Welcome to Journal of Theoretical and Applied Information Technology

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In any distance and open learning system, the following functions are mandatory: Content Authoring/Acquisition, Structuring of content (logic and format), storage, processing, distribution and access. How these are done will determine the nature and effectiveness of the system. In turn these are dependent on what the purpose of the system is and how it is to be used. In many modern e-learning systems, these are not clearly defined and we find, very often, that the pedagogical design objectives of a course of study and its purpose are poorly met, even when the presentation of the contents are excellent. They are usually also of the one size fits all type, i.e. they are not flexible and assume all learners are the same and do not cater for learning styles and personal differences in ability of the learning community. In this paper we report a prototype system based on a framework which enables course and content designers to develop personalized learning systems which are important for distance learning where the availability of personal help from teachers and instructors may be poor.

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In the environment of automation and emergence of the Mechatronics field in an industrial sector especially in Computer Numeric Controlled (CNC) machines, use of Artificial Intelligence (AI) techniques is emerged as novel scheme to control the process more effectively. This further enhances the operational availability of CNC machines to help improve the productivity of the organization.
This paper presents a rule-based fuzzy logic controller applied to a closed loop control of an Induction Motor (IM) with slip regulation. The IM has modeled in terms of dq windings, with synchronous frame associated with the frequency ωs of the stator excitation. This Induction motor has installed in Makino A77 CNC machine at Mahindra and Mahindra Limited, Nagpur, India. The results obtained are interesting, considering the power failure at 0.5 seconds and the power and machine operational recovery at 1 second. The fuzzy logic used in the program is taking care of the actual environment prior to the occurrence of power failure.

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This paper presented an autonomous Secretary Agent (SA) that can perform meeting scheduling task on behalf of their respective user through negotiations. Previous study of searching strategy uses relaxation process to allow agents negotiate by relaxing their preference when conflict arises. However, this increased the cost of searching process. As a result, an improvement of relaxation searching strategy by adapting Neural Network (NN) learning mechanism is proposed. The back-propagation learning method is used in this research to intelligently predict the participants’ preferences and guide the host in selecting proposals that are more likely to get accepted. Hence, higher quality solution can be found in lower communication cost. The comparison result between the proposed and two previous estimation strategies showed improvement of quality of the solution as well as the communication cost of the proposed strategy.

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A popular subfield of data mining is the area of association mining that searches for frequently co-occurring items in a market-basket type database. A market basket is the list of items a customer purchases at the super market. We can analyze past transaction data to discover customer behaviors such that quality of business decision can be improved. The Input of Association mining is large set of transactions each consisting of a list of items a customer has purchased at a supermarket. This paper focuses on the composition of the list of frequent itemsets that may change in time as the purchasing habits get affected by season, fashion and introduction of new products. The output of this algorithm is to get the frequent items which occur after a particular item or item sets

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The strategy formulation in organizations will be useless unless it is well implemented. Monitoring and measuring the implementation process is responsibility of the stakeholders. By understanding the elements in strategy implementation such as the complexity of environment and dynamic changing in decision making can be considered as metrics to develop a performance tool and measurement kit. This metric mechanism can then deploy into a knowledge management system (Strategy Monitoring-Knowledge Management System). This paper explains the emerging metrics for measuring and monitoring strategy implementation at higher education institution in Malaysia. The concept of the balanced scorecard, the skandia navigator, the Okumus framework and the Cynafin framework will be used as the basic. The paper proposes a research framework in monitoring and measuring the success of strategy implementation and to ensure it aligns with organizational objectives

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Many advanced techniques are being developed for Power Systems Stability Assessment. We can use Eigen value techniques for the purpose of increasing the calculation performance of eigen-algorithms for Power System Small Signal Stability Analysis.
Firstly, we discuss a bulge chasing algorithm called the BR algorithm which is a novel and efficient method to find all Eigen values of upper Hessenberg matrices and has never been applied to eigen-analysis for power system small signal stability analysis. This paper analyzes differences between the BR and the QR algorithms with performance comparison in terms of CPU time based on stopping criteria and storage requirement.
Secondly, we discuss a method of small-signal stability analysis of power systems with microgrids which is based on the development of an integrated model in quadratic form and subsequent development of the transition matrix of the overall system. The Eigen values of the transition matrix provide the small-signal stability properties of the system.

