VP17.01: Exploring a new paradigm for the fetal anomaly ultrasound scan: artificial intelligence in real‐time

AbstractOur contemporary world is on the verge of crucial changes of an unparalleled pace. The ‘technological changeover’ is the new paradigm caused by the unprecedented evolution of the disruptive technologies. The present world has the tendency to evolve at least exponential, therefore future educational environment is fairly different than its present layout. An entire array of nowadays studies widely recognizes that the progress of the disruptive technologies will pose a meaningful impact over the educational system evolution. Among the most spectacular technologies with disruptive features we should encounter Artificial Intelligence, Blockchain Technology, Cloud Computing, and the like. In an era of technological disruption the education is seen as the new currency. With the help of Artificial Intelligence, for instance, the education system could track how people learn from kindergarten to retirement. Besides, the technology domain will move the centre of gravity from the institutional area to that of the education’s beneficiaries, as we might expect that they will recruit and employ the needed teacher staff, not the institutions. Moreover, the education’s recipients will be the main creators of tomorrow’s professions and within their community the overarching events will happen and the main decisions will be taken in the educational domain.

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Artificial Intelligence-based Decision-Making Algorithms, Internet of Things Smart Devices, and Real-Time Process Monitoring in Sustainable Industry 4.0

Economics Management and Financial Markets ◽

10.22381/emfm15220204 ◽

2020 ◽

Vol 15 (2) ◽

pp. 30

Keyword(s):

Artificial Intelligence ◽

Decision Making ◽

Internet Of Things ◽

Real Time ◽

Process Monitoring ◽

Industry 4.0 ◽

Smart Devices ◽

Time Process

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

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i10.7197 ◽

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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Special issue on artificial intelligence and machine learning for real-time image processing

Journal of Real-Time Image Processing ◽

10.1007/s11554-021-01157-0 ◽

2021 ◽

Author(s):

S. Balamurugan

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Image Processing ◽

Real Time ◽

Special Issue ◽

Real Time Image Processing ◽

Real Time Image ◽

Time Image

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Design of a dynamic and self-adapting system, supported with artificial intelligence, machine learning and real-time intelligence for predictive cyber risk analytics in extreme environments – cyber risk in the colonisation of Mars

Safety in Extreme Environments ◽

10.1007/s42797-021-00025-1 ◽

2021 ◽

Author(s):

Petar Radanliev ◽

David De Roure ◽

Kevin Page ◽

Max Van Kleek ◽

Omar Santos ◽

...

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Real Time ◽

Cyber Security ◽

Extreme Environments ◽

Learning Technologies ◽

Cyber Attacks ◽

Edge Computing ◽

Mathematical Formulas ◽

Cyber Risk

AbstractMultiple governmental agencies and private organisations have made commitments for the colonisation of Mars. Such colonisation requires complex systems and infrastructure that could be very costly to repair or replace in cases of cyber-attacks. This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real-time intelligence in edge computing. The paper presents a new mathematical approach for integrating concepts for cognition engine design, edge computing and Artificial Intelligence and Machine Learning to automate anomaly detection. This engine instigates a step change by applying Artificial Intelligence and Machine Learning embedded at the edge of IoT networks, to deliver safe and functional real-time intelligence for predictive cyber risk analytics. This will enhance capacities for risk analytics and assists in the creation of a comprehensive and systematic understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when Artificial Intelligence and Machine Learning technologies are migrated to the periphery of the internet and into local IoT networks.

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