Good Technical Skills(C,C Concepts on Network and OS

RESUME

Ravi Kumar N

S/o L.Nagaraj, Kadathanamale

Arakere Post, Hesarghatta Hobli

Bangalore North. Email: *************@*****.***

Phone: +91-805*******

Objective:

Intend to build a career with leading corporate of Hi-tech environment with committed & dedicated people, which will help me to explore myself fully and realize my potential. Willing to work as a key player in challenging & creative environment.

Education:

QUALIFICATION

BOARD\UNIVERSITY

YEAR

AGGREGATE

Bachelor of Engineering(CSE)

VTU

2015

60.07%

PUC

STATE

2011

81.33%

SSLC

KSSEB

2009

86.56%

Technical Skills:

Programming Languages: C, C++

Concepts: Networks, OS

Training/Certificates:

Member in organizing committee in the National conference on "Electronics, Computers and Computation" held in BMSIT.

Personal Skills:

Possess effective communication skills, leadership skills.

Able to grasp things quickly with the self-learning ability, motivated and determinate team player.

Project Done:

Project Title: "Feature Selection by Online Learning with Full or Partial Inputs"

Environment : JAVA & J2EE

Operating System : Windows 2000/XP/7/8 & above

Team Size : 4.

Role : Design, Development

Organization : B.M.S Institute of Technology & Mgmt. Bangalore.

Brief Overview of Project:

Feature selection is an important technique for data mining. Despite its importance, most studies of feature selection are restricted to batch learning. Unlike traditional batch learning methods, online learning represents a promising family of efficient and scalable machine learning algorithms for large-scale applications. Most existing studies of online learning require accessing all the attributes/features of training instances. Specifically, this article addresses two different tasks of online feature selection: (1) learning with full input where a learner is allowed to access all the features to decide the subset of active features, and (2) learning with partial input where only a limited number of features are allowed to be accessed for each instance by the learner. Feature selection has been an active and fruitful field of research area in pattern recognition, machine learning, statistics and data mining communities. Feature subset selection can be viewed as the process of identifying and removing as many irrelevant and redundant features as possible. By removing irrelevant and redundant features, feature selection can improve the performance of prediction models by alleviating the effect of the curse of dimensionality, enhancing the generalization performance, speeding up the learning process, and improving the model interpretability. Feature selection has found applications in many domains, especially for the problems involved high dimensional data.

Personal Profile:

Name Ravi Kumar.N

Date of Birth 02-07-1993

Father's name L Nagaraj

Mother's name Nagarathnamma

Gender Male

Marital Status Single

Languages Known English, Kannada, and Telugu

Nationality Indian

Address Ravi Kumar.N S/o L.Nagaraj, Kadathanamale

Arakere Post, Bangalore North-562163.

Declaration:

I hereby declare that the information furnished above is true to the best of my knowledge.

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