Browsing by Author "Bhattacharjee, S."

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A Comparative Study of Temporal and Static Networks in Modeling Pathogen Transmission within School Environments
(Institute of Electrical and Electronics Engineers Inc., 2024) Shetty, R.D.; Bhattacharjee, S.
In the domain of epidemiology and infectious disease control, understanding the dynamics of pathogen transmission is crucial. This study shows the dynamics of pathogen spread in school environments by comparing two distinct network models: temporal networks and static networks. The objective is to assess their respective effectiveness in modeling the transmission of pathogens within school settings. Temporal networks take into account the evolving nature of social interactions over time, offering a more realistic representation of human contact patterns. In contrast, static networks provide a simplified but stable view of social connections. This research investigates how these two modeling approaches impact our ability to analyze, and control the spread of pathogens, such as viruses, in school environments. We Propose T-GSI (Temporal Global Structure Influence) to measure the influence of the temporal networks, which outperformed the state-of-the-art methods. Our findings reveal the strengths and weaknesses of temporal and static networks in capturing the influence of pathogen transmission within school communities. Â© 2024 IEEE.
A Temporal Metric-Based Efficient Approach toÂ Predict Citation Counts ofÂ 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 publicationâ€™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. Â© 2023, IFIP International Federation for Information Processing.
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 Ï„ score. This method is useful for modeling disease dynamics and identifying highly influential contacts by considering both node and edge properties. Â© 2022 IEEE.
Analyzing Derived Network Feature Importance to Identify Location Influence in LBSN
(Institute of Electrical and Electronics Engineers Inc., 2023) Shetty, R.D.; Dewangan, S.K.; Bhattacharjee, S.
Location-based social networks (LBSN) data is of greater importance in various domains of studies, such as viral marketing, location recommendation, influence maximization problem, etc. Identifying influential or hotspot locations of disease spread is of great importance during epidemic outbreaks, in case of limited vaccination, to make timely decisions about stay-at-home policy, restricting the transportation/human movement from one geographical location to another, etc. LBSN data can be used to construct the location-based weighted spatio-temporal network with its defined feature sets, including its rich collection of user movement information with respect to space and time. Here, we design two algorithms to generate spatio-temporal edge weights in such networks. We then examine four benchmark algorithms to identify the importance of these derived edge features to evaluate location influence in the context of contagious disease spread. It is observed that deriving edge features play an important role in the application scenario, especially to understand the need of dense network compared to the sparse network, generated from the LBSN data. Â© 2023 IEEE.
Comparative Analysis of Religious Texts: NLP Approaches to the Bible, Quran, and Bhagavad Gita
(Association for Computational Linguistics (ACL), 2025) Mahit Nandan, A.D.; Godbole, I.; Kapparad, P.; Bhattacharjee, S.
Religious texts have long influenced cultural, moral, and ethical systems, and have shaped societies for generations. Scriptures like the Bible, the Quran, and the Bhagavad Gita offer insights into fundamental human values and societal norms. Analyzing these texts with advanced methods can help improve our understanding of their significance and the similarities or differences between them. This study uses Natural Language Processing (NLP) techniques to examine these religious texts. Latent Dirichlet allocation (LDA) is used for topic modeling to explore key themes, while GloVe embeddings and Sentence transformers are used to comapre topics between the texts. Sentiment analysis using Valence Aware Dictionary and sEntiment Reasoner (VADER) assesses the emotional tone of the verses, and corpus distance measurement is done to analyze semantic similarities and differences. The findings reveal unique and shared themes and sentiment patterns across the Bible, the Quran, and the Bhagavad Gita, offering new perspectives in computational religious studies. Â© 2025 Association for Computational Linguistics.
Comparative analysis of the impact of epidemiological modeling on COVID-19
(De Gruyter, 2023) Bhattacharjee, S.; Das, K.; Zaman, S.; Sadhu, A.; Roy, S.K.; Naha, A.; Khan, F.S.; Sarkar, B.
