Výsledky vyhledávání - "Rana, Mashud"

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A Semi-supervised Approach for Activity Recognition from Indoor Trajectory Data

Autor: Rana, Mashud, Rahman, Ashfaqur, Smith, Daniel

The increasingly wide usage of location aware sensors has made it possible to collect large volume of trajectory data in diverse application domains. Machine learning allows to study the activities or behaviours of moving objects (e.g., people, vehic

Externí odkaz: http://arxiv.org/abs/2301.03134

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Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective

Autor: Baumgartner, Peter, Smith, Daniel, Rana, Mashud, Kapoor, Reena, Tartaglia, Elena, Schutt, Andreas, Rahman, Ashfaqur, Taylor, John, Dunstall, Simon

Data-driven decision making is becoming an integral part of manufacturing companies. Data is collected and commonly used to improve efficiency and produce high quality items for the customers. IoT-based and other forms of object tracking are an emerg

Externí odkaz: http://arxiv.org/abs/2210.01344

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Solar Power Time Series Forecasting Utilising Wavelet Coefficients

Autor: Almaghrabi, Sarah, Rana, Mashud, Hamilton, Margaret, Rahaman, Mohammad Saiedur

Publikováno v: Neurocomputing Neurocomputing Volume 508, 7 October 2022, Pages 182-207

Accurate and reliable prediction of Photovoltaic (PV) power output is critical to electricity grid stability and power dispatching capabilities. However, Photovoltaic (PV) power generation is highly volatile and unstable due to different reasons. The

Externí odkaz: http://arxiv.org/abs/2210.00269

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Deep Learning for Prawn Farming: Forecasting and Anomaly Detection

Autor: Dabrowski, Joel Janek, Rahman, Ashfaqur, Hellicar, Andrew, Rana, Mashud, Arnold, Stuart

Publikováno v: Advances in Knowledge Discovery and Data Mining. PAKDD 2022. Lecture Notes in Computer Science, vol 13282. Springer, Cham

We present a decision support system for managing water quality in prawn ponds. The system uses various sources of data and deep learning models in a novel way to provide 24-hour forecasting and anomaly detection of water quality parameters. It provi

Externí odkaz: http://arxiv.org/abs/2205.06359

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SSDNet: State Space Decomposition Neural Network for Time Series Forecasting

Autor: Lin, Yang, Koprinska, Irena, Rana, Mashud

In this paper, we present SSDNet, a novel deep learning approach for time series forecasting. SSDNet combines the Transformer architecture with state space models to provide probabilistic and interpretable forecasts, including trend and seasonality c

Externí odkaz: http://arxiv.org/abs/2112.10251

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