Faculty Publications
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Item Solar Irradiation Prediction Hybrid Framework Using Regularized Convolutional BiLSTM-Based Autoencoder Approach(Institute of Electrical and Electronics Engineers Inc., 2023) Chiranjeevi, M.; Karlamangal, S.; Moger, T.; Jena, D.Solar irradiance prediction is an essential subject in renewable energy generation. Prediction enhances the planning and management of solar installations and provides several economic benefits to energy companies. Solar irradiation, being highly volatile and unpredictable makes the forecasting task complex and difficult. To address the shortcomings of the traditional approaches, this research developed a hybrid resilient architecture for an enhanced solar irradiation forecast by employing a long short-term memory (LSTM) autoencoder, convolutional neural network (CNN), and the Bi-directional Long Short Term Memory (BiLSTM) model with grid search optimization. The suggested hybrid technique is comprised of two parts: feature encoding and dimensionality reduction using an LSTM autoencoder, followed by a regularized convolutional BiLSTM. The encoder is tasked with extracting the key features in order to deduce the input into a compact latent representation. The decoder network then predicts solar irradiance by analyzing the encoded representation's attributes. The experiments are conducted on three publicly available data sets collected from Desert Knowledge Australia Solar Centre (DKASC), National Solar Radiation Database (NSRDB), and Hawaii Space Exploration Analog and Simulation (HI-SEAS) Habitat. The analysis of univariate and multivariate-multi step ahead forecasting performed independently and it is compared with the conventional approaches. Several benchmark forecasting models and three performance metrics are utilized to validate the hybrid approach's prediction performance. The results show that the proposed architecture outperforms benchmark models in accuracy. © 2013 IEEE.Item Knowledge distillation: A novel approach for deep feature selection(Elsevier B.V., 2023) C, D.; Shetty, A.; Narasimhadhan, A.V.High dimensional data in hyperspectral remote sensing leads to computational, analytical, and storage complexities. Dimensionality reduction serves as an efficient tool to remove redundant, irrelevant, and highly correlated features. Recently, deep learning approaches have received remarkable progress in hyperspectral data analysis. In this paper, a new end-to-end deep learning framework based on a teacher-student network inspired by knowledge distillation is proposed for deep feature selection. Initially, a complicated teacher deep neural network is employed on complex high dimensional data to learn its corresponding best low dimensional representation. Then, the knowledge from the network is transferred to a simple student network that performs feature selection. Hence, it eventually leads to deep neural network compression which is of prime concern in hyperspectral remote sensing. Limited studies have been carried out to explore the benefits of knowledge distillation on hyperspectral data. The proposed method could be employed to choose deep features for both supervised and unsupervised tasks. Experimental results reveal the performance of the proposed scheme using limited features. In comparison to 1D and simple autoencoder models, the 2D model based on convolutional autoencoder delivers greater classification accuracies, with a classification accuracy value of 96.15% for the Indian Pines dataset and 97.82% for the Pavia University dataset. A similar trend is reported with unsupervised learning as well. Furthermore, the proposed model has a low degree of sensitivity to parameter selection. © 2022 National Authority of Remote Sensing & Space ScienceItem Dimensionality reduction using neural networks for lattice-based cryptographic keys(Taylor and Francis Ltd., 2024) Wahlang, R.; Chandrasekaran, K.Post Quantum Cryptography has received an increasing amount of active research. This has been made prominent by the ever-growing field of quantum computing which poses a formidable threat to the modern cybersecurity landscape. Recently, post quantum schemes have been standardized for adoption into existing security services and protocols. In comparison to contemporary cryptographic schemes, these approaches are lagging behind in terms of performance with regards to functional speed and package sizes. A study into the various applications of neural networks used in classical and post quantum cryptographic schemes has been explored to demonstrate their different possible applications within the various fields of cybersecurity. The main contribution of this work is to investigate the feasibility of dimensionality reduction using the autoencoder neural network on lattice-based keys generated by the Kyber key encapsulation mechanism scheme. Moreover, this work also presents a comparative analysis of the different implemented autoencoder models to showcase their relative performance. © 2024 Informa UK Limited, trading as Taylor & Francis Group.Item Accelerating randomized image secret sharing with GPU: contrast enhancement and secure reconstruction using progressive and convolutional approaches(Springer, 2024) Holla, M.; Suma, D.; Pais, A.R.Image Secret Sharing (ISS) is a cryptographic technique used to distribute secret images among multiple users. However, current Visual Secret Sharing (VSS) schemes produce a halftone image with only 50% contrast when reconstructing the original image. To overcome this limitation, the Randomized Image Secret Sharing (RISS) scheme was introduced. RISS achieves a higher contrast of 70% when extracting the secret image but comes with a high computational cost. This research paper presents a novel approach called Graphics Processing Unit (GPU)-based Randomized Image Secret Sharing (GRISS), which utilizes data parallelism within the RISS pipeline. The proposed technique also incorporates an Autoencoder-based Single Image Super-Resolution (ASISR) to enhance the contrast of the recovered image. The performance of GRISS is evaluated against RISS, and the contrast of the ASISR images is compared to current benchmark models. The results demonstrate that GRISS outperforms state-of-the-art models in both efficiency and effectiveness. © The Author(s) 2024.Item JSON document clustering based on schema embeddings(SAGE Publications Ltd, 2024) Uma Priya, D.U.; Santhi Thilagam, P.S.The growing popularity of JSON as the data storage and interchange format increases the availability of massive multi-structured data collections. Clustering JSON documents has become a significant issue in organising large data collections. Existing research uses various structural similarity measures to perform clustering. However, differently annotated JSON structures may also encode semantic relatedness, necessitating the use of both syntactic and semantic properties of heterogeneous JSON schemas. Using the SchemaEmbed model, this paper proposes an embedding-based clustering approach for grouping contextually similar JSON documents. The SchemaEmbed model is designed using the pre-trained Word2Vec model and a deep autoencoder that considers both syntactic and semantic information of JSON schemas for clustering the documents. The Word2Vec model learns the attribute embeddings, and a deep autoencoder is designed to generate context-aware schema embeddings. Finally, the context-based similar JSON documents are grouped using a clustering algorithm. The effectiveness of the proposed work is evaluated using both real and synthetic datasets. The results and findings show that the proposed approach improves clustering quality significantly, with a high NMI score of 75%. In addition, we demonstrate that clustering results obtained by contextual similarity are superior to those obtained by traditional semantic similarity models. © The Author(s) 2022.