Browsing by Author "Sundaram, V.M."

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A deep dive into Hyperledger
(Institution of Engineering and Technology, 2021) Punathumkandi, S.; Sundaram, V.M.; Prabhavathy, P.
Hyperledger is an open -source, network -oriented effort made to propel cross industry blockchain developments. It is a worldwide facilitated exertion remembering pioneers for banking, cash, Internet of Things, manufacturing, supply chains, and advancement. The Linux Foundation has Hyperledger under the establishment. This chapter gives an elevated level overview of Hyperledger: why it was made, how it is represented, and what it would like to accomplish. The core of this chapter presents five convincing uses for big business blockchain in various ventures. It depicts how the Hyperledger guarantees the secure, progressively solid, and increasingly streamlined communication. © The Institution of Engineering and Technology 2021.
Dual attention guided deep encoder-decoder network for change analysis in land use/land cover for Dakshina Kannada District, Karnataka, India
(Springer Science and Business Media Deutschland GmbH, 2023) Naik, N.; Chandrasekaran, K.; Sundaram, V.M.; Prabhavathy, P.
The Earth is frequently changed by natural occurrences and human actions that have threatened our environment to a certain extent. Therefore, accurate and timely monitoring of transformations at the surface of the Earth is crucial for precisely facing their harmful effects and consequences. This paper aims to perform a change detection (CD) analysis and assessment of the Dakshina Kannada region, being one of the coastal districts of Karnataka, India. The spatial and temporal variations in land use and land cover (LULC) are being monitored and examined from the data received as LULC maps from the National Remote Sensing Agency, Indian Space Research Organization, India. The time-series data from advanced wide-field sensor (AWiFS) Resourcesat2 satellite as LULC maps (1:250k) are analyzed using a deep learning approach with an encoder–decoder architecture with dual-attention modules for the change analysis. The model provides an overall accuracy and meanIOU(intersection over union) of 94.11% and 74.1%. The LULC maps from 2005 to 2018 (13 years) are utilized to decide the variations in the LULC, including urban development, agricultural variations, vegetation dynamics, forest areas, barren land, littoral swamp, and water bodies, current fallow, etc. The multiclass area-wise changes in terms of percentage show a decline in most LULC classes, which raises a point of concern for the environmental safety of the considered area, which is highly exposed to coastal flooding due to increased urbanization. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Interoperable permissioned-blockchain with sustainable performance
(MDPI, 2021) Punathumkandi, S.; Sundaram, V.M.; Prabhavathy, P.
Bitcoin is an innovative and path-breaking technology that has influenced numerous industries across the globe. It is a form of digital currency (cryptocurrency) that can be used for trading and has the potential to replace fiat money, where the underlying infrastructure is called Blockchain. The Blockchain is an open ledger that provides decentralization, transparency, immutability, and confidentiality. Blockchain can be used in enormous applications, such as healthcare, logistics, supply chain management, the IoT, and so forth. Most of the industrial applications rely on the permissioned blockchain. However, the permissioned blockchain fails in some aspects, such as interoperability among different platforms. This paper suggests a sustainable system to solve the interoperability issue of the permissioned blockchain by designing a new infrastructure. This work has been tested in ethereum and hyperledger frameworks, which obtained a success rate of 100 percent. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Preface
(Springer Science and Business Media Deutschland GmbH, 2018) Arun Kumar, A.K.; Thangavelu, A.; Sundaram, V.M.
[No abstract available]
Spatiotemporal Assessment of Satellite Image Time Series for Land Cover Classification Using Deep Learning Techniques: A Case Study of Reunion Island, France
(MDPI, 2022) Navnath, N.N.; Chandrasekaran, K.; Stateczny, A.; Sundaram, V.M.; Prabhavathy, P.
Current Earth observation systems generate massive amounts of satellite image time series to keep track of geographical areas over time to monitor and identify environmental and climate change. Efficiently analyzing such data remains an unresolved issue in remote sensing. In classifying land cover, utilizing SITS rather than one image might benefit differentiating across classes because of their varied temporal patterns. The aim was to forecast the land cover class of a group of pixels as a multi-class single-label classification problem given their time series gathered using satellite images. In this article, we exploit SITS to assess the capability of several spatial and temporal deep learning models with the proposed architecture. The models implemented are the bidirectional gated recurrent unit (GRU), temporal convolutional neural networks (TCNN), GRU + TCNN, attention on TCNN, and attention of GRU + TCNN. The proposed architecture integrates univariate, multivariate, and pixel coordinates for the Reunion Island’s landcover classification (LCC). the evaluation of the proposed architecture with deep neural networks on the test dataset determined that blending univariate and multivariate with a recurrent neural network and pixel coordinates achieved increased accuracy with higher F1 scores for each class label. The results suggest that the models also performed exceptionally well when executed in a partitioned manner for the LCC task compared to the temporal models. This study demonstrates that using deep learning approaches paired with spatiotemporal SITS data addresses the difficult task of cost-effectively classifying land cover, contributing to a sustainable environment. © 2022 by the authors.