Welcome to Journal of Theoretical and Applied Information Technology

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The problem of cost estimation in software engineering has been addressed by several researchers. Research shows that among many factors that affect the development cost, size of the product plays an important role. In addition to the size, product properties like complexity, cohesion and coupling are also mentioned as cost factors. We focus on the relationship between complexity of a product to its development cost. The aim of this research is to identify the use of complexity metrics as important parameters in similarity matching for retrieval of cases in case based effort estimation. The objective is to estimate the development effort of student programs based on the values of certain attributes using case based reasoning model. We have developed a case based estimation model using complexity metrics and have validated it upon student data. Our focus is on object oriented programs.

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Antnet is an agent based routing algorithm based on real ants' behavior. Ants are able to find shortest path to food source .In real life, ants drop some kind of chemical substances to mark the path that they used. Then on their way back they choose the path with the highest pheromones which becomes the shortest path. But Antnet Algorithms may cause the network congestion and stagnation. Here, M-shortest optimal paths are proposed with negligible overhead in spite of single optimal path in Antnet routing algorithm and consequently the load is distributed over those paths with some strategy.

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The Feedforward Multilayer Perceptrons network is a widely used model in Artificial Neural Network using the backpropagation algorithm for real world data. There are two common ways to construct Feedforward Multilayer Perceptrons network, that is, either taking a large network and then pruning away the irrelevant nodes or starting from a small network and then adding new relevant nodes. An Artificial Neural Network model is often avoided due to the large size of network and the training that would be too slow to be tolerable. For improving the efficiency and to provide accurate results on the basis of same behaviour data, a serial algorithm for the training of data is proposed that uses two data mining techniques, that is, cluster analysis, which partitions large dataset into similar n blocks and then these n blocks are inputted to Feedforward Multilayer Perceptrons network to perform serial training for improving the efficiency.

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Induction motors are widely used in industries due to various techno-economic advantages .Voltage unbalance causes a lot of ill effects on induction motors. The problem of voltage unbalanced has attracted special attention amongst electrical engineers dealing with power quality issues in recent past. The adverse effects of voltage unbalance in Induction motors are Over heating, line-current unbalance, derating, torque pulsation, and inefficiency. The overheating leads to winding insulation degradation. This paper examines the proper application of the three phase induction motors when supplied by unbalance Voltage. The proposed work show that poor power quality voltages have bad effects over the motor performance: loss temperature rise, rated hp, efficiency, reliability. Finally this paper presents some recommendations to improve the performance.

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In the past decades, electricity markets have significantly restructured in both developed and developing countries. In it, Optimal Power Flow (OPF) is emerging as the main function of power generation, operation and control. OPF is an optimization problem, in which the utility strives to minimize its generation costs while satisfying all its equality and inequality constraints, while the system is operating within its security limits. In recent years, the incorporation of subsets of transmission High Voltage Direct Current (HVDC) in AC transmission networks brought significant techno-commercial changes in the transmission of the electric power in developing countries. This paper aims at (1) presenting Genetic Algorithm approach to solve OPF, (2) problem formulation with incorporation of HVDC link in a AC transmission system, (3) demonstrating the proposed methodology for IEEE test system and a complex and real power system of India and (4) to assess the performance of GAOPF with the traditional OPF method. The paper concludes that the proposed scheme is effective for the real network situation in developing countries

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This paper presents a simple three phase load flow method to solve three-phase unbalanced radial distribution system (RDS). It solves a simple algebraic recursive expression of voltage magnitude, and all the data are stored in vector form. The algorithm uses basic principles of circuit theory and can be easily understood. Mutual coupling between the phases has been included in the mathematical model. The proposed algorithm has been tested with several unbalanced distribution networks and the result of an unbalanced RDS is presented in the article. The application of the proposed method is also extended to find optimum location for reactive power compensation and network reconfiguration for planning and day-to-day operation of distribution networks.

