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Employee Performance Identification Portal

N. Prasanna Balaji, Madhavi Pingili

Abstract


Employee Performance and Recognition Portal is online Performance Appraisal and Recognition system used by all the employees in different sections of the company. Salary hike and promotion depends upon the employee performance. This portal is a one stop shop for all the employees to provide details like tasks performed and performance measures improved etc to their superiors. It allows superiors to evaluate and analyze the employee’s performance and work done by him and target achieved in a given period of time and to rate him. It provides a very good interface between superiors and subordinates. Based on these ratings and overall performance shown by employees ranks will be allotted to them among the group of employees with same designation. This rank is called consolidated rank. This is the basic criteria for recognizing employee’s performance and to provide salary hike/promotion to any employee. This application maintains the entire data in a centralized and secured database server to maintain consistency in report generation and allows users to access from any location. This is an online application that allows multi user access of system and to track or manage the data simultaneously. Various roles and authentications have been provided and access to various areas in the tool is restricted according to the role given to users. The aim of this application is to reduce the manual effort needed to manage the details of tasks and performance measures of each and every employee which is very tedious. And maintaining historical data used by HR team in generating consolidated data is not possible. This portal helps them in generating consolidated rank or required reports with a single click. Also this application provides an interface to management and other users to manage the details of and to generate required reports. This helps to prevent unnecessary delays and human errors. This system helps in generating foolproof reports with in not time by users which is very difficult in current system (manual system). This system design is modularized into various categories. This system has enriched UI so that a novice user did not feel any operational difficulties. This system mainly concentrated in designing various reports requested by the users as well as higher with export to excel options.


Keywords


Functional requirements, Non-functional requirements

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References


Java How to program 5th edition Deitel and Deitel (Prentice Hall of India).

Internet & World Wide Web How to program 3rd edition by Deitel & Deitel and Goldberg (Pearson education).

Web enabled commercial application development using Java 2. 0 by Ivan Bayross (Prentice Hall of India).

Data base System Concepts 4th edition by Silbershatz, Korth, and Sudharshan (Tata McGraw Hill).

Fundamentals of Data base systems 4th edition by Ramez Elmasri and Shamkant B. Navathe (Pearson education).

Java Servlet Programming by O’relly publishers

Java Complete Reference 5th edition by Herbert Schildt (Tata McGraw Hill).

Algorithm and applications in java 3rd edition by Satraj Sahni (TataMcGraw Hill).

Classical Data Structures by Samantha (Pearson education).

Java Server Programming 2. 0 with complete J2EE concepts included(apress).

Software Engineering practice and principles 6th edition by Roger Pressmen (Tate McGraw Hill).

Core java volume-II Advanced features 7th edition by Cay S. Horstmann and Gary Cornell (Pearson education).

The Nature of Employee Turnover, 2002. Article. CCH-EXP, HRMPersonnel.26 Oct. 2002 (http://80-health.cch.com).

Society for Human Resource Management, Employee Turnover:Analyzing Employee Movement Out of the Organization. SHRM White Papers. June 1993. (http://www.shrm.org).

Leonard B. Turnover at the Top. HR Magazine 2001; 46:46.

Racz S. Finding the right talent through sourcing and recruiting. Strategic Finance 2000; 82:38.

Hampton L. Five ways to really irritate your employees. Business Credit 2001; 103:20.

Bai B, Ghiselli R, LaLopa J. Job satisfaction, life satisfaction and turnover intent. Cornell Hotel & Restaurant Administration Quarterly 2001; 42:28.

Jatinder N. D. Gupta, Randall S. Sexton, Enar A. Tunc, Selecting Scheduling Heuristics Using Neural Networks, INFORMS Journal on Computing, v. 12 n. 2, p. 150-162, April 2000 [doi>10. 1287/ijoc. 12. 2.150. 11893] .

