Results 1 to 10 of about 25,665,833 (303)

Visualizing Semantic Data [PDF]

, 2018
Visualizing semantic data describes the task of using the additional self describing features of semantic data (Linked Data/RDF) to inform the process of creating vector or bitmap drawings.
openaire   +2 more sources

Deep learning based detection of wild bee parasites under natural conditions

Ecological Informatics
Wild bees are threatened by numerous parasites that can significantly impair their health and even lead to death. Such parasites can weaken entire colonies, ultimately causing their eradication.
Svetlana Ionova, Henri Greil, Patrick Mäder, Marco Seeland   +3 more
doaj   +1 more source

Improving Cell Detection and Tracking in Microscopy Images Using YOLO and an Enhanced DeepSORT Algorithm

Sensors
Accurate and automated detection and tracking of cells in microscopy images is a persistent challenge in biotechnology and biomedical research. Effective detection and tracking are crucial for understanding biological processes and extracting meaningful ...
Mokhaled N. A. Al-Hamadani, Richard Poroszlay, Gabor Szeman-Nagy, Andras Hajdu, Stathis Hadjidemetriou, Luca Ferrarini, Balazs Harangi   +6 more
doaj   +1 more source

A visualization framework for hierarchical structure enhanced mind mapping

Visual Informatics
Policy and regulatory documents are typically lengthy and hierarchically complex, making them difficult to interpret even for domain experts. Mind maps provide an intuitive way to summarize key concepts and reveal document structure, but existing ...
Yang Liu, Heyu Wang, Lu Wei, Wei Chen, Yuanxiang Ji   +4 more
doaj   +1 more source

Annotated Pap cell images and smear slices for cell classification

Scientific Data
Machine learning-based systems have become instrumental in augmenting global efforts to combat cervical cancer. A burgeoning area of research focuses on leveraging artificial intelligence to enhance the cervical screening process, primarily through the ...
David Kupas, Andras Hajdu, Ilona Kovacs, Zoltan Hargitai, Zita Szombathy, Balazs Harangi   +5 more
doaj   +1 more source

Investigating the Performance of Retrieval-Augmented Generation and Domain-Specific Fine-Tuning for the Development of AI-Driven Knowledge-Based Systems

Machine Learning and Knowledge Extraction
Generative large language models (LLMs) have revolutionized the development of knowledge-based systems, enabling new possibilities in applications like ChatGPT, Bing, and Gemini.
Róbert Lakatos, Péter Pollner, András Hajdu, Tamás Joó   +3 more
doaj   +1 more source

Unsupervised Annotation Transfer in Phase-Contrast Microscopy Using a CycleGAN

Sensors
Domain shift between microscopy imaging domains poses a significant challenge for deploying deep learning-based cell detection models across different experimental setups.
Mokhaled N. A. Al-Hamadani, Stathis Hadjidemetriou, Gabor Szeman-Nagy, Paris A. Skourides, Andras Hajdu, Balázs Harangi   +5 more
doaj   +1 more source

Concept transfer of synaptic diversity from biological to artificial neural networks

Nature Communications
Recent developments in artificial neural networks have drawn inspiration from biological neural networks, leveraging the concept of the artificial neuron to model the learning abilities of biological nerve cells.
Martin Hofmann, Moritz Franz Peter Becker, Christian Tetzlaff, Patrick Mäder   +3 more
doaj   +1 more source

Reinforcement Learning Algorithms and Applications in Healthcare and Robotics: A Comprehensive and Systematic Review

Sensors
Reinforcement learning (RL) has emerged as a dynamic and transformative paradigm in artificial intelligence, offering the promise of intelligent decision-making in complex and dynamic environments.
Mokhaled N. A. Al-Hamadani, Mohammed A. Fadhel, Laith Alzubaidi, Harangi Balazs   +3 more
doaj   +1 more source

Neuromorphic LiDAR-Based Bird’s Eye View Object Detection Using Energy-Efficient Spiking Neural Networks

IEEE Access
Autonomous driving perception demands accurate and efficient processing of three-dimensional sensor data under strict power constraints. Traditional convolutional neural networks achieve strong detection accuracy but are computationally intensive ...
Sambit Mohapatra, Senthil Yogamani, Heinrich Gotzig, Patrick Mader   +3 more
doaj   +1 more source