Zobrazeno 1 - 10
of 11 316
pro vyhledávání: '"A. Mahendran"'
Autor:
S. Lakshmana Kumar, V. Jacintha, A. Mahendran, R. M. Bommi, M. Nagaraj, Umamahesawari Kandasamy
Publikováno v:
Advances in Materials Science and Engineering, Vol 2022 (2022)
In this present paper, the machine learning approach is used to optimize, model, and predict the factors during drilling Nimonic C263 under dry mode. Nimonic C263 is tough to machine aero alloys, and it is required to find a predictive model and to o
Externí odkaz:
https://doaj.org/article/622fc7ad785445aaab4b771d8ecb2681
BioBERT-based Deep Learning and Merged ChemProt-DrugProt for Enhanced Biomedical Relation Extraction
This paper presents a methodology for enhancing relation extraction from biomedical texts, focusing specifically on chemical-gene interactions. Leveraging the BioBERT model and a multi-layer fully connected network architecture, our approach integrat
Externí odkaz:
http://arxiv.org/abs/2405.18605
Locomotion gaits are fundamental for control of soft terrestrial robots. However, synthesis of these gaits is challenging due to modeling of robot-environment interaction and lack of a mathematical framework. This work presents an environment-centric
Externí odkaz:
http://arxiv.org/abs/2402.03617
Autor:
Narayanan, Mahendran
Movies wield significant influence in our lives, playing a pivotal role in the tourism industry of any country. The inclusion of picturesque landscapes, waterfalls, and mountains as backdrops in films serves to enhance the allure of specific scenario
Externí odkaz:
http://arxiv.org/abs/2312.00098
Autor:
Narayanan, Mahendran
Convolutional Neural Networks (CNNs) have revolutionized image classification by extracting spatial features and enabling state-of-the-art accuracy in vision-based tasks. The squeeze and excitation network proposed module gathers channelwise represen
Externí odkaz:
http://arxiv.org/abs/2311.10807
Publikováno v:
Journal of Physical Chemistry C 128, 1709 (2024)
Transition metal dichalcogenides are investigated for various applications at the nanoscale thanks to their unique combination of properties and dimensionality. For many of the anticipated applications, heat conduction plays an important role. At the
Externí odkaz:
http://arxiv.org/abs/2310.09405
Autor:
N, Mahendran
In this paper, we introduce Handwritten augmentation, a new data augmentation for handwritten character images. This method focuses on augmenting handwritten image data by altering the shape of input characters in training. The proposed handwritten a
Externí odkaz:
http://arxiv.org/abs/2308.13791
Autor:
N, Mahendran
Convolutional neural networks have spatial representations which read patterns in the vision tasks. Squeeze and excitation links the channel wise representations by explicitly modeling on channel level. Multi layer perceptrons learn global representa
Externí odkaz:
http://arxiv.org/abs/2308.13343
Autor:
Mahendran, Arun Niddish, Freeman, Caitlin, Chang, Alexander H., McDougall, Michael, Vela, Patricio A., Vikas, Vishesh
The adaptability of soft robots makes them ideal candidates to maneuver through unstructured environments. However, locomotion challenges arise due to complexities in modeling the body mechanics, actuation, and robot-environment dynamics. These facto
Externí odkaz:
http://arxiv.org/abs/2307.16385
Autor:
NV, Mahendran
Convolutional neural networks learns spatial features and are heavily interlinked within kernels. The SE module have broken the traditional route of neural networks passing the entire result to next layer. Instead SE only passes important features to
Externí odkaz:
http://arxiv.org/abs/2304.06502