Information Technology

Travelling salesman problem is an NP complete problem and can be solved using approximation algorithms. It is a minimization problem starting and finishing at a specified vertex after having visited every other vertex exactly once. Often, the model is a complete graph. An algorithm that returns near-optimal solutions is called approximation algorithms. Through analyzing the Metric TSP, the performance of the approximation algorithm can be improved significantly using parallelization that implements a program to find a path with approximately minimum travelling cost through parallel depth-first search and the result can be verified using graphical analysis of spanning trees.

Cloud computing provides the way to share distributed resource and services that belong to different organizations. Since cloud computing share distributed resources via network in the open environment thus it makes security problems. All types of users who require the secure transmission or storage of data in any kind of media or network. Since, the data transmissions on the internet or over any networks are vulnerable to the hackers attack. I’m in great need of encrypting the data. I propose a method to build a trusted computing environment for cloud computing system. In this method some important security service including authentication, encryption, decryption and compression are provided in the cloud computing system. Need of this software is divided in to three modules: Encryption, Decryption, and Compression.

Elliptic curves are algebraic curves that have been studied by many mathematicians since the seventeenth century. In 1985, Neal Koblitz and Victor Miller independently proposed public key cryptosystems using the group of points on an elliptic curve. The elliptic curve cryptosystem (ECC) was thus created. Since then, numerous researchers and developers have spent years researching the strength of ECC and improving techniques for its implementation. Secure applications in smart cards present implementation challenges particular to the platform’s memory, bandwidth, and computation constraints. ECC’s unique properties make it especially well suited to smart card applications. This paper describes the Elliptic Curve Cryptography algorithm and its security over RSA algorithm.

Static analysis means examining a program code and overcoming all possible errors that tend to occur at run-time. Static analysis tools are efficient in finding bugs and correction of defects that arise due to improper functioning of the code, before the actual execution of the code [10]. In recent times, technology has facilitated us with new tools that do deeper and more efficient code analysis and have a higher defect detection rate along with low fake warning ratio. This paper aims to deal with buffer overrun anomalies occurring in many areas of source code in Java.

Security has become the greatest problem within and outside the organizations. User ID, passwords and firewalls are the common steps that organizations take to secure their computers. However, these are not so effective mediums in current context of unsecure eon. Intruders and attackers are so advanced that they access the computer and manipulate it, so they cannot be traced easily. Through this contribution objective is to find out & present existing intrusion detection system (IDS) with their pros and cons that will be helpful to select the best one and provide the secure environment so that system will be protected from intrusion and attacks.

The advent of compression standards has led to the proliferation of innovative techniques in the compressed domain has become an active research topic. This paper presents an overview of the latest video compression standards related to the H.26xSeries and this paper is specifically covered including its latest standard, H.265 otherwise known as AVC (Advanced Video Coding). It also provides an overview of compression rules of thumb for different standards and the corresponding performance requirements for real-time implementations.

In recent years, we have witnessed a dramatic popularity of online social networking services, in which millions of people publicly communicate for a kind of mutual friendship relations. Social network research is one of the fastest growing academic areas as it is continuously expanding within our society. One key element of this field of research is social network visualization, which refers to the use of sociogram / illustrative diagrams of the joins that connect various actors in social networks. Visualization of social networks has a rich history, particularly within the social science since at least the 1930s. The use of graphical representations is one of the main defining properties of social networks. Researchers make use of pictorial images of social networks in order to communicate and understand the content and patterns within social networks. However, visual diagrams of social networks often suffer from a range of problems, the most common of which being the high density of edges and complex structures in large networks, providing sociograms that often appear as unclear set of nodes and edges. In this paper, we have made every possible effort to remove the fear from mind of people that understanding networks is a difficult process as it is difficult to visualize, navigate, and find patterns in networks. For this, we begin by defining what constitutes a social network site and then present our introduction of basic concepts of social networks, social network sites and then discussed about visualization needs and problems, benefits of using data visualization, implementations of visualization available over time.

Classification is an important task in data mining and machine learning, in which a model is generated based on training dataset and that model is used to predict class label of unknown dataset. Today most real-world data are stored in relational format. So to classify objects in one relation, other relations provide useful information. Relational data are the popular format for structured data which consist of tables connected via relations (primary key/ foreign key). Relational data are simply too complex to analyze with a propositional (single table learning) algorithm of data mining. So to classify from relational data there is a need of multi relational classification which is used to analyze relational data and predict unknown pattern automatically. This paper contains Multi Relational Classification with weighted voting algorithm for learning from relational data which result in increase accuracy. Also to decrease the running time voting technique is used compared to stacking as a combination method. The experimental study along with results demonstrate the effectiveness of algorithm respect to other existing techniques.

In last two decades continues increase of comput-ational power and recent advance in the web technology cause to provide large amounts of data. That needs large scale data processing mechanism to handle this volume of data. MapReduce is a programming model for large scale distributed data processing in an efficient and transparent way. Due to its excellent fault tolerance features, scalability and the ease of use. Currently, there are several options for using MapReduce in cloud environments, such as using MapReduce as a service, setting up one’s own MapReduce cluster on cloud instances, or using specialized cloud MapReduce runtimes that take advantage of cloud infrastructure services. Cloud computing has recently emerged as a new paradigm that provide computing infrastructure and large scale data processing mechanism in the network. The cloud is on demand, scalable and high availability so implement of MapReduce on the top of cloud services cause faster, scalable and high available MapReduce framework for large scale data processing. In this paper we explain how to implement MapReduce in the cloud and also have a comparison between implementations of MapReduce on AzureCloud, Amazon Cloud and Hadoop at the end.

A person’s learning of a new language starts with learning alphabets of the language. The validation of a learning process is its recognition under any circumstances. The application developed in this paper, is aimed at making the “recognition process of alphabets” after different distortions on the original structure and frame of the alphabet. The system creates the distorted alphabet with graphical transformations and recognition is done by a back propagation neural network. The graphical transformations include scaling, rotation and translation functions written in Open GL. The distorted character is recognized by the neural network coded in C#. The system can be deployed for any distorted image recognition application. The system has shown satisfactory performance for distorted character recognition.