Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Bhattacharjee, S.

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    Dual band operation using metamaterial structure for Radio Frequency Identification (RFID) system
    (IEEE Computer Society help@computer.org, 2014) Saha, R.; Bhattacharjee, S.; Bhunia, C.T.; Maity, S.
    The important functions of Radio Frequency Identification (RFID) technology are radiation and detection. So efficient antenna design and fabrication is the unique solution which perform a very crucial role for many applications. The reader and tag intense are component of the RFID system. In this paper designed has done on RFID antenna for tag and reader application, which also includes the different types of antenna (UHF application, wideband, multiband antenna) and the general consideration for design, them which are currently used for tag and reader application. In this paper we proposed a rectangular patch antenna and then designed a metamaterial structure on the patch to improve the antenna performance. © 2014 IEEE.
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    Searchable encryption through obfuscation and multi-cloud search
    (Institute of Electrical and Electronics Engineers Inc., 2016) Chatterjee, S.; Bhattacharjee, S.; Chandrasekaran, K.
    In this paper different searchable encryption techniques are studied along with some implementation of searchable encryption in a cloud environment. Symmetric searchable encryption is implemented through trapdoor function to selectively expose keyword for search. A new method of achieving searchable encryption in the random oracle model is proposed through one way indistinguishability obfuscation. Indistinguishability obfuscation is achievable through mimicry function and one way cryptographic hash function. Security of the model is also analyzed with non adaptable indistinguishability security. Using this method an efficient idea of multi-cloud search environment is also proposed. In multi cloud searchable encryption several independent data providers collaborate in a federated way to provide search in their data through different independent cloud service provider revealing only keywords associated with the data. © 2015 IEEE.
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    Enhancing reliability of cloud system through proactive identification of under performing components
    (Institute of Electrical and Electronics Engineers Inc., 2017) Bhattacharjee, S.; Annappa, B.
    Services involving cloud computing are advertised by the providers such that they are available all the time; but many of the recent past events have shown how a single or a bunch of component seizures can lead to a system failure affecting the business continuity of consumers. Though reliability of a cloud system depends heavily on the capability of the participating components as a whole, but there needs to be some methodology to monitor and make the whole system work efficiently upto the maximum capability of each respective individual components. In this paper, a mechanism for quantifying the reliability of cloud components is proposed so as to provide reliability as a service. Reliability of cloud is dependent on the nature and status of its individual components. Each component's credibility is evaluated based on the reliability score it obtains while executing its assigned jobs. From these scores faulty or error prone components are identified proactively and the non performing components are given a second chance to revive itself, failing which the defective components undergo further checks of the component's importance, history of past failures and other quality factors. If after these checks it is seen that the component is safe to remove and the system will not be affected in anyway by the removal, then the component is quarantined otherwise the defective component is identified and handled suitably. This method is aimed at giving a highly reliable and non-intermittent cloud service to the consumers not at the expense of getting rid of critical components. © 2016 IEEE.
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    A Weighted Hybrid Centrality for Identifying Influential Individuals in Contact Networks
    (Institute of Electrical and Electronics Engineers Inc., 2022) Shetty, R.D.; Bhattacharjee, S.
    In the context of infectious disease spread, one of the most challenging tasks is to identify influential nodes in the human contact networks. In recent years, the research community has acquired strong evidences of complex and heterogeneous connections and diverse patterns in a variety of real human contact networks. The heterogeneity of network topologies has a deep influence on understanding the spreading dynamics of disease. In the process of infection spread, network edges are the critical communication channels. Many existing approaches for identifying prominent nodes in such networks rely on node attributes. Further, in unweighted networks, all the edges are considered to be equal, which imposes an unrealistic assumption for epidemic spreading through frequent interactions between humans. Here, we present an edge weighting technique, named as, Weighted Hybrid Centrality left( {{W_{{C_H}}}} right), that takes into account multiple centrality indicators, including degree, k-shell, and eigenvector centrality, as well as the frequency of interactions be-tween any two users (nodes) which is considered as potential edge weight. To analyze the performance of {W_{{C_H}}}, we have utilized the weighted susceptible-infected-recovered (W-SIR) simulator and have carried out a comparative experiment on six real-world heterogeneous networks. The proposed technique outperforms the baseline methods with 0.067 to 0.641 averaged Kendall's τ score. This method is useful for modeling disease dynamics and identifying highly influential contacts by considering both node and edge properties. © 2022 IEEE.
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    Stock Price Prediction Using Corporation Network and LSTM
    (Institute of Electrical and Electronics Engineers Inc., 2022) Bisarya, U.; Parekh, V.; Bhattacharjee, S.
    The problem of stock price prediction is addressed in this work by incorporating additional stock-related factors and using them to model relations between stocks. We have built a corporation network that displays the relation between stocks based on common shareholders and their shareholding ratio. The network is constructed by including all involved corporations based on investment facts from the real market. In this work, we have used a deep learning-based model, long short-term memory (LSTM) for the prediction of stock prices. We have considered node embedding methods that can store the properties of the nodes in the network, and use this information to make the model more accurate. The results produced by an initial and a revised LSTM model are compared, which have achieved a minimum mean average percentage error (MAPE) value of 4.121% for the initial LSTM model, and 1.788% for the revised LSTM model. © 2022 IEEE.
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    Pattern Analysis of COVID-19 Based On Geotagged Social Media Data with Sociodemographic Factors
    (Institute of Electrical and Electronics Engineers Inc., 2022) Sabareesha, S.S.S.; Bhattacharjee, S.; Shetty, R.D.
