121 citations · 193 across the 15 of their papers we have counts for
15 papers
An Unsupervised Anomaly Detection in Electricity Consumption Using Reinforcement Learning and Time Series Forest Based Framework
Jihan Ghanim, Mariette Awad
Anomaly detection (AD) plays a crucial role in time series applications, primarily because time series data is employed across real-world scenarios. Detecting anomalies poses signi…
On The Potential of The Fractal Geometry and The CNNs Ability to Encode it
Julia El Zini, Bassel Musharrafieh, Mariette Awad
The fractal dimension provides a statistical index of object complexity by studying how the pattern changes with the measuring scale. Although useful in several classification task…
Spatio-Temporal Graph Neural Networks: A Survey
Zahraa Al Sahili, Mariette Awad
Graph Neural Networks have gained huge interest in the past few years. These powerful algorithms expanded deep learning models to non-Euclidean space and were able to achieve state…
An Asymmetric Loss with Anomaly Detection LSTM Framework for Power Consumption Prediction
Jihan Ghanim, Maha Issa, Mariette Awad
Building an accurate load forecasting model with minimal underpredictions is vital to prevent any undesired power outages due to underproduction of electricity. However, the power…
CEnt: An Entropy-based Model-agnostic Explainability Framework to Contrast Classifiers' Decisions
Julia El Zini, Mohammad Mansour, Mariette Awad
Current interpretability methods focus on explaining a particular model's decision through present input features. Such methods do not inform the user of the sufficient conditions…
Beyond Model Interpretability: On the Faithfulness and Adversarial Robustness of Contrastive Textual Explanations
Julia El Zini, Mariette Awad
Contrastive explanation methods go beyond transparency and address the contrastive aspect of explanations. Such explanations are emerging as an attractive option to provide actiona…
On the Explainability of Natural Language Processing Deep Models
Julia El Zini, Mariette Awad
While there has been a recent explosion of work on ExplainableAI ExAI on deep models that operate on imagery and tabular data, textual datasets present new challenges to the ExAI c…
On the Evaluation of the Plausibility and Faithfulness of Sentiment Analysis Explanations
Julia El Zini, Mohamad Mansour, Basel Mousi +1
Current Explainable AI (ExAI) methods, especially in the NLP field, are conducted on various datasets by employing different metrics to evaluate several aspects. The lack of a comm…
The Power of Transfer Learning in Agricultural Applications: AgriNet
Zahraa Al Sahili, Mariette Awad
Advances in deep learning and transfer learning have paved the way for various automation classification tasks in agriculture, including plant diseases, pests, weeds, and plant spe…
Towards Cross-Disaster Building Damage Assessment with Graph Convolutional Networks
Ali Ismail, Mariette Awad
In the aftermath of disasters, building damage maps are obtained using change detection to plan rescue operations. Current convolutional neural network approaches do not consider t…
BLDNet: A Semi-supervised Change Detection Building Damage Framework using Graph Convolutional Networks and Urban Domain Knowledge
Ali Ismail, Mariette Awad
Change detection is instrumental to localize damage and understand destruction in disaster informatics. While convolutional neural networks are at the core of recent change detecti…
Learning to run a Power Network Challenge: a Retrospective Analysis
Antoine Marot, Benjamin Donnot, Gabriel Dulac-Arnold +7
Power networks, responsible for transporting electricity across large geographical regions, are complex infrastructures on which modern life critically depend. Variations in demand…
Generative Adversarial Stacked Autoencoders for Facial Pose Normalization and Emotion Recognition
Ariel Ruiz-Garcia, Vasile Palade, Mark Elshaw +1
In this work, we propose a novel Generative Adversarial Stacked Autoencoder that learns to map facial expressions, with up to plus or minus 60 degrees, to an illumination invariant…
An Optimized and Energy-Efficient Parallel Implementation of Non-Iteratively Trained Recurrent Neural Networks
Julia El Zini, Yara Rizk, Mariette Awad
Recurrent neural networks (RNN) have been successfully applied to various sequential decision-making tasks, natural language processing applications, and time-series predictions. S…
Change your singer: a transfer learning generative adversarial framework for song to song conversion
Rema Daher, Mohammad Kassem Zein, Julia El Zini +2
Have you ever wondered how a song might sound if performed by a different artist? In this work, we propose SCM-GAN, an end-to-end non-parallel song conversion system powered by gen…