… problems. I consider the book to be a great addition to the area of Applications of Learning
and Analytics in Intelligent Systems. … and explore further Learning and Analytics methods and …

… Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are three … represent
intelligent systems or software. The position of machine learning and deep learning within …

… systems are also briefly studied. The primary goal of this work was to identify common issues
… many energy perspectives on significant opportunities and challenges. It is noted that if the …

… limitations of machine learning, including the need for large amounts of data, long model
training times, and difficulties for end users in understanding how the models work. …

… the results of this work will be of great importance for broad international interest, especially
for low- and middle-income countries and applications and not only for the local application. …

… is a ML method concerns expanding artificially intelligent systems that can continue to learn
new tasks. It uses novel inputs as well as retains previously accumulated knowledge. The …

… The primary motivation of this work is to present a review of machine learning-based traffic
police force scheduling and duty allocation based on the real-time traffic density. At present, …

… deep learning and artificial intelligence approaches. This work can be extended to discuss
the role of deep learning … of multiple intelligent systems driven by machine learning and deep …

… most notably deep learning (DL) [5], graphical processing units, and reusable software. AI/ML
and data analytics pose both challenges and opportunities for SDF. The challenges arise …

… applications edge intelligence is a necessary evolutionary need, and thus we provide a
summary of the challenges and opportunities that … drones, how these opportunities can be taken …