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Audio coding is widely used in application such as digital broadcasting, Internet audio or music database to reduce the bit rate of high quality audio signal without comprising the perceptual quality.
In this paper work high quality audio codec at low bit rate using wavelet transform and improvement in reconstructed wave using post filtering has been proposed. The major issues concerning the development of audio codec are choosing optimal wavelets for audio signals, decomposition level in the digital wavelet transform and threshold criteria for coefficient truncation which is the basis to provide compression ratio for audio with suitable peak signal to noise ratio (PSNR), wavelet packet compression technique has also been used to compare the performance of audio codec using wavelet transform. After reconstructing the audio signal a post filtering technique is used to improve the quality of reconstructed audio signal. The proposed audio codec has been implemented in MATLAB 7.0 and various audio signals of different time duration have been tested. Result obtained show that the proposed codec improves quality of the reconstructed audio signal after post filtering

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In this paper, we introduce the new concept of interactive automata by proposing that states are not virtual elements but real objects containing useful information. They are used as models for structured data and system architecture, and then implemented in two developing environments, the XML environment and the client-server environment. These implementations produce, respectively, a generic markup language called Automata Markup Language (AML) and a client-server system, which together realize a homogeneous and uniform platform. An example is given in the E-learning domain where AML is a questionnaire description language and the automata client-server system plays the role of an assessment platform

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In this paper, a stomach cancer detection system based on Artificial Neural Network (ANN), and the Discrete Cosine Transform (DCT), is developed. The proposed system extracts classification features from stomach microarrays using the DCT. The features extracted from the DCT coefficients are then applied to an ANN for classification (tumor or non—tumor). The microarray images used in this study were obtained from the Stanford Medical Database (SMD). Simulation results showed that the proposed system produces a very high success rate.

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Construction of Virtual Backbone (VB) in Mobile Ad-Hoc Network (MANET) is a very familiar method to avoid broadcast storm problem. Most of the routing protocols in wireless domain based on flooding information and the result will be performance degradation. Existing research works concentrate on construction of Virtual Backbone, no work give the effective solution for Reconfiguration virtual backbone (R-VB) during mobility. If a node in movement topology maintenance is a difficult process. In this paper we propose a packet based solution for Reconfigurable virtual backbone (R-VB) to support mobility in MANET. The R-VB in MANET is achieved by exchange of ten bit control packet between backbone nodes and non backbone nodes. The fields in the packet indicate movement and battery power of the node. Exchange of packet between backbones and nodes locally, in the way every update support to maintain topology during the mobility. Exchange of small control packet between nodes, control overhead will be minimized, which will give the extra life time to the network, because to process the tiny packet less power required for each node. We present the performance of R-VB in terms of backbone size, delay and packet transmitted per each node. The performance of our algorithm is witnessed by simulation results.

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The recent advancements in wireless technology have lead to the development of a new wireless system called Mobile Adhoc Networks. A Mobile Adhoc Network is a self configuring network of wireless devices connected by wireless links. The traditional protocol such as TCP/IP has limited use in Mobile adhoc networks because of the lack of mobility and resources. This has lead to the development of many routing protocols such as proactive, reactive and hybrid. Reactive routing protocols have been found to be user friendly and efficient when compared to other routing protocols. The main boon of Reactive routing protocols when compared with Proactive and Hybrid routing protocols is the relatively unconditional low storage requirements, higher mobility and the availability of routes when needed. There are a variety of reactive routing protocols such as AODV, DSR, LAR1, LMR, ABR, SSI, TORA, RDMAR, MSR, AOMDV, MRAODV, ARA. However there is no study done over the efficiency of any reactive routing protocols. In this study a comparison and performance evaluation of three reactive routing protocols AODV, DSR and LAR1 are done using QualNet Simulator to identify the protocol that is best suited for MANET’s

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Phoneme recognition is important for successful development of speech recognizers in most real world applications. While speaker dependent phoneme recognizers have achieved close to 100% accuracy, the speaker independent phoneme recognition systems have poor accuracy not exceeding 75%.In this paper we describe a two-module speaker independent phoneme recognition system for all-Indian English speech. The first module performs classification of phonemes recognition using Probabilistic neural networks. The second module executes the recognized phonemes from the classified phonemes employing Recurrent Neural Networks. The system was trained by Indian English speech consisting of 1000 words uttered by 50 speakers. The test samples comprised 500 words spoken by a different set of 30 speakers. The recognition accuracy is found to be 98% which is well above the previous results.