Welcome to Journal of Theoretical and Applied Information Technology

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 Classification of voluminous protein data based on the structural and functional properties is a challenging task for researchers in bioinformatics field. In this paper a faster, accurate and efficient classification tool has been developed, which is a hybridization of Sequence Arithmetic, Rough Set Theory and Concept Lattice. It reduces the domain search space to 9% without losing the potentially of classification of protein.

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 The general consideration in Spatial General Epidemic Model is controlling the propagation of the disease starting from an infected individual. By a suitable control process, this epidemic can be rooted out. Here, we suggest the control process using the percolation probability to be attached with the formation of edges having the suitable probabilities, giving the random graphs which will explain the extinction probability of the epidemic.

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 Recently mobile ad hoc network has attracted great interest with a variety of real applications. Rigorous research work, mainly on efficient routing protocol designs has been done and numerous MANET routing protocols have been developed. While designing the routing protocols it is assumed that the network is fully connected (i. e. there are no partitions). Mobility plays an important role in MANET. Relative node movement can break links and thus change the topology. In this paper we propose a physical implementation of a topology control algorithm for MANETs. The proposed algorithm maintains the topology without any control message. There is no need to change routing table as connectivity of the network is maintained all through. Each mobile node is equipped with a transceiver and a GPS receiver. Every node in the network is free to travel with its own velocity. Individual node can take the decision on its own to change the position for maintaining the connectivity with the reference node. They can roam around a reference node. Results obtained through the experimentation with the prototype developed, demonstrate that the connectivity and hence the topology of the network is always maintained.

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 Artificial Immune Recognition System (AIRS) is immune inspired classifier that competes with famous classifiers. Many researches have been conducted to improve the accuracy of AIRS and to investigate the source of power of AIRS. Some of these researches have focused on resource allocation method of AIRS.
This study investigates the difference between the accuracy of AIRS with fuzzy resource allocation and the accuracy of original AIRS, by using the reliable statistical method. The combination of ten fold cross validation and t-test was used as evaluation method and algorithms tested on ten benchmark datasets of  UCI machine learning repository. Based on the results of experiments, using fuzzy resource allocation increases the accuracy of AIRS in majority of datasets but the increase is significant in minority of datasets.

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 The concept of Orthogonal Frequency Division Multiplexing (OFDM) has been known since 1966, but it only reached sufficient maturity for deployment in standard systems during 1990s. OFDM is an attractive modulation technique for transmitting large amounts of digital data over radio waves. One major disadvantage of OFDM is that the time domain OFDM signal which is a sum of several sinusoids leads to high peak to average power ratio (PAPR). Number of techniques have been proposed in the literature for reducing the PAPR in OFDM systems. In this paper the various techniques proposed for reducing the PAPR and the selection criteria for choosing these techniques have been discussed. The goal is to convey the fundamental ideas and intuitive understanding of the concept introduced. This is done primarily to give an overview of the various techniques known today for PAPR reduction.

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 Companies have been collecting data for decades, building massive data warehouses in which to store it. Even though this data is available, very few companies have been able to realize the actual value stored in it. The question these companies are asking is how to extract this value. The answer is Data mining. There are many technologies available to data mining practitioners, including Artificial Neural Networks, Regression, and Decision Trees. Many practitioners are wary of Neural Networks due to their black box nature, even though they have proven themselves in many situations. This paper is an overview of artificial neural networks and questions their position as a preferred tool by data mining practitioners.

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 An electrical power utility is expected to maintain power quality and reliability. Electrical power reaches the consumer through a network of 400, 220, 132, 33 and 11 kV feeders. An electrical power utility is responsible for minimizing the number of interruption of these feeders. A regression based statistical method has been developed to forecast the number of interruptions. This approach utilizes historical data for evolving a strategy to take corrective actions to minimize the number of future interruptions. This paper proposes a scientific way for facilitating uninterrupted power supply to consumers.

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 This paper proposes an application of Markov model based reliability assessment for EHT Transmission system involving 132 kV & 220 kV level voltages in Chittoor District.

