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2020 – today
- 2024
[j30]Amrita Mukherjee
, Avijit Paul, Kuntal Ghosh:
Deep learning models for perception of brightness related illusions. Appl. Intell. 54(21): 10259-10283 (2024)- [j29]Keerthi S. Chandran, Kuntal Ghosh:
A deep learning based cognitive model to probe the relation between psychophysics and electrophysiology of flicker stimulus. Brain Informatics 11(1): 18 (2024) - [j28]Snigdha Agarwal, Adarsh Raj, Anjan Chowdhury, Geetanjali Aich, Rajdeep Chatterjee, Kuntal Ghosh:
Investigating the impact of standard brain atlases and connectivity measures on the accuracy of ADHD detection from fMRI data using deep learning. Multim. Tools Appl. 83(25): 67023-67057 (2024) - [j27]Anjan Chowdhury, Sriram Srinivasan, Animesh Mukherjee, Sanjukta Bhowmick, Kuntal Ghosh:
Improving Node Classification Accuracy of GNN through Input and Output Intervention. ACM Trans. Knowl. Discov. Data 18(1): 17:1-17:31 (2024) - [i5]Aniket Roy, Anirban Roy, Soma Mitra, Kuntal Ghosh:
BRI3L: A Brightness Illusion Image Dataset for Identification and Localization of Regions of Illusory Perception. CoRR abs/2402.04541 (2024) - [i4]Kuntal Ghosh, Shadan Sadeghian:
The Impact of AI on Perceived Job Decency and Meaningfulness: A Case Study. CoRR abs/2406.14273 (2024) - [i3]Swayamjit Saha, Kuntal Ghosh, Garga Chatterjee, J. Edward Swan II:
Exploring and Visualizing COVID-19 Trends in India: Vulnerabilities and Mitigation Strategies. CoRR abs/2409.05876 (2024) - 2023
- [j26]Smriti Gupta, Sabita Pal, Kundan Kumar, Kuntal Ghosh:
Coupling Effect of Exploration Rate and Learning Rate for Optimized Scaled Reinforcement Learning. SN Comput. Sci. 4(5): 638 (2023) - [c21]Anjan Chowdhury, Swarup Chattopadhyay, Kuntal Ghosh:
Analyzing the Progression of Alzheimer's Disease in Human Brain Networks. ASONAM 2023: 415-418 - [c20]Anjan Chowdhury, Swarup Chattopadhyay, Kuntal Ghosh, Gautam Das:
NeuroANATOP: An Effective Tool to Assess the Progression of Alzheimer's Disease in Human Brain Networks. ICKG 2023: 108-116 - 2022
- [j25]Rajdeep Das, Ajoy Mondal, Tapan Chakraborty, Kuntal Ghosh:
Deep neural networks for automatic grain-matrix segmentation in plane and cross-polarized sandstone photomicrographs. Appl. Intell. 52(3): 2332-2345 (2022) - [j24]Ashish Bakshi, Sourya Roy, Arijit Mallick, Kuntal Ghosh:
A discrete magno-parvo additive model in early vision for explaining brightness perception in varying contrastive contexts. Biol. Cybern. 116(1): 5-21 (2022) - [j23]Shilpi Bose, Chandra Das, Kuntal Ghosh, Matangini Chattopadhyay, Samiran Chattopadhyay:
A framework for neighbourhood configuration to improve the KNN based imputation algorithms on microarray gene expression data. Int. J. Bioinform. Res. Appl. 18(3): 141-190 (2022) - [j22]Anjan Chowdhury, Sriram Srinivasan, Sanjukta Bhowmick, Animesh Mukherjee, Kuntal Ghosh:
Constant community identification in million-scale networks. Soc. Netw. Anal. Min. 12(1): 70 (2022) - 2021
- [j21]Abidhan Bardhan, Pijush Samui, Kuntal Ghosh, Amir H. Gandomi, Siddhartha Bhattacharyya:
ELM-based adaptive neuro swarm intelligence techniques for predicting the California bearing ratio of soils in soaked conditions. Appl. Soft Comput. 110: 107595 (2021) - [j20]Swarup Chattopadhyay, Tanmay Basu, Asit Kumar Das, Kuntal Ghosh, Late C. A. Murthy:
Towards effective discovery of natural communities in complex networks and implications in e-commerce. Electron. Commer. Res. 21(4): 917-954 (2021) - [j19]Rajdeep Das, B. Uma Shankar, Tapan Chakraborty, Kuntal Ghosh:
Automatic grain segmentation in cross-polarized photomicrographs of sedimentary rocks using psychophysics inspired models. Innov. Syst. Softw. Eng. 17(2): 167-183 (2021) - [j18]Debasis Mazumdar, Soma Mitra, Kuntal Ghosh, Kamales Bhaumik:
