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Barua pepe imethibitishwa kwa ufanisi.
mathura,
india
Ni saa 4:38 AM hapa
Alijiunga Mei 24, 2012
4
Mapendekezo
Shiv K.
@SuiGenSolutions
7.3
7.3
91%
91%
mathura,
india
98%
Kazi Zilizokamilika
96%
Kwa Bajeti
91%
Kwa wakati
32%
Kiwango cha Kuajiriwa Tena
Scripting | Web Scraping | Custom Web Development
Wasiliana na Shiv K. kuhusu kazi yako
Ingia ili kujadili maelezo yoyote kwenye gumzo.
Portfolio
Portfolio
Yellow Page Directory Scrapper
Amazon Search Wordpress Plugin
Bulk Profile Creator in C#
Real Time Floor Dashboard Charting App
Real Time Floor Dashboard Charting App
Social Media Tracking for US Presidential Election
Android Application for Tow Portal
Yellow Page Directory Scrapper
Amazon Search Wordpress Plugin
Bulk Profile Creator in C#
Real Time Floor Dashboard Charting App
Real Time Floor Dashboard Charting App
Social Media Tracking for US Presidential Election
Android Application for Tow Portal
Kaguzi
Mabadiliko yamehifadhiwa
Inaonyesha 1 - 5 kutoka kwa 50+ - hakiki
€220.00 EUR
PHP
Java
Python
Web Scraping
Software Architecture
+1 zaidi
S
•
$510.00 USD
Java
Android
Objective C
MySQL
AngularJS
•
$475.00 USD
PHP
Python
Software Architecture
MySQL
Data Mining
+1 zaidi
•
$180.00 USD
D
•
$200.00 NZD
•
Uzoefu
Director
Ago 2012 - Sasa
Full stack consultant
CDO - GSP
Jul 2011 - Mei 2012 (10 months, 1 day)
Full stack Java developer
Elimu
M. Tech.
(2 years)
Sifa
CCNA
Cisco Systems
2010
Chapisho
A Reduce Time Complexity Technique For Finding Frequent Item Sets
ICDM 2009 held at IMT Gaziabad, "Data Management" pp 258-264. , 2009
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Intrusion Detection System based on Real Time Rule Accession and Honeypot
International Conference on Computer Network Security and Applications, 15-17 July 2011. LNCS Springer
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Design and implementation of IDS using Snort, Entropy and Alert ranking system
IEEE International Conference on Signal, Communication and Network, Kanyakumari, 21-22 July 2011
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Local Subspace Based Outlier Detection
IC3 2009, CCIS 40, pp. 149-157, 2009. Springer-Verlag Berlin Heidelberg 2009.
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
An Enhanced Density Based Spatial Clustering of Applications with Noise
Advance Computing Conference, 2009. IACC 2009. IEEE International, pp. 1475-1478
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
Entity Ranking and Relationship Queries Using an Extended Graph Model
18 th International Conference on Management of Data (COMAD 2012)
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
Wasiliana na Shiv K. kuhusu kazi yako
Ingia ili kujadili maelezo yoyote kwenye gumzo.
Udhibitisho
Vyeti
Ujuzi wa Juu
Angalia Wafanyakazi Huru sawia:
Pitia Onyesho Sawia
Mwaliko umetumwa kwa mafanikio
Asante! Tumekutumia kiungo cha kudai mkopo wako bila malipo kwa barua pepe.
Hitilafu fulani imetokea wakati wa kutuma barua pepe yako. Tafadhali jaribu tena.
Imeshindwa kunakili kwenye ubao wa kunakili, tafadhali jaribu tena baada ya kurekebisha ruhusa zako.
Imenakiliwa kwenye ubao wa kunakili.
Onyesho la kukagua linapakia
Ruhusa imetolewa kwa Uwekaji wa Kijiografia.
Muda wako wa kuingia umeisha na umetoka nje. Tafadhali ingia tena.