… To date, 2 retrospective studies have shown that statistical machine learning and deep
learning can improve or outperform rule-based algorithms [51,52]. Further validation and impact …

… Deep Learning has been used extensively on the data generated by IoT sensors in a smart
… state‐of‐the art on usage of Deep Learning on Smart City data. Several future research …

… comparing the performances of several popular foundation models with many state-of-the-art
fully supervised task-speciic machine learning or deep learning models on various tasks …

… is that it can be overwhelming for an RL algorithm to learn from … deep learning to produce
another subfield of ML called deep … deep learning, however with a focus on embedded deep …

… Advances in Deep Learning (DL) technologies showed great modeling capabilities in
complex network scenarios [5]–[7]. We argue that DL techniques can be leveraged to extract …

… by incorporating quantum deep learning into the current classical deep hedging … learning
techniques to investigate the feasibility of establishing a novel quantum framework for deep …

… deep learning approaches are employed in the field of HAR and what pros and cons each
of them have, and Q 3 : What challenges we are facing in this field and what opportunities and …

… Finally, this review presented 10 open research challenges for future … in medical image
diagnosis through deep learning. … In addition, their discussion on deep learning techniques was …

… Throughout the discussion, we highlight recent progresses meeting with major challenges.
We also identify some next challenges still ahead to realize integrated photonic neural …

… GPT by OpenAI or Bard by Google, also poses several challenges. In this article, I will
discuss a range of challenges and opportunities for higher education, as well as conclude with …