The core objective of ontological representation is to provide better domain knowledge in specific areas. Ontologies are being developed to share common understanding regarding the structure of information among the people concerned in particular domain. This paper explains about the ways in which ontological representation can be inherited into the teaching-learning process of Software Engineering course. The process starts with concept design followed by concepts hierarchy, setting relations, and constraints in the software engineering domain.

Mobile Ad Hoc Network (MANET) is a large collection of mobile nodes which are equipped with wireless communication devices. These devices communicate peer-to- peer in a network with no fixed infrastructure even when moving. It uses various routing mechanism in order to choose the path for data transfer .A 2-layer analysis routing algorithm is proposed which aims to reduce network traffic and to improve efficiency, reduce power requirements for similar algorithms and improve reliability.

Distributed peer-to-peer systems rely on voluntary participation of peers to effectively manage a storage pool. In such systems, data is generally replicated for performance and availability. If the storage associated with replication is not monitored and provisioned, the underlying benefits may not be realized. Resource constraints, performance scalability, and availability present diverse considerations. Availability and performance scalability, in terms of response time, are improved by aggressive replication, whereas resource constraints limit total storage in the network. Identification and elimination of redundant data pose fundamental problems for such systems. In this paper, we present a novel and efficient solution that addresses availability and scalability with respect to management of redundant data.Specifically, we address the problem of duplicate elimination in the context of systems connected over an unstructured peer-to-peer network in which there is no a priori binding between an object and its location. We propose two randomized protocols to solve this problem in a scalable and decentralized fashion that does not compromise the availability requirements of the application. Performance results using both large-scale simulations and a prototype built on Planet Lab demonstrate that our protocols provide high probabilistic guarantees while incurring minimal administrative overheads.

Cloud Computing has developed as a well-known model in computing world in which resources of computing infrastructure are provided as services over the Internet. From the consumer’s viewpoint the profit behind the idea is to be able to dynamically adjust computing power up or down to meet the demand for that power at particular moment. However, Cloud Computing present an added level of risk because fundamental services are often outsourced to a third party, which make it harder to sustain data security and privacy, support data and service accessibility and demonstrate compliance. The purpose of this paper is to discuss the concept of Cloud Computing to achieve a complete description of what a cloud is and what security concern are required to be properly address and managed to realize the full prospective of Cloud Computing.

This paper presents a comprehensive survey of different face detection approaches based on Adaboost algorithm and its variants. Face detection precedes the process of Face recognition and analysis. Precise and accurate face detection lays down a robust foundation for many real world face analysis applications. A face detection algorithm tells us, if and where faces exist in a digital image. A number of face detection techniques have been introduced but most of them are context specific, rather than being generic. Here, our aim is to compare with each other, the different techniques used by Viola and Jones. Also, we will try to compare the Viola and Jones method for face detection with an enhanced face detection approach that uses a combination of cascaded adaboost algorithm and a hierarchal neural network. Based on our review, we will try to propose possible future enhancements in the field of Face Detection.

In this paper we analyzed the different views of various researchers about License Plate Recognition. For accuracy enhancement of license plate recognition, some is used AdaBoost algorithm to remove non-character region or boundaries and some is used hole filling algorithm for remove color stains and many researcher used multiple classifier for character recognition and accuracy enhancement of a license plate recognition. The result from various surveys done by various researchers and agencies are support Ada Boost algorithm for improve the accuracy enhancement of a license plate recognition. An approach of license plate recognition based on automatic license plate recognition. In this papers proposed multiple classifier and removing color stains on license plate recognition based on hole filling algorithm ,detection and character recognition are two steps contains by license plate recognition. For remove the boundaries proposed a efficient Ada Boost algorithm. The algorithm works based on license plate detected algorithm, it follow many rules estimation of character height, estimation of width, identification of block and segmentation. Much higher accuracy of recognition to access a high recognition rate 3 classifier used svm, BP, Ann, and minimum distance classifier whose arranged in structure of voting manners. Segmentation and recognition is two main approaches are used for clean license plate. 

There are various search techniques among which Genetic algorithms (GAs) are powerful search techniques that are used successfully to solve problems in many different disciplines. Genetic Algorithms are particularly easy to implement and promise substantial gains in performance. As such, there has been extensive research in this field. Biometric authentication systems are also becoming a solution to a wide number of authentication and identity management problems. Unique parts of the human body can be used to identify a person for example fingerprints, iris, lips etc. Different methods used so far for fingerprint and face recognition is slow and expensive. In this paper we give an introduction to genetic algorithms and explain how it can be used for palm recognition. Genetic algorithm does not require special equipment’s and can be used in system where fast detection is required.

Applications of wireless sensor networks witnessed tremendous growth in recent years. In this article, a Least –Disruptive Topology Repair (LeDiR) algorithm is proposed for random simultaneous failure of multiple node in wireless sensor-actor networks (WSANs). The recovery plan is presented with minimal topology change. LeDiR is a localized algorithm with advantage that no additional pre-failure communication overhead is introduced in the network. The performance of LeDiR for simultaneous node failure is compared with the LeDiR for single node. The performance metrics used are: Total distance travelled, No of nodes moved and is analyzed in MATLAB.

A good application is the one that has good user interface. This is because users will judge an application based on its user interface. A good user interface is the one that is designed based on principles of user interface design. Since the user of an application is human, then designers must also understand and apply cognitive psychology in their designs. This is to know what human really need. From a survey we carried out, it is proven that the user interface that applies both principles of user interface design and cognitive psychology is preferred by the most respondent.