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Monidipa Das
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- affiliation: Nanyang Technological University, Singapore
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2020 – today
- 2024
- [j22]Suparna Dutta
, Monidipa Das
Toward Causality-Based Explanation of Aerial Scene Classifiers. IEEE Geosci. Remote. Sens. Lett. 21: 1-5 (2024) - [j21]Rahul Dasharath Gavas
Spatial-SMOTE for handling imbalance in spatial regression tasks. Multim. Tools Appl. 83(5): 14111-14132 (2024) - [c23]Arju Bano, Monidipa Das:
A Guided Input Sampling-Based Perturbative Approach for Explainable AI in Image-Based Application. ICPR (4) 2024: 144-162 - 2023
- [j20]Suparna Dutta
An autonomous lightweight model for aerial scene classification under labeled sample scarcity. Appl. Intell. 53(19): 22216-22227 (2023) - [j19]Monidipa Das
GrapHiSM: a graph-based hierarchical semantics-driven model for aerial scene classification under scarcity of labelled samples. Appl. Intell. 53(21): 25919-25930 (2023) - [j18]Suparna Dutta
Remote sensing scene classification under scarcity of labelled samples - A survey of the state-of-the-arts. Comput. Geosci. 171: 105295 (2023) - [j17]Sucheta Dawn, Monidipa Das, Sanghamitra Bandyopadhyay:
SoURA: a user-reliability-aware social recommendation system based on graph neural network. Neural Comput. Appl. 35(25): 18533-18551 (2023) - [c22]Sayan Saha, Monidipa Das, Sanghamitra Bandyopadhyay:
GraphEx: A User-Centric Model-Level Explainer for Graph Neural Networks. Tiny Papers @ ICLR 2023 - 2022
- [j16]Sucheta Dawn, Monidipa Das
GraMMy: Graph representation learning based on micro-macro analysis. Neurocomputing 506: 84-95 (2022) - [j15]Monidipa Das
Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements. Remote. Sens. 14(8): 1906 (2022) - [j14]Monidipa Das
A Multilayered Adaptive Recurrent Incremental Network Model for Heterogeneity-Aware Prediction of Derived Remote Sensing Image Time Series. IEEE Trans. Geosci. Remote. Sens. 60: 1-13 (2022) - [c21]Sayan Saha
A Model-Centric Explainer for Graph Neural Network based Node Classification. CIKM 2022: 4434-4438 - 2021
- [j13]Monidipa Das
Analyzing impact of parental occupation on child's learning performance: a semantics-driven probabilistic approach. Int. J. Data Sci. Anal. 12(1): 31-44 (2021) - [j12]Monidipa Das
Real-time prediction of spatial raster time series: a context-aware autonomous learning model. J. Real Time Image Process. 18(5): 1591-1605 (2021) - [j11]Monidipa Das
Does Climate Variability Impact COVID-19 Outbreak? An Enhanced Semantics-Driven Theory-Guided Model. SN Comput. Sci. 2(6): 452 (2021) - [j10]Monidipa Das
Reducing Parameter Value Uncertainty in Discrete Bayesian Network Learning: A Semantic Fuzzy Bayesian Approach. IEEE Trans. Emerg. Top. Comput. Intell. 5(3): 361-372 (2021) - [c20]Monidipa Das
Analyzing Impact of Climate Variability on COVID-19 Outbreak: A Semantically-enhanced Theory-guided Data-driven Approach. COMAD/CODS 2021: 1-9 - [c19]Sucheta Dawn, Monidipa Das, Sanghamitra Bandyopadhyay:
CateReR: A Graph Neural Network-Based Model for Category-Wise Reliability-Aware Recommendation. PReMI 2021: 200-210 - [c18]Suparna Dutta, Monidipa Das
PReLim: A Modeling Paradigm for Remote Sensing Image Scene Classification Under Limited Labeled Samples. PReMI 2021: 545-555 - [c17]Monidipa Das
SELFIE: A Semantically-Enhanced Load Forecasting Approach with Indirect Estimate of Spatial Influences. TENCON 2021: 687-692 - 2020
- [b1]Monidipa Das, Soumya K. Ghosh:
Enhanced Bayesian Network Models for Spatial Time Series Prediction - Recent Research Trend in Data-Driven Predictive Analytics. Studies in Computational Intelligence 858, Springer 2020, ISBN 978-3-030-27748-2 - [j9]Monidipa Das
Data-Driven Approaches for Spatio-Temporal Analysis: A Survey of the State-of-the-Arts. J. Comput. Sci. Technol. 35(3): 665-696 (2020) - [j8]Monidipa Das
