Information Technology

In the context of this article, the construction of software for language proficiency level and difficulty measurement of language proficiency tests was sought. The main software goal can not help with identification issues, and also the recognition of the text can not be mitigated by using common scoring systems, including the types of recognition calculations. This software accepts various Italian language test subjects whether they are graded or not, and after analyzing the text, ranks these texts at the corresponding Italian language proficiency levels and calculates the degree of difficulty of the test. The selection of subjects and their inclusion is of course done by the sampling method.

Essbase is multidimensional database software that is optimized for planning, analysis, and management-reporting applications. Essbase uniquely blends an innovative technical design with an open, client-server architecture. The product enables you to extend decision support systems beyond ad hoc queries and reports on historical performance to dynamic, operational systems that combine historical analysis and future planning .Oracle Hyperion Essbase is the industry-leading multi-dimensional online analytical processing server, providing a rich environment for effectively developing custom analytic and enterprise performance management applications. By leveraging its self-managed, rapid application development capabilities, business users can quickly model complex business scenarios. In this paper we see how Oracle Hyperion Essbase supports extremely fast query response times for vast numbers of users, large data sets, and complex business models.

In this study, two different type of Artificial Neural Networks (ANNs) were analyzed in simulating the daily inflow into Taleghan reservoir in Iran. These types include: General Regression Neural Network with standardized inputs (GRNN1) and with non-standardized inputs (GRNN1), and Radial Basis Networks with standardized inputs (RBN1) and with non-standardized inputs (RBN2). An iterative algorithm was designed to assess different architecture of these models. Results revealed the potential of these models, as suitable tools for simulating the daily reservoir inflow. Also, it was concluded that multiday averaging can improve the simulation results considerably.

Web is an enormous warehouse of knowledge and frequent hyperlinks. Web also serves a wide diversity of user communities and worldwide information service centers. Every minutes the knowledge in web page upwards swiftly. Web page is used to transmit the knowledge to web users. Such voluminous size of web makes a complexity of web information retrieval, web content filtering, web usage mining and web structure mining. Hence, it is essential to perform proper categorization of web pages. This paper formulates the web page categorization problem as multi classification task and provides an appropriate solution using support vector machine. The classification model is generated by learning the features that have been extracted from different category of HTML structures and URLs of the web pages. The experimental results of support vector machine with various kernels have been evaluated and observed that accuracy of web page categorization model with RBF kernel (98.5%) performs well than linear and polynomial kernel.

Current scenario in healthcare sector in Uttarakhand is disappointing. Public health service run by Government is overburdened and collapsing. Hilly geographical size, increase population density, lack of transport, inaccessibility, illiteracy, poverty, poor nutritional status, diversity in food habit and life style are various impediments. Government priorities for providing health service to rural areas is yet to be fulfilled. At this stage low budget from state for health, lack of funds and coordination have triggered down trend in health services in rural and hilly areas. As medical science is fast developing and information resource is pouring in, there is urgent need for dissemination knowledge by interlinking primary, secondary and tertiary level health centers using the ICT and e-Governance applications. This will help health personal to deliver high quality services. IT giants are experimenting for e-Governance application in health sector both in Government and private hospitals, This paper reviews use of e-Governance through ICT applications at National Level and also in Uttarakhand province. It presents facts on tele-medicine, tele-referal services and health information dissemination by Video conferencing, Some suggested services using ICT in healthcare are explained in this paper also. Use of Mobile phone to communicate health related alerts using SMS services in rural areas suggested here in this paper.

Implementation of this guidance, or indeed any IT best practice, should be consistent with your organization’s management style and the way your organization deals with risk management and delivery of IT value. All analysts currently agree that probably the biggest risk and concern to top management today is failing to align IT to real business needs, and a failure to deliver, or be seen to be delivering, value to the business. Since IT can have such a dramatic effect on business performance and competitiveness and particularly in security management issues, a failure to manage IT effectively can have a very serious impact on the business as a whole. In this paper, the notion and impact of governance is analyzed in the context of IT security management decisions. In doing so, two case studies are used to identify possible factors that may affect managers in developing successful governance strategies.

In order to save the energy and provide the humanized hint and operation of vending machine, we design the function and application combing with solar energy and light emitting diode (LED) screen, the vending machine updated the intelligent device system to data query and the alarm hints to sound. The theoretical analysis and practical results implies that it is feasible to provide better service for client by the intelligent device system.

Construction and development of classifier that work with more accuracy and perform efficiently for large database is one of the key task of data mining techniques [l7] [18]. Secondly training dataset repeatedly produces massive amount of rules. It’s very tough to store, retrieve, prune, and sort a huge number of rules proficiently before applying to a classifier [1]. In such situation FP is the best choice but problem with this approach is that it generates redundant FP Tree. A Frequent pattern tree (FP-tree) is a type of prefix tree [3] that allows the detection of recurrent (frequent) item set exclusive of the candidate item set generation [14]. It is anticipated to recuperate the flaw of existing mining methods. FP –Trees pursues the divide and conquers tactic. In this paper we have adopt the same idea of author [17] to deal with large database. For this we have integrated a positive and negative rule mining concept with frequent pattern (FP) of classification. Our method performs well and produces unique rules without ambiguity

Neural Networks are being used for character recognition from last many years. This paper presents creating the Character Recognition System, in which Creating a Character Matrix and a corresponding Suitable Network Structure is key. The Feed Forward Algorithm gives insight into the enter workings of a neural network; followed by the Back Propagation Algorithm which compromises Training, Calculating Error, and Modifying Weights. We have made an attempt to recognize handwritten English characters by using a multilayer perceptron with one hidden layer. In addition, an analysis has been carried out to determine the number of hidden nodes to achieve high performance of back propagation network in the recognition of handwritten English characters. The results showed that the MLP networks trained by the error back propagation algorithm are superior in recognition accuracy and memory usage. The result indicates that the back propagation network provides good recognition accuracy of more than 70% of Handwritten English characters.

In previous research reports the Excel™ outlier replacement protocol called: The Average of the Nearest Neighbor Panel-points: [ANN] was investigated. The authors reported that there seems to be a predilection relative to chance for ANN-protocols to produce a reduction in the standard error of the OLS-two-parameter linear regression [OLSR] model compared to that produced by the Basic or unmodified Panel. This is usually termed a Smoothing-Effect and results in more narrow Confidence or Capture Intervals [CI]—i.e., enhanced precision. There were idiosyncratic anecdotal conjectures offered as to why such a penchant may be created by ANN-protocols. We will consider Dysfunctional or Gaming Considerations: If Smoothing is inherent for ANN-protocols this offers an opportunity to make the decision to apply or eschew the application of the ANN-protocol based upon the intention to engineer the forecasting CIs. This being the case, two research questions are begged: Is there a Panel-length that: (i) sufficiently mollifies the Smoothing- or Provoking-events, or (ii) results in a balance between Smoothing- and Provoking-events either of which would render the gaming decision moot. We offer inferential tests re: (i) the conjecture that the length of the Panel systematically mollifies the ANN-impact on precision, and (ii) the conjecture that the seriousness of an ANN-impact on the OLSR-CIs is symmetrically balanced. We demonstrate inferentially that: Using the Medians of various ANN-protocols tested over various sample-sizes that mollification is likely the state of nature. However, despite mollification there seems likely to be asymmetry in favor of Smoothing. This suggests that gaming must be entertained as an opportunistic possibility. Given this, an organizational solution is suggested to mitigate against gaming the application of the ANN-protocols.