As the demand for energy-efficient Artificial Intelligence (AI) grows, Spiking Neural Networks (SNNs) have emerged as a leading neuromorphic alternative to traditional deep learning. However, the complex, non-linear temporal dynamics of SNNs often result in a…
Data Mining (DM) refers to the analysis of observational datasets to find relationships and to summarize the data in ways that are both understandable and useful. Many DM techniques exist. Compared with other DM techniques, Intelligent Systems (ISs) based appr…
As optical systems advance in capability, they present new engineering challenges. This work focuses on two such challenges: controlling stray light in imaging systems with increasingly demanding out-of-field stray light requirements and understanding the rela…
This study presents a unified framework for the analysis and optimization of truss structures through the integration of Graph Neural Networks (GNNs) and metaheuristic algorithms. The proposed approach performs simultaneous size, shape, and topology optimizati…
Wir untersuchen die Verwendung globaler Graphmerkmale, um die Ausdruckskraft und die Vorhersageleistung von graph neural networks (GNNs) zu verbessern. Während Message Passing Neural Networks (MPNNs) ein gängiger Ansatz für das Lernen von Graphrepräsentationen…
Graph Neural Networks (GNNs) are powerful deep learning models for predicting molecular properties but they require large datasets and significant computational resources. My research focuses on enhancing their efficiency and accuracy by improving how these mo…
Real-world problems often require identifying a single class of interest (e.g., normal patterns, relevant profiles, confirmed causal relations), while the non-interest space is broad, unknown, heterogeneous, and costly to label. This setting motivates One-Clas…
This thesis presents the mathematical framework underlying two central architectures in modern deep learning: the feedforward neural network and the transformer model. Beginning from first principles, the report introduces the perceptron model and builds towar…
Graph Neural Networks (GNNs) have emerged as the standard paradigm for machine learning on graph-structured data, demonstrating remarkable success in diverse applications such as molecular design, anomaly detection within networks, and recommendation systems.…
Schreiberidentifizierung (Writer Identification) zielt darauf ab, ein handschriftliches Dokument seinem korrekten Autor zuzuordnen, während Schreibersuche (Writer Retrieval) darauf abzielt, Dokumente desselben Autors aus einer Datenbank abzurufen. Während aktu…
Information stored as unary and binary facts is often referred to as a knowledge graph (KG). KGs are used in many applications, and different methods for learning on them have been proposed. Among these approaches, graph neural networks (GNNs) are effective an…
Graph neural networks, with their ability to capture relations well, are a modern and attractive field of research. The thesis analyses existing recommendation systems that exploit graph neural networks, namely SR-GNN and TGN, to capture complex patterns in us…
أنا مساعد المستودع. اسألني عن الرسائل والأطاريح حسب الموضوع أو الجامعة أو الدولة أو السنة أو الدرجة، أو اطلب أعداداً وإحصاءات.
يجيب من قاعدة بيانات المستودع فقط، ولا يخترع سجلات أو أرقاماً. لا يُحفظ ما تكتبه على الخادم.
قد يخطئ المساعد أحياناً، فراجع المصادر المرفقة.
