Dimitris Askounis

Sotiris Pelekis, Evangelos Karakolis, Theodosios Pountridis et al.

This paper presents DeepTSF, a comprehensive machine learning operations (MLOps) framework aiming to innovate time series forecasting through workflow automation and codeless modeling. DeepTSF automates key aspects of the ML lifecycle, making it an ideal tool for data scientists and MLops engineers engaged in machine learning (ML) and deep learning (DL)-based forecasting. DeepTSF empowers users with a robust and user-friendly solution, while it is designed to seamlessly integrate with existing data analysis workflows, providing enhanced productivity and compatibility. The framework offers a front-end user interface (UI) suitable for data scientists, as well as other higher-level stakeholders, enabling comprehensive understanding through insightful visualizations and evaluation metrics. DeepTSF also prioritizes security through identity management and access authorization mechanisms. The application of DeepTSF in real-life use cases of the I-NERGY project has already proven DeepTSF's efficacy in DL-based load forecasting, showcasing its significant added value in the electrical power and energy systems domain.

Georgios Tzoumanekas, Michail Chatzianastasis, Loukas Ilias et al.

Social media platforms, including X, Facebook, and Instagram, host millions of daily users, giving rise to bots-automated programs disseminating misinformation and ideologies with tangible real-world consequences. While bot detection in platform X has been the area of many deep learning models with adequate results, most approaches neglect the graph structure of social media relationships and often rely on hand-engineered architectures. Our work introduces the implementation of a Neural Architecture Search (NAS) technique, namely Deep and Flexible Graph Neural Architecture Search (DFG-NAS), tailored to Relational Graph Convolutional Neural Networks (RGCNs) in the task of bot detection in platform X. Our model constructs a graph that incorporates both the user relationships and their metadata. Then, DFG-NAS is adapted to automatically search for the optimal configuration of Propagation and Transformation functions in the RGCNs. Our experiments are conducted on the TwiBot-20 dataset, constructing a graph with 229,580 nodes and 227,979 edges. We study the five architectures with the highest performance during the search and achieve an accuracy of 85.7%, surpassing state-of-the-art models. Our approach not only addresses the bot detection challenge but also advocates for the broader implementation of NAS models in neural network design automation.

Panagiota Rempi, Sotiris Pelekis, Alexandros Menelaos Tzortzis et al.

Enhancing energy efficiency in residential buildings is a crucial step toward mitigating climate change and reducing greenhouse gas emissions. Retrofitting existing buildings, which account for a significant portion of energy consumption, is critical particularly in regions with outdated and inefficient building stocks. This study presents an Artificial Intelligence (AI) and Machine Learning (ML)-based framework to recommend energy efficiency measures for residential buildings, leveraging accessible building characteristics to achieve energy class targets. Using Latvia as a case study, the methodology addresses challenges associated with limited datasets, class imbalance and data scarcity. The proposed approach integrates Conditional Tabular Generative Adversarial Networks (CTGAN) to generate synthetic data, enriching and balancing the dataset. A Multi-Layer Perceptron (MLP) model serves as the predictive model performing multi-label classification to predict appropriate retrofit strategies. Explainable Artificial Intelligence (XAI), specifically SHapley Additive exPlanations (SHAP), ensures transparency and trust by identifying key features that influence recommendations and guiding feature engineering choices for improved reliability and performance. The evaluation of the approach shows that it notably overcomes data limitations, achieving improvements up to 54% in precision, recall and F1 score. Although this study focuses on Latvia, the methodology is adaptable to other regions, underscoring the potential of AI in reducing the complexity and cost of building energy retrofitting overcoming data limitations. By facilitating decision-making processes and promoting stakeholders engagement, this work supports the global transition toward sustainable energy use in the residential building sector.