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Dr. Sandeep M.
@svmandr
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Researcher, Academician, Trainer
$36 USD / hora
・
India (1:09 a. m.)
・
Se unió el febrero 12, 2021
$36 USD / hora
・
In academics for more than 36 years
Has coding expertise from 8085 to Python
Has coding experience in Weka, R and Python
Has experience in segmenting and recognising natural objects in colour images
Experience of working with statistical tools
Has been working in the field detecting image forgery.
Has experience in building projects on Machine learning, Artificial intelligence, Deep learning.
Working on Cognitive Radio using machine learning techniques to sence the occupancy of any channel with any particular modulation scheme.
Mentoring research and research paper writing- more than 25 years of experience in Mentoring in report writing, research paper writing.
Refer my articles on:
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Cambios guardados
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Experiencia
Professor, Dean-R&D
jun, 2023 - Presente
•
1 año, 6 meses
Sri Krishna Institute of Technology
jun, 2023 - Presente
•
1 año, 6 meses
Mentoring research activities
Mentoring Project proposals writing and execution
Bengaluru, India
jun, 2023 - Presente
•
1 año, 6 meses
Professor
jul, 2007 - dic, 2022
•
15 años, 5 meses
Jayaprakash Narayan College of Engineering
jul, 2007 - dic, 2022
•
15 años, 5 meses
Research Mentoring
Teaching: Subjects include Machine Learning, Artificial Intelligence, Data Analytics, IoT
Project Mentoring: 150+ UG projects, 30+ PG projects
Mentoring Research Scholars working in the field of Cognitive Radio, Machine Learning, Deep Learning, OpenCV, Data Analytics, Artificial Intelligence, Image Processing, Forgery Detection to name a few
Expertise in: Python, OpenCV, R, gretl, MatLab, Simulink, Arduino based Circuit design and building, IoT
jul, 2007 - dic, 2022
•
15 años, 5 meses
Educación
Visvesvaraya Technological University
2004 - 2011
•
7 años
Ph. D.
India
2004 - 2011
•
7 años
Gulbarga University
1997 - 1999
•
2 años
ME(CSE)
India
1997 - 1999
•
2 años
Gulbarga University
1983 - 1988
•
5 años
BE(ECE)
India
1983 - 1988
•
5 años
Calificaciones
R programming A-Z: R for Data science
2019
Udemy
Statistical information of the data will be a great resource while analysing data and building models to forecast or predictions. R programming is very powerful and handy tool for these purposes.
Starting from the raw data to cleaning to visualization, all the stages of data analytics are explained in depth. The course objective is to simplify the data analytics activities and provide effective outcomes.
2019
Python for computer vision with OpenCV and Deep learning
2019
Udemy
Complete and detailed insights of how python helps the developers in building artificial intelligent agents through OpenCV and Deep learning. Computer vision is a major component of machine learning and artificial intelligence.
2019
Publicaciones
Skeletal distance mapped functional features for improved CBIR
International Congress for Global Science and Technology (ICGST)-GVIP, 2011,11(3), 47-56
The size of the skeleton of the object is used for Content Based Image Retrieval. It makes use of a fast and efficient distance mapping technique DSFT.
Citations: 3
A scale and rotation invariant fast image mining for shapes
IEEE Conference on AI Tools in Engineering, Pune, India
An efficient and fast distance mapping tool called "Distance mapping through Scanning and Filling Technique" DSFT makes it possible to find the size of the object boundary and the inner and outer layers. This information allows us to find the shape signature for any given object. This makes the image mining invariant to rotation and scaling.
Citations: 6
Level set issues for efficient image segmentation
International Journal of Image and Data Fusion 2(1):75-92 DOI: 10.1080/19479832.2010.491802
This article presents the impact of several distance mapping and level set methods suggested in the literature and provides an effective way of handling it. Further, this article emphasises the need of periodic reinitialisation of the level set function to a signed distance function which makes curvature term become redundant. Frequent reinitialisation of the level set to signed distance function overcomes this limitation and increases the speed of evolution.
Citations: 6
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