Results 21 to 30 of about 1,130,914 (300)
A theory of consciousness from a theoretical computer science perspective: Insights from the Conscious Turing Machine [PDF]
Proceedings of the National Academy of Sciences of the United States of America, 2021 Significance This paper provides evidence that a theoretical computer science (TCS) perspective can add to our understanding of consciousness by providing a simple framework for employing tools from computational complexity theory and machine learning ...
L. Blum, M. Blum
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Machine Learning: Algorithms, Real-World Applications and Research Directions
SN Computer Science, 2021 In the current age of the Fourth Industrial Revolution (4IR or Industry 4.0), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc.
Iqbal H. Sarker
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Categorisation of Computer Science Research Papers using Supervised Machine Learning Techniques [PDF]
International Journal of Computing and Digital Systems, 2020 Hemrajsingh Gheeseewan +1 more
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CancersArtificial intelligence (AI), the wide spectrum of technologies aiming to give machines or computers the ability to perform human-like cognitive functions, began in the 1940s with the first abstract models of intelligent machines. Soon after, in the 1950s and 1960s, machine learning algorithms such as neural networks and decision trees ignited ...
Michele Avanzo +4 more
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Computer Science and Machine Learning Trends 2023 [PDF]
, 2023 Bob Zigon, Fengguang Song
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International Journal of Environmental Research and Public Health, 2022 Computer-aided diagnostic (CAD) systems can assist radiologists in detecting coal workers’ pneumoconiosis (CWP) in their chest X-rays. Early diagnosis of the CWP can significantly improve workers’ survival rate.
Liton Devnath +6 more
semanticscholar +1 more source
Provenance Data in the Machine Learning Lifecycle in Computational Science and Engineering [PDF]
2019 IEEE/ACM Workflows in Support of Large-Scale Science (WORKS), 2019 Machine Learning (ML) has become essential in several industries. In Computational Science and Engineering (CSE), the complexity of the ML lifecycle comes from the large variety of data, scientists' expertise, tools, and workflows. If data are not tracked properly during the lifecycle, it becomes unfeasible to recreate a ML model from scratch or to ...
Souza, Renan +12 more
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Extending the reach of quantum computing for materials science with machine learning potentials
AIP Advances, 2022 Solving electronic structure problems represents a promising field of applications for quantum computers. Currently, much effort is spent in devising and optimizing quantum algorithms for near-term quantum processors, with the aim of outperforming classical counterparts on selected problem instances using limited quantum resources.
Julian Schuhmacher +6 more
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Researcher reasoning meets computational capacity: Machine learning for social science
Social Science Research, 2022 Computational power and big data have created new opportunities to explore and understand the social world. A special synergy is possible when social scientists combine human attention to certain aspects of the problem with the power of algorithms to automate other aspects of the problem. We review selected exemplary applications where machine learning
Ian Lundberg +2 more
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Trend Prediction for Computer Science Research Topics Using Extreme Learning Machine
Procedia Engineering, 2012 Novita Sari +2 more
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