Rajendra Sahu

Integrating Internet, telephones, and call centers for delivering better quality e-governance to all citizens

Abstract This article examines the present approach of providing e-government services through the Internet. Since the Internet is not accessible to most of the populations of the world, the article advocates adopting a multi-platform approach in which mobile and fixed line phones can be used to enhance the Internet in the delivery of e-government services. The article also suggests the concept of Government Call Centers to overcome the limitations posed by the digital divide. The study concludes that integration of the Internet, phones, and call centers can enable governments to deliver e-government to every citizen of a nation. Finally, the article makes specific recommendations to spread e-government services to more citizens through the approach suggested in this paper.

Many-Objective Comparison of Twelve Grid Scheduling Heuristics

Different class of stakeholders of Computational Grid has their own perspective and preferences, which result in different, often contradictory, criteria for scheduling (main step of grid resource management). To increase the level of satisfaction of different class of stakeholders grid management system must use the scheduling heuristic, which provides compromise solution (i.e. a compromise schedule) using the many conflicting objectives. Present work analysed, conflicting as well as harmonious, interactions of Many-Objectives and performed many objective comparison to find the most suited heuristics out of the twelve popular heuristics by1Visualization objectives of using 3D Bar Chart and Radar Chart in manner suggested, 2Non-dominated Ranking of Heuristics and 3-Qualitative Comparison. Emphasis is given to computation time taken by heuristics.

Portfolio evaluation using OWA-heuristic algorithm and data envelopment analysis

Purpose - The purpose of this paper is to evaluate two different asset selection methodologies and further examine these by forming optimal portfolios. Design/methodology/approach - This paper deals with the problem of portfolio formation, broadly in two steps: asset selection and asset allocation by using the two different approaches for the first step and then well-known mean variance portfolio optimization. In addition, the resulting portfolios are compared using Sharpe ratio. Findings - The empirical observations prove the applicability of the methodology adopted in the research design, ordered weighted averaging (OWA)-heuristic algorithm gives us a better portfolio from the sample observations. Also the asset selection procedures adopted in the research proves to be of help when an investor has to narrow down the number of assets to invest in. Practical implications - The analysis provides two different methodologies for portfolio formation – though the asset allocation is based on the mean variance portfolio optimization, the asset selection methods adopted provide a systematic approach to select the efficient securities. Originality/value - This paper shows that OWA can be used to decide the order of inputs for the heuristic algorithm. Also an attempt is made to use data envelopment analysis to find a solution to the problem of portfolio formation.

A WAVELET APPROACH FOR CLASSIFICATION OF MICROARRAY DATA

Microarray technologies facilitate the generation of vast amount of bio-signal or genomic signal data. The major challenge in processing these signals is the extraction of the global characteristics of the data due to their huge dimension and the complex relationship among various genes. Statistical methods are used in broad spectrum in this domain. But, various limitations like extensive preprocessing, noise sensitiveness, requirement of critical input parameters and prior knowledge about the microarray dataset emphasise the need for better exploratory techniques. Transform oriented signal processing techniques are successful in many data processing techniques like image and video processing. But, the use of wavelets in analyzing the microarray bio-signals is not sufficiently probed. The aim of this paper is to propose a wavelet power spectrum based technique for dimensionality reduction through gene selection and classification problem of gene microarray data. The proposed method was administered on such datasets and the results are encouraging. The present method is robust to noise since no preprocessing has been applied. Also, it does not require any critical input parameters or any prior knowledge about the data which is required in many existing methods.

Classification of multi class dataset using wavelet power spectrum

Data mining techniques are widely used in many fields. One of the applications of data mining in the field of the Bioinformatics is classification of tissue samples. In the present work, a wavelet power spectrum based approach has been presented for feature selection and successful classification of the multi class dataset. The proposed method was applied on SRBCT and the breast cancer datasets which are multi class cancer datasets. The selected features are almost those selected in previous works. The method was able to produce almost 100% accurate classification results. The method is very simple and robust to noise. No extensive preprocessing is required. The classification was performed with comparatively very lesser number of features than those used in the original works. No information is lost due to the initial pruning of the data usually performed using a threshold in other methods. The method utilizes the inherent nature of the data in performing various tasks. So, the method can be used for a wide range of data.

A framework for evaluating the status of supply chain integration

Supply chain management (SCM) is among the most frequently discussed topics in the corporate world today. Constant efforts are being made to develop value-added processes that deliver innovative, high-quality, low-cost products on time with greater responsiveness than ever before. Supply chain integration plays an important role to achieve the same. Unfortunately, the SCM concept has not been implemented in totality and there is a huge disconnect between supplier and customer. Many issues need to be addressed to integrate the supply chains of many industries. But the first step is the recognition of the present status of integration so that false notions are removed and fruitful work starts towards better integration and thus higher supply chain (SC) profitability. The present work tries to find out the critical parameters related to supply chain integration, which if addressed properly can provide improvements in supply chain integration effectiveness. Thereafter, the study after establishing the comprehensive list of information that flows through a generic SC and identifying their impact on the SC cost. Finally, a framework has been articulated to enable an organisation to identify its state of supply chain integration.

Optimal portfolio allocation using portfolio theory and heuristics driven evolutionary technique

The problem of portfolio optimization involves selecting appropriate stocks for investment by maximising the returns from the portfolio at a pre‐specified level of risk. The current approaches center around Markowitz’s mean variance optimization method that suffers from several pitfalls like instability of beta, and are either computation extensive or lead to sub‐optimal solutions. The present work suggests a heuristics and evolutionary approaches to portfolio optimization. The approach is computationally less intensive. It further extends the approach to include cardinality constraints that limit a portfolio to have a specified number of assets, and to impose limits on the proportion of the portfolio held in a given asset. The heuristics technique is employed for asset selection while the evolutionary technique is used for allocation of funds among the already selected assets. The approach is capable of handling a large number of instruments and scenarios, and is relatively stable to minor variations of the inputs, as is practiced in real life situations. The performance from this approach compares well with the Markowitz’s model, and performs better than the stock market indices of US and India.

Obstacle Detection for Vehicle Navigation by Chaining of Adoptive Declivities Using Geometrical Constrains

Here we present an approach of meaningful curve identification with its depth estimation by chaining of the edge points, to locate and track the obstacles with stereo matching for automatic vehicle navigation. We use a self adoptive and nonlinear principle of extended declivity to obtain the edge points (horizontal declivities) in the images. These edge points include lots of noise and hence matching is not effective directly. The large size of the matching problem does not allow us to use effective matching algorithm properly. We use basic assumptions of continuity in the shape of expected obstacles to reduce the problem size and match less number of features effectively. Vertical chaining is used to obtain features which can be used for the tracking or stereo and obtain obstacles in the region of interest. These newly proposed curves are defined with their features and a matching algorithm is used to obtain results.

A real time motion estimation for traffic monitoring using sloped diamond search

This paper focus on fast block based motion estimation for traffic monitoring using a stationary camera. As the traffic needs to be monitored continuously a real time motion estimation algorithm is required which could provide good match as well. Most of the existing block matching algorithms fail to give good match when the speed of motion varies too much. This algorithm (RTME-SDS) overcomes this problem by estimating the initial position to be searched using the motion vector of previous frame. At this position we apply sloped diamond search. Since the traffic move along a fixed path at different speed, this algorithm gives a good initial position to continue with the estimation. RTME-SDS improves over the above mentioned algorithm by more than 45% in terms of complexity giving a match close to above mentioned algorithm.