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Flag of India Mathura, India
Member since May, 2012
4 Recommendations


We are a team of experienced and talented professionals based in India. Our team, led by an enthusiastic management comprising a couple of IITians, has expertise in different domains including Mobile App development, Data Analytics, Web App development, Web Scraping etc. We have experience of over 8 years in the field of web scraping, web analytics and other custom web development solutions. Our expertise - Fields - Scripting, Web scraping, Server Administration, E-Commerce solutions, Custom web development, Data Mining Languages - Shell, Python, C#, Java, , Php Framework - Wordpress, Magento, Spring, Hibernate, AWS, Heroku, Django We provide quality solutions in timely manner. Hire us and you will feel the difference!
$30 USD/hr
96 Reviews
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Recent Reviews


Analytics Specialist

Jul 2001 - May 2013 (11 years)


M. Tech.

2009 - 2011 (2 years)


CCNA (2010)

Cisco Systems


Entity Ranking and Relationship Queries Using an Extended Graph Model

There is a large amount of textual data on the Web and in Wikipedia, where mentions of entities (such as Gandhi) are annotated with a link to the disambiguated entity (such as M. K. Gandhi). Such annotation may have been done manually (as in Wikipedia) or can be done using named entity recognition/disambiguation techniques. Such an annotated corpus allows queries to return entities, instead of documents. Entity ranking queries retrieve entities that are related to keywords in the query and belong to a giv

An Enhanced Density Based Spatial Clustering of Applications with Noise

DBSCAN is a pioneer density based clustering algorithm. It can find out the clusters of different shapes and sizes from the large amount of data which is containing noise and outliers. But the clusters detected by it contain large amount of density variation within them. It can not handle the local density variation that exists within the cluster. For good clustering a significant density variation may be allowed within the cluster because if we go for homogeneous clustering, a large number of smaller unimp

Local Subspace Based Outlier Detection

Existing studies in outlier detection mostly focus on detecting outliers in full feature space. But most algorithms tend to break down in highdimensional feature spaces because classes of objects often exist in specific subspace of the original feature space. Therefore, subspace outlier detection has been recently defined. As a novel solution to tackle this problem, we propose here a local subspace based outlier detection technique, which uses different subspaces for different objects. Using this concept we

Design and implementation of IDS using Snort, Entropy and Alert ranking system


Intrusion Detection System based on Real Time Rule Accession and Honeypot


A Reduce Time Complexity Technique For Finding Frequent Item Sets