Potential Contribution of Artificial Intelligence to Nephrology

Artificial Intelligence Based Commercial Risk Management Framework for SMEs

Sustainability ◽

10.3390/su11164501 ◽

2019 ◽

Vol 11 (16) ◽

pp. 4501

Author(s):

Gerda Žigienė ◽

Egidijus Rybakovas ◽

Robertas Alzbutas

Keyword(s):

Artificial Intelligence ◽

Risk Assessment ◽

Risk Management ◽

Academic Research ◽

Building Blocks ◽

Learning Technologies ◽

Potential Contribution ◽

Risk Management Framework ◽

Development Goals ◽

Risk Assessment And Management

Risk management in commercial processes is among the most important procedures affecting the competitiveness of small and medium-sized enterprises (SMEs), their innovativeness and potential contribution to global sustainable development goals (SDGs). The ecosystem of commercial processes is the prerequisite to manage risk faced by SMEs. Commercial risk assessment and management using elements of artificial intelligence, big data, and machine learning technologies could be developed and maintained as external services for a group of SMEs allowing to share costs and benefits. This paper aims to provide a conceptual framework of commercial risk assessment and management solution based on elements of artificial intelligence. This conceptualization is done on the background of scientific literature, policy documents, and risk management standards. Main building blocks of the framework in terms of commercial risk categories, data sources and workflow phases are presented in the article. Business companies, state policy, and academic research focused recommendations on the further development of the framework and its implementation are elaborated.

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A Novel Neural Network-Based Method for Decoding and Detecting of the DS8-PSK Scheme in an OCC System

Applied Sciences ◽

10.3390/app9112242 ◽

2019 ◽

Vol 9 (11) ◽

pp. 2242 ◽

Cited By ~ 2

Author(s):

Tung Lam Pham ◽

Huy Nguyen ◽

Trang Nguyen ◽

Yeong Min Jang

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Wireless Communication ◽

Visible Light Communication ◽

Research Area ◽

Modulation Scheme ◽

Potential Contribution ◽

Channel Condition ◽

Shift Keying ◽

Novel Method

This paper proposes a novel method of training and applying a neural network to act as an adaptive decoder for a modulation scheme used in optical camera communication (OCC). We present a brief discussion on trending artificial intelligence applications, the contemporary ways of applying them in a wireless communication field, such as visible light communication (VLC), optical wireless communication (OWC) and OCC, and its potential contribution in the development of this research area. Furthermore, we proposed an OCC vehicular system architecture with artificial intelligence (AI) functionalities, where dimmable spatial 8-phase shift keying (DS8-PSK) is employed as one out of two modulation schemes to form a hybrid waveform. Further demonstration of simulating the blurring process on a transmitter image, as well as our proposed method of using a neural network as a decoder for DS8-PSK, is provided in detail. Finally, experimental results are given to prove the effectiveness and efficiency of the proposed method over an investigating channel condition.

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