Browsing by Author "Raghavendra, M."

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AuthNet: A Deep Learning Based Authentication Mechanism Using Temporal Facial Feature Movements (Student Abstract)
(Association for the Advancement of Artificial Intelligence, 2021) Raghavendra, M.; Omprakash, P.; Mukesh, B.R.
Deep learning algorithms are widely used to extend modern biometric authentication mechanisms in resource-constrained environments like smartphones, providing ease-of-use and user comfort, while maintaining a non-invasive nature. In this paper, an alternative is proposed, that uses both facial recognition and the unique movements of that particular face while uttering a password. The proposed model is language independent, the password doesn't necessarily need to be a set of meaningful words or numbers, and also, is a contact-less system. When evaluated on the standard MIRACL-VC1 dataset, the proposed model achieved a testing accuracy of 98.1%, underscoring its effectiveness. Â© Â© 2021, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved
Predicting cross-tissue hormone-gene relations using balanced word embeddings
(Oxford University Press, 2022) Jadhav, A.; Kumar, T.; Raghavendra, M.; Loganathan, T.; Narayanan, M.
Motivation: Inter-organ/inter-tissue communication is central to multi-cellular organisms including humans, and mapping inter-tissue interactions can advance system-level whole-body modeling efforts. Large volumes of biomedical literature have fostered studies that map within-tissue or tissue-agnostic interactions, but literature-mining studies that infer inter-tissue relations, such as between hormones and genes are solely missing. Results: We present a first study to predict from biomedical literature the hormone-gene associations mediating inter-tissue signaling in the human body. Our BioEmbedS∗ models use neural network-based Biomedical word Embeddings with a Support Vector Machine classifier to predict if a hormone-gene pair is associated or not, and whether an associated gene is involved in the hormone's production or response. Model training relies on our unified dataset Hormone-Gene version 1 of ground-truth associations between genes and endocrine hormones, which we compiled and carefully balanced in the embedded space to handle data disparities, such as between poorly- versus well-studied hormones. Our BioEmbedS model recapitulates known gene mediators of tissue-tissue signaling with 70.4% accuracy; predicts novel inter-tissue communication genes in humans, which are enriched for hormone-related disorders; and generalizes well to mouse, thereby holding promise for its extension to other multi-cellular organisms as well. © 2022 The Author(s).
Representation Learning in Continuous-Time Dynamic Signed Networks
(Association for Computing Machinery, 2023) Sharma, K.; Raghavendra, M.; Lee, Y.-C.; Anand Kumar, M.A.; Kumar, S.
Signed networks allow us to model conflicting relationships and interactions, such as friend/enemy and support/oppose. These signed interactions happen in real-time. Modeling such dynamics of signed networks is crucial to understanding the evolution of polarization in the network and enabling effective prediction of the signed structure (i.e., link signs) in the future. However, existing works have modeled either (static) signed networks or dynamic (unsigned) networks but not dynamic signed networks. Since both sign and dynamics inform the graph structure in different ways, it is non-trivial to model how to combine the two features. In this work, we propose a new Graph Neural Network (GNN)-based approach to model dynamic signed networks, named SEMBA: Signed link's Evolution using Memory modules and Balanced Aggregation. Here, the idea is to incorporate the signs of temporal interactions using separate modules guided by balance theory and to evolve the embeddings from a higher-order neighborhood. Experiments on 4 real-world datasets and 3 different tasks demonstrate that SEMBA consistently and significantly outperforms the baselines by up to 80% on the tasks of predicting signs of future links while matching the state-of-the-art performance on predicting existence of these links in the future. We find that this improvement is due specifically to superior performance of SEMBA on the minority negative class. Code is made available at https://github.com/claws-lab/semba. Â© 2023 Copyright held by the owner/author(s). ACM ISBN 979-8-4007-0124-5/23/10.
SGR: Secure geographical routing in Wireless Sensor Networks
(2015) Lata, B.T.; Tejaswi, V.; Shaila, K.; Raghavendra, M.; Venugopal, K.R.; Iyengar, S.S.; Patnaik, L.M.
Geographical Routing Technique is a new trend in Wireless Sensor Networks in which the sensor nodes are enabled using Global Positioning Systems(GPS). This helps to easily detect the position of their neighboring nodes. The power consumption is more in the existing routing algorithms, since the nodes build the routing tables and the neighboring node IDs are determined by searching the routing table. In this paper, we have proposed Secure Geographical Routing (SGR) algorithm in which the data traffic and energy consumption is minimized using single copy data transfer. In SGR, initially one copy is transmitted to the next node using greedy approach and another copy is preserved in the sending station. If acknowledgment is not received even after timeout then the second copy is transmitted. This dynamic single copy scheme reduces the data traffic in Wireless Sensor Networks. Security algorithms are incorporated in every sensor node to prevent any malicious node attack that disturb the normal functioning of the network. Simulation result shows that the performance of the proposed algorithm is better in terms of packet delivery probability and energy consumption in comparison with existing algorithms. � 2014 IEEE.
SGR: Secure geographical routing in Wireless Sensor Networks
(Institute of Electrical and Electronics Engineers Inc., 2015) Lata, B.T.; Tejaswi, V.; Shaila, K.; Raghavendra, M.; Venugopal, K.R.; Iyengar, S.S.; Patnaik, L.M.
Geographical Routing Technique is a new trend in Wireless Sensor Networks in which the sensor nodes are enabled using Global Positioning Systems(GPS). This helps to easily detect the position of their neighboring nodes. The power consumption is more in the existing routing algorithms, since the nodes build the routing tables and the neighboring node IDs are determined by searching the routing table. In this paper, we have proposed Secure Geographical Routing (SGR) algorithm in which the data traffic and energy consumption is minimized using single copy data transfer. In SGR, initially one copy is transmitted to the next node using greedy approach and another copy is preserved in the sending station. If acknowledgment is not received even after timeout then the second copy is transmitted. This dynamic single copy scheme reduces the data traffic in Wireless Sensor Networks. Security algorithms are incorporated in every sensor node to prevent any malicious node attack that disturb the normal functioning of the network. Simulation result shows that the performance of the proposed algorithm is better in terms of packet delivery probability and energy consumption in comparison with existing algorithms. Â© 2014 IEEE.