Implementation of Hand Detection based Techniques for Human Computer Interaction

The computer industry is developing at a fast pace. With this development almost all of the fields under computers have advanced in the past couple of decades. But the same technology is being used for human computer interaction that was used in 1970s. Even today the same type of keyboard and mouse is used for interacting with computer systems. With the recent boom in the mobile segment touchscreens have become popular for interaction with cell phones. But these touchscreens are rarely used on traditional systems. This paper tries to introduce methods for human computer interaction using the users hand which can be used both on traditional computer platforms as well as cell phones. The methods explain how the users detected hand can be used as input for applications and also explain applications that can take advantage of this type of interaction mechanism.

Predicting Students' Performance Using ID3 And C4.5 Classification Algorithms

An educational institution needs to have an approximate prior knowledge of enrolled students to predict their performance in future academics. This helps them to identify promising students and also provides them an opportunity to pay attention to and improve those who would probably get lower grades. As a solution, we have developed a system which can predict the performance of students from their previous performances using concepts of data mining techniques under Classification. We have analyzed the data set containing information about students, such as gender, marks scored in the board examinations of classes X and XII, marks and rank in entrance examinations and results in first year of the previous batch of students. By applying the ID3 (Iterative Dichotomiser 3) and C4.5 classification algorithms on this data, we have predicted the general and individual performance of freshly admitted students in future examinations.