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.

Energy-Efficient Cluster based Routing Schemes for Static and Mobile Sensor Networks

The theme of the thesis is centred around one important aspect of wireless sensor networks; the energy-efficiency.The limited energy source of the sensor nodes calls for design of energy-efficient routing protocols. The schemes for protocol design should try to minimize the number of communications among the nodes to save energy. Cluster based techniques were found energy-efficient. In this method clusters are formed and data from different nodes are collected under a cluster head belonging to each clusters and then forwarded it to the base station.Appropriate cluster head selection process and generation of desirable distribution of the clusters can reduce energy consumption of the network and prolong the network lifetime. In this work two such schemes were developed for static wireless sensor networks.In the first scheme, the energy wastage due to cluster rebuilding incorporating all the nodes were addressed. A tree based scheme is presented to alleviate this problem by rebuilding only sub clusters of the network. An analytical model of energy consumption of proposed scheme is developed and the scheme is compared with existing cluster based scheme. The simulation study proved the energy savings observed.The second scheme concentrated to build load-balanced energy efficient clusters to prolong the lifetime of the network. A voting based approach to utilise the neighbor node information in the cluster head selection process is proposed. The number of nodes joining a cluster is restricted to have equal sized optimum clusters. Multi-hop communication among the cluster heads is also introduced to reduce the energy consumption. The simulation study has shown that the scheme results in balanced clusters and the network achieves reduction in energy consumption.The main conclusion from the study was the routing scheme should pay attention on successful data delivery from node to base station in addition to the energy-efficiency. The cluster based protocols are extended from static scenario to mobile scenario by various authors. None of the proposals addresses cluster head election appropriately in view of mobility. An elegant scheme for electing cluster heads is presented to meet the challenge of handling cluster durability when all the nodes in the network are moving. The scheme has been simulated and compared with a similar approach.The proliferation of sensor networks enables users with large set of sensor information to utilise them in various applications. The sensor network programming is inherently difficult due to various reasons. There must be an elegant way to collect the data gathered by sensor networks with out worrying about the underlying structure of the network. The final work presented addresses a way to collect data from a sensor network and present it to the users in a flexible way.A service oriented architecture based application is built and data collection task is presented as a web service. This will enable composition of sensor data from different sensor networks to build interesting applications. The main objective of the thesis was to design energy-efficient routing schemes for both static as well as mobile sensor networks. A progressive approach was followed to achieve this goal.

SPATIAL CLUSTERING ALGORITHMS-AN OVERVIEW.

An Overview of known spatial clustering algorithms The space of interest can be the two-dimensional abstraction of the surface of the earth or a man-made space like the layout of a VLSI design, a volume containing a model of the human brain, or another 3d-space representing the arrangement of chains of protein molecules. The data consists of geometric information and can be either discrete or continuous. The explicit location and extension of spatial objects define implicit relations of spatial neighborhood (such as topological, distance and direction relations) which are used by spatial data mining algorithms. Therefore, spatial data mining algorithms are required for spatial characterization and spatial trend analysis. Spatial data mining or knowledge discovery in spatial databases differs from regular data mining in analogous with the differences between non-spatial data and spatial data. The attributes of a spatial object stored in a database may be affected by the attributes of the spatial neighbors of that object. In addition, spatial location, and implicit information about the location of an object, may be exactly the information that can be extracted through spatial data mining

Detection of Flock Movement in Spatio-Temporal Database using Clustering Techniques - an Experience

In this paper, moving flock patterns are mined from spatio- temporal datasets by incorporating a clustering algorithm. A flock is defined as the set of data that move together for a certain continuous amount of time. Finding out moving flock patterns using clustering algorithms is a potential method to find out frequent patterns of movement in large trajectory datasets. In this approach, SPatial clusteRing algoRithm thrOugh sWarm intelligence (SPARROW) is the clustering algorithm used. The advantage of using SPARROW algorithm is that it can effectively discover clusters of widely varying sizes and shapes from large databases. Variations of the proposed method are addressed and also the experimental results show that the problem of scalability and duplicate pattern formation is addressed. This method also reduces the number of patterns produced

Spectral clustering independent component analysis for tissue classification from brain MRI

A spectral angle based feature extraction method, Spectral Clustering Independent Component Analysis (SC-ICA), is proposed in this work to improve the brain tissue classification from Magnetic Resonance Images (MRI). SC-ICA provides equal priority to global and local features; thereby it tries to resolve the inefficiency of conventional approaches in abnormal tissue extraction. First, input multispectral MRI is divided into different clusters by a spectral distance based clustering. Then, Independent Component Analysis (ICA) is applied on the clustered data, in conjunction with Support Vector Machines (SVM) for brain tissue analysis. Normal and abnormal datasets, consisting of real and synthetic T1-weighted, T2-weighted and proton density/fluid-attenuated inversion recovery images, were used to evaluate the performance of the new method. Comparative analysis with ICA based SVM and other conventional classifiers established the stability and efficiency of SC-ICA based classification, especially in reproduction of small abnormalities. Clinical abnormal case analysis demonstrated it through the highest Tanimoto Index/accuracy values, 0.75/98.8%, observed against ICA based SVM results, 0.17/96.1%, for reproduced lesions. Experimental results recommend the proposed method as a promising approach in clinical and pathological studies of brain diseases