This chapter provides a comprehensive review on different existing epidemiological models proposed for analyzing the impact of COVID-19. Since December 2019, COVID-19 emerged as an alarming threat to mankind. To mitigate the impact of pandemic, several preventive measures have been practiced by nations. But due to mutation of the virus, the pandemic prevails. This review provides a vivid description of the contributions of different existing epidemiological models on COVID-19. A comparative analysis of SIR, ESIR, SEPIR, SEIR, SEIJR, SEIAR, SEIR-P, SIRD, SEIRD, R-SEIRD, SEIRDH, SEIQARDT, SIDARTHE, θ-SEIHRD, and SIRDV models have been highlighted. Effects of important parameters like infection rate and recovery rate on different epidemiological models have been addressed. Model parameters, assumptions about the model, techniques used, and contributions and drawbacks of the respective models have also been discussed. Apart from epidemical models, this chapter aims to focus on precise illustration on multiple strains of SARS-CoV-2. Comprehensive analysis on the impact of vaccination on multiple strains has also been reported. © 2023 Walter de Gruyter GmbH, Berlin/Boston.
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. Â© 2022 IEEE.
Disaster Classification Using Multimodality Techniques byÂ Integrating Images andÂ Text
(Springer Science and Business Media Deutschland GmbH, 2025) Medapati, B.M.R.; Pais, S.M.; Bhattacharjee, S.
In todayâ€™s digital era, the abundance of information shared through social media platforms in times of disaster has emerged as a crucial asset for enhancing disaster response operations. This research initiative was specifically dedicated to enhance disaster categorization by integrating image and tweet text data. The devised model comprises two distinct modules aimed at optimizing the classification process. The primary module focuses on extracting insights from text and images independently using VGG-16 for images, and Bidirectional Long Short-Term Memory and Convolutional Neural Network for texts, subsequently executing the classification task. Conversely, the secondary module is designed to learn the interconnectedness between textual content and images using Contrastive Learning Image Pretraining (CLIP). After this late fusion is used to combine the outcomes of these modules and later softmax classification is used for the classification of the incident into one of seven humanitarian categories thereby enhancing the precision and efficacy of disaster classification. The developed model gives an accuracy of 75% with no data and image augmentations and the result was improved to 93% with different combinations of augmentations. Â© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Does Degree Capture It All? A Case Study of Centrality and Clustering in Signed Networks
(Association for Computing Machinery, Inc, 2025) Murali, S.S.; Abhin, B.; Shetty, R.D.; Bhattacharjee, S.
Signed graph networks are used to model systems that contain both positive and negative components. By incorporating signed information into Graph Neural Networks (GNNs), allow for the analysis of complex interactions between nodes, facilitating tasks such as sentiment analysis and trust prediction in social networks. Our main goal in this study is to improve feature selection in a benchmark GNN, Signed Graph Attention (SiGAT) by including centrality and clustering measures other than degree. Our studies reveal that using both degree and centrality features slightly improves signed link prediction performance. Further, our ablation studies revealed that 6 degree features and 16 attention heads optimally encode information and reduce noise. Â© 2024 Copyright held by the owner/author(s).
Downscaled XCO2 Estimation Using Data Fusion and AI-Based Spatio-Temporal Models
(Institute of Electrical and Electronics Engineers Inc., 2024) Pais, S.M.; Bhattacharjee, S.; Anand Kumar, M.; Chen, J.
One of the well-known greenhouse gases (GHGs) produced by anthropogenic human activity is carbon dioxide (CO2). Understanding the carbon cycle and how negatively it affects the ecosystem requires analysis of the rise in CO2 concentration. This work aims to map CO2 concentration for the entire surface, making it useful for regional carbon cycle analysis. Here, column-averaged CO2 dry mole fraction, called XCO2, measured by the orbiting carbon observatory-2 (OCO-2) satellite, is used. Because of spectral interference by the clouds and aerosols, there are many missing footprints in the Level-2 swath of OCO-2, making it disruptive to understand any assessment related to the carbon cycle. The objective of this work is to predict 1 km2 XCO2 using data resampling and machine learning models. This work achieves a minimum mean absolute error (MAE) and root mean square error (RMSE) of 0.3990 and 0.8090 ppm, using the monthly models. © 2004-2012 IEEE.
Drought Detection in India using Spatio-Temporal Graph Neural Networks
(Institute of Electrical and Electronics Engineers Inc., 2025) Sudhakara, B.; Maheshwari, A.; Palakuru, S.; Bhattacharjee, S.