SARDINE: A Self-Adaptive Recurrent Deep Incremental Network Model for Spatio-Temporal Prediction of Remote Sensing Data. ACM Trans. Spatial Algorithms Syst. 6(3): 16:1-16:26 (2020) - [c16]Monidipa Das, Mahardhika Pratama, Jie Zhang, Yew-Soon Ong:
A Skip-Connected Evolving Recurrent Neural Network for Data Stream Classification under Label Latency Scenario. AAAI 2020: 3717-3724 - [c15]Monidipa Das
Online Prediction of Derived Remote Sensing Image Time Series: An Autonomous Machine Learning Approach. IGARSS 2020: 1496-1499 - [c14]Monidipa Das
A Self-Evolving Mutually-Operative Recurrent Network-based Model for Online Tool Condition Monitoring in Delay Scenario. KDD 2020: 2775-2783
2010 – 2019
- 2019
- [j7]Monidipa Das
FB-STEP: A fuzzy Bayesian network based data-driven framework for spatio-temporal prediction of climatological time series data. Expert Syst. Appl. 117: 211-227 (2019) - [c13]Monidipa Das
Space-time Prediction of High Resolution Raster Data: An Approach based on Spatio-temporal Bayesian Network (STBN). COMAD/CODS 2019: 129-135 - [c12]Atul Patel, Monidipa Das
Short-Term Load Forecasting: An Intelligent Approach Based on Recurrent Neural Network. HIS 2019: 52-62 - [c11]Monidipa Das
MUSE-RNN: A Multilayer Self-Evolving Recurrent Neural Network for Data Stream Classification. ICDM 2019: 110-119 - [c10]Monidipa Das
FERNN: A Fast and Evolving Recurrent Neural Network Model for Streaming Data Classification. IJCNN 2019: 1-8 - 2018
- [j6]Monidipa Das
Data-driven approaches for meteorological time series prediction: A comparative study of the state-of-the-art computational intelligence techniques. Pattern Recognit. Lett. 105: 155-164 (2018) - [c9]Monidipa Das
FORWARD: A Model for FOrecasting Reservoir WAteR Dynamics Using Spatial Bayesian Network (SpaBN) (Extended Abstract). ICDE 2018: 1799-1800 - 2017
- [j5]Monidipa Das
semBnet: A semantic Bayesian network for multivariate prediction of meteorological time series data. Pattern Recognit. Lett. 93: 192-201 (2017) - [j4]Monidipa Das
Measuring Moran's I in a Cost-Efficient Manner to Describe a Land-Cover Change Pattern in Large-Scale Remote Sensing Imagery. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 10(6): 2631-2639 (2017) - [j3]Monidipa Das
A Deep-Learning-Based Forecasting Ensemble to Predict Missing Data for Remote Sensing Analysis. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 10(12): 5228-5236 (2017) - [j2]Monidipa Das
FORWARD: A Model for FOrecasting Reservoir WAteR Dynamics Using Spatial Bayesian Network (SpaBN). IEEE Trans. Knowl. Data Eng. 29(4): 842-855 (2017) - [c8]Monidipa Das
Spatio-temporal Autocorrelation Analysis for Regional Land-cover Change Detection from Remote Sensing Data. CODS 2017: 8:1-8:10 - [c7]Monidipa Das
BESTED: An Exponentially Smoothed Spatial Bayesian Analysis Model for Spatio-temporal Prediction of Daily Precipitation. SIGSPATIAL/GIS 2017: 55:1-55:4 - [c6]Monidipa Das
Spatio-temporal Prediction under Scarcity of Influencing Variables: A Hybrid Probabilistic Graph-based Approach. ICAPR 2017: 1-6 - [c5]Monidipa Das
Spatio-Temporal Prediction of Meteorological Time Series Data: An Approach Based on Spatial Bayesian Network (SpaBN). PReMI 2017: 615-622 - 2016
- [j1]Monidipa Das
Deep-STEP: A Deep Learning Approach for Spatiotemporal Prediction of Remote Sensing Data. IEEE Geosci. Remote. Sens. Lett. 13(12): 1984-1988 (2016) - [c4]Monidipa Das
Modeling Spatio-temporal Change Pattern using Mathematical Morphology. CODS 2016: 4:1-4:10 - [c3]Shrutilipi Bhattacharjee, Monidipa Das
Prediction of meteorological parameters: an a-posteriori probabilistic semantic kriging approach. SIGSPATIAL/GIS 2016: 38:1-38:10 - [c2]Monidipa Das
A cost-efficient approach for measuring Moran's index of spatial autocorrelation in geostationary satellite data. IGARSS 2016: 5913-5916 - 2015
- [c1]Monidipa Das
Detection of climate zones using multifractal detrended cross-correlation analysis: A spatio-temporal data mining approach. ICAPR 2015: 1-6
Coauthor Index
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