Zobrazeno 1 - 10
of 152
pro vyhledávání: '"Ramnath, Rajiv"'
Autor:
Mohammadshirazi, Ahmad, Firoozsalari, Ali Nosrati, Zhou, Mengxi, Kulshrestha, Dheeraj, Ramnath, Rajiv
Automating the annotation of scanned documents is challenging, requiring a balance between computational efficiency and accuracy. DocParseNet addresses this by combining deep learning and multi-modal learning to process both text and visual data. Thi
Externí odkaz:
http://arxiv.org/abs/2406.17591
Traffic accidents pose a severe global public health issue, leading to 1.19 million fatalities annually, with the greatest impact on individuals aged 5 to 29 years old. This paper addresses the critical need for advanced predictive methods in road sa
Externí odkaz:
http://arxiv.org/abs/2406.13968
Autor:
Biswas, Biplob, Ramnath, Rajiv
Expansion-enhanced sparse lexical representation improves information retrieval (IR) by minimizing vocabulary mismatch problems during lexical matching. In this paper, we explore the potential of jointly learning dense semantic representation and com
Externí odkaz:
http://arxiv.org/abs/2405.13173
Autor:
Monsefi, Amin Karimi, Karisani, Payam, Zhou, Mengxi, Choi, Stacey, Doble, Nathan, Ji, Heng, Parthasarathy, Srinivasan, Ramnath, Rajiv
Standard modern machine-learning-based imaging methods have faced challenges in medical applications due to the high cost of dataset construction and, thereby, the limited labeled training data available. Additionally, upon deployment, these methods
Externí odkaz:
http://arxiv.org/abs/2402.06190
Autor:
Monsefi, Amin Karimi, Shiri, Pouya, Mohammadshirazi, Ahmad, Monsefi, Nastaran Karimi, Davies, Ron, Moosavi, Sobhan, Ramnath, Rajiv
Reducing traffic accidents is a crucial global public safety concern. Accident prediction is key to improving traffic safety, enabling proactive measures to be taken before a crash occurs, and informing safety policies, regulations, and targeted inte
Externí odkaz:
http://arxiv.org/abs/2402.05151
Autor:
Mohammadshirazi, Ahmad, Nadafian, Aida, Monsefi, Amin Karimi, Rafiei, Mohammad H., Ramnath, Rajiv
Cost-effective sensors are capable of real-time capturing a variety of air quality-related modalities from different pollutant concentrations to indoor/outdoor humidity and temperature. Machine learning (ML) models are capable of performing air-quali
Externí odkaz:
http://arxiv.org/abs/2308.01438
Autor:
Moosavi, Sobhan, Ramnath, Rajiv
Driving risk prediction has been a topic of much research over the past few decades to minimize driving risk and increase safety. The use of demographic information in risk prediction is a traditional solution with applications in insurance planning,
Externí odkaz:
http://arxiv.org/abs/2305.03740
Road construction projects maintain transportation infrastructures. These projects range from the short-term (e.g., resurfacing or fixing potholes) to the long-term (e.g., adding a shoulder or building a bridge). Deciding what the next construction p
Externí odkaz:
http://arxiv.org/abs/2209.06813
Web-based interactions can be frequently represented by an attributed graph, and node clustering in such graphs has received much attention lately. Multiple efforts have successfully applied Graph Convolutional Networks (GCN), though with some limits
Externí odkaz:
http://arxiv.org/abs/2112.15530