Internet Banking and Customer Acceptance: The Indian Scenario

Indian economy is witnessing stellar growth over the last few years. There have been rapid developments in infrastructural and business front during the growth period.Internet adoption among Indians has been increasing over the last one decade.Indian banks have also risen to the occasion by offering new channels of delivery to their customers.Internet banking is one such new channel which has become available to Indian customers.Customer acceptance for internet banking has been good so far.In this study the researcher tried to conduct a qualitative and quantitative investigation of internet banking customer acceptance among Indians. The researcher tried to identify important factors that affect customer's behavioral intention for internet banking .The researcher also proposes a research model which has extended from Technology Acceptance Model for predicting internet banking acceptance.The findings of the study would be useful for Indian banks in planning and upgrading their internet banking service.Banks could increase internet banking adoption by making their customer awareness about the usefulness of the service.It is seen that from the study that the variable perceived usefulness has a positive influence on internet banking use,therefore internet banking acceptance would increase when customers find it more usefulness.Banks should plan their marketing campaigns taking into consideration this factor.Proper marketing communications which would increase consumer awareness would result in better acceptance of internet banking.The variable perceived ease of use had a positive influence on internet banking use.That means customers would increase internet banking usage when they find it easier to use.Banks should therefore try to develop their internet banking site and interface easier to use.Banks could also consider providing practical training sessions for customers at their branches on usage of internet banking interface.

Median filtering: structure analysis and application

Median filtering is a simple digital non—linear signal smoothing operation in which median of the samples in a sliding window replaces the sample at the middle of the window. The resulting filtered sequence tends to follow polynomial trends in the original sample sequence. Median filter preserves signal edges while filtering out impulses. Due to this property, median filtering is finding applications in many areas of image and speech processing. Though median filtering is simple to realise digitally, its properties are not easily analysed with standard analysis techniques,

FEATURE SELECTION AND COMPARISON OF TWO NAÏVE BAYES CLASSIFICATION METHODS IN THE CONTEXT OF SPAM FILTERING

Treating e-mail filtering as a binary text classification problem, researchers have applied several statistical learning algorithms to email corpora with promising results. This paper examines the performance of a Naive Bayes classifier using different approaches to feature selection and tokenization on different email corpora

Ultrasound Image Despeckling using Local Binary Pattern Weighted Linear Filtering

Speckle noise formed as a result of the coherent nature of ultrasound imaging affects the lesion detectability. We have proposed a new weighted linear filtering approach using Local Binary Patterns (LBP) for reducing the speckle noise in ultrasound images. The new filter achieves good results in reducing the noise without affecting the image content. The performance of the proposed filter has been compared with some of the commonly used denoising filters. The proposed filter outperforms the existing filters in terms of quantitative analysis and in edge preservation. The experimental analysis is done using various ultrasound images

Morphological filtering on hypergraphs

The focus of this article is to develop computationally efficient mathematical morphology operators on hypergraphs. To this aim we consider lattice structures on hypergraphs on which we build morphological operators. We develop a pair of dual adjunctions between the vertex set and the hyper edge set of a hypergraph H, by defining a vertex-hyperedge correspondence. This allows us to recover the classical notion of a dilation/erosion of a subset of vertices and to extend it to subhypergraphs of H. Afterward, we propose several new openings, closings, granulometries and alternate sequential filters acting (i) on the subsets of the vertex and hyperedge set of H and (ii) on the subhypergraphs of a hypergraph

Automatic Segmentation Framework for Primary Tumors From Brain MRIs Using Morphological Filtering Techniques

This paper describes a novel framework for automatic segmentation of primary tumors and its boundary from brain MRIs using morphological filtering techniques. This method uses T2 weighted and T1 FLAIR images. This approach is very simple, more accurate and less time consuming than existing methods. This method is tested by fifty patients of different tumor types, shapes, image intensities, sizes and produced better results. The results were validated with ground truth images by the radiologist. Segmentation of the tumor and boundary detection is important because it can be used for surgical planning, treatment planning, textural analysis, 3-Dimensional modeling and volumetric analysis