Impact of Quality on Ethics and Social Responsibility in Marketing, in Industries in Kerala in the present Indian Scenario

The paper ‘Impact of Quality on Ethics and Social Responsibility in Marketing in Industries in Kerala in the present Indian scenario’ highlights the observations, based on a descriptive research carried out in five leading industries in Kerala, in the private and public sector. Ethics and social responsibilities, practiced in these industries, are reflected in the results of the survey conducted on specific queries like awareness of products/services provided by them, total understanding of the requirements of the customer, open discussion on technical matters, accountability of employees to the society and social needs, consumer ethics vis a vis business ethics etc. Team working goes a long way, in building relations, which in turn, results in a progressive and effective marketing strategy. This assumes paramount importance, considering the severe competition we are facing in the light of liberalization, privatization and globalization, which encompasses the globe. The prediction of India becoming a lead nation, along with USA, China and Japan, in this decade, can get fructified only if we follow a very high standards of ethics and social responsibility, in all domains including marketing. Organizations like TRW.Rane, Sundaram Fasteners, TVS Motors, in Chennai are a few among others in India, who have achieved the highest distinction in quality viz Deming Prize, and these demonstrate their commitment to quality, society and humanity at large. Cost effectiveness, without jeopardizing quality has become the need of the hour and MRTP has become history. This trait is being brought out through the survey and the results speak for themselves. Unethical practices like switch and bait, not only brings shame to the organization, and country but also results in the company getting wiped out from the market. Adherence to standards like ISO 14000 helps to maintain the minimum level of social responsibility and environmental friendliness. Like quality audit, safety audit etc, social audit is being insisted in all progressive countries to ensure that the organization comply with the minimum statutory requirements. The paper also touches upon Corporate Social Responsibility practiced in the industries and this becomes crystal clear through their commitment to improve the community. Green Marketing lays a lot of importance on the three Rs of environmentalism viz Reduce, Reuse and Recycle. The objective of any business is to achieve optimal profit and this is possible only by reducing the cost as well as waste. In this context, management tools like brainstorming, suggestion schemes, benchmarking etc becomes helpful. These characteristics are brought out through the analysis of survey results. The conclusions drawn throw a lot of information on the desirable practices with respect to Ethics and Social Responsibility in Marketing

Development of Hierarchical Clustering Techniques for Gridded Data from Mixed Data Sequences

Knowledge discovery in databases is the non-trivial process of identifying valid, novel potentially useful and ultimately understandable patterns from data. The term Data mining refers to the process which does the exploratory analysis on the data and builds some model on the data. To infer patterns from data, data mining involves different approaches like association rule mining, classification techniques or clustering techniques. Among the many data mining techniques, clustering plays a major role, since it helps to group the related data for assessing properties and drawing conclusions. Most of the clustering algorithms act on a dataset with uniform format, since the similarity or dissimilarity between the data points is a significant factor in finding out the clusters. If a dataset consists of mixed attributes, i.e. a combination of numerical and categorical variables, a preferred approach is to convert different formats into a uniform format. The research study explores the various techniques to convert the mixed data sets to a numerical equivalent, so as to make it equipped for applying the statistical and similar algorithms. The results of clustering mixed category data after conversion to numeric data type have been demonstrated using a crime data set. The thesis also proposes an extension to the well known algorithm for handling mixed data types, to deal with data sets having only categorical data. The proposed conversion has been validated on a data set corresponding to breast cancer. Moreover, another issue with the clustering process is the visualization of output. Different geometric techniques like scatter plot, or projection plots are available, but none of the techniques display the result projecting the whole database but rather demonstrate attribute-pair wise analysis

Provenance, Isolation and Characterisation of Organic Matter in the Cochin Estuarine Sediment-“a Diagenetic Amino Acid Marker Scenario