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 Image segmentation and its performance evaluation are very difficult but important problems in computer vision. A major challenge in segmentation evaluation comes from the fundamental conflict between generality and objectivity: For general-purpose segmentation, the ground truth and segmentation accuracy may not be well defined, while embedding the evaluation in a specific application, the evaluation results may not be extensible to other applications. This paper analyzes the performance of Normalized Cut (NC) and Efficient Graph (EG) methods of Image Segmentation. We treat image segmentation as graph partitioning problem and propose novel global criterion, NC for segmenting the graph. The NC criterion measures both total dissimilarity between the different groups as well as the total similarity within the groups. We apply efficient graph based image segmentation method to image segmentation using two different kinds of local neighbourhood in constructing the graph. We also present a special strategy to compare and analysis the above two graph-based methods

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 Among the different approaches to investigate in detail the extinction of population of rare species the Birth and Death process has given the simplest approach to settledown the solution. Here, an attempt is made to obtain the extinction probabilities and to generate a different levels of population sizes. A random quad-tree method is footforth. We give the basic concepts and results used in the subsequent sections.

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 In this work, Particle Swarm Optimization (PSO) for the solution of the optimal power flow (OPF) with use of controllable FACTS devices is studied. Two types of FACTS devices, thyristor controlled series compensator (TCSC) and thyristor-controlled phase shifters (TCPS) are considered in this method. The specified power flow control constraints due to the use of FACTS devices are included in the OPF problem in addition to normal conventional constraints. The sensitivity analysis is carried out for the location of FACTS devices. This method provides an enhanced economic solution with the use of controllable FACTS devices. IEEE standard 30-bus system is taken and results have been compared with GA to show the feasibility and potential of this PSO approach.

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A commonly used method for thinning regions on binary images consists of examining windows of 3 x 3 pixels throughout an image, and erasing the enter pixel if thinning criteria are met. Critical study will be made in present research to develop a new thinning algorithm based on kXk windows for the categorization and skeleton analysis of images. The advantage of this algorithm is they peel thick layers from the boundaries of image regions and also reduces overall iterations of thinning algorithm and reduces overall complexity of these proposed methods. 

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 Image segmentation and its performance evaluation are very difficult but important problems in computer vision. A major challenge in segmentation evaluation comes from the fundamental conflict between generality and objectivity: For general-purpose segmentation, the ground truth and segmentation accuracy may not be well defined, while embedding the evaluation in a specific application; the evaluation results may not be extensible to other applications. In this paper, we compare the performances of the two popular region-based image segmentation methods namely the Watershed method and the Mean-shift method. The watershed method, also called the watershed transform, is an image segmentation approach based on mathematical morphology. Mean-shift method is a data-clustering method that searches for the local maximal density points and then groups all the data to the clusters defined by these maximal density points.

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 This paper presents two new computationally efficient Fuzzy-Stochastic algorithms for solving Security Constrained Economic Dispatch (ED) in interconnected power system. The proposed algorithms are based on the combined application of Fuzzy logic strategy incorporated in both Evolutionary Programming (EP) and Tabu-Search (TS) algorithms. The main objective of the Multi Area Economic Dispatch (MAED) is to determine the generation allocation of each committed unit in the system and power exchange between areas so as to minimize the total generation cost without violating the tie-line security constraint. The proposed methods are tested on IEEE – 30 bus interconnected three area system. The investigation reveals that the proposed methods can provide accurate solution with fast convergence and has the potential to be applied to other power engineering problems.

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 This paper describes about the modeling of errors (like ionospheric delays, atmospheric delays, Tropospheric delays, Multipath effects and dilution of precision etc.,) affecting the GPS signals as they travel from satellite to user on Earth. These errors degrade the accuracy of GPS position. An attempt is made to improve the accuracy in locating the GPS receiver by filtering the range measurements with the datum conversion between Universal Transverse Mercator (UTM) and World Geodetic System (WGS - 84) using single frequency ML-250 hand held GPS receiver and smoothening of these coordinates using Kalman filter. A linear recursive filtering technique, Kalman filter is used for greater accuracy in estimating the position of user by considering the initial state of the system, statistics of system noise and measurement errors from sensor noise measurements. The results of proposed Kalman filter technique give better accuracy with more consistency and are found superior to the standard one