… 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 …

… 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. …

… to facilitate machine learning for intelligent systems. The advancement in embedded
systems and machine sensing in the industry has resulted in the production of large volumes of …

… machine learning techniques and introduce 10 most critical challenges in advancing the
intelligent 6G system. … on the construction of machine learning based 6G systems. To realize the …

… intelligence (AI), machine learning (ML), and deep reinforcement learning (DRL) in the
evolution of smart … optimal policy regarding various smart city-oriented complex problems. In this …

… article discusses the learning ability of buildings with a system-level … of reinforcement
learning applications to intelligent buildings. These categories are summarized in the ‘Application’ …

… real-life smart systems, like smart cities, smart homes, smart healthcare, the large … systems
has introduced new security challenges [5,6,7]. Furthermore, since IoT devices generally work …

… existing power systems problems. In this work, a detailed literature survey of machine
learning-based solutions for a wide range of smart grid applications is presented and discussed in …

… some common types of machine learning models. Finally, we discuss some opportunities
and challenges for the materials community to fully utilize the capabilities of machine learning. …