Analysing the patterns of spatial contrast discontinuities in natural images for robust edge detection. Pattern Anal. Appl. 24(3): 1403-1425 (2021) - [j17]Shilpi Bose, Chandra Das, Abhik Banerjee, Kuntal Ghosh, Matangini Chattopadhyay, Samiran Chattopadhyay, Aishwarya Barik:
An ensemble machine learning model based on multiple filtering and supervised attribute clustering algorithm for classifying cancer samples. PeerJ Comput. Sci. 7: e671 (2021) - [j16]Swarup Chattopadhyay, Tanujit Chakraborty, Kuntal Ghosh, Asit Kumar Das:
Modified Lomax model: a heavy-tailed distribution for fitting large-scale real-world complex networks. Soc. Netw. Anal. Min. 11(1): 43 (2021) - [c19]Anjan Chowdhury, Sriram Srinivasan, Sanjukta Bhowmick, Animesh Mukherjee, Kuntal Ghosh:
Constant community identification in million scale networks using image thresholding algorithms. ASONAM 2021: 116-120 - [c18]Swarup Chattopadhyay, Tanujit Chakraborty, Kuntal Ghosh, Asit Kumar Das:
Uncovering patterns in heavy-tailed networks : A journey beyond scale-free. COMAD/CODS 2021: 136-144 - [c17]Anjan Chowdhury, Rajdeep Chatterjee, Geetanjali Aich, Kuntal Ghosh:
ADHDNet: A DNN Based Framework for Efficient ADHD Detection from fMRI Dataset. PReMI 2021: 137-147 - [i2]Rajdeep Das, Ajoy Mondal, Tapan Chakraborty, Kuntal Ghosh:
Deep Neural Networks for Automatic Grain-matrix Segmentation in Plane and Cross-polarized Sandstone Photomicrographs. CoRR abs/2111.07102 (2021) - 2020
- [j15]Kuntal Ghosh, Sushmita Mitra:
Preface. Fundam. Informaticae 176(2): 139-140 (2020) - [j14]Swarup Chattopadhyay, Asit Kumar Das, Kuntal Ghosh:
Finding patterns in the degree distribution of real-world complex networks: going beyond power law. Pattern Anal. Appl. 23(2): 913-932 (2020) - [j13]Ajoy Mondal, Kuntal Ghosh:
State-of-the-art fuzzy active contour models for image segmentation. Soft Comput. 24(19): 14411-14427 (2020) - [i1]Ajoy Mondal, Kuntal Ghosh:
State-of-The-Art Fuzzy Active Contour Models for Image Segmentation. CoRR abs/2008.00175 (2020)
2010 – 2019
- 2019
- [c16]Aparup Khatua, Erik Cambria, Kuntal Ghosh, Nabendu Chaki, Apalak Khatua:
Tweeting in Support of LGBT?: A Deep Learning Approach. COMAD/CODS 2019: 342-345 - [c15]Chandra Das, Shilpi Bose, Arnab Chanda, Sandeep Singh, Sumanta Das, Kuntal Ghosh:
Impact of Prerequisite Subjects on Academic Performance Using Association Rule Mining. ICACIE (2) 2019: 227-236 - [c14]Sandipa Roy, Arpan Kumar Maiti, Indira Ghosh, Indranil Chatterjee, Gopal K. Basak, Kuntal Ghosh:
An app based unified approach to enhance language comprehension and mathematical reasoning ability of the hearing impaired using contrast words. TENCON 2019: 1711-1716 - [p1]Sanjit Maitra, Ratul Ghosh, Kuntal Ghosh:
Applications of Deep Learning in Medical Imaging. Handbook of Deep Learning Applications 2019: 111-127 - 2018
- [j12]Ashish Bakshi, Kuntal Ghosh:
A parsimonious model of brightness induction. Biol. Cybern. 112(3): 237-251 (2018) - [j11]Aniket Roy, Arpan Kumar Maiti, Kuntal Ghosh:
An HVS Inspired Robust Non-blind Watermarking Scheme in YCbCr Color Space. Int. J. Image Graph. 18(3): 1850015:1-1850015:23 (2018) - [c13]Amrita Mukherjee, Avijit Paul, Rajarshi Roy, Shibsankar Roy, Kuntal Ghosh:
Perceptual Filling-in of Blind-Spot for Surrounding Color Gradient Stimuli. IHCI 2018: 194-204 - 2017
- [j10]Avijit Paul, Amrita Mukherjee, Apurba Das, Kuntal Ghosh:
Computational intelligence in telecommunication networks: a review. Int. J. Comput. Intell. Stud. 6(2/3): 189-228 (2017) - [c12]Keerthi S. Chandran, Swati Banerjee, Kuntal Ghosh:
A Study on Crossmodal Correspondence in Sensory Pathways Through Forced Choice Task and Frequency Based Correlation in Sound-Symbolism. MIKE 2017: 212-220 - [e2]B. Uma Shankar, Kuntal Ghosh, Deba Prasad Mandal, Shubhra Sankar Ray, David Zhang, Sankar K. Pal:
Pattern Recognition and Machine Intelligence - 7th International Conference, PReMI 2017, Kolkata, India, December 5-8, 2017, Proceedings. Lecture Notes in Computer Science 10597, Springer 2017, ISBN 978-3-319-69899-1 [contents] - 2016
