Výsledky vyhledávání - "SEKH, ARIF AHMED"

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Is Grad-CAM Explainable in Medical Images?

Autor: Suara, Subhashis, Jha, Aayush, Sinha, Pratik, Sekh, Arif Ahmed

Explainable Deep Learning has gained significant attention in the field of artificial intelligence (AI), particularly in domains such as medical imaging, where accurate and interpretable machine learning models are crucial for effective diagnosis and

Externí odkaz: http://arxiv.org/abs/2307.10506

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Akademický článek

Future educational environment – Identification of smart campus topic trends using text mining

Autor: Joshy, K.X., Thakurta, Rahul, Sekh, Arif Ahmed

Publikováno v: International Journal of Educational Management, 2023, Vol. 37, Issue 4, pp. 884-906.

Externí odkaz: http://www.emeraldinsight.com/doi/10.1108/IJEM-01-2023-0005

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Auxiliary Network: Scalable and agile online learning for dynamic system with inconsistently available inputs

Autor: Agarwal, Rohit, Sekh, Arif Ahmed, Agarwal, Krishna, Prasad, Dilip K.

Streaming classification methods assume the number of input features is fixed and always received. But in many real-world scenarios demand is some input features are reliable while others are unreliable or inconsistent. In this paper, we propose a no

Externí odkaz: http://arxiv.org/abs/2008.11828

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Simulation-supervised deep learning for analysing organelles states and behaviour in living cells

Autor: Sekh, Arif Ahmed, Opstad, Ida S., Agarwal, Rohit, Birgisdottir, Asa Birna, Myrmel, Truls, Ahluwalia, Balpreet Singh, Agarwal, Krishna, Prasad, Dilip K.

In many real-world scientific problems, generating ground truth (GT) for supervised learning is almost impossible. The causes include limitations imposed by scientific instrument, physical phenomenon itself, or the complexity of modeling. Performing

Externí odkaz: http://arxiv.org/abs/2008.12617

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Neural network based country wise risk prediction of COVID-19

Autor: Pal, Ratnabali, Sekh, Arif Ahmed, Kar, Samarjit, Prasad, Dilip K.

Publikováno v: Applied Sciences, 2020

The recent worldwide outbreak of the novel coronavirus (COVID-19) has opened up new challenges to the research community. Artificial intelligence (AI) driven methods can be useful to predict the parameters, risks, and effects of such an epidemic. Suc

Externí odkaz: http://arxiv.org/abs/2004.00959

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