    The world has faced a catastrophic global crisis of COVID-19 caused by coronavirus and called for analyzing the affected areas in any country. The study helps to understand how the second wave affected different states in India concerning sociodemographic factors, such as population density, economy, and unemployment rate. During the lockdown, the sudden impact of staying at home has led to increased social media usage, where people expressed their opinions on multiple topics. Twitter provides timestamp and sometimes spatial information of the tweets generated. Using the geotagged Twitter dataset, a study in India is performed in this work considering the second wave of COVID-19, which occurred approximately from April to June 2021. It analyses the temporal and spatial patterns of the geotagged tweets generated from all the states during the period mentioned above. Also, topic modeling and sentiment analysis are performed to understand the concerns discussed by the people. We use different states' sociodemographic factors and machine learning algorithms to divide the population into high and low categories to understand the topic prevalence in different socioeconomic groups. This study reveals that the low socioeconomic groups have shared more concerns, urging the government to help fight the COVID-19 pandemic. © 2022 IEEE.
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    Crop Yield Analysis using SIF and Climate Variables: A Case Study in Punjab, India
    (Institute of Electrical and Electronics Engineers Inc., 2022) Gautam, P.K.; Bhattacharjee, S.
    Regular and faster crop yield prediction can mitigate the extreme effects of severe weather events, such as drought, heavy rainfall, etc. This work explores a precise, scalable, and automatic way to understand rice yield dynamics and its correlation with satellite-based solar-induced fluorescence (SIF), climate variables, such as temperature and rainfall, as they exhibit a significant correlation with rice yield. A district-wise analysis in Punjab, India, is carried out using Pearson correlation coefficient and different regression techniques, such as linear, ridge, lasso, and elastic net for the Kharif season. A comparative study shows that the elastic net performs better than the other models, with the best coefficient of determination (R2) of 0.792 and root mean square error (RMSE) of 300.5 kg/ha. This study can be extended in multiple dimensions by including a variety of crops, climate factors, and multi-satellite SIF data for any crop yield pattern analysis and prediction. © 2022 IEEE.
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    Weighted GNN-based Betweenness Centrality Considering Stability and Connection Structure
    (Institute of Electrical and Electronics Engineers Inc., 2023) Shetty, R.D.; Bhattacharjee, S.
    Epidemic spreading or information spreading in a network leads to a broader range of propagation due to the presence of bridge nodes, as defined by the betweenness centrality. The existing literature shows that computing betweenness centrality by considering connection topology is computationally expensive and less efficient. Moreover, exploiting the edge features in the network can determine how strongly the nodes are connected. This work proposes a technique to leverage connection topology and tie strength for modeling Weighted Betweenness Graph Neural Network (WBGNN) for a faster-ranking estimation. This measure is based on shortest path calculations, which take connection strength into account for determining the fastest path for contagion or information spread in the network. A variant of statistical weighted betweenness centrality, namely Stable Betweenness Centrality (CSB) and the proposed GNN-based WBGNN are compared to analyze the effectiveness of the proposed model. Further, the WBGNN model is also compared with different machine learning regressors and a neural network-based model to examine the efficacy of the proposed model. The experimental outcome has revealed that the WBGNN model has achieved a 0.203 to 0.536 improvement in Kendall's τ score, and also takes less time for node ranking estimation compared to CSB and other machine learning-based regressor models. © 2023 IEEE.
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    Spatio-temporal Analysis and Modeling of Coastal areas for Water Salinity Prediction
    (Institute of Electrical and Electronics Engineers Inc., 2023) Sudhakara, B.; Priyadarshini, R.; Bhattacharjee, S.; Kamath S․, S.; Umesh, P.; Gangadharan, K.V.; Ghosh, S.K.
    Salinity is an important parameter affecting the quality of water, and excessive amounts adversely affect vege-tation growth and aquatic organism populations. Natural factors like tidal waves, natural calamities etc., and man-made factors like unchecked disposal of industrial wastes, domestic/ urban sewage, and fish hatchery activities can cause significant increases in water salinity. In this article, an approach that utilizes multimodal data like Landsat 8 optical observations and the SMAP salinity data product for predicting water salinity indices in the coastal region is proposed. Machine Learning models such as K-nearest neighbor (KNN), Gradient Boost (GB), Extremely Randomized Tree (ERT), Random Forest Regression (RFR), Decision Tree (DT), Multiple Linear Regression (MLR), Lasso Regression (LR), and Ridge Regression (RR) are used for salinity prediction. Empirical experiments revealed that the ERT model outperformed other ML models, with a R2 of 0.92 and RMSE of 0.25 psu. © 2023 IEEE.
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    A Temporal Metric-Based Efficient Approach to Predict Citation Counts of Scientists
    (Springer Science and Business Media Deutschland GmbH, 2023) Dewangan, S.K.; Bhattacharjee, S.; Shetty, R.D.
    Citation count is one of the essential factors in understanding and measuring the impact of a scientist or a publication. Estimating the future impact of scientists or publications is crucial as it assists in making decisions about potential awardees of research grants, appointing researchers for several scientific positions, etc. Many studies have been proposed to estimate publication’s future citation count; however, limited research has been conducted on forecasting the citation-based influence of the scientists. The authors of the scientific manuscripts are connected through common publications, which can be captured in dynamic network structures with multiple features in the nodes and the links. The topological structure is an essential factor to consider as it reveals important information about such dynamic networks, such as the rise and fall in the network properties like in-degree, etc., over time for nodes. In this work, we have developed an approach for predicting the citation count of scientists using topological information from dynamic citation networks and relevant contents of individual publications. This framework of the citation count prediction is formulated as the node classification task, which is accomplished by using seven machine learning-based classification models for various class categories. The highest average accuracy of 85.19% is achieved with the XGBoost classifier on the High Energy Physics - Theory citation network dataset. © 2023, IFIP International Federation for Information Processing.