Browsing by Author "Thummar, D."

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Democratizing University Seat Allocation using Blockchain
(Institute of Electrical and Electronics Engineers Inc., 2022) Jahnavi, Y.; Prathyusha, M.; Shahanaz, S.; Thummar, D.; Ghosh, B.C.; Addya, S.K.
Online seat allocation processes such as Joint Seat Allocation Authority in India have streamlined the university seat allocation process and reduced the risk of seats being vacant. Similar centralized online counseling processes are used for many universities in different countries. In-spite of being a collaborative process involving different stakeholders, such systems are centralized having their inherent limitations including lack of transparency, risk of censorship, manipulation, and single point of failure. In this demonstration, we showcase a decentralized ledger technology based system and application for democratizing the university seat allocation process. We demonstrate that the user experience of the proposed system is almost identical to the traditional centralized one, in spite of having the additional benefits of transparency, auditability, and non-repudiability of the decentralized architecture. Â© 2022 IEEE.
DeSAT: Towards Transparent and Decentralized University Counselling Process
(Institute of Electrical and Electronics Engineers Inc., 2022) Thummar, D.; Jahnavi, Y.; Prathyusha, M.; Shahanaz, S.; Ghosh, B.C.; Addya, S.K.
The admission process in academic institutions (universities, colleges, etc.) is more digitized than ever. Starting from standardized tests to application processing, to shortlisting on the basis of merit, to even document verification, everything is carried out through online processes now. However, in spite of having huge benefits in terms of convenience, existing admission processes severely lack transparency. The entire process is dependent on certain central authoritative entities such as the testing authorities followed by the institutes themselves. Moreover, critical tasks such as verifying educational and identity-related documents of students is a tedious affair and the effort is duplicated across all institutions. In this work, we attempt to overcome these limitations of the existing workflow of academic institutes' admission process by designing a distributed ledger based framework that involves the academic institutes, testing authorities, document and credential validators, as well as the students. Our framework DeSAT uses verifiable credentials together with a permissioned ledger to remove the duplicate efforts in verification of test scores as well as validation of students' documents. In addition, it makes the entire process transparent and auditable while enforcing fair merit-based seat allotment through smart contracts. Through a prototype implementation using Hyperledger Fabric, Indy, and Aries, we demonstrate the practicality of DeSAT and show that our system offers acceptable performance while scaling with the number of participating institutions. Â© 2022 IEEE.
Importance of Knee Angle and Trunk Lean in the Detection of an Abnormal Walking Pattern Using Machine Learning
(Springer Science and Business Media Deutschland GmbH, 2023) Pandit, P.; Thummar, D.; Verma, K.; Gangadharan, K.V.; Das, B.; Kamat, Y.
Human gait can be quantified using motion capture systems. Three-dimensional (3D) gait analysis is considered the gold standard for gait assessment. However, the process of three-dimensional analysis is cumbersome and time-consuming. It also requires complex software and a sophisticated environment. Hence, it is limited to a smaller section of the population. We, therefore, aim to develop a system that can predict abnormal walking patterns by analyzing trunk lean and knee angle information. A vision-based OpenPose algorithm was used to calculate individual trunk lean and knee angles. Web applications have been integrated with this algorithm so that any device can use it. A Miqus camera system of Qualisys 3D gait analysis system was used to validate the OpenPose algorithm. The validation method yielded an error of Â± 9Â° in knee angle and Â± 8Â° in trunk lean. The natural walking pattern of 100 healthy individuals was compared to simulated walking patterns in an unconstrained setting in order to develop a machine learning program. From the collected data, an RNN-based LSTM machine learning model was trained to distinguish between normal and abnormal walkings. LSTM-based models were able to distinguish between normal and abnormal gaits with an accuracy of 80%. This study shows that knee angle and trunk lean patterns collected during walking can be significant indicators of abnormal gait. Â© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Machine learning approach for optimization and performance prediction of triangular duct solar air heater: A comprehensive review
(Elsevier Ltd, 2023) Nidhul, K.; Thummar, D.; Yadav, A.K.; Anish, S.
This paper presents a comprehensive review of various kinds of distinct artificial roughness employed in rectangular and triangular duct solar air heaters to aid prospective researchers in finding a critical gap in the domain of solar air heaters. A Machine Learning (ML) model is developed using 72 distinct rib combinations compiled to 454 datasets and trained using an Artificial Neural Network (ANN) to predict the performance of ribbed triangular duct Solar Air Heater (SAH). The developed ML model predicts the data with an average deviation of <3%. Owing to reasonably accurate predictions, the same could be increased when more cases (geometric or operating parameters) are added to the databases by retraining the ANN. Further, a second law analysis of the rib configurations features collector efficiency and entropy generation variation with Re for various rib parameters. For the Re range of 4000 to 18000, optimum parameters such as rib height, pitch, chamfer angle, and inclinations are obtained for triangular duct SAH. This could help design engineers obtain the performance parameters of ribbed triangular duct SAH with other artificial roughness designs, possibly with a combination of different geometrical and operating parameters, without having to perform tests. © 2023 International Solar Energy Society
Machine Learning for Vortex Flowmeter Design
(Institute of Electrical and Electronics Engineers Inc., 2022) Thummar, D.; Reddy, Y.J.; Venugopal, V.
Vortex flowmeters are one of the broadly used flow measurement devices in various industrial applications. The shape of the bluff body is the most critical parameter in the design of vortex flowmeter. The conventional approach of bluff body design relies on parametric shape optimization of a bluff body using experimentation and computational fluid dynamics simulations, which are expensive and time-consuming. In this study, we propose a novel machine learning (ML)-based approach to design bluff body shapes. Two ML models are developed using supervised ML using an artificial neural network (ANN). The first model predicts new optimum bluff body shapes for a given input flow characteristic. The second model predicts the deviation in Strouhal number for a given bluff body to determine its optimality. Data from the literature on the geometry of bluff bodies and fluid flow properties such as blockage ratio, Reynolds number, and Strouhal number are used for training ML models. The obtained ML results are in close agreement (±3.0%) compared with the computational fluid dynamics simulation results. This approach may find broad applicability for designing other fluid flowmeters. © 1963-2012 IEEE.
Mitigating Blackhole attack of Underwater Sensor Networks
(Institute of Electrical and Electronics Engineers Inc., 2021) Zala, D.; Thummar, D.; Chandavarkar, B.R.
Underwater wireless sensor network(UWSN) is an emerging technology for exploring and research inside the ocean. Since it is somehow similar to the normal wireless network, which uses radio signals for communication purposes, while UWSN uses acoustic for communication between nodes inside the ocean and sink nodes. Due to unattended areas and the vulnerability of acoustic medium, UWNS are more prone to various malicious attacks like Sybil attack, Black-hole attack, Wormhole attack, etc. This paper analyzes blackhole attacks in UWSN and proposes an algorithm to mitigate blackhole attacks by forming clusters of nodes and selecting coordinator nodes from each cluster to identify the presence of blackholes in its cluster. We used public-key cryptography and the challenge-response method to authenticate and verify nodes. Â© 2021 IEEE.