Introduction: Video Processing, Granular Computing, Rough Sets, Deep Learning and IoT

2021 ◽  
pp. 1-43
Keyword(s):  
Deep Learning ◽  
Video Processing ◽  
Rough Sets ◽  
Granular Computing

scholarly journals Scene Illustration of Terrestrial Animals with Its Monitoring, Tracking and Recognizing Through Deep Learning in Relation with Granular Computing

2019 ◽  
Vol 9 (2) ◽  
pp. 2538-2543
Keyword(s):  
Deep Learning ◽  
Granular Computing ◽  
Formal Model ◽  
Terrestrial Animal ◽  
Learning Technique

Combining Deep Learning Technique with Granular Computing employs an inductive paradigm for the terrestrial animal’s elucidation. The proposed method frames the object (terrestrial animal) in arbitrary-shaped and sized granules rather than fixed and rectangular shaped, so that object can effectively mine and recognized. The goal is to present a formal model which automatically focus only on representative pixel of each granule rather than converting pixels from entire image through scanning. Thus, this work entails the process of recognizing not only the static animal in the background, but also depicts moving animal in foreground separately.

Granular Computing and Rough Sets

2006 ◽  
pp. 535-561 ◽  
Author(s):  
Tsau Young (’T. Y.’) Lin ◽  
Churn-Jung Liau
Keyword(s):  
Rough Sets ◽  
Granular Computing

Multi-Granular Computing through Rough Sets

2014 ◽  
pp. 1-34 ◽  
Author(s):  
B. K. Tripathy
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Equivalence Relation ◽  
Rough Sets ◽  
Intelligent Systems ◽  
Granular Computing ◽  
Rough Set Theory ◽  
Point Of View ◽  
Information Granules ◽  
Computing Platform

Granular Computing has emerged as a framework in which information granules are represented and manipulated by intelligent systems. Granular Computing forms a unified conceptual and computing platform. Rough set theory put forth by Pawlak is based upon single equivalence relation taken at a time. Therefore, from a granular computing point of view, it is single granular computing. In 2006, Qiang et al. introduced a multi-granular computing using rough set, which was called optimistic multigranular rough sets after the introduction of another type of multigranular computing using rough sets called pessimistic multigranular rough sets being introduced by them in 2010. Since then, several properties of multigranulations have been studied. In addition, these basic notions on multigranular rough sets have been introduced. Some of these, called the Neighborhood-Based Multigranular Rough Sets (NMGRS) and the Covering-Based Multigranular Rough Sets (CBMGRS), have been added recently. In this chapter, the authors discuss all these topics on multigranular computing and suggest some problems for further study.

Business Applications of Deep Learning

2020 ◽  
pp. 942-964
Author(s):  
Armando Vieira
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Video Processing ◽  
Autonomous Vehicles ◽  
Video Annotation ◽  
Business Applications ◽  
Personal Assistants ◽  
Efficient Learning ◽  
High Level ◽  
Almost All

Deep Learning (DL) took Artificial Intelligence (AI) by storm and has infiltrated into business at an unprecedented rate. Access to vast amounts of data extensive computational power and a new wave of efficient learning algorithms, helped Artificial Neural Networks to achieve state-of-the-art results in almost all AI challenges. DL is the cornerstone technology behind products for image recognition and video annotation, voice recognition, personal assistants, automated translation and autonomous vehicles. DL works similarly to the brain by extracting high-level, complex abstractions from data in a hierarchical and discriminative or generative way. The implications of DL supported AI in business is tremendous, shaking to the foundations many industries. In this chapter, I present the most significant algorithms and applications, including Natural Language Processing (NLP), image and video processing and finance.

scholarly journals On a Hybridization of Deep Learning and Rough Set Based Granular Computing

Algorithms ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 63 ◽  
Author(s):  
Krzysztof Ropiak ◽  
Piotr Artiemjew
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Rough Set ◽  
Granular Computing ◽  
High Efficiency ◽  
Opportunity To Learn ◽  
Original Data ◽  
Learning Tools ◽  
Approximation Techniques ◽  
Decision Systems

The set of heuristics constituting the methods of deep learning has proved very efficient in complex problems of artificial intelligence such as pattern recognition, speech recognition, etc., solving them with better accuracy than previously applied methods. Our aim in this work has been to integrate the concept of the rough set to the repository of tools applied in deep learning in the form of rough mereological granular computing. In our previous research we have presented the high efficiency of our decision system approximation techniques (creating granular reflections of systems), which, with a large reduction in the size of the training systems, maintained the internal knowledge of the original data. The current research has led us to the question whether granular reflections of decision systems can be effectively learned by neural networks and whether the deep learning will be able to extract the knowledge from the approximated decision systems. Our results show that granulated datasets perform well when mined by deep learning tools. We have performed exemplary experiments using data from the UCI repository—Pytorch and Tensorflow libraries were used for building neural network and classification process. It turns out that deep learning method works effectively based on reduced training sets. Approximation of decision systems before neural networks learning can be important step to give the opportunity to learn in reasonable time.

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