Information Technology

The aim of this article is to analyze the role of metadata in the use of learning objects. Firstly, an overview of metadata is provided and the role of educational metadata for learning objects is considered. Then the purposes, the uses and the values of metadata are investigated in this regard. In the continuation, the study reviews other related issues such as metadata categories, metadata creation, and metadata attributes or characteristics. Finally a collaborative approach for metadata creation is proposed. This research concludes that metadata has an invaluable role in facilitating the widespread use of learning objects.

Expert systems, which are the most commercially successful result of research in artificial intelligence, are software entities that follow the cognitive abilities of human experts in complex decision making situation. As one of the primary activities in computer science and dependent heavily on the rapid development in computer technology, Expert systems have been eagerly adopted by industries and applied to a wide range of applications. Expert systems belongs to a field of intelligence knowledge based systems the constitute one of the principle field of activity of computational intelligence, a field which been referred to as the science that attempts to reproduced human intelligence using computational means.

Learning objects provide easily accessible and individualized learning facilitated through flexible deployment of networks of small, reusable components from multiple sources. A good learning object helps students understand a worthwhile concept accurately, concretely, and well-presented. They also take students through a structured experience that draws on creative pedagogy. Despite their time-honored use and discussion in the area of information technology, learning objects are still the Cinderella sister of the other materials and objects used in education. The aim of this article is, therefore, to investigate the role of learning objects in enabling and enhancing learning. Firstly, learning objects are defined and the evolution of the learning object movement is explored. Then the need for learning objects is considered and the attributes or characteristics of learning objects are examined. The structure of learning content and the issue of granularity is also investigated. Finally, the benefits and risks associated with adopting a learning object approach are considered and learning object repositories are investigated.

In this work we have proposed a new application on mobile to cater the well being of heart and we designing a new mobile which is incorporating with stethoscope. We call it as “Mobile Steth” with automatic application for suggesting the food and heart related problems. Mobile steth will record the user datum; with this datum critical label will be checked. If the subject (patient) reaches the critical label, mobile steth will initiates an alarm to their family members and if the subject needs it will fix appointment in the nearest hospital.

It is a significant challenge to search, comprehend and use the semi-structured HTML, XML, database-service-engine information stored on the web. This data is more complex and dynamic than commercial databases’ data. Data mining has been applied to web-page ranking to supplement keyboard-based indexing. In this context, data mining improves the quality and efficiency of search results[1]. For the web to be at its best, we must improve its usability. Data mining can play a vital role in the development of intelligent web. It will make the web a more exhaustive, intuitive and intelligent, usable resource. The paper shows how data mining can be applied to discover and catalog important links and patterns that will make our web interactions directed and intelligent.

We have long used mechanical devices to compensate or physical disability, it may be possible to augmented mental capacity to add memory or carbon-based neural net 'wetware' brains-at a point in history, when we might be able to imprint the circuitry of the human brain using transistors on a silicon chip? Brain Chip-a direct interface between the biological information processing system of the brain and neurons system and the artificial information processing system of microprocessors and silicon electronics. The brain is a parallel processor. The colorful blue jay, I see flitting from tree to tree in my garden appears as a single image. But the brain divides what it sees into four component-color, motion, shape & depth. These are individually processed-at the same time-and compared to my stored memories. My brain combines all of these processes into one image, that's just vision aspect of a multiplexed moment of perception..- The human cortex has about 22 billion neurons and 220 trillion synapses. In this paper, we purpose to initiate ethics of implanting computer chip in the Brain and focuses on issues of manufacturing and scientific responsibility, anxieties about possible usage in children, and most troubling, issues of privacy and autonomy.

This paper develops a heuristic algorithm for the multi-index fixed charge transportation problem. The efficiency of this algorithm for solving the multi-index fixed charge transportation problem has been proved by comparing the results obtained by using this algorithm with the existing exact method of solving multi-index fixed charge transportation problem. Further the proposed algorithm is extended to multi-index fixed charge fuzzy transportation problem in which all the parameters are considered as trapezoidal fuzzy numbers. A numerical example is presented to illustrate the proposed method.

The paper is about cloud computing which focus on understanding the entire scope of the entire cloud computing spectrum from storage to security. In this paper, cloud computing is presented as an emerging business model, taking in view its evolution and its changing approach in society and reinvention of present technologies in commercial ways. It highlights this technology as certainly a compelling alternative to running all applications within a traditional corporate data centre and its distinctive features in terms of its architecture, models, reliability which are helping IT enterprise professionals to maximize performance, minimize cost and improve the scalability of their Enterprise IT endeavors.

Incorrect classification of risks or Policyholders can lead to the wrong choice for insurer, and in individual accident insurance, analyzing risk and determining premiums are based on the individual jobs in a job class. Therefore, the risk of the false classification of risks and Policyholders can lead to significant financial losses for insurance companies or policyholders. So in order to recognize and optimize the classification of job`s risk and thus determine a reasonable price for the policy insurance, the frequency and severity of losses can be considered as determining variables. In this study the collected data from Dana Insurance Company database, were entered in the SOM neural network in the form of three variables to assess the occupational risk. After reaching the overview of clustering resulted from SOM, and determining the optimal number of clusters based on Silhouette index, the separation of final clusters using different algorithms, K-Means and TwoStep, was done. Finally, the precise rules found for describing the outputs of the algorithms based on C5.0 algorithm with accuracy of 100%. Due to the overlapping clusters, there was no possibility of allocating jobs to different occupational classes. There is also another notable result and that is the significant negative correlation between the two factors, the current risk level and the damage ratio (the ratio of claims paid to premiums received)

Document segmentation is a method of rending the document into distinct regions. A document is an assortment of information and a standard mode of conveying information to others. Pursuance of data from documents involves ton of human effort, time intense and might severely prohibit the usage of data systems. So, automatic information pursuance from the document has become a big issue. It is been shown that document segmentation will facilitate to beat such problems. This paper proposes a new approach to segment and classify the document regions as text, image, drawings and table. Document image is divided into blocks using Run length smearing rule and features are extracted from every blocks. Discipulus tool has been used to construct the Genetic programming based classifier model and located 97.5% classification accuracy.