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Sebastian Kujawa, Gniewko Niedbała • Agriculture
(2021)
Artificial neural networks are one of the most important elements of machine learning and artificial intelligence. They are inspired by the human brain structure and function as if they are based on interconnected nodes in which simple proc...
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DOI: https://doi.org/10.3390/agriculture11060497
Günter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter • arXiv (Cornell University)
(2017)
Deep Learning has revolutionized vision via convolutional neural networks (CNNs) and natural language processing via recurrent neural networks (RNNs). However, success stories of Deep Learning with standard feed-forward neural networks (FNN...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1706.02515
Dong-Ling Deng, Xiaopeng Li, S. Das Sarma • Physical Review X
(2017)
Machine learning has recently gained attention as a possible way to understand phase transitions in many-body quantum systems. A new entanglement analysis reveals crucial properties of the data structures that encode quantum states in a neu...
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DOI: https://doi.org/10.1103/physrevx.7.021021
Saad Albawi, Tareq Abed Mohammed, Saad Al-Azawi (2017)
The term Deep Learning or Deep Neural Network refers to Artificial Neural Networks (ANN) with multi layers. Over the last few decades, it has been considered to be one of the most powerful tools, and has become very popular in the literatur...
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DOI: https://doi.org/10.1109/icengtechnol.2017.8308186
Roheen Qamar, Baqar Ali Zardari • Mesopotamian Journal of Computer Science
(2023)
Neural networks, also known as artificial neural networks (ANNs) or artificially generated neural networks (SNNs) are a subset of machine learning that provide the foundation of deep learning techniques. Their name and form are inspired by ...
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DOI: https://doi.org/10.58496/mjcsc/2023/015
Amey Thakur • International Journal for Research in Applied Science and Engineering Technology
(2021)
The purpose of this study is to familiarise the reader with the foundations of neural networks. Artificial Neural Networks (ANNs) are algorithm-based systems that are modelled after Biological Neural Networks (BNNs). Neural networks are an ...
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Open Access
DOI: https://doi.org/10.22214/ijraset.2021.37362
Brian David Ripley • Cambridge University Press eBooks
(1996)
This 1996 book is a reliable account of the statistical framework for pattern recognition and machine learning. With unparalleled coverage and a wealth of case-studies this book gives valuable insight into both the theory and the enormously...
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DOI: https://doi.org/10.1017/cbo9780511812651
Paul Refenes • Wiley eBooks
(1995)
PART ONE: NEURAL NETWORKS: Introduction Design Considerations Methods for Optimal Network Design Data Modelling Considerations Testing Strategies and Metrics PART TWO: EQUITY APPLICATIONS: Modelling Stock Returns in the Framework of APT: A ...
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Aarush Saxena • International Journal for Research in Applied Science and Engineering Technology
(2022)
Abstract: The field of machine learning has taken a dramatic twist in re- cent times, with the rise of the Artificial Neural Network (ANN). These biologically inspired computational models can far exceed the per- performance of previous for...
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Open Access
DOI: https://doi.org/10.22214/ijraset.2022.47789
Qiang Yang, Yang Liu, Tianjian Chen, Yongxin Tong • ACM Transactions on Intelligent Systems and Technology
(2019)
Today’s artificial intelligence still faces two major challenges. One is that, in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and security. We propose a possible solution to t...
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Open Access
DOI: https://doi.org/10.1145/3298981
Sašo Džeroski, Luc De Raedt, Stefan Wrobel (2005)
This volume, which is also available online from http://www.machinelearning.org, contains the papers accepted for presentation at ICML-2005, the 22nd lnternational Conference on Machine Learning, which was held at the University of Bonn in ...
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Pratik Gajane, Pechenizkiy, Mykola • arXiv (Cornell University)
(2017)
Machine learning algorithms for prediction are increasingly being used in critical decisions affecting human lives. Various fairness formalizations, with no firm consensus yet, are employed to prevent such algorithms from systematically dis...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1710.03184
• Journal of Machine Learning Technologies
(2024)
The Journal of Machine Learning Technologies, ISSN: 2229–3981 & ISSN: 2229-399X, aims to publish all the latest and outstanding research articles, reviews and letters in all areas of Machine Learning Technologie. Each issue contains a serie...
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DOI: https://doi.org/10.9735/2229-3981
Masoud Yazdani • Chapman & Hall eBooks
(1986)
Part 1. Principles of artificial intelligence, John Campbell. Part 2 Tools and techniques: artificial intelligence programming environments and the POPLOG system, John Gibson LISP, lists and pattern-matching, Tony Hasemer. Part 3 Applicatio...
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Felipe Oviedo, Juan Lavista Ferres, Tonio Buonassisi, Keith T. Butler • Accounts of Materials Research
(2022)
High Resolution Image Download MS PowerPoint Slide Conspectus Machine learning has become a common and powerful tool in materials research. As more data become available, with the use of high-performance computing and high-throughput experi...
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DOI: https://doi.org/10.1021/accountsmr.1c00244
Jirawat Tangpanitanon, Supanut Thanasilp, Ninnat Dangniam, Marc-Antoine Lemonde et al. • Physical Review Research
(2020)
Parametrized quantum evolution is the main ingredient in variational quantum algorithms for near-term quantum devices. In digital quantum computing, it has been shown that random parametrized quantum circuits are able to express complex dis...
DOAJ
Open Access (DOAJ)
DOI: 10.1103/PhysRevResearch.2.043364
Vu Tuan Hai, Nguyen Tan Viet, Jesus Urbaneja, Nguyen Vu Linh et al. • Machine Learning: Science and Technology
(2024)
Quantum compilation is the process of converting a target unitary operation into a trainable unitary represented by a quantum circuit. It has a wide range of applications, including gate optimization, quantum-assisted compiling, quantum sta...
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Open Access (DOAJ)
DOI: 10.1088/2632-2153/ad9705
Hashim Hussain, Shailendra Mishra, Reem Alshenaifi • PLoS ONE
(2026)
Healthcare IoT systems increasingly rely on interconnected, resource-constrained devices that are vulnerable to both classical and emerging quantum-enabled cyber threats, but introduced heightened cybersecurity risks, particularly from emer...
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Open Access (DOAJ)
DOI: 10.1371/journal.pone.0348600
Salik Siddiqui, Justin Holzer, Joshua Malcarne, Galahad M. B. Wernsing et al. • IEEE Access
(2025)
While quantum computing is already being employed in different domains to solve large scale and highly complex optimization problems, the wireless communications community has only recently begun exploring the potential of Quantum Machine L...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2025.3572416
Ivana Nikoloska, Osvaldo Simeone • Frontiers in Quantum Science and Technology
(2024)
Simulation is an indispensable tool in both engineering and the sciences. In simulation-based modeling, a parametric simulator is adopted as a mechanistic model of a physical system. The problem of designing algorithms that optimize the sim...
DOAJ
Open Access (DOAJ)
DOI: 10.3389/frqst.2024.1394533