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Facets of computer science, information theory, quantum mechanics combines to form Quantum computing. The repression architectural of Von Neumann computers with computational complexity of classical Algorithms which often deigh down, it makes to identity a better way to handle with skill for visual information. In classical computer the storage done in hardware is of bits. The bits are Independent to each other. The connectivity for those Independent bits is given by the software components. Inter connectivity between the bits in the memory leads to information to be lost. Each independent bit will represent some property of the associated image, like spatial, light strength. Retrieval of Image is done by the fetching the binary data from hardware if memory, by the way of independent property of bits. The reciprocal relationship between the bits of the image which is needed to understand properly about the Image is left out due to the Von Neumann architecture. An attempt has been made to improve visual information using QFT transform in the remote sensing data’s for linear feature identification. Here the images are recorded using the Qubit Lattice and algorithms for an image in qubit have been done in serial methods.

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Unlimited mobility free of any fundamental communications is presented by Mobile Networks. In such networks, the information access between unacquainted nodes and server is a usual thing that happens frequently. This creates more chance to take the advantage of accessing information illegally by any invalid user node. The threat of apprehensive surroundings, information mishandling, etc is the eventual outcome. The fortification of classified and susceptible data in mobile networks can be done by the vital system, cryptosystem that spots the legitimacy. In such circumstances, the fundamental pre-requisite for better authentication is the performance of authentication by a middle agent, say Authentication server. Hereby, we are proposing a protocol that provides a secure transfer of information between the both unaccustomed parties. The most common public-key cryptographic scheme, elliptic curve cryptography is exploited to devise the protocol. Subsequently, the mobile networks employ the protocol for the authentication of user node. As the protocol utilizes the elliptic curve cryptography, it offers security more than enough with a reasonable key length. Hence the protocol develops an environment in which the user node which is unfamiliar to the Main server can transfer information without any eavesdropping of un-authenticated nodes.

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This article presents a new approach to indirect vector control of permanent magnet synchronous machine. In this order the scrambled controller is added to the controller of sliding mode with integral action in order to guarantee the stability of the system. The new hybrid controller obtained is applied to the permanent magnet synchronous machine PMSM. In a first stage, we present the design of the order by fuzzy logic. This new order is applied to the PMSM. The second stage is devoted to the development of an order with variable structure. The latter is the order by sliding mode with integral action. In the third stage, we suggest a new scrambled controller of mode slipping for the ordering of position. In this case, the scrambled controller is added to the controller of sliding mode with integral action. We present the simulation results of the evolution of the real PMSM sizes, as well as the dynamic characteristics of the total system, during the starting of the PMSM. To justify our work, an evaluation of the two approaches is presented. Finally, the results of simulation prove that the system to be regulated is powerful and stable vis-a-vis the external disturbances.

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Research efforts have produced a lot of applications and hardware devices for healthcare services especially in the developed world. However, much still left to be desire in mobile wireless healthcare research that produces semantic web services applications solution especially for the developing African countries. The first successful implementation of a mobile wireless application for the healthcare industry in the United States of America (USA) was announced on 13 October, 2008 by InfoLogic.
This paper presents our research in the design and implementation of a framework for cost cutting electronic healthcare delivery services for rural/suburban communities. This is achieved through the development of a semantic web services framework that would be deployed to provide wireless mobile healthcare delivery services and health management services for rural African communities. Currently no e-health application making use of semantic web services are known or reported in literature to have been implemented in any real life situation in Africa. The product of this research will be deployed in some selected rural communities in south-western Nigeria. Software artifacts will be implemented on the proposed framework. The software will be deployed for use by patients and healthcare practitioner in some selected hospitals in south-western Nigeria.

