Hasan Al-Marzouqi

Lyes Saad Saoud, Hasan AlMarzouqi, Ramy Hussein

Sustainability requires increased energy efficiency with minimal waste. The future power systems should thus provide high levels of flexibility iin controling energy consumption. Precise projections of future energy demand/load at the aggregate and on the individual site levels are of great importance for decision makers and professionals in the energy industry. Forecasting energy loads has become more advantageous for energy providers and customers, allowing them to establish an efficient production strategy to satisfy demand. This study introduces two hybrid cascaded models for forecasting multistep household power consumption in different resolutions. The first model integrates Stationary Wavelet Transform (SWT), as an efficient signal preprocessing technique, with Convolutional Neural Networks and Long Short Term Memory (LSTM). The second hybrid model combines SWT with a self-attention based neural network architecture named transformer. The major constraint of using time-frequency analysis methods such as SWT in multistep energy forecasting problems is that they require sequential signals, making signal reconstruction problematic in multistep forecasting applications.The cascaded models can efficiently address this problem through using the recursive outputs. Experimental results show that the proposed hybrid models achieve superior prediction performance compared to the existing multistep power consumption prediction methods. The results will pave the way for more accurate and reliable forecasting of household power consumption.

Hasan AlMarzouqi, Lyes Saad Saoud

Semantic segmentation necessitates approaches that learn high-level characteristics while dealing with enormous amounts of data. Convolutional neural networks (CNNs) can learn unique and adaptive features to achieve this aim. However, due to the large size and high spatial resolution of remote sensing images, these networks cannot analyze an entire scene efficiently. Recently, deep transformers have proven their capability to record global interactions between different objects in the image. In this paper, we propose a new segmentation model that combines convolutional neural networks with transformers, and show that this mixture of local and global feature extraction techniques provides significant advantages in remote sensing segmentation. In addition, the proposed model includes two fusion layers that are designed to represent multi-modal inputs and output of the network efficiently. The input fusion layer extracts feature maps summarizing the relationship between image content and elevation maps (DSM). The output fusion layer uses a novel multi-task segmentation strategy where class labels are identified using class-specific feature extraction layers and loss functions. Finally, a fast-marching method is used to convert all unidentified class labels to their closest known neighbors. Our results demonstrate that the proposed methodology improves segmentation accuracy compared to state-of-the-art techniques.

Alabi Bojesomo, Panos Liatsis, Hasan Al Marzouqi

In this paper, we present our solution to the Traffic4cast2020 traffic prediction challenge. In this competition, participants are to predict future traffic parameters (speed and volume) in three different cities: Berlin, Istanbul and Moscow. The information provided includes nine channels where the first eight represent the speed and volume for four different direction of traffic (NE, NW, SE and SW), while the last channel is used to indicate presence of traffic incidents. The expected output should have the first 8 channels of the input at six future timing intervals (5, 10, 15, 30, 45, and 60min), while a one hour duration of past traffic data, in 5mins intervals, are provided as input. We solve the problem using a novel sedenion U-Net neural network. Sedenion networks provide the means for efficient encoding of correlated multimodal datasets. We use 12 of the 15 sedenion imaginary parts for the dynamic inputs and the real sedenion component is used for the static input. The sedenion output of the network is used to represent the multimodal traffic predictions. Proposed system achieved a validation MSE of 1.33e-3 and a test MSE of 1.31e-3.

Hasan Al-Marzouqi, Yuting Hu, Ghassan AlRegib

Image retrieval is an important problem in the area of multimedia processing. This paper presents two new curvelet-based algorithms for texture retrieval which are suitable for use in constrained-memory devices. The developed algorithms are tested on three publicly available texture datasets: CUReT, Mondial-Marmi, and STex-fabric. Our experiments confirm the effectiveness of the proposed system. Furthermore, a weighted version of the proposed retrieval algorithm is proposed, which is shown to achieve promising results in the classification of seismic activities.