| Engineering projects |
| WIRELESS SUPPLY CHAIN INTEGRATION SYSTEM |
|
ABSTRACT
The project aims to build a complete framework for supply chain integration system, to develop and integrate supply chain database server, supply chain web application server and provide accessibility through a wireless device. The wireless supply chain integration system has four modules - consumer module, retailer module, manufacturer module and administrator module. This project uses J2ME Wireless Toolkit to implement the front end for a wireless device which communicates with the web server implemented in J2EE.
Download Complete Project
|
|
|
| USING GENETIC ALGORITHMS FOR DATA MINING OPTIMIZATION IN AN EDUCATIONAL WEB-BASED SYSTEMS |
|
ABSTRACT
The main objective is to classify the students in order to predict their final grade Based on features extracted from logged data in an educational web based system. We can give the grades to students through data mining. In data mining we have multiple classifiers. A combination of multiple classifiers leads to a significant improvement in classification performance. We use genetic algorithm to optimize the prediction accuracy.
Download Complete Project
|
|
|
| CREDIT MANAGEMENT SYSTEM |
|
ABSTRACT
The purpose of the Credit Management System project is to provide a standard approach to the collection of revenue in the organization, through an integrated financial package linked to PeopleSoft Financials.The project objective is to eliminate the myriad of satellite receipting systems and point of sale systems, providing a single integrated platform for revenue collection . Further, the projects objective is to provide transparency of all revenue collections, with tight financial and audit controls.
Download Complete Project
|
|
|
| SOFTWARE METRICS |
|
ABSTRACT
Now - a - days in modern applications large volumes of data arrive continuously, which is not possible to store in some form of memory. Like in telecommunication for example call records are generated continuously. Typically most processing is done by examining a call record once after which records are archived and not examined again. In this condition a model is necessary which take less amount of memory to cluster the data. Generally, various clustering algorithms operate on data in main memory and of smaller volumes. The new clustering algorithm (streaming algorithm) is very efficient on streaming, continuous and dynamic data over a network, which works on large sets of data stored in secondary memory. The project clusters the similar kind of data together into semantically equal groups for efficient retrieval. In this we present such a model.
Download Complete Project
|
|
|
|
|
|
|
|
|
|
|
