Zobrazeno 1 - 10
of 11 641
pro vyhledávání: '"Vasan A"'
Publikováno v:
Journal of Hydroinformatics, Vol 26, Iss 5, Pp 972-997 (2024)
The present study aims to evaluate the potentiality of Bidirectional Long Short-Term Memory (Bi-LSTM), Convolutional Neural Networks (CNNs), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), and Random Forest (RF) for predi
Externí odkaz:
https://doaj.org/article/cedd6a384bc9468bbbdca5fb5e2d1cc6
Autor:
Abdullah Kahraman, Murat Tunc, Emrah Ramazanoglu, Vasan Almarie, Ramazan Sezer, Fatma Basdemir, Cengiz Kaya, Mehmet Senbayram
Publikováno v:
Acta Agriculturae Scandinavica. Section B, Soil and Plant Science, Vol 74, Iss 1 (2024)
Lentil yield in semi-arid regions is limited by heat, drought stress and nutrient deficiencies induced by high soil pH and water scarcity. In this study, we investigated the effect of foliar fertiliser treatments on lentil yield and yield components
Externí odkaz:
https://doaj.org/article/54abf4f426004c5e93300e8b5958fe8b
We propose a diffusion model-based approach, FloAtControlNet to generate cinemagraphs composed of animations of human clothing. We focus on human clothing like dresses, skirts and pants. The input to our model is a text prompt depicting the type of c
Externí odkaz:
http://arxiv.org/abs/2411.15028
Autor:
Goyal, Sahil, Mahajan, Abhinav, Mishra, Swasti, Udhayanan, Prateksha, Shukla, Tripti, Joseph, K J, Srinivasan, Balaji Vasan
Graphic designs are an effective medium for visual communication. They range from greeting cards to corporate flyers and beyond. Off-late, machine learning techniques are able to generate such designs, which accelerates the rate of content production
Externí odkaz:
http://arxiv.org/abs/2411.14959
Autor:
Naghdi, Parisa, Bhurwani, Mohammad Mahdi Shiraz, Rahmatpour, Ahmad, Mondal, Parmita, Udin, Michael, Williams, Kyle A, Nagesh, Swetadri Vasan Setlur, Ionita, Ciprian N
This study evaluates a multimodal machine learning framework for predicting treatment outcomes in intracranial aneurysms (IAs). Combining angiographic parametric imaging (API), patient biomarkers, and disease morphology, the framework aims to enhance
Externí odkaz:
http://arxiv.org/abs/2411.14407
Autor:
Mondal, Parmita, Williams, Kyle A, Naghdi, Parisa, Rahmatpour, Ahmad, Bhurwani, Mohammad Mahdi Shiraz, Nagesh, Swetadri Vasan Setlur, Ionita, Ciprian N
In intracranial aneurysm (IA) treatment, digital subtraction angiography (DSA) monitors device-induced hemodynamic changes. Quantitative angiography (QA) provides more precise assessments but is limited by hand-injection variability. This study evalu
Externí odkaz:
http://arxiv.org/abs/2411.14475
We consider the problem of conditional text-to-image synthesis with diffusion models. Most recent works need to either finetune specific parts of the base diffusion model or introduce new trainable parameters, leading to deployment inflexibility due
Externí odkaz:
http://arxiv.org/abs/2411.10800
Autor:
Rahmatpour, Ahmad, Shields, Allison, Mondal, Parmita, Naghdi, Parisa, Udin, Michael, Williams, Kyle A, Bhurwani, Mohammad Mahdi Shiraz, Nagesh, Swetadri Vasan Setlur, Ionita, Ciprian N
This study leverages convolutional neural networks to enhance the temporal resolution of 3D angiography in intracranial aneurysms focusing on the reconstruction of volumetric contrast data from sparse and limited projections. Three patient-specific I
Externí odkaz:
http://arxiv.org/abs/2411.09632
Autor:
Mondal, Parmita, Shields, Allison, Bhurwani, Mohammad Mahdi Shiraz, Williams, Kyle A, Nagesh, Swetadri Vasan Setlur, Siddiqui, Adnan H, Ionita, Ciprian N
This study aims to mitigate these biases and enhance QA analysis by applying a path-length correction (PLC) correction, followed by singular value decomposition (SVD)-based deconvolution, to angiograms obtained through both in-silico and in-vitro met
Externí odkaz:
http://arxiv.org/abs/2411.08185
Autor:
Vasan, Vivek, Agrawal, Anuj, Nico-Katz, Alexander, Horgan, Jerry, Bash, Boulat A., Kilper, Daniel C., Ruffini, Marco
We consider quantum networks, where entangled photon pairs are distributed using fibre optic links from a centralized source to entangling nodes. The entanglement is then stored (via an entanglement swap) in entangling nodes' quantum memories until u
Externí odkaz:
http://arxiv.org/abs/2411.07410