- [j9]Debasis Mazumdar, Soma Mitra, Kuntal Ghosh, Kamales Bhaumik:
A DOG filter model of the occurrence of Mach bands on spatial contrast discontinuities. Biol. Cybern. 110(2-3): 229-236 (2016) - 2015
- [j8]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. Int. J. Image Graph. 15(4): 1550015:1-1550015:15 (2015) - [c11]Aparup Khatua, Apalak Khatua, Kuntal Ghosh, Nabendu Chaki:
Can #Twitter_Trends Predict Election Results? Evidence from 2014 Indian General Election. HICSS 2015: 1676-1685 - 2013
- [e1]Pradipta Maji, Ashish Ghosh, M. Narasimha Murty, Kuntal Ghosh, Sankar K. Pal:
Pattern Recognition and Machine Intelligence - 5th International Conference, PReMI 2013, Kolkata, India, December 10-14, 2013. Proceedings. Lecture Notes in Computer Science 8251, Springer 2013, ISBN 978-3-642-45061-7 [contents] - 2012
- [c10]Ashish Bakshi, Kuntal Ghosh:
Scaling Properties of Mach Bands and Perceptual Models. PerMIn 2012: 66-74 - 2011
- [c9]Apurba Das, Anirban Roy, Kuntal Ghosh:
Proposing a CNN Based Architecture of Mid-level Vision for Feeding the WHERE and WHAT Pathways in the Brain. SEMCCO (1) 2011: 559-568 - 2010
- [j7]Kuntal Ghosh, Sankar K. Pal:
Some Insights Into Brightness Perception of Images in the Light of a New Computational Model of Figure-Ground Segregation. IEEE Trans. Syst. Man Cybern. Part A 40(4): 758-766 (2010)
2000 – 2009
- 2009
- [j6]Kuntal Ghosh, Sandip Sarkar, Kamales Bhaumik:
A possible mechanism of stochastic resonance in the light of an extra-classical receptive field model of retinal ganglion cells. Biol. Cybern. 100(5): 351-359 (2009) - 2007
- [j5]Kuntal Ghosh, Sandip Sarkar, Kamales Bhaumik:
Understanding image structure from a new multi-scale representation of higher order derivative filters. Image Vis. Comput. 25(8): 1228-1238 (2007) - [c8]Sandip Sarkar, Kuntal Ghosh, Kamales Bhaumik:
Stochastic Resonance in the Perceptual Interpretation of Zero Crossing Maps: A Low Level Center Surround Model of Vision. IICAI 2007: 2123-2139 - [c7]Kuntal Ghosh, Sankar K. Pal:
Figure-Ground Segregation: Cues from Early Vision. IICAI 2007: 2238-2254 - [c6]Kuntal Ghosh, Sankar K. Pal:
Attention in Early Vision: Some Psychophysical Insights. WAPCV 2007: 381-398 - 2006
- [j4]Kuntal Ghosh, Sandip Sarkar, Kamales 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. Biol. Cybern. 94(2): 89-96 (2006) - [j3]Kuntal Ghosh, Sandip Sarkar, Kamales Bhaumik:
Proposing new methods in low-level vision from the Mach band illusion in retrospect. Pattern Recognit. 39(4): 726-730 (2006) - [j2]Subhajit Karmakar, Kuntal Ghosh, Sandip Sarkar, Swapan Sen:
Design of a low-pass filter by multi-scale even order Gaussian derivatives. Signal Process. 86(12): 3923-3933 (2006) - [c5]Kuntal Ghosh, Sandip Sarkar, Kamales Bhaumik:
New Vision Tools from the Comparative Study of an "Old" Psychophysical and a "Modern" Computational Model. BioADIT 2006: 236-251 - [c4]Kuntal Ghosh, Sandip Sarkar, Kamales Bhaumik:
Early Vision and Image Processing: Evidences Favouring a Dynamic Receptive Field Model. ICVGIP 2006: 216-227 - 2005
- [j1]Kuntal Ghosh, Sandip Sarkar, Kamales Bhaumik:
A possible mechanism of zero-crossing detection using the concept of the extended classical receptive field of retinal ganglion cells. Biol. Cybern. 93(1): 1-5 (2005) - [c3]Kuntal Ghosh, Sandip Sarkar, Kamales Bhaumik:
A New Mechanism of "Fuzzy" Edge Detection by Multi-scale Gaussian Filters in the light of Human Visual System. IICAI 2005: 3234-3251 - [c2]Sandip Sarkar, Kuntal Ghosh, Kamales Bhaumik:
Edge Detection and Enhancement by a New Model for Neural Engineering. IICAI 2005: 3375-3385 - [c1]Kuntal Ghosh, Sandip Sarkar, Kamales Bhaumik:
Image Enhancement by High-Order Gaussian Derivative Filters Simulating Non-classical Receptive Fields in the Human Visual System. PReMI 2005: 453-458
Coauthor Index
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