Artificial Intelligence Applications and Innovation

doi:10.3390/books978-3-0365-9950-2

Download Url(s)

https://mdpi.com/books/pdfview/book/8609

Contributor(s)

Rodrigues, João M. F. (editor)

Cardoso, Pedro J. S. (editor)

Chinnici, Marta (editor)

Language

English

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Abstract

The idea of an intelligent machine has fascinated humans for centuries. Intelligence can be defined as the capacity for learning, reasoning, understanding, or the aptitude for grasping truths, relationships, facts, or meanings. Any of these perspectives requires the capacity to acquire data from the surrounding environment and possibly act over that environment. In short, the building of autonomous agents served with sensors and actuators, capable of learning and producing educated answers was long foreseen. Artificial Intelligence (AI) is impacting our day-to-day lives, our cities, and even our free time. AI is still closely associated with some popular misconceptions that cause the public to either have unrealistic fears about it or unrealistic expectations about how it will change our workplace and life in general. It is important to show that such fears are unfounded, and that new trends, innovations, and technologies will be able to improve the way we live, benefiting society without replacing humans in their core activities. This Special Issue delves into mutually dependent subfields including, but not restricted to, machine learning, computer vision, data analysis, data science, big data, Internet of Things, sentiment analysis, natural language processing, privacy and ethics, and robotics. The chapters build a comprehensive collection of research and development trends on contemporary “Artificial Intelligence Applications and Innovation” that will serve as a convenient reference for AI experts, as well as newly arrived practitioners, introducing them to the field’s trends.

URI

https://directory.doabooks.org/handle/20.500.12854/137455

Keywords

transfer learning; autoencoder; lung cancer; malignancy assessment; data assimilation; deep learning; neural network; thermal imaging; Raynaud phenomenon; systemic sclerosis; evolutionary networks; graph representation; community detection; deep sparse autoencoder; specific learning disorders; dyslexia; artificial intelligence; virtual reality; adaptive learning; inclusive teaching; artificial neural networks; biometrics; document handling; face recognition; Internet of Things; network architecture; smart cities; person ReID; computer vision; deep neural networks; image enhancement; butterfly optimization algorithm; electric energy consumption prediction; long short-term memory network; time series analysis; transformation methods; electric vehicle; real driving cycle; recurrent neural network; simulation; state of charge; temporal attention; equipment selection; construction robot; decision support system; axiomatic design; decision-theoretic expert system; construction industry; industry 4.0; continual learning; neural networks; catastrophic forgetting; object recognition; decision support systems; uncertainty; info-incompleteness; machine learning; football market; athlete evaluation; sentiment analysis; LSTM; stock market; forecasting; fundamental analysis; technical analysis; human-centered AI; data analysis; data science; big data