Diploma Theses for the Academic Year 2024-2025

Brief Description:

This thesis focuses on the use of computational intelligence techniques to detect and address spam emails. The uncontrolled sending of spam messages is a significant problem for both users and the infrastructure of email service providers. The purpose of this thesis is to examine and compare modern artificial intelligence methods, such as machine learning, neural networks, and evolutionary algorithms, for the effective detection and filtering of spam. Initially, the basic principles of spam emails and the characteristics that distinguish them from regular emails will be presented. Then, classical filtering techniques, such as rule-based filters and statistical models, will be analysed. This thesis will primarily focus on the use of more advanced techniques, such as neural networks (MLP, CNN, LSTM), classification algorithms (SVM, Naive Bayes), and evolutionary algorithms (PSO, SCA), and will examine how they can improve the accuracy and speed of the filtering process. Furthermore, an analysis of the advantages and disadvantages of each approach will be conducted, and the challenges concerning the ongoing evolution of spam messages and the need for filters to adapt to new spamming techniques will be discussed. This thesis will also include experimental results from the application of these techniques, demonstrating their effectiveness in practice. Finally, the potential for improving the respective algorithms will be presented, as well as the possibility of creating new, more advanced, and adaptive algorithms to counter the threats posed by spammers. Additionally, a Gmail account will be used to implement the codes of the aforementioned methods in order to evaluate their performance on real data.

Required background knowledge: Linux, Docker, Python, AI, Machine Learning, Neural Networks

Place: ZB203 – Ερευνητικό Εργαστήριο TelSiP

Supervisor: Dr. Grigorios Koulouras, Associate Professor

Student: Konstantina Pano

Brief Description:

Transformers have recently emerged as a promising and versatile deep neural architecture. Since the introduction of Vision Transformers (ViT) in 2020, the Computer Vision & Pattern Recognition community has witnessed an explosion of Transformer-based Computer Vision models. This thesis presents an opportunity and at the same time a challenge to explore the various relevant topologies and open challenges in designing transformer models for Computer Vision.

Required background knowledge: Computer Vision, Pattern Recognition, Image Processing, Computational Intelligence, Matlab, Python

Place: ZB203 – Research Laboratory TelSiP

Supervisor: Dr. Elias Zois, Associate Professor

Student: –

Brief Description:

Automatic cloud type recognition of ground-based infrared images is still a challenging task. A novel cloud classification method is proposed to group images into five cloud types based on manifold and texture features. Compared with statistical features in Euclidean space, manifold features extracted on symmetric positive definite (SPD) matrix space can describe the non-Euclidean geometric characteristics of the infrared image more effectively. The proposed method comprises three stages: pre-processing, feature extraction and classification. The datasets are comprised of the zenithal and whole-sky images taken by the Whole-Sky Infrared Cloud-Measuring System (WSIRCMS).

Required background knowledge: Linear Algebra and Geometry in Computer Vision, Pattern Recognition, Image Processing, Computational Intelligence, Matlab,  C/C++, Python

Place: ZB203 – Research Laboratory TelSiP

Supervisor: Dr. Elias Zois, Associate Professor

Student: –

Brief Description:

This thesis is related to the description and operation of various deep machine learning topologies based on convolutional neural networks. It is a comprehensive plan to apply cutting-edge techniques in machine learning and computer vision.

Required background knowledge: Computer Vision, Pattern Recognition, Image Processing, Computational Intelligence, Matlab,  C/C++, Python

Place: ZB203 – Research Laboratory TelSiP

Supervisor: Dr. Elias Zois, Associate Professor

Student: –

Brief Description:

This thesis will focus on the theoretical framework and experimental results of applying deep learning techniques to non-Euclidean spaces, with particular emphasis on SPD manifolds, for addressing computer vision applications. In particular, the proposed study will focus on a deep learning framework designed for processing complex data representations that cannot be efficiently handled by traditional linear methods. By understanding the geometric characteristics of the SPD manifold and how these are preserved during feature extraction, the candidate will explore how this methodology enhances classification efficiency by addressing the challenges of both within-class variability and diversity between them. The aim will therefore be to engage with the theoretical foundations of manifold learning and its practical application to deep networks, thereby enhancing the understanding of these sophisticated concepts in modern machine learning.

Required background knowledge: Linear Algebra, Computer Vision, Pattern Recognition, Image Processing, Computational Intelligence, Matlab,  C/C++, Python

Place: ZB203 – Research Laboratory TelSiP

Supervisor: Dr. Elias Zois, Associate Professor

Student: –

Brief Description:

The task of visual recognition in the space of the Riemannian geometry with the use of Symmetric Positive Definite (SPD) matrices, provides high discriminative power. Still, the use of a high-dimensional Riemannian manifold of SPD matrices, becomes computationally expensive. This thesis aims to methods and algorithms which will map the high-dimensional SPD matrices by mapping to low-dimensional ones with the use of an orthonormal transformation. This manifold oriented dimensionality reduction is achieved in both supervised and unsupervised scenarios. It all comes to an optimization problem on a Grassmann manifold; Its evaluation on computer vision tasks shows that this approach results towards an enhanced accuracy which can be compared to state-of-the-art methods.

Required background knowledge: Linear Algebra and Geometry in Computer Vision, Pattern Recognition, Image Processing, Computational Intelligence, Matlab, Python

Place: ZB203 – Research Laboratory TelSiP

Supervisor: Dr. Elias Zois, Associate Professor

Student: –