Information Technology

The problem of Multiple Sequence Alignment (MSA) is one of the most significant problems in bio-informatics world. Solving this problem helps to reconstruct the phylogenetic tree, predict protein structure and its function. Several algorithms are proposed to solve this problem. Genetic algorithm is one of them which has been proposed in different versions to solve the MSA problem. In this article we provide a specific type of genetic algorithm to solve the MSA problem and we will explain it in detail. Once the problem is described, it will also be explained how to formulate the problem and how to define crossover and mutation operators. Finally, using the BALiBASE 3.0 database the performance of the algorithm is evaluated and the results are reported.

Control architecture for resource allocation in satellite networks is proposed, along with the specification of performance indexes and control strategies. The latter, besides being based on information on traffic statistics and network status, rely upon some knowledge of the fading conditions over the satellite network channels. The resource allocation problem consists of the assignment, by a master station, of a total available bandwidth among traffic earth stations in the presence of different traffic types. Traffic stations are assumed to measure continuously their signal fade level, but this information may either be used only locally or also communicated to the master station. According to the information made available on-line to the master station on the level of the fading attenuation of the traffic stations, the assignment can be made static, based on the a priori knowledge of long-term fading statistics, or dynamic, based on the updated measurements. In any case, the decisions can be adapted to slowly time-varying traffic characteristics. At each earth station, two basic traffic types are assumed to be present, namely guaranteed bandwidth, real-time, synchronous data (stream traffic), and best effort traffic (datagram traffic). Numerical results are provided for a specific architecture in the dynamic case, in a real environment, based on the Italian satellite national coverage payload characteristics.

Data mining is the process of extracting previously unknown patterns from large amount of data. Association rule mining is one of very important data mining models. Swarm intelligence is a new subfield of artificial intelligence which studies the collective behavior of groups of simple agents. In this paper, a new efficient algorithm is proposed for exploring high-quality association rules by firefly algorithm. The proposed method mines interesting and understandable association rules without relying upon the minimum support and the minimum confidence thresholds in only single run. Experimental evaluation shows the efficiency of proposed algorithm in terms of computation time.

Push-to-Talk (PTT) is a useful capability for rapidly deployable wireless mesh networks used by first responders. PTT allows several users to speak with each other while using a single, half-duplex, communication channel, such that only one user speaks at a time while all other users listen. This paper presents the architecture and protocol of a fault tolerant PTT service for PTT in all IP Networks.

The problem of Reoptimization of Steiner Tree is a NP Hard problem. Given a graph and a optimal solution of the Steiner tree and then after a slight modification is done in the initial instance, then a new Steiner minimal tree is to be determined. This is known as reoptimization of Steiner Tree. Several cases of reoptimization of Steiner tree have been discussed. This paper presents a comparative analysis of some of the previous work done on reoptimization of Steiner Tree.

Cloud Computing (CC) becomes the most promising technology to reach the advanced educational services, because it essentially provides a huge computing and storage capacities. Cloud computing provides reliable and tailored dynamic computing environments for education services. On the other side, e-learning has been realized as an efficient way of learning. The increasing number of students, services, education contents and resource as well as the way of adapting e-learning becomes problematic. As a potential technology to overcome the problems in e-learning, this study explores the potential impacts and the measure of how the educational services can be benefited by cloud. For that purpose the study attempt to adapt a proposed framework for virtual learning system in an extended cloud computing environment. This framework can be applied everywhere where there is a need for intensive teaching and learning in higher education. The applied case study findings of implementing the proposed framework equate the study expectations, where the student’s satisfaction significantly increased compared with the existing system.

Communication is the god given gift that enables intellectual and cultural exchange and builds up our competence in social behaviour. So now we are living in the information age. The internet and cloud computing has taken communication to unimaginable attitudes. Cloud computing entrusts remote services with a user's data, software and computation. But many questions arise when we think of security. Is Cloud computing communication private and security? But encrypted messages can still be tracked revealing who is talking to whom. The term Cloud Computing refers to the concept where the shared servers provide resources such as data, software to the clients. In order to use a Cloud service all you need is a web browser and an internet. The biggest Disadvantage in cloud computing is the data security. Because the data that is being stored in the cloud will be stored in the cloud provider’s server and hence this results in hacking of data by unauthorized person. In the business model using software as a service, users are provided access to application software and databases. The cloud providers manage the infrastructure and platforms on which the applications run. In this paper we gave our proposal, how we can secure our data in cloud computing. Our idea is based on implementing the Spread spectrum Image. Steganography’s (SSIS) in cloud computing platforms .we hope it will be very beneficial for the user who loves cloud platform but hesitating to use because of the data security issue.

Discretization transforms a continuous attribute values into a finite number of intervals and associates with each interval a numerical, discrete value. For mixed-mode (continuous and discrete) data, discretization is usually performed prior to the learning process and plays an important role in KDD process. CAIM is very efficient, supervised discretization algorithm. Recent data mining technology is found to be slow to handle data of very large scale. In addition, data mining needs to be a continuous, online process, rather than an occasional one-shot process which has created a need for incremental approach for effective model preparation and updating. Incremental classification is proposed in literature needs online discretization, has created a need for fast and efficient discretization algorithm. The Modified CAIM (MoCAIM) algorithm is proposed and used as online discretization for the Novel ID3 (NID3) algorithm where CAIR is used as attribute selection criteria and CAIM is used for online discretization. Improved NID3 (INID3) is proposed to improve the classification accuracy by considering the unclassified instances of the test and predication phases of the classification process. The outperforming results of MoCAIM and INID3 algorithms in term of classification accuracy and Execution time motivate to explore the process further for the streamed data classification.

For the many people in world social networks are playing major role day to day life. Depending on the user’s behavior and interaction with each other, the social networking sites are reshaped. Growing interest and development of social network sites like Facebook, Twitter, Flicker, YouTube etc. imposing many research challenges. And hence this is allowing researchers to do many research studies using data mining concepts. The main challenge of such online social networking websites is to find out the individuals behavior over social network. Understanding the user’s behavior on social networking websites is called as collective behavior. There are many data mining techniques presented to identify the behavior of individuals. Such methods of collective behavior allows to learn and predict the users online behavior and based on it assign the appropriate label to actor in network. But the another main problem occurs in such methods is the networks scalability due to which this systems becomes poor in performance and many not be work if the network size is too big. To overcome this problem we need to have scalable learning of collective behavior to deal with any size of social networks. Recently one such method presented, in this method an edge-centric clustering technique is presented to extract social network dimensions. With sparse social dimensions, the proposed approach can efficiently handle networks of any size. In this paper we are presenting the detailed discussion on this method.

The application takes care of certain problems in agriculture and health care by concurrently capturing images, audio and video and sending them to a specified server. Agricultural Scientists or doctors can view or listen to this images/video/audio information and provide proper solutions, accordingly. The Technology used is Content Based Image Processing which matches the histogram of two samples and detect the plant disease at the Server End.