Zobrazeno 1 - 10
of 3 252
pro vyhledávání: '"P Bhavsar"'
Autor:
Goel, Anoushkrit, Singh, Bipanjit, Joshi, Ankita, Jha, Ranjeet Ranjan, Ahuja, Chirag, Nigam, Aditya, Bhavsar, Arnav
White matter tract segmentation is crucial for studying brain structural connectivity and neurosurgical planning. However, segmentation remains challenging due to issues like class imbalance between major and minor tracts, structural similarity, subj
Externí odkaz:
http://arxiv.org/abs/2411.08187
Autor:
Joshi, Ankita, Sharma, Ashutosh, Goel, Anoushkrit, Jha, Ranjeet Ranjan, Ahuja, Chirag, Bhavsar, Arnav, Nigam, Aditya
Fiber tractography is a cornerstone of neuroimaging, enabling the detailed mapping of the brain's white matter pathways through diffusion MRI. This is crucial for understanding brain connectivity and function, making it a valuable tool in neurologica
Externí odkaz:
http://arxiv.org/abs/2411.05757
This study evaluates the performance of various deep learning models, specifically DenseNet, ResNet, VGGNet, and YOLOv8, for wildlife species classification on a custom dataset. The dataset comprises 575 images of 23 endangered species sourced from r
Externí odkaz:
http://arxiv.org/abs/2408.00002
Autor:
Jena, Sushovan, Pulkit, Arya, Singh, Kajal, Banerjee, Anoushka, Joshi, Sharad, Ganesh, Ananth, Singh, Dinesh, Bhavsar, Arnav
With the rapid advances in deep learning and smart manufacturing in Industry 4.0, there is an imperative for high-throughput, high-performance, and fully integrated visual inspection systems. Most anomaly detection approaches using defect detection d
Externí odkaz:
http://arxiv.org/abs/2407.02968
What makes a good Large Language Model (LLM)? That it performs well on the relevant benchmarks -- which hopefully measure, with some validity, the presence of capabilities that are also challenged in real application. But what makes the model perform
Externí odkaz:
http://arxiv.org/abs/2406.14051
In this work, we delve into the EEG classification task in the domain of visual brain decoding via two frameworks, involving two different learning paradigms. Considering the spatio-temporal nature of EEG data, one of our frameworks is based on a CNN
Externí odkaz:
http://arxiv.org/abs/2406.07153
We explore a discrete-time, coined quantum walk on a quantum network where the coherent superposition of walker-moves originates from the unitary interaction of the walker-coin with the qubit degrees of freedom in the quantum network. The walk dynami
Externí odkaz:
http://arxiv.org/abs/2406.01558
Perceptual hashing algorithms (PHAs) are utilized extensively for identifying illegal online content. Given their crucial role in sensitive applications, understanding their security strengths and weaknesses is critical. This paper compares three maj
Externí odkaz:
http://arxiv.org/abs/2406.00918
Autor:
Jena, Sushovan, Saini, Vishwas, Shaw, Ujjwal, Jain, Pavitra, Raihal, Abhay Singh, Banerjee, Anoushka, Joshi, Sharad, Ganesh, Ananth, Bhavsar, Arnav
Unsupervised anomaly detection encompasses diverse applications in industrial settings where a high-throughput and precision is imperative. Early works were centered around one-class-one-model paradigm, which poses significant challenges in large-sca
Externí odkaz:
http://arxiv.org/abs/2405.06467
Autor:
Bhavsar, Sujal, DE, Santanu
This study introduces a new approach to optimize the geometrical parameters of pipe diffusers in centrifugal compressors for Micro Gas Turbines, tailored for a 100 kW unit. The methodology draws insights from optimized airfoil-type diffusers and addr
Externí odkaz:
http://arxiv.org/abs/2404.11828