Randall S. Sexton, Robert E. Dorsey, John D. Johnson, Toward global optimization of neural networks: a comparison of the genetic algorithm and back propagation, Decision Support Systems, v. 22 n. 2, p. 171-185,Feb. 1998 [doi>10. 1016/S0167-9236(97)00040-7]

Randall S. Sexton, Robert E. Dorsey, Reliable classification using neural networks: a genetic algorithm and back propagation comparison,Decision Support Systems, v. 30 n. 1, p. 11-22, Dec. 15 2000 [doi>10. 1016/S0167-9236(00)00086-5]

Randall S. Sexton, Jatinder N. D. Gupta, Comparative evaluation of genetic algorithm and back propagation for training neural networks, Information Sciences—Informatics and Computer Science: An International Journal, v. 129 n. 1-4, p. 45-59, Nov. 2000 [doi>10.1016/S0020-0255(00)00068-2]

Gupta JND, Sexton RS. Comparing back propagation with a genetic algorithm for neural network training. OMEGA The International Journal of Management Science 1999;27:679-84.

Randall S. Sexton, Robert E. Dorsey, Naheel A. Sikander, Simultaneous optimization of neural network function and architecture algorithm,Decision Support Systems, v. 36 n. 3, p. 283-296, January 2004.

Burkitt AN. Optimisation of the architecture of feed-forward neural nets with hidden layers by unit elimination. Complex Systems 1991;5:371-80.

Fogel DB. A comparison of evolutionary programming and genetic algorithms on selected constrained optimization problems. Simulation 1995;64(6):399-406.

www.javaworld.com

www.apaache.org

www.java.sun.com

www.w3schools.com

www.itpapers.com

Kamimura R. Internal representation with minimum entropy in recurrent neural networks: minimizing entropy through inhibitory connections.Network Computation in Neural Systems 1993;4:423-40.

Prechelt L. A study of experimental evaluations of neural network learning algorithm: current research practice, technical report 19/94,Fakultat fur Informatik, Universitat Karlsruhe, D-76128 Karsruhe,Germany, 1994. Anonymous FTP:/pub/papers/techreports/1994/1994-19.ps. Z on ftp.ira.uka.de.

Dorsey RE, Johnson JD, Mayer WJ. A genetic algorithm for the training of feedforward neural networks. Johnson JD. Whinston AB., editors.Advances in artificial intelligence in economics, finance, and management, vol. 1. Greenwich, CT: JAI Press Inc. ; 1994. pp. 93-111.

Drucker H, LeCun Y. Improving generalization performance using double back propagation. IEEE Transactions on Neural Networks 1992;3:991-7.

Karmin ED. A simple procedure for pruning back-propagation trained networks. IEEE Transactions on Neural Networks 1990;1:239-42.

Kruschke JK. Distributed bottlenecks for improved generalization in back-propagation networks. International Journal of Neural Networks Research and Applications 1989;1:187-93.

Dorsey RE, Johnson JD, Van Boening MV. The use of artificial neural networks for estimation of decision surfaces in first price sealed bid auctions. In: Cooper WW, Whinston AB., editors. New Direction in Computational Economics. Netherlands: Kluwer Academic Publishers; 1994. pp. 19-40.

Dorsey RE, Mayer WJ. Genetic algorithms for estimation problems with multiple optima, non-differentiability, and other irregular features. Journal of Business and Economic Statistics 1995;13(1):53-66.

Sexton RS, Sriram RS, Etheridge H. Improving decision effectiveness of artificial neural networks: a modified genetic algorithm approach. Decision Sciences 2003;34(3):421-42.

Romaniuk SG. Pruning divide and conquer networks. Network: Computation in Neural Systems 1993;4:481-94.

Chan WS, Tong H. On tests for non-linearity in time series analysis. Journal of forecasting 1986;5:217-28.

Cottrell M, Girard B, Girard Y, Mangeas M. Time series and neural network: a statistical method for weight elimination. In: Verlysen M., editor. European Symposium on Artificial Neural Networks. Brussels: D. facto; 1993. pp. 157-64.


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