
Dany Varghese
About
My research project
I am currently pursuing a PhD in the area of Computer Vision which incorporates the advantages of one-shot meta interpretive learning in the domain of plant disease detection.
Previously, I was worked as Assistant Professor, Department of Computer Science & Engineering, Jyothi Engineering College(NACC & NBA Accredited), Kerala, India. My research interest lies in the area of Image Processing & Machine Learning.
Supervisors
I am currently pursuing a PhD in the area of Computer Vision which incorporates the advantages of one-shot meta interpretive learning in the domain of plant disease detection.
Previously, I was worked as Assistant Professor, Department of Computer Science & Engineering, Jyothi Engineering College(NACC & NBA Accredited), Kerala, India. My research interest lies in the area of Image Processing & Machine Learning.
ResearchResearch interests
Humanity is facing a great challenge to feed the growing population of 7.7 billion people and food security remains threatened by a number of factors including new plant diseases. Moreover, the excessive use of chemicals (to fight the plant diseases) has led to the adverse effects on agro-ecosystem. Presently there is an immediate need for early and precise diagnostic techniques to control the plant diseases for the sustainability of the ecosystem. The state-of-the-art algorithm for this problem requires building a new model from a very large number of previous cases and there is
currently no algorithm that could learn an accurate model only from a new case, e.g. a single image.
Proposed Method:
We develop a new framework called One-Shot Meta-Interpretive Learning (OSMIL) for the problem
of plant diseases detection from a single image. MIL has been already used in a new computer
vision framework called Logical Vision (LV). Logical Vision (LV) was shown to overcome some of
the limitations of statistical-based algorithms. LV first uses background knowledge on symbols
to guide the sampling of low-level features like pixel value, shape, edge, colour and then it uses the
sampled results to revise previously conjectured mid-level symbols. With the extracted mid-level
feature symbols as background knowledge, a generalized MIL setting is used to learn high-level
visual concepts. This will enhance the constructive paradigm of LV through its ability to learn
recursive theories, inventing predicates and learning from a single example.
Research interests
Humanity is facing a great challenge to feed the growing population of 7.7 billion people and food security remains threatened by a number of factors including new plant diseases. Moreover, the excessive use of chemicals (to fight the plant diseases) has led to the adverse effects on agro-ecosystem. Presently there is an immediate need for early and precise diagnostic techniques to control the plant diseases for the sustainability of the ecosystem. The state-of-the-art algorithm for this problem requires building a new model from a very large number of previous cases and there is currently no algorithm that could learn an accurate model only from a new case, e.g. a single image.
Proposed Method:
We develop a new framework called One-Shot Meta-Interpretive Learning (OSMIL) for the problem of plant diseases detection from a single image. MIL has been already used in a new computer vision framework called Logical Vision (LV). Logical Vision (LV) was shown to overcome some of the limitations of statistical-based algorithms. LV first uses background knowledge on symbols to guide the sampling of low-level features like pixel value, shape, edge, colour and then it uses the sampled results to revise previously conjectured mid-level symbols. With the extracted mid-level feature symbols as background knowledge, a generalized MIL setting is used to learn high-level visual concepts. This will enhance the constructive paradigm of LV through its ability to learn recursive theories, inventing predicates and learning from a single example.
Teaching
Currently, I am working as lab coordinator for the course Data Mining & Machine Learning lab.
I also worked as an assistant professor at Jyothi Engineering College, India. I taught different subjects like;
- Theory of Computation
- Compiler Design(PG, UG)
- Advanced Data Structures(PG)
- Graph Theory & Combinatorics
- Logic for Computer Science
- Operating Systems
- C, Python