Company information Tracking System .net b-tech projects

CITS is a powerful human resource tool for maintaining employee and company information. More than a data storage program, CITS helps you manage your employees. CITS offers a wide variety of Reports that give you exactly the information you need. View payroll information by department, or find everyone who is receiving company Benefits. CITS gives you the power of information with different report categories.
CITS allows you to add and remove employees from the program and provides access to all employee information categories from Address History to Work Information. Organization files keep track of your company information. From this screen you can create, modify, and Remove company data. You can adjust data for company benefits, departments, evaluation categories, and Positions. It is a good idea to define your departments and positions before adding employees. You must also set up your company benefits and evaluations before adding them to your employee files. When you create a new category such as an Additional department or position, it is immediately available for selection in every applicable employee screen. Checklists assist you in office management by creating a list of items that need to be completed for a particular event. For example, you may want to make a checklist of everything that needs to be done when someone is hired.
CITS allows you to preview and print different reports that range from individual Work History to department Headcounts. Each report screen has different options. You can change the name of the report by editing the Report Title field. This will not change the name of the report in the drop down box, only the name as it appears on the report. The header includes report information such as the report title and date. Your company name (if selected) and report title will appear in every header. If you choose to have your company logo displayed on your report, it will appear in the top left corner. The date range of the report is displayed below the title. The date of the report is in the top right corner directly above the page number. The header may also contain information such as the employee name, department, and Selection Criteria
Main Module’
Employee Files:
 Address History:
 Benefits:
 Evaluation:
 Warning:
Organization Files:
 Company Benefits,
 Evaluation Categories
 Positions
Check Lists:
 New Employee Hires
 Termination Checklist
Reports:
 Certification List,
 Address History,
 Personal Information,
 Achievement List,
 Education and training Report,
 Department Salary Profile,
 Employee Compensation Status.

A Communication Perspective on Automatic Text Categorization –java/dotnet

Abstract
The basic concern of a Communication System is to transfer information from its source to a destination some distance away. Textual documents also deal with the transmission of information. Particularly, from a text categorization system point of view, the information encoded by a document is the topic or category it belongs to. Following this initial intuition, a theoretical framework is developed where Automatic Text Categorization(ATC) is studied under a Communication System perspective. Under this approach, the problematic indexing feature space dimensionality reduction has been tackled by a two-level supervised scheme, implemented by a noisy terms filtering and a subsequent redundant terms compression. Gaussian probabilistic categorizers have been revisited and adapted to the concomitance of sparsity in ATC. Experimental results pertaining to 20 Newsgroups and Reuters-21578 collections validate the theoretical approaches. The noise filter and redundancy compressor allows an aggressive term vocabulary reduction (reduction factor greater than 0.99) with a minimum loss (lower than 3 percent) and, in some cases, gain (greater than 4 percent) of final classification accuracy. The adapted Gaussian Naive Bayes classifier reaches classification results similar to those obtained by state-of-the-art Multinomial Naive Bayes (MNB) and Support Vector Machines (SVMs).

A Divide-and-Conquer Approach for Minimum Spanning Tree-Based Clustering –java

Abstract
Due to their ability to detect clusters with irregular boundaries, minimum spanning tree-based clustering algorithms have been widely used in practice. However, in such clustering algorithms, the search for nearest neighbor in the construction of minimum spanning trees is the main source of computation and the standard solutions take O(N2) time. In this paper, we present a fast minimum spanning tree-inspired clustering algorithm, which, by using an efficient implementation of the cut and the cycle property of the minimum spanning trees, can have much better performance than O(N2).

Ranking and Suggesting Popular Items java Project

Abstract
We consider the problem of ranking the popularity of items and suggesting popular items based on user feedback. User feedback is obtained by iteratively presenting a set of suggested items, and users selecting items based on their own preferences either from this suggestion set or from the set of all possible items. The goal is to quickly learn the true popularity ranking of items (unbiased by the made suggestions), and suggest true popular items. The difficulty is that making suggestions to users can reinforce popularity of some items and distort the resulting item ranking. The described problem of ranking and suggesting items arises in diverse applications including search query suggestions and tag suggestions for social tagging systems. We propose and study several algorithms for ranking and suggesting popular items, provide analytical results on their performance, and present numerical results obtained using the inferred popularity of tags from a month-long crawl of a popular social bookmarking service. Our results suggest that lightweight, randomized update rules that require no special configuration parameters provide good performance.