The semantic web goal is to share and integrate data across different domains and organizations. The knowledge representations of semantic data are made possible by ontology. As the usage of semantic web increases, construction of the semantic web ontologies is also increased. Moreover, due to the monolithic nature of the ontology various semantic web operations like query answering, data sharing, data matching, data reuse and data integration become more complicated as the size of ontology increases. Partitioning the ontology is the key solution to handle this scalability issue. In this work, we propose a revision and an enhancement of K-means clustering algorithm based on a new semantic similarity measure for partitioning given ontology into high quality modules. The results show that our approach produces meaningful clusters than the traditional algorithm of K-means.

In mobile ad hoc networks (MANETs), providing reliable and stable communication paths between wireless devices is critical. This paper presents a fuzzy logic stable-backbone-based multipath routing protocol (FLSBMRP) for MANET that provides a high-quality path for communication between nodes. The proposed protocol has two main phases. The first phase is the selection of candidate nodes using a fuzzy logic technique. The second phase is the construction of a routing backbone that establishes multiple paths between nodes through the candidate nodes, thus forming a routing backbone. If any candidate node in the path fails due to a lack of bandwidth, residual energy or link quality, an alternate path through another candidate node is selected for communication before the route breaks, because a candidate node failure may lead to a broken link between the nodes. Simulation results demonstrate that the proposed protocol performs better in terms of the packet delivery ratio, overhead, delay and packet drop ratio than the major existing ad hoc routing protocols.

This paper proposed an algorithm for logic circuits verification using neural networks where a model is built to be trained and tested. The proposed algorithm for combinational circuits' verification is based on merging two of the well-known learning algorithms for neural networks. The first one is the Perceptron Convergence Procedure, which is used for learning the functions of the standard logic gates in order to simulate the whole circuit. While the second is a modified learning algorithm of Back-propagation neural networks to be used for the verification of the hardware design. The algorithm can predict the gates that cause the malfunction in the circuit design.
This work may be considered as a step toward building Distributed Computer Aided Design Environments depending on the parallel processing architecture, particularly in the Neurocomputer architecture.

This paper derives a new procedure for age classification of facial image based on the local region of facial image. The local region of facial image is extracted from a Significant Binary Pattern of Local Maximum Edge (SBPLME). The SBPLME is generated by calculating the absolute value of local difference between the average of local 3×3 sub window pixel values and its neighbors instead of the center pixel value. In the case of Local Maximum Edge Binary Pattern (LMEBP) calculating the absolute value of local difference between the center pixel value of local 3×3 sub window and its neighbors. The proposed SBPLME can generate 512 (0 to 511) different patterns. The present paper utilized Prominent LBP (PLBP) on the proposed SBPLME. The PLBP contains the significant patterns of Uniform LBP (ULBP) and Non Uniform LBP (NULBP). Thus the derived Significant PLBP of Local Maximum Edge (SPBPLME) becomes an efficient image classification and analysis, which will have a significant role in many areas. The novelty of the proposed SPBPLME method is, it has shown excellent age classification results by reducing the overall dimension, thus reducing the overall complexity.

The level of complexity and risks associated with software are increasing exponentially because of competing environment especially in geographically distributed projects. Global software development (GSD) face challenges like distance, communication and coordination challenges. The coordination and communication challenges are the main causes of failure in GSD. Project Oriented Risk Management (PRORISK) is one of the models to address the importance of risk management and project management processes in standard software projects. However, existing model is not proposed to handle GSD associated risks. This warrants the proposal of new PRORISK model to manage the risks of GSD. Survey is used as a research design to validate the proposed solution. We anticipate that the proposed solution will help the software companies to cater the risks associated with GSD.

This paper presents the performance analysis of smart PCS based system for an accurate oxygen content measurement in the aqueous electrolyte solution. The system intends to measure the photo catalytic activity of the PCS which is sensitive to O2 by integrating the software and hardware design technique for more accurate results. The threshold voltage of the PCS changes due to photo catalysis process causing fluctuations in the PCS readout which is sensed by the microcontroller via an inbuilt 10 bit A/D converter. The digitized signal is processed and converted into corresponding value of O2 content. The proposed digital system is fast as compared to conventional flow injection analysis which is tiresome and time consuming job. The approach for this system relies on the fast operation of PCS which operates under sub threshold conditions and reduced computation time. The system is more user friendly and the outcomes of simulation are fairly in agreement with the theoretical estimations. The system is highly favourable for water quality monitoring applications due to reduced response time.

The increasing speed of today's computer networks directly affects the performance of Network Intrusion Detection Systems (NIDS) in terms of speed of detection of threads. Therefore, the performance of the existing algorithms needs to be improved to enhance the speed of detection engine used in NIDS applications. Hence, this paper defines a new platform NIDS to enhance the speed of detection engine based on Weighted Exact Matching Algorithm (WEMA). Furthermore, this platform can be run in sequential and in parallel mode, using the pthread techniques, in order to increase the total performance of NIDS applications.

The World Wide Web is a huge source of hyperlinked information; it is growing every moment in context of web documents. So it has become an enormous challenge to manage the local repository (storage module of search engine) for to handling the web documents efficiently that leads to less access time of web documents and proper utilization of available resources. This research paper proposes an architecture of search engine with the clustered repository, organized in a better manner to make task easy for user to retrieving the web pages in reasonable amount of time. The research focuses on coordinator module which not only indexes the documents but also uses compression technique to increase the storage capacity of repository.

The traditional manual attendance system wastes time over students’ responses, but it has worked well for small numbers of students. This research presents a real-time group face-detection system. This system will be used later for student class attendance through automatic student identification. The system architecture and its algorithm will be described in details. The algorithm for the system was based on analyzing facial properties and features in order to perform face detection for checking students’ attendance in real time. The classroom’s camera captures the students’ photo. Then, face detection will be implemented automatically to generate a list of detected student faces. Many experiments were adopted based on real time video captured using digital cameras. The experimental results showed that our approach of face detection offers real-time processing speed with good acceptable detection ratio equal to 94.73%.

Web service selection is an indispensable process for web service composition. However it became a difficult task as many web services are increased on the web and mostly they offer similar functionalities, which service will be the best. User preferences are the key to retain only the best services for the composition. In this paper, we have proposed a web service composition model based on user preferences. To improve the process of web service composition we propose a case-based planning approach with user preferences which uses successful experiences in past to solve similar problems. In this paper we integrate user preferences in the phase of selection, adaptation and planning. Our main contributions are a new method of case retrieval, an extended algorithm of adaptation and planning with user preferences. Results obtained offer more than a solution to the user and taking both functional and non-functional requirements.