Faculty Publications
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Item Virtual medical board: A distributed Bayesian agent based approach(Knowledge Systems Institute Graduate School office@ksi.edu, 2013) Dutta, A.; Acharya, S.; Krishna, A.; Bhattacharya, S.Distributed Decision Making has become of increasing importance to get solution of different real life problems. Application of agent and multi agent system in this Distributed Decision Support System is an evolving paradigm. One of such real life problem is medical board formation. But always formation of a medical board with a group of expert physicians may not be always possible due to lack of infrastructure, availability, time etc. In these situations the role of multi agent based distributed decision making can comes into play. In this paper we develop a Virtual Medical Board System in which a number of software agents (expert agents) act as a group of expert physicians with knowledge base(KB), reasoning capability. They coordinate with each other to diagnose a patient. © © 2013 by Knowledge Systems Institute Graduate School.Item Multi agent based railway scheduling and optimization(Institute of Electrical and Electronics Engineers Inc., 2015) Dalapati, P.; Singh, A.J.; Dutta, A.; Bhattacharya, S.This paper proposes a multi agent based timetable scheduling algorithm for railway system which handles the in-between time delay of the newly introduced train. The delay management indeed optimizes the total journey time, hence increases the total utility of the whole railway system as well. Here we show that schedule generated by our proposed algorithm is the most optimized schedule. It is done by using the notion of DCOP(Distributed Constraint Optimization Problem), where we define some metric to analyze the system to achieve our goals. We use JADE(Java Agent DEvelopment Framework) platforms to simulate our work and test it using a small network. We also take a small case study to compare our proposed work with the existing one and the results are therefore presented. © 2014 IEEE.Item Experimental and Numerical Study of the Hydrodynamics of a Thin Film Reactor (TFR) for the Decarboxylation of Anacardic Acid(Walter de Gruyter GmbH, 2018) Shrutee, L.; van Geel, T.; Rene, E.R.; Raj Mohan, B.; Dutta, A.A newly designed laboratory scale thin film reactor (TFR) was tested for the decarboxylation of anacardic acid in Cashew Nut Shell Liquid (CNSL) and to investigate the fluid flow behaviour under the influence of temperature since the fluid properties like viscosity and density have strong dependence on temperature. The CNSL containing 60-65 % anacardic acid was decarboxylated to produce cardanol and CO2 at wall temperatures ranging between 393 K and 433 K, respectively. The characteristics of the CNSL, essentially a non-Newtonian fluid, was analysed at different temperatures and its rheological behaviour was studied using the well-known power law model. It was observed that CNSL follows a pseudoplastic behaviour and its viscosity, along with the liquid residence time, was found to decrease till 413 K, while a further increase in temperature resulted in product degradation due to charring, accompanied by an increase in viscosity and residence time. Using measured values for the viscosity, the film thickness was calculated for each wall temperature within the 393-433 K temperature range, showing an increase of the film thickness with temperature and viscosity. Computational Fluid Dynamics (CFD) studies were carried out for the first time for this reactor configuration, using the volume of fluid (VOF) model for the reactive flow. The results obtained from these simulations were in concurrence with the experimental outcomes: velocity profiles along the length of the reactor show its highest values at a wall temperature of 413 K, while lower velocity values were observed when the temperatures were lower or greater than 413 K. © 2018 Walter de Gruyter GmbH, Berlin/Boston 2018.Item 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.Item Phenolic profile of unripe areca nuts cultivated in various districts of Karnataka, India(John Wiley and Sons Ltd, 2024) Hugar, P.; Dutta, A.; Srilakshmi, S.; Belur, P.D.; Raval, K.; Iyyaswami, R.Background: Annual production of areca nut in Karnataka state exceeds 1.08 million tonnes, contributing 80% and 49% to Indian and global production, respectively. Areca nut (pericarp of Areca catechu L.) is found to be a rich source of valuable phenolic compounds. Total phenolic content (TPC) and total flavonoid content (TFC) were estimated in 21 unripe areca nut samples collected from the major areca nut growing regions of Karnataka state. Arecoline, a prominent alkaloid present in areca nut was estimated and phenolic profile of one areca nut sample was generated using UHPLC–MS/MS studies. Results: A significant variability was found in TPC, TFC and arecoline content among the samples, belongs to different agroclimatic zones. Flavonoids were found to be the major phenolic compounds present in these unripe areca nut samples. The median values of TPC, TFC and arecoline were found to be 99.609 ± 0.002 mg gallic acid equivalent, 78.86 ± 0.007 mg catechin equivalent and 2.17 ± 0.13 mg/g of the sample on fresh weight basis. A positive correlation was found to exist between TPC and arecoline content in the green unripe areca nuts of 6–7 months' maturity. Through UHPLC–MS/MS studies, 61 prominent compounds have been identified. Conclusion: The TPC and TFC of areca nuts collected from different districts of Karnataka falling in various agroclimatic zones varied significantly. In general, the arecoline content found in all these samples was less than that reported elsewhere, and the variability among the samples was also found to be minimal. A positive correlation was observed between TPC and arecoline. UHPLC–MS/MS studies showed the presence of about 52 unique phenolic compounds. © 2023 The Authors. JSFA Reports published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.