from microscopic images. This paper mainly focuses on SCA novel segment...

A wide range of non-parametric classifiers have been suggested and developed in the recent years in order to overcome the compulsion of using the classical parametric Maximum Likelihood Classifier (MLC) for non-normal data. The most advanced of these classifiers being the ones based on the Artificial Neural Network (ANN) algorithm, the Support Vector Machines (SVMs) and the Random Forests (RFs)...

As medical technology advances, has accumulated a large number of health-related data. Faced with increasingly complex analytical requirements, predictive data mining has become an essential instrument for hospital management and medical research. In this study, the breast cancer dataset is collected from a regional teaching hospital in central Taiwan between 2002 and 2009. The prognostic facto...

This paper discusses the different methods for optical character recognition (OCR), which has been an important field to research from a few decades due its huge necessity to convert paper documents or books in computer readable format. Though Bangla (widely used as Bengali) is one of the top uses language among the other languages, but there is no reliable character recognizer for this. Our wo...

An entropy-based self organized learning algorithm is presented for classifier design. The method, which is based on ideas from information theory, maximizes the information index during feed forward learning in ANN. The neuron in the architecture divides the subspace based on the division of previous neurons which are connected to the current neuron by some probability distribution. Utilizing ...

The specification and verification of algorithms is vital for safety-critical autonomous systems which incorporate deep learning elements. We propose an integrated process verifying artificial neural network (ANN) classifiers. This consists off-line on-line performance prediction phase. intended to verify ANN classifier generalisation performance, this end makes use dataset dissimilarity measur...