All Outputs (3)

Multivariate Procedure for Modeling and Prediction of Temperature in Punjab, Pakistan (2024)
Book Chapter
Kanwal, B., Ashraf, Z., Mehmood, T., Kanwal, S., Dashtipour, K., & Gogate, M. (2024). Multivariate Procedure for Modeling and Prediction of Temperature in Punjab, Pakistan. In W. Boulila, J. Ahmad, A. Koubaa, M. Driss, & I. Riadh Farah (Eds.), Decision Making and Security Risk Management for IoT Environments (99-124). Springer. https://doi.org/10.1007/978-3-031-47590-0_6

Climate study often relies upon global climate models (GCM) to project future scenarios of change in climate behavior. This study aims to refine GCM results to fill the gap between local scale surface weather with regional atmospheric predictors. The... Read More about Multivariate Procedure for Modeling and Prediction of Temperature in Punjab, Pakistan.

Statistical Downscaling Modeling for Temperature Prediction (2024)
Book Chapter
Ashraf, Z., Kanwal, B., Hussain, I., Dashtipour, K., Gogate, M., & Kanwal, S. (2024). Statistical Downscaling Modeling for Temperature Prediction. In W. Boulila, J. Ahmad, A. Koubaa, M. Driss, & I. Riadh Farah (Eds.), Decision Making and Security Risk Management for IoT Environments (147-169). Springer. https://doi.org/10.1007/978-3-031-47590-0_8

The application compares the Statistical Downscaling Model (SDSM) and partial least square (PLS) to bridge the gap between (minimum and maximum) daily temperatures of 11 sites in Punjab between 1961 and 2013 with atmospheric variables. The data set w... Read More about Statistical Downscaling Modeling for Temperature Prediction.

Federated Learning for Market Surveillance (2024)
Book Chapter
Song, P., Kanwal, S., Dashtipour, K., & Gogate, M. (2024). Federated Learning for Market Surveillance. In W. Boulila, J. Ahmad, A. Koubaa, M. Driss, & I. Riadh Farah (Eds.), Decision Making and Security Risk Management for IoT Environments (199-218). Springer. https://doi.org/10.1007/978-3-031-47590-0_10

The data utilized in market surveillance is highly sensitive; what may be available for machine learning is limited. In this paper, we examine how federated learning for time series data can be used to identify potential market abuse while maintainin... Read More about Federated Learning for Market Surveillance.