Cochin estuarine system is among the most productive aquatic environment along the Southwest coast of India, exhibits unique ecological features and possess greater socioeconomic relevance. Serious investigations carried out during the past decades on the hydro biogeochemical variables pointed out variations in the health and ecological functioning of this ecosystem. Characterisation of organic matter in the estuary has been attempted in many investigations. But detailed studies covering the degradation state of organic matter using molecular level approach is not attempted. The thesis entitled Provenance, Isolation and Characterisation of Organic Matter in the Cochin Estuarine Sediment-“ A Diagenetic Amino Acid Marker Scenario” is an integrated approach to evaluate the source, quantity, quality, and degradation state of the organic matter in the surface sediments of Cochin estuarine system with the combined application of bulk and molecular level tools. Sediment and water samples from nine stations situated at Cochin estuary were collected in five seasonal sampling campaigns, for the biogeochemical assessment and their distribution pattern of sedimentary organic matter. The sampling seasons were described and abbreviated as follows: April- 2009 (pre monsoon: PRM09), August-2009 (monsoon: MON09), January-2010 (post monsoon: POM09), April-2010 (pre monsoon: PRM10) and September- 2012 (monsoon: MON12). In order to evaluate the general environmental conditions of the estuary, water samples were analysed for water quality parameters, chlorophyll pigments and nutrients by standard methods. Investigations suggested the fact that hydrographical variables and nutrients in Cochin estuary supports diverse species of flora and fauna. Moreover the sedimentary variables such as pH, Eh, texture, TOC, fractions of nitrogen and phosphorous were determined to assess the general geochemical setting as well as redox status. The periodically fluctuating oxic/ anoxic conditions and texture serve as the most significant variables controlling other variables of the aquatic environment. The organic matter in estuary comprise of a complex mixture of autochthonous as well as allochthonous materials. Autochthonous input is limited or enhanced by the nutrient elements like N and P (in their various fractions), used as a tool to evaluate their bioavailability. Bulk parameter approach like biochemical composition, stoichiometric elemental ratios and stable carbon isotope ratio was also employed to assess the quality and quantity of sedimentary organic matter in the study area. Molecular level charactersation of free sugars and amino acids were carried out by liquid chromatographic techniques. Carbohydrates are the products of primary production and their occurrence in sediments as free sugars can provide information on the estuarine productivity. Amino acid biogeochemistry provided implications on the system productivity, nature of organic matter as well as degradation status of the sedimentary organic matter in the study area. The predominance of carbohydrates over protein indicated faster mineralisation of proteinaceous organic matter in sediments and the estuary behaves as a detrital trap for the accumulation of aged organic matter. The higher lipid content and LPD/CHO ratio pointed towards the better food quality that supports benthic fauna and better accumulation of lipid compounds in the sedimentary environment. Allochthonous addition of carbohydrates via terrestrial run off was responsible for the lower PRT/CHO ratio estimated in thesediments and the lower ratios also denoted a detrital heterotrophic environment. Biopolymeric carbon and the algal contribution to BPC provided important information on the better understanding the trophic state of the estuarine system and the higher values of chlorophyll-a to phaeophytin ratio indicated deposition of phytoplankton to sediment at a rapid rate. The estimated TOC/TN ratios implied the combined input of both terrestrial and autochthonous organic matter to sedimentsAmong the free sugars, depleted levels of glucose in sediments in most of the stations and abundance of mannose at station S5 was observed during the present investigation. Among aldohexoses, concentration of galactose was found to be higher in most of the stationsRelative abundance of AAs in the estuarine sediments based on seasons followed the trend: PRM09-Leucine > Phenylalanine > Argine > Lysine, MON09-Lysine > Aspartic acid > Histidine > Tyrosine > Phenylalanine, POM09-Lysine > Histadine > Phenyalanine > Leucine > Methionine > Serine > Proline > Aspartic acid, PRM10-Valine > Aspartic acid > Histidine > Phenylalanine > Serine > Proline, MON12-Lysine > Phenylalanine > Aspartic acid > Histidine > Valine > Tyrsine > MethionineThe classification of study area into three zones based on salinity was employed in the present study for the sake of simplicity and generalized interpretations. The distribution of AAs in the three zones followed the trend: Fresh water zone (S1, S2):- Phenylalanine > Lysine > Aspartic acid > Methionine > Valine ῀ Leucine > Proline > Histidine > Glycine > Serine > Glutamic acid > Tyrosine > Arginine > Alanine > Threonine > Cysteine > Isoleucine. Estuarine zone (S3, S4, S5, S6):- Lysine > Aspartic acid > Phenylalanine > Leucine > Valine > Histidine > Methionine > Tyrosine > Serine > Glutamic acid > Proline > Glycine > Arginine > Alanine > Isoleucine > Cysteine > Threonine. Riverine /Industrial zone (S7, S8, S9):- Phenylalanine > Lysine > Aspartic acid > Histidine > Serine > Arginine > Tyrosine > Leucine > Methionine > Glutamic acid > Alanine > Glycine > Cysteine > Proline > Isoleucine > Threonine > Valine. The abundance of AAs like glutamic acid, aspartic acid, isoleucine, valine, tyrosine, and phenylalanine in sediments of the study area indicated freshly derived organic matter.