Artificial Intelligence: Models, Algorithms and Applications

The destination of vessels is important decision makers of maritime trading. However, shipping companies keep this kind of data inclusively, which results in the absence of complete information of destination for every vessel. However, other information such as the position can be available due to Automated Identification Systems (AIS). Hence, predicting the vessels’ destination port becomes possible. To give a baseline of how to make use of AIS data for vessel destination prediction with machine learning, several AIS data preprocessing approaches and machine learning approaches for vessel destination prediction are introduced in the literature. The chapter aims to give the audience an idea of how to link between AIS data, trajectories, and numerous machine learning models for the purpose of predicting arrival ports for maritime services. Furthermore, the discussion points out the current state of researches on this topic and where the potential future work may possibly lie in.

Managing the prevalence of mental health problems is both socially and economically challenging. Technological advancement in recent decades has provided potential solutions to this issue. In particular, Artificial Intelligence (AI) is one of the research areas that has expanded into several behavioural, neurological and mental healthcare areas by fulfilling the main objectives of P4 medicine - personalized, predictive, preventive, and participative medicine. In this chapter, we give an overview of recent applications of AI for screening, detection, and treatment of mental health problems; summarize the economic and social implications; present a systematic overview of which AI technologies are used for the different mental disorders and identify challenges for further development. Based on this, we identify some future research questions that could be solved with the use of AI in mental healthcare. The chapter concludes with an in-depth discussion on the key challenges of the application of AI in mental health interventions.

Recent developments in artificial intelligence (AI), particularly deep learning (DL) algorithms have demonstrated remarkable progress in image recognition and prediction tasks. These are now being applied in the field of radiology on medical images to quantify, characterize, classify as well as monitor various pathologies. Such DL based quantifications facilitate greater support to the visual assessment of image characteristics that is performed by the physician. Furthermore it aids in reducing interreader variability as well as assists in speeding up the radiology workflow. In this chapter, we provide an insightful motivation for employing DL based framework followed by an overview of recent applications of DL in radiology and present a systematic summary of specific DL algorithms pertaining to image perception and recognition tasks. Finally, we discuss the challenges in clinical implementation of these algorithms and provide a perspective on how the domain could be advanced in the next few years.

Artificial intelligence (AI) as the name suggests is a new way of automatically deciding upon the operation and control of real time machines and processes in industry. The advantages of using artificial intelligence in industry are many. First of all the decisions are dynamic and real time without any human intervention. Next, it is not based on any formula which in the past required updating for different process conditions and time. Operational technologies did not deploy complete intelligence systems and there used to be much more complexity in tuning the processes and systems together. Training of the operational people was very much crucial and required periodic updates from time to time as the operational technologies changed. AI can overcome most of these complexities, due to the fact that it obtains a data driven solution in real time. Intelligence could be distributed right from the sensory levels to higher levels of distributed computerized systems. Internet of Things (IoTs) and data analytics can provide dynamic information on the performance of machines and processes. Industries which would benefit from technologies based on Artificial Intelligence (AI), Machine Learning (ML) and Deep learning (DL) are in general any process or manufacturing industries including healthcare, petroleum, power sector, automotive etc. In fact this would lead to applicability of Industry 4.0. In this chapter different concepts and applicability of AI in industry have been described. Off course it is possible due to the powerful computational tools, which help not only in doing computations, but also in terms of the capability of communication control, plus data storage, transmission and intelligent decision making.

In recent years, the interest for artificial intelligence has gone from the computing department to the board room. Business leaders are in a rush to explore the possibilities presented by the abundance of data, processing power and the methods of AI to create business value and business opportunities. In this chapter we will adapt the view of a manager and explore some of technologies used in machine learning. We will also look at how managers should approach artificial intelligence. The chapter will close with a discussion of some cases of usage of the different technologies. One case will come from marketing and discusses the use of reinforcement learning in real-time bidding on an e-commerce platform. The next two cases are from the education industry, one case will discuss reinforcement learning in intelligent tutoring systems, and the final case will discuss neural networks in grading of tests and assignments. There are many exciting use cases for artificial intelligence, it is important for business managers to understand the possibilities, and equally important for programmers to understand how businesses create value.

Real estate appraisal plays a vital role in people’s daily life. People rely on the estimation of decisions on buying houses. It is well recognized that the criminal activities around the house have significant impacts on house prices. House buyers can make more reasonable decisions if they are aware of the criminal activities around the house. Therefore, a machine learning-based method is proposed by combining house attributes and criminal activities. Specifically, the method firstly infers the intensity of criminal activities from historical crime records.Then, a novel machine learning algorithm, extremely randomized trees (ExtraTrees), is implemented to estimate the house price based on the extracted comprehensive crime intensity and real-world house attributes.The experimental results show that the proposed method outperforms contemporary real estate appraisal methods by achieving higher accuracy and robustness.

Radical innovation is disruptive. It is a change that sweeps away much of a firm’s, or an entire industry’s, existing investment in technological assets, skills, and knowledge. Such innovation has occurred throughout history and wealth has been accumulated in its wake. Companies have flourished because of such ingenuity, yet there is no evidence in the literature that radical innovation is a result of senior management’s decision, rather it takes place through learning. To understand what drives a knowledge-based organization, one must look at the inside of the firm where implemented structures and tools are supporting employees’ empowerment to unleash their creativity. What the firm knows is stored in the employees’ head and in the firm’s procedural structure, and the firm learns in two ways - by its employees and through hiring new employees. Thus, development of radical innovation, such as artificial intelligence (AI), will either be learned by the firm’s employees and/or through hiring experts. Whenever management prevents new methods from being applied, or employees refuse to acknowledge and learn new techniques, productivity suffers, resulting in firm and industry obsolescence. This is exactly what is happening in the case of AI. Almost eighty years after Alan Turing introduced AI theory, we see a world flabbergasted by its potential impact on productivity. Our case study is based on interviews of a dozen or so R&D managers in private and public sectors. Although our observations are not a guarantee to lead to a consistent agreement or interpretation, valid knowledge that can lead to better performance and organizational survival, may nevertheless provide useful learning for relevant readers.

After a short introduction to neural networks generally, a more detailed presentation of the structure of a feed forward neural network is done, using mathematical language, functions, matrices and vectors.