The rapid proliferation of Artificial Intelligence applications necessitates scalable solutions that perform efficiently under real-world constraints. Heterogeneous accelerators combining specialized analog and digital units offer localized, energy-e... read more
This study presents an annotated multi-sensor, multimodal, and hyperspectral dataset designed to support deep learning-based classification and segmentation of bulky waste. The dataset comprises four distinct sensor modalities: high-resolution visibl... read more
Green AI aims to design and train machine learning models while taking into consideration sustainable resource usage without sacrificing model efficiency. The exponential growth of training data has led to results in increasing computational cost and... read more
Interpretability remains one of the major challenges in the clinical adoption of deep learning models for medical image analysis. In ophthalmology, particularly for glaucoma screening, explainable artificial intelligence (XAI) methods are essential f... read more
The clinical utility of doxorubicin (DOX) has been widely hampered by a dose-dependent systemic toxicity, in particular cardiotoxicity. While nanocarrier systems represent encouraging solutions, their optimization is not an easy task due to complex, ... read more
Accurate regional input-output (IO) tables are indispensable for economic analysis and policy-making, yet their availability remains limited due to data constraints. This paper develops a novel hybrid framework combining Generative Adversarial Networ... read more
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.