Significant Publications:

1.Swarup Chattopadhyay, Tanmay Basu, Asit K Das, K Ghosh , CA Murthy A similarity based generalized modularity measure towards effective community discovery in complex networks, Physica A: Statistical Mechanics and its Applications, vol. 527, 2019

2. Swarup Chattopadhyay, Asit K Das, K Ghosh , Finding patterns in the degree distribution of real-world complex networks: going beyond power law, Pattern Analysis and Applications, 2019

3. Losiana Nayak, Abhijit Dasgupta, Ritankar Das, K Ghosh , K De Rajat, Computational neuroscience and neuroinformatics: Recent progress and resources, Journal of biosciences, vol. 43, 2018

4. Soma Mitra, Debasis Mazumdar,K Ghosh , Kamales Bhaumik , An adaptive scale Gaussian filter to explain White’s illusion from the viewpoint of lightness assimilation for a large range of variation in spatial frequency of the grating and aspect ratio of the targets, PeerJ, vol. 6, 2018

5. Aniket Roy, Arpan Kumar Maiti,K Ghosh , An HVS inspired robust non-blind watermarking scheme in YCbCr color space, International Journal of Image and Graphics, vol. 18, 2018

6. Ashish Bakshi, K Ghosh , A parsimonious model of brightness induction, Biological cybernetics, vol. 112, 2018

7. Avijit Paul, Amrita Mukherjee, Apurba Das,K Ghosh , Computational intelligence in telecommunication networks: a review, International Journal of Computational Intelligence Studies, vol. 6, 2017

8. A Bakshi, S Roy, A Mallick and K Ghosh , Limitations of the Oriented Difference of Gaussian filter in special cases of brightness perception illusions. Perception, vol. 45 (3), 2016.

9. Debasis Mazumdar, Soma Mitra, K Ghosh , Kamales Bhaumik, A DOG filter model of the occurrence of Mach bands on spatial contrast discontinuities. Biological Cybernetics, vol.110, 2016.

10. Debjyoti Bhattacharjee, Ashish Bakshi, Kuntal Ghosh, Comparison Between an HVS Inspired Linear Filter and the Bilateral Filter in Performing" Vision at a Glance" through Smoothing with Edge Preservation, International Journal of Image and Graphics, vol. 15, 2015.

11. A Bakshi and K Ghosh , Some insights into why the perception of Mach bands is strong for luminance ramps and weak or vanishing for luminance steps. Perception, 41(11), 2012.

12. K. Ghosh, A possible role and basis of visual pathway selection in brightness induction. Spatial Vision, 25 (2), 2012.

13. K. Ghosh and K. Bhaumik, Complexity in human perception of brightness: a historical review on the evolution of the philosophy of visual perception, Online Journal of Biological Sciences, 10(1), 2010.

14. K. Ghosh, S. Sarkar and K. Bhaumik, A Possible Mechanism of Stochastic Resonance in the Light of an Extra-classical Receptive Field Model of Retinal Ganglion Cells, Biological Cybernetics, vol.100, 2009.

15. K. Ghosh, K. Bhaumik, and S. Sarkar, Retinomorphic Image Processing, Progress in Brain Research, vol. 168, pp. 175-191, 2008.

16. K. Ghosh, S. Sarkar and K. Bhaumik, Understanding Image Structure from a New Multi-scale Representation of Higher Order Derivative Filters, Image and Vision Computing, vol. 25, 2007.

17. K. Ghosh, S. Sarkar and K. Bhaumik, Proposing New Methods in Low-level Vision from the Mach Band Illusion in Retrospect, Pattern Recognition, vol. 39, 2006.

18. K. Ghosh, S. Sarkar and K. Bhaumik, A Possible Explanation of the Low-level Brightness-contrast Illusions in the Light of an Extended Classical Receptive Field Model of Retinal Ganglion Cells, Biological Cybernetics, vol. 94, pp. 89-96, 2006.

19. S. Karmakar, K. Ghosh, S. Sarkar and S. K. Sen, Design of a Lowpass Filter by Multi-scale Even Order Gaussian Derivatives, Signal Processing, vol. 86, pp. 3923-3929, 2006.

20. B. Bal and K. Ghosh, A Thermomechanical Model of Earthquakes, Modelling Critical and Catastrophic Phenomena in Geoscience: A Statistical Physics Approach. In: Lecture Notes in Physics, vol. 705, Springer-Verlag, Berlin Heidelberg, 2006.

21. K. Ghosh, S. Sarkar and K. Bhaumik, A Possible Mechanism of Zero-crossing Detection Using the Concept of Extended Classical Receptive Field Model of Retinal Ganglion Cells, Biological Cybernetics, vol. 93, 2005.


22. Aparup Khatua, Erik Cambria, K Ghosh , Nabendu Chaki, Apalak Khatua, Tweeting in Support of LGBT?: A Deep Learning Approach, Proceedings of the ACM India Joint International Conference on Data Science and Management of Data, 2019

23. Aparup Khatua, Apalak Khatua, K. Ghosh, Nabendu Chaki, Can# Twitter_Trends Predict Election Results? Evidence from 2014 Indian General Election, 48th Hawaii International Conference on System Sciences (HICSS), 2015