Zobrazeno 1 - 10
of 4 279
pro vyhledávání: '"A. Harshavardhan"'
Autor:
Kaushik Sekaran, A. Harshavardhan, N. Sandhya, C. Sudha, Gujjeti Nagaraju, Hanumanthu Bukya, Rashmi Sahay, J. Kalaivani
Publikováno v:
IEEE Access, Vol 12, Pp 83140-83153 (2024)
Parkinson’s disease (PD) is a progressive neurodegenerative disease with multiple motor and non-motor characteristics. PD patients commonly face vocal impairments during the early stages of the disease. Therefore, diagnosis systems based on vocal d
Externí odkaz:
https://doaj.org/article/44b046b176ca480881a7248cd70d8a9e
Publikováno v:
Journal of King Saud University: Computer and Information Sciences, Vol 35, Iss 10, Pp 101848- (2023)
Alzheimer's disease (AD) is a neurological disorder characterized by cognitive decline and memory loss. An early and precise diagnosis of Alzheimer's disease is critical for effective therapy and management. The electroencephalogram (EEG) has shown p
Externí odkaz:
https://doaj.org/article/a61de5c67a3641bf9b9dc8d5f1b809c8
Autor:
Bukas, Christina, Subramanian, Harshavardhan, See, Fenja, Steinchen, Carina, Ezhov, Ivan, Boosarpu, Gowtham, Asgharpour, Sara, Burgstaller, Gerald, Lehmann, Mareike, Kofler, Florian, Piraud, Marie
High-throughput image analysis in the biomedical domain has gained significant attention in recent years, driving advancements in drug discovery, disease prediction, and personalized medicine. Organoids, specifically, are an active area of research,
Externí odkaz:
http://arxiv.org/abs/2410.14612
Autor:
Kamarthi, Harshavardhan, Sasanur, Aditya B., Tong, Xinjie, Zhou, Xingyu, Peters, James, Czyzyk, Joe, Prakash, B. Aditya
Hierarchical time-series forecasting (HTSF) is an important problem for many real-world business applications where the goal is to simultaneously forecast multiple time-series that are related to each other via a hierarchical relation. Recent works,
Externí odkaz:
http://arxiv.org/abs/2407.02657
Multi-variate time series forecasting is an important problem with a wide range of applications. Recent works model the relations between time-series as graphs and have shown that propagating information over the relation graph can improve time serie
Externí odkaz:
http://arxiv.org/abs/2407.02641
Autor:
Liu, Haoxin, Kamarthi, Harshavardhan, Kong, Lingkai, Zhao, Zhiyuan, Zhang, Chao, Prakash, B. Aditya
Time-series forecasting (TSF) finds broad applications in real-world scenarios. Due to the dynamic nature of time-series data, it is crucial to equip TSF models with out-of-distribution (OOD) generalization abilities, as historical training data and
Externí odkaz:
http://arxiv.org/abs/2406.09130
Autor:
Liu, Haoxin, Xu, Shangqing, Zhao, Zhiyuan, Kong, Lingkai, Kamarthi, Harshavardhan, Sasanur, Aditya B., Sharma, Megha, Cui, Jiaming, Wen, Qingsong, Zhang, Chao, Prakash, B. Aditya
Time series data are ubiquitous across a wide range of real-world domains. While real-world time series analysis (TSA) requires human experts to integrate numerical series data with multimodal domain-specific knowledge, most existing TSA models rely
Externí odkaz:
http://arxiv.org/abs/2406.08627
Autor:
Warrier, Sreeraj Rajan, Reddy, D Sri Harshavardhan, Bada, Sriya, Achampeta, Rohith, Uppapalli, Sebastian, Dontabhaktuni, Jayasri
Underwater images taken from autonomous underwater vehicles (AUV's) often suffer from low light, high turbidity, poor contrast, motion-blur and excessive light scattering and hence require image enhancement techniques for object recognition. Machine
Externí odkaz:
http://arxiv.org/abs/2404.13130
Autor:
Timperley, Christopher S., van der Hoorn, Gijs, Santos, André, Deshpande, Harshavardhan, Wąsowski, Andrzej
Publikováno v:
ROBUST: 221 bugs in the Robot Operating System CS Timperley, G van der Hoorn, A Santos, H Deshpande, A W\k{a}sowski Empirical Software Engineering 29 (3), 57, 2024
As robotic systems such as autonomous cars and delivery drones assume greater roles and responsibilities within society, the likelihood and impact of catastrophic software failure within those systems is increased.To aid researchers in the developmen
Externí odkaz:
http://arxiv.org/abs/2404.03629
Transformers are the backbone of powerful foundation models for many Vision and Natural Language Processing tasks. But their compute and memory/storage footprint is large, and so, serving such models is expensive often requiring high-end hardware. To
Externí odkaz:
http://arxiv.org/abs/2403.06082