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Automatic detection and classification of cardiac arrhythmias from ECG signals is of considerable importance in critical care or operating room patient monitoring. By examining the sequence of events on the ECG, cardiologists are able to diagnose cardiac arrhythmias. We propose in this paper a method to accurately classify the heartbeat of ECG signals through the artificial neural networks (ANN) and a solution to reduce time spent by specialist on long time ECG registers visual inspection.
We develop two neural classifiers from a global classifier we implemented a specialized classifier obtained after a second on-line training based on brief patient-specific ECG data (specialization by patient).
Our results demonstrate that Specialized classifier provides significantly enhanced performance (sensitivity Se=93.5185% ; 100% ; 100% correct classification CC= 96.1956% ; 100% ; 95.1220%) compared to Global classifier (sensitivity Se= 64.1711% ; 34.6154% ; 40% correct classification CC=79.5419% ; 37.0975% ; 69.4495%) respectively for the classification of Premature ventricular contraction, Right and Left bundle branch block.

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The protein superfamily classification problem, which consists of determining the superfamily membership of a given unknown protein sequence, is very important for a biologist for many practical reasons, such as drug discovery, prediction of molecular function and medical diagnosis. In this work, we propose a new approach for protein classification based on a Probabilistic Neural Network and feature selection. Our goal is to predict the functional family of novel protein sequences based on the features extracted from the protein’s primary structure i.e., sequence only. For this purpose, the datasets are extracted form Protein Data Bank(PDB), a curated protein family database, are used as training datasets. In these conducted experiments, the performance of the classifier is compared to other known data mining approaches / sequence comparison methods. The computational results have shown that the proposed method performs better than the other ones and looks promising for problems with characteristics similar to the problem

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This paper presents a novel method to determine the best locations for capacitor placement in unbalanced radial distribution networks and simple GA is used to find the optimal sizing of the capacitor bank. The objective function formulated includes the energy cost, capacitor installation cost and purchase cost, so that the fitness function is to be maximized for the net saving.

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Advances in data storage and image acquisition technologies have enabled the creation of large image datasets. In this scenario, it is necessary to develop appropriate information systems to efficiently manage these collections. The most common approaches use Content-Based Image Retrieval (CBIR). The goal of CBIR systems is to support image retrieval based on content e.g., shape, color, texture. In this paper color extraction and comparison were performed using the three color histograms, conventional color histogram (CCH), invariant color histogram (ICH) and fuzzy color histogram (FCH) .The conventional color histogram (CCH) of an image indicates the frequency of occurrence of every color in an image. The appealing aspect of the CCH is its simplicity and ease of computation. There are however, several difficulties associated with the CCH. The first of these is the high dimensionality of the CCH, even after drastic quantization of the color space. Another downside of the CCH is that it does not take into consideration color similarity across different bins and cannot handle rotation and translation. To address the problem of rotation and translation an invariant color histograms(ICH) based on the color gradients is used and to address the problem of spatial relationship fuzzy linking color histogram (FCH) is used.

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KNN algorithm is one of the best and the most usable classification algorithms which is used largely in different applications. One of the problems of this algorithm is the equal effect of all attributes in calculating the distance between the new record and the available records in training dataset ,how ever ,may be some of these attributes are less important to the classification and some of these attributes are more important. This results in misleading of classification process and decreasing the accuracy of classification algorithm. A major approach to deal with this problem is to weight attributes differently when calculating the distance between two records. In this research we used association rules to weight attributes and suggested new classification algorithm K-Nearest-Neighbor-Based-Association (KNNBA) that improves accuracy of KNN algorithm.
We experimentally tested KNNBA accuracy, using the 15 UCI data sets [1], and compared it with other classification algorithms NB, NN, C4.4, NBTREE, VFI, LWL and IBK. The experimental results show that KNNBA outperforms these seven algorithms.

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In this paper, a study of the synonymy in a dictionary of verbs is presented and a new approach to solve the polysemy problem is proposed. More precisely, it is about dispatching verb synonyms in groups called “sense components”, each corresponding to a verb meaning. The dictionary is represented as a graph and the sense component is obtained by studying circuits in this graph. This study is based on the following idea: verbs on a circuit may/must belong to the same component of sense. Our study has resulted in a graphical interface for automatic dictionary exploitation.