Droughts, which are defined by extended periods of water scarcity, offer significant difficulties to agriculture, ecosystems, and human populations. Drought detection that requires timely and precise assessment is critical for effective mitigation and resource planning. This work proposes a novel technique for drought detection using satellite imagery with the capabilities of Graph Neural Networks (GNNs). The proposed GNN-based model captures spatio-temporal dependencies by representing 671 districts across India as nodes, connected based on geographical proximity. The spatio-temporal model achieved its best performance with an RMSE of 6.849, MAE of 4.367, and R2 of 0.903 for the Normalized Vegetation Supply Water Index (NVSWI). This work is one of the initial attempts to predict the drought over the Indian region using graph neural networks. Â© 2025 IEEE.
Dual band operation using metamaterial structure for Radio Frequency Identification (RFID) system
(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.
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. Â© 2014 IEEE.
Enhancing reliability of cloud system through proactive identification of under performing components
(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.
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. Â© 2016 IEEE.
Forecasting of Fine-Grained SIF of OCO-2 Using Multi-source Data and AI-Based Techniques
(Springer, 2025) Pais, S.M.; Bhattacharjee, S.; Anand Kumar, M.
The emission of unused energy absorbed from the sunlight by plants and other photosynthetic organisms is known as solar-induced fluorescence (SIF). SIF is a direct proxy for the photosynthetic activity of the plants used to monitor drought, crop yield estimation, ecological processes, and carbon cycles. Comprehending the SIF dynamics beforehand helps gain an understanding of vegetation dynamics, carbon cycle, and crop phenology. This study explores the potential of using Orbiting Carbon Observatory-2 (OCO-2) SIF data for forecasting SIF at regional scales. The research utilizes machine learning models and data fusion to forecast the SIF data, by establishing relationships between observed SIF from past timestamps and the Enhanced Vegetation Index. The lasso regression achieves minimal error of RMSE 0.0355 Wm-2nm-1sr-1 and MAPE of 16.9093% for forecasting monthly SIF data. In contrast, the light gradient boosting machine model (LG) performs well for a larger non-linear dataset, i.e., seasonal models achieving a RMSE of 0.0389 Wm-2nm-1sr-1 and MAPE of 17.4895%, respectively. Karnataka and Maharastra, the two Indian states, are considered as the study areas for this work for a temporal window of 2017–2019. Fine-grained, uniformly distributed SIF forecasting provides valuable insights for understanding vegetation responses to environmental changes, optimizing agricultural practices, and developing climate change mitigation strategies. © Indian Society of Remote Sensing 2025.
GSI: An Influential Node Detection Approach in Heterogeneous Network Using Covid-19 as Use Case
(Institute of Electrical and Electronics Engineers Inc., 2023) Shetty, R.D.; Bhattacharjee, S.; Dutta, A.; Namtirtha, A.
The growth of COVID-19, caused by the SARS-CoV-2 virus, has turned into an unprecedented pandemic in the last century. It is crucial to identify superspreading nodes to prevent the pandemic's progress. Most available superspreader identification techniques consider only a single or few network metrics related to the complex network's topological structure. Furthermore, it is more challenging to determine influential spreaders from heterogeneous structures of networks. In a disease transmission network, the degree of heterogeneity is essential to locate the path of the infection spread. Therefore, it is required to have an extended degree of centrality to collect information from various neighborhood levels. This article presents an approach, namely, global structure influence (GSI), which considers network nodes' local and global influence. This method can gather information from multiple levels of the neighborhood. Evaluation of our proposed method is done by considering different types of networks, i.e., social networks, highly heterogeneous human contact networks, and epidemiological networks, and also by using the benchmark susceptible-infected-recovered (SIR) epidemic model. The GSI technique provides real-spreading dynamics across various network structures and has outperformed the baseline techniques with an average Kendall's τ improvement range from 0.017 to 0.278. This study will help to identify the superspeaders in real applications, where pathogens spread quickly because of close contact, such as the recently witnessed COVID-19 pandemic. © 2014 IEEE.
High-Resolution Soil Moisture Estimation: A Case Study in Coastal India
(Springer, 2025) Sudhakara, B.; Bhattacharjee, S.
