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Academic Articles & Research Papers
Showing 20 of 739 academic articles
Frank Cremer, Barry Sheehan, Michael Fortmann, Arash Negahdari Kia et al. • The Geneva Papers on Risk and Insurance Issues and Practice
(2022)
Cybercrime is estimated to have cost the global economy just under USD 1 trillion in 2020, indicating an increase of more than 50% since 2018. With the average cyber insurance claim rising from USD 145,000 in 2019 to USD 359,000 in 2020, th...
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DOI: https://doi.org/10.1057/s41288-022-00266-6
PMC: 8853293
Saqib Saeed, Salha A. Altamimi, Norah A. Alkayyal, Ebtisam Alshehri et al. • Sensors
(2023)
This systematic literature review explores the digital transformation (DT) and cybersecurity implications for achieving business resilience. DT involves transitioning organizational processes to IT solutions, which can result in significant...
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DOI: https://doi.org/10.3390/s23156666
PMC: 10422504
Gulsebnem Bishop • CRC Press eBooks
(2025)
This chapter will explore the complex world of cybersecurity and its surrounding culture. It sets out to highlight the distinctions between the two and underscore their unique characteristics. It is crucial that these differences are clarif...
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DOI: https://doi.org/10.1201/9781003368496-1
Joydeep Bhattacharjee • Apress eBooks
(2019)
In the previous chapters, we took a look at different machine learning algorithms and then we worked on how to work with data for those algorithms. Those data sets were largely tabular, and the individual values were either numerical or cat...
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DOI: https://doi.org/10.1007/978-1-4842-5121-8_5
Yang Liu, Meng Zhang • Computational Linguistics
(2017)
Deep learning has attracted dramatic attention in recent years, both in academia and industry. The popular term deep learning generally refers to neural network methods. Indeed, many core ideas and methods were born years ago in the era of ...
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DOI: https://doi.org/10.1162/coli_r_00312
Matthieu Courbariaux, Itay Hubara, Daniel Soudry, Ran El‐Yaniv et al. • arXiv (Cornell University)
(2016)
We introduce a method to train Binarized Neural Networks (BNNs) - neural networks with binary weights and activations at run-time. At training-time the binary weights and activations are used for computing the parameters gradients. During t...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1602.02830
Peter L. Bartlett, Dylan J. Foster, Matus Telgarsky • arXiv (Cornell University)
(2017)
This paper presents a margin-based multiclass generalization bound for neural networks that scales with their margin-normalized "spectral complexity": their Lipschitz constant, meaning the product of the spectral norms of the weight matrice...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1706.08498
Jianlin Cheng, Zheng Wang, Gianluca Pollastri • International Joint Conference on Neural Networks (IJCNN)
(2008)
Ordinal regression is an important type of learning, which has properties of both classification and regression. Here we describe an effective approach to adapt a traditional neural network to learn ordinal categories. Our approach is a gen...
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Open Access
DOI: https://doi.org/10.1109/ijcnn.2008.4633963
Igor I. Baskin, David Alan Winkler, Igor V. Tetko • Expert Opinion on Drug Discovery
(2016)
INTRODUCTION: Neural networks are becoming a very popular method for solving machine learning and artificial intelligence problems. The variety of neural network types and their application to drug discovery requires expert knowledge to cho...
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DOI: https://doi.org/10.1080/17460441.2016.1201262
Mikhail Yurochkin, Mayank Agarwal, Ghosh, Soumya, Kristjan H. Greenewald et al. • arXiv (Cornell University)
(2019)
In federated learning problems, data is scattered across different servers and exchanging or pooling it is often impractical or prohibited. We develop a Bayesian nonparametric framework for federated learning with neural networks. Each data...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1905.12022
Tommy W. S. Chow, Siu‐Yeung Cho • Series in electrical and computer engineering
(2007)
This book covers neural networks with special emphasis on advanced learning methodologies and applications. It includes practical issues of weight initializations, stalling of learning, and escape from a local minima, which have not been co...
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DOI: https://doi.org/10.1142/p487
Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami et al. • ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
(2019)
Representation learning in heterogeneous graphs aims to pursue a meaningful vector representation for each node so as to facilitate downstream applications such as link prediction, personalized recommendation, node classification, etc. This...
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Open Access
DOI: https://doi.org/10.1145/3292500.3330961
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
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El‐Yaniv et al. • arXiv (Cornell University)
(2016)
We introduce a method to train Quantized Neural Networks (QNNs) --- neural networks with extremely low precision (e.g., 1-bit) weights and activations, at run-time. At train-time the quantized weights and activations are used for computing ...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1609.07061
Matipa Ricky Ngandu, David Risinamhodzi, Godwin Pedzisai Dzvapatsva, Courage Matobobo • Discover Education
(2023)
Abstract ICT tools in education are widely used to support the aim of achieving learning outcomes by improving critical areas such as student engagement, participation, and motivation. In this study, we examine literature to explore how gam...
DOAJ
Open Access (DOAJ)
DOI: 10.1007/s44217-023-00069-4
Zeng Yun, Wu Wenxiu, Yu Dankui • Shiyou jixie
(2018)
Under the rapid development of the world’s powder technology, powder materials are used in a wide range of fields such as metallurgy, chemical industry and petroleum. Turbo air classifier is one of the most important mechanical equipment in...
DOAJ
Open Access (DOAJ)
César González-Pérez, Brian Henderson‐Sellers (2008)
This book focuses on metamodelling as a discipline, exploring its foundations, techniques and results. It presents a comprehensive metamodel that covers process, product and quality issues under a common framework. Issues covered include: *...
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Ioana Rus, Mikael Lindvall • IEEE Software
(2002)
Software organizations’ main assets are not plants, buildings, or \nexpensive machines. A software organization’s main asset is its intellectual \ncapital, as it is in sectors such as consulting, law, investment \nbanking, and advertising. ...
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DOI: https://doi.org/10.1109/ms.2002.1003450
Ce Zhang, Wei-Gong Lv, Sheng Sheng, Jin-Yong Wang et al. • Applied Sciences
(2022)
From the perspective of FDR (fault detection rate), which is an indispensable component in reliability modeling, this paper proposes two kinds of