Vijay Mahantesh SM (@vijaym123)
  • Stars
    star
    30
  • Global Rank 477,323 (Top 17 %)
  • Followers 41
  • Following 20
  • Registered about 12 years ago
  • Most used languages
    Python
    90.0 %
    Ruby
    10.0 %
  • Location ๐Ÿ‡ฎ๐Ÿ‡ณ India
  • Country Total Rank 18,866
  • Country Ranking
    Ruby
    2,075
    Python
    3,737

Top repositories

1

Congressional-Data-Analysis

This project is on collecting data from govtrack.us and analysing it.
Python
6
star
2

StereoVision

Python
4
star
3

Boredom-Detection-In-Class

This project is based
Python
3
star
4

Prediction-of-arrival-of-nodes-in-a-Scale-Free-Network

We present in this article, a novel strategy to partially predict the order of node arrivals in such an evolved network. We show that our proposed method outperforms other centrality measure based approaches.
Python
3
star
5

twitminer

TwitMiner is a Machine learning contest conducted by Computer Science and Automation department of Indian Institute of Science, Bangalore. The challenge is to predict whether a particular tweet text can be classified to a category of โ€˜Politicsโ€™ or โ€˜Sportsโ€™.
Python
2
star
6

K-walk-dominating-set

A dominating set for a graph G = (V, E) is a subset D of V, where the set can be reached from any other vertices in less than or equal k steps with a probability involved is greater than half.
Python
1
star
7

ingenuitas.github.com

Summer Of Code Page
Ruby
1
star
8

AcademicCV

My CV
1
star
9

Testing

Hello World in Python
Python
1
star
10

Intrusion-detection-system

1
star
11

BlameGame

Python
1
star
12

Human-Navigation-Algorithm

Human navigation has been a topic of interest in spatial cognition from the past few decades. It has been experimentally observed that humans accomplish the task of way-finding a destination in an unknown environment by recognizing landmarks. Investigations using network analytic techniques reveal that humans, when asked to way-find their destination, learn the top ranked nodes of a network. In this paper we report a study simulating the strategy used by humans to recognize the centers of a network. We show that the paths obtained from our simulation has the same properties as the paths obtained in human based experiment. The simulation thus performed leads to a novel way of path-finding in a network. We discuss the performance of our method and compare it with the existing techniques to find a path between a pair of nodes in a network
Python
1
star