# Recent Trends in AI-Based Intelligent Sensing
###### Abstract  
In recent years, intelligent sensing has gained significant attention because of its autonomous decision-making ability to solve complex problems. Today, smart sensors complement and enhance the capabilities of human beings and have been widely embraced in numerous application areas. Artificial intelligence (AI) has made astounding growth in domains of natural language processing, machine learning (ML), and computer vision. The methods based on AI enable a computer to learn and monitor activities by sensing the source of information in a real-time environment. The combination of these two technologies provides a promising solution in intelligent sensing. This survey provides a comprehensive summary of recent research on AI-based algorithms for intelligent sensing. This work also presents a comparative analysis of algorithms, models, influential parameters, available datasets, applications and projects in the area of intelligent sensing. Furthermore, we present a taxonomy of AI models along with the cutting edge approaches. Finally, we highlight challenges and open issues, followed by  
used to extract information but are also involved in more complex tasks such as execution of different instructions based on the pattern of data sequence. Indeed, we encounter a vast amount of data in different forms on a daily basis. To extract useful information from the plethora of data, smart sensors are designed that perceive the environment, make decisions, and draw conclusions. Intelligent sensing is important for various reasons. It can be applied in different areas such as self-driving cars, autonomous flying droids, and Amazon Kiva systems.  
In light of recent successes, AI is a trending field in the research areas of management science [2], operational research [3], and technology [4]. There is a broad array of applications of AI, ranging from expert systems to computer vision, which improves the everyday lives of ordinary people. For example, [5] investigated the application of machine learning to medicine and reported the diagnostic performance and caution of machine learning in dermatology, radiology, pathology, and microscopy. [6] examined the serious issues of modern transport systems and how AI techniques can be used to tackle the issues. The recent improvements in AI algorithms and computer hardware are expected to exceed human intelligence shortly. The current research on AI, including machine learning (ML) and deep learning (DL), uses real-time algorithms to enable machines to learn information from the sensing parameters. Recently, several AI-based approaches have witnessed rapid growth due to their sensing capability to learn feature representations for decision making and control problems [7]. Furthermore, the critical aspect of AI is to design efficient learning algorithms to unlock new possibilities in the field of intelligent sensing. Algorithms based on AI have been successfully utilized in myriads of areas such as mobile applications [8], social media analytics [9], healthcare [10], agriculture [11], manufacturing processes [12], logistics [13] and environmental engineering [14].