most citedAutomatic Roof Type Classification Through Machine Learning for Regional Wind Risk Assessment

9 citations · 10 across the 3 of their papers we have counts for

collaborators

5 papers

cs.LG2024

High-Quality and Full Bandwidth Seismic Signal Synthesis using Operational GANs

Ozer Can Devecioglu, Serkan Kiranyaz, Zafer Yilmaz +3

Vibration sensors are essential in acquiring seismic activity for an accurate earthquake assessment. The state-of-the-art sensors can provide the best signal quality and the highes…

eess.IV2024

FUELVISION: A Multimodal Data Fusion and Multimodel Ensemble Algorithm for Wildfire Fuels Mapping

Riyaaz Uddien Shaik, Mohamad Alipour, Eric Rowell +3

Accurate assessment of fuel conditions is a prerequisite for fire ignition and behavior prediction, and risk management. The method proposed herein leverages diverse data sources i…

cs.LG20239 cited

Automatic Roof Type Classification Through Machine Learning for Regional Wind Risk Assessment

Shuochuan Meng, Mohammad Hesam Soleimani-Babakamali, Ertugrul Taciroglu

Roof type is one of the most critical building characteristics for wind vulnerability modeling. It is also the most frequently missing building feature from publicly available data…

cs.SD20231 cited

Sound-to-Vibration Transformation for Sensorless Motor Health Monitoring

Ozer Can Devecioglu, Serkan Kiranyaz, Amer Elhmes +6

Automatic sensor-based detection of motor failures such as bearing faults is crucial for predictive maintenance in various industries. Numerous methodologies have been developed ov…

cs.SI2023

Edge Ranking of Graphs in Transportation Networks using a Graph Neural Network (GNN)

Debasish Jana, Sven Malama, Sriram Narasimhan +1

Many networks, such as transportation, power, and water distribution, can be represented as graphs. Crucial challenge in graph representations is identifying the importance of grap…