G. Sahoo

Development and performance evaluation of FLANN based model for forecasting of stock markets

A trigonometric functional link artificial neural network (FLANN) model for short (one day) as well as long term (one month, two months) prediction of stock price of leading stock market indices: DJIA and S&P 500 is developed in this paper. The proposed FLANN model employs the least mean square (LMS) as well as the recursive least square (RLS) algorithms in different experiments to train the weights of the model. The historical index data transformed into various technical indicators as well as macro economic data as fundamental factors are considered as inputs to the proposed models. The mean absolute percentage error (MAPE) with respect to actual stock prices is selected as the performance index to gauge the quality of prediction of the models. Extensive simulation and test results show that the application of FLANN to the stock market prediction problem gives out results which are comparable to other neural network models. In addition the proposed models are structurally simple and requires less computation during training and testing as the model contains only one neuron and one layer. Between the two models proposed the FLANN-RLS requires substantially less experiments to train compared to the LMS based model. This feature makes the RLS-based FLANN model more suitable for online prediction.

Efficient prediction of stock market indices using adaptive bacterial foraging optimization (ABFO) and BFO based techniques

The present paper introduces the use of BFO and ABFO techniques to develop an efficient forecasting model for prediction of various stock indices. The structure used in these forecasting models is a simple linear combiner. The connecting weights of the adaptive linear combiner based models are optimized using ABFO and BFO by minimizing its mean square error (MSE). The short and long term prediction performance of these models are evaluated with test data and the results obtained are compared with those obtained from the genetic algorithm (GA) and particle swarm optimization (PSO) based models. It is in general observed that the new models are computationally more efficient, prediction wise more accurate and show faster convergence compared to other evolutionary computing models such as GA and PSO based models.

Efficient prediction of exchange rates with low complexity artificial neural network models

In recent years forecasting of financial data such as interest rate, exchange rate, stock market and bankruptcy has been observed to be a potential field of research due to its importance in financial and managerial decision making. Survey of existing literature reveals that there is a need to develop efficient forecasting models involving less computational load and fast forecasting capability. The present paper aims to fulfill this objective by developing two novel ANN models involving nonlinear inputs and simple ANN structure with one or two neurons. These are: functional link artificial neural network (FLANN) and cascaded functional link artificial neural network (CFLANN). These have been employed to predict currency exchange rate between US

Securing Software as a Service Model of Cloud Computing: Issues and Solutions

to British Pound, Indian Rupees and Japanese Yen. The performance of the proposed models have been evaluated through simulation and have been compared with those obtained from standard LMS based forecasting model. It is observed that the CFLANN model performs the best followed by the FLANN and the LMS models.

Cloud Computing: Future Framework for e-Governance

Cloud computing, undoubtedly, has become the buzzword in the IT industry today. Looking at the potential impact it has on numerous business applications as well as in our everyday life, it can certainly be said that this disruptive technology is here to stay. Many of the features that make cloud computing attractive, have not just challenged the existing security system, but have also revealed new security issues. This paper provides an insightful analysis of the existing status on cloud computing security issues based on a detailed survey carried by the author. It also makes an attempt to describe the security challenges in Software as a Service (SaaS) model of cloud computing and also endeavors to provide future security research directions.

Identification of nonlinear systems using particle swarm optimization technique

The success of the forthcoming endeavors of human civilization depends on the proper utilization of the resources which are becoming scarce day by day. It is observed that while some places have plenty of resources, other places suffer from the lack of it. This discrimination can be wiped out by a proper management and strategy adopted by the Governments of different countries in the form of a properly implemented and managed e-Governance. The existing e-Governance framework can not address all categories of users. In this paper, we propose a new effective framework of e-Governance based on cloud computing concept, which would be intelligent as well as accessible by all.

Stock market prediction of S&P 500 and DJIA using Bacterial Foraging Optimization Technique

System identification in noisy environment has been a matter of concern for researchers in many disciplines of science and engineering. In the past the least mean square algorithm (LMS), genetic algorithm (GA) etc. have been employed for developing a parallel model. During training by LMS algorithm the weights rattle around and does not converge to optimal solution. This gives rise to poor performance of the model. Although GA always ensures the convergence of the weights to the global optimum but it suffers from slower convergence rate. To alleviate the problem we propose a novel Particle Swarm Optimization (PSO) technique for identifying nonlinear systems. The PSO is also a population based derivative free optimization technique like GA, and hence ascertains the convergence of the model parameters to the global optimum, there by yielding the same performance as provided by GA but with a faster speed. Comprehensive computer simulations validate that the PSO based identification is a better candidate even under noisy condition both in terms of convergence speed as well as number of input samples used.

A Novel Method of Edge Detection using Cellular Automata

The present paper introduces the bacterial foraging optimization (BFO) technique to develop an efficient forecasting model for prediction of various stock indices. The connecting weights of the adaptive linear combiner based model are optimized by the BFO so that its mean square error(MSE) is minimized. The short and long term prediction performance of the model is evaluated with test data and the results obtained are compared with those obtained from the multilayer perceptron (MLP) based model. It is in general observed that the proposed model is computationally more efficient, prediction wise more accurate and takes less training time compared to the standard MLP based model.

A charged system search approach for data clustering

Edge detection is one of the most commonly used operations in image analysis. Several edge detectors have been proposed in literature for enhancing and detecting of edges. In this paper a new and optimal approach of edge detection based on Cellular Automata (CA) has been proposed. The idea is simple but effective technique for edge detection that greatly improves the performances of complicated images. The comparative analysis of various image edge detection methods is presented and shown that cellular automata based algorithm performs better than all these operators under almost all scenarios. General Terms: Edge Detection, Image Processing, Computation Time.

Predication of Parkinson's disease using data mining methods: A comparative analysis of tree, statistical, and support vector machine classifiers

This paper presents a charged system search optimization method for finding the optimal cluster centers in a given dataset. CSS algorithm utilizes the Coulomb and Gauss laws from electrostatics to initiate the local search, and Newton second law of motion from mechanics is employed for global search. The efficiency and capability of the proposed algorithm are evaluated on seven datasets and compared with existing