An Artificial Intelligence Platform for Asset Management Contributes to Better Decision-making Tools for Operations, Maintenance, and Utility Management

2018 ◽  
Vol 90 (4) ◽  
pp. 355-375 ◽  
Author(s):  
D. Sen ◽  
A. Fashokun ◽  
R. Angelotti ◽  
M. Brooks ◽  
H. Bhaumik ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Asset Management

scholarly journals A Human-Centric Model for Sustainable Asset Management in Railway: A Case Study

2022 ◽  
Vol 14 (2) ◽  
pp. 936
Author(s):  
Ravdeep Kour ◽  
Miguel Castaño ◽  
Ramin Karim ◽  
Amit Patwardhan ◽  
Manish Kumar ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Virtual Reality ◽  
Asset Management ◽  
Mixed Reality ◽  
Research Work ◽  
Design Model ◽  
User Experience Design ◽  
Operation And Maintenance ◽  
Railway Industry

The ongoing digital transformation is changing asset management in the railway industry. Emerging digital technologies and Artificial Intelligence is expected to facilitate decision-making in management, operation, and maintenance of railway by providing an integrated data-driven and model-driven solution. An important aspect when developing decision-support solutions based on AI and digital technology is the users’ experience. User experience design process aims to create relevance, context-awareness, and meaningfulness for the end-user. In railway contexts, it is believed that applying a human-centric design model in the development of AI-based artefacts, will enhance the usability of the solution, which will have a positive impact on the decision-making processes. In this research, the applicability of such advanced technologies i.e., Virtual Reality, Mixed Reality, and AI have been reviewed for the railway asset management. To carry out this research work, literature review has been conducted related to available Virtual Reality/Augmented Reality/Mixed Reality technologies and their applications within railway industry. It has been found that these technologies are available, but not applied in railway asset management. Thus, the aim of this paper is to propose a human-centric design model for the enhancement of railway asset management using Artificial Intelligence, Virtual Reality, and Mixed Reality technologies. The practical implication of the findings from this work will benefit in increased efficiency and effectiveness of the operation and maintenance processes in railway.

Vallejo Sanitation and Flood Control District – Using Asset Management for Better Infrastructure Decision Making

2016 ◽  
Vol 2016 (9) ◽  
pp. 3725-3747
Author(s):  
Johnson Ho ◽  
Mark Tomko ◽  
Gage Muckleroy ◽  
Roop Lutchman ◽  
Mert Muftugil
Keyword(s):  
Decision Making ◽  
Asset Management ◽  
Flood Control

Perceived Effectiveness of Artificial Intelligence based Decision Support System in a Clinical Setting: A Systematic Literature Review (Preprint)

2020 ◽  
Author(s):  
Avishek Choudhury
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Literature Review ◽  
Decision Support System ◽  
Support System ◽  
Clinical Setting ◽  
Clinical Decision Making ◽  
Theoretical Perspective ◽  
Care Providers

UNSTRUCTURED Objective: The potential benefits of artificial intelligence based decision support system (AI-DSS) from a theoretical perspective are well documented and perceived by researchers but there is a lack of evidence showing its influence on routine clinical practice and how its perceived by care providers. Since the effectiveness of AI systems depends on data quality, implementation, and interpretation. The purpose of this literature review is to analyze the effectiveness of AI-DSS in clinical setting and understand its influence on clinician’s decision making outcome. Materials and Methods: This review protocol follows the Preferred Reporting Items for Systematic Reviews and Meta- Analyses reporting guidelines. Literature will be identified using a multi-database search strategy developed in consultation with a librarian. The proposed screening process consists of a title and abstract scan, followed by a full-text review by two reviewers to determine the eligibility of articles. Studies outlining application of AI based decision support system in a clinical setting and its impact on clinician’s decision making, will be included. A tabular synthesis of the general study details will be provided, as well as a narrative synthesis of the extracted data, organised into themes. Studies solely reporting AI accuracy an but not implemented in a clinical setting to measure its influence on clinical decision making were excluded from further review. Results: We identified 8 eligible studies that implemented AI-DSS in a clinical setting to facilitate decisions concerning prostate cancer, post traumatic stress disorder, cardiac ailment, back pain, and others. Five (62.50%) out of 8 studies reported positive outcome of AI-DSS. Conclusion: The systematic review indicated that AI-enabled decision support systems, when implemented in a clinical setting and used by clinicians might not ensure enhanced decision making. However, there are very limited studies to confirm the claim that AI based decision support system can uplift clinicians decision making abilities.

Investment Management and the Investment Chain

2017 ◽  
Author(s):  
Diane-Laure Arjaliès ◽  
Philip Grant ◽  
Iain Hardie ◽  
Donald MacKenzie ◽  
Ekaterina Svetlova
Keyword(s):  
Decision Making ◽  
Asset Management ◽  
Investment Decisions ◽  
Investment Management ◽  
Money Flows ◽  
Central Argument ◽  
A Chain ◽  
Asset Managers

Chapter 1 introduces the idea of the chain as related to investment management. It highlights the increasing importance and influence of the asset management industry and argues that, despite this fact, the behaviour and decision-making of asset managers has been little studied. The chapter suggests that investment decisions today cannot be understood by focusing on isolated investors. Rather, most of their money flows through a chain: a sequence of intermediaries that ‘sit between’ savers and companies/governments. The chapter introduces the central argument of the book that investment management is shaped profoundly by the opportunities and constraints that this chain creates.

scholarly journals BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13849-13850
Author(s):  
Donghyeon Lee ◽  
Man-Je Kim ◽  
Chang Wook Ahn
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Real Time ◽  
Large Scale ◽  
Outcome Predictor ◽  
Short Term ◽  
Rts Game

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.

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