The high-resolution soil moisture (SM) is significant for agricultural production, hydrological modelling, weather prediction, and climate studies at the regional scale. Current satellite and derived SM products span a wide range of coarse spatial resolutions. This work predicts high-resolution and precise SM estimates for the USA and the western coastal regions of India by utilizing remote sensing data. The study implements and compares five models for SM prediction in the study regions, including machine learning and deep learning models. The extra trees regressor model outperformed all other models in the USA and India datasets. It achieved the best performance metrics, with an MAE of 1.080 mm, RMSE of 1.715 mm, and R2 of 0.958 for the USA, and an MAE of 0.847 mm, RMSE of 1.554 mm, and R2 of 0.955 for India. The study validates the predicted data using 20 stations from the International Soil Moisture Network in the USA. It aims to support decision-making for agricultural practices, hydrology, and climate adaptation by providing valuable insights into the area’s spatio-temporal dynamics of SM. © Indian Society of Remote Sensing 2025.
High-resolution Soil Moisture Prediction from SMOS using Machine Learning Models
(Institute of Electrical and Electronics Engineers Inc., 2025) Sudhakara, B.; Maheshwari, A.; Periasamy, M.; Bhattacharjee, S.
Soil moisture is essential for the land carbon cycle, surface and groundwater circulation, heat transport, energy exchange between these systems and other processes. SMOS's (Soil Moisture and Ocean Salinity) 36-kilometer spatial resolution and 3-day temporal resolution offer valuable insights into soil moisture dynamics. This research paper introduces an innovative approach to enhance our understanding and prediction of SMOS values by applying advanced machine learning models. Our research focuses on developing and implementing advanced downscaling techniques, leveraging advanced machine learning algorithms. The primary objective is to establish a robust framework for estimating soil moisture levels at multiple geographic locations within the study region of Oklahoma, USA. To achieve this, three years of SMOS (Soil Moisture and Ocean Salinity) data was integrated with remotely captured images spanning the full range of the electromagnetic spectrum, from visible to infrared wavelengths. The LSTM model performed significantly better in predicting soil moisture values with 0.041 RMSE (m3/m3) and 0.869 (R2) than the other models. Â© 2025 IEEE.
High-Spatial-Resolution Estimation of XCO2 Using a Stacked Ensemble Model
(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Pais, S.M.; Bhattacharjee, S.; Anand Kumar, M.; Balamurugan, V.; Chen, J.
Highlights: What are the main findings? The study develops a customized stacked ensemble model that generalizes (Formula presented.) predictions across multiple country, such as Germany, France, and Japan. It produces gap-filled high-resolution monthly, seasonal, and yearly maps, highlighting vegetation dynamics and seasonal cycles. What is the implication of the main finding? The customized stacked ensemble model provides reliable cross-country (Formula presented.) predictions at 1 (Formula presented.) resolution, validated against TCCON and CAMS, supporting large-scale environmental monitoring. Seasonal and yearly analyses show vegetation dynamics and photosynthetic activity significantly influence (Formula presented.), enhancing the model’s adaptability for agriculture, different climate assessments, and future global mapping. One of the leading causes of climate change and global warming is the rise in carbon dioxide ((Formula presented.)) levels. For a precise assessment of (Formula presented.) ’s impact on the climate and the creation of successful mitigation methods, it is essential to comprehend its distribution by analyzing (Formula presented.) sources and sinks, which is a challenging task using sparsely available ground monitoring stations and airborne platforms. Therefore, the data retrieved by the Orbiting Carbon Observatory-2 (OCO-2) satellite can be useful due to its extensive spatial and temporal coverage. Sparse and missed retrievals in the satellite make it challenging to perform a thorough analysis. This work trains machine learning models using the Orbiting Carbon Observatory-2 (OCO-2) (Formula presented.) retrievals and auxiliary features to obtain a monthly, high-spatial-resolution, gap-filled (Formula presented.) concentration distribution. It uses a multi-source aggregated (MSD) dataset and the generalized stacked ensemble model to predict country-level high-resolution (1 (Formula presented.)) (Formula presented.). When evaluated with TCCON, this country-level model can achieve an RMSE of 1.42 ppm, a MAE of 0.84 ppm, and (Formula presented.) of 0.90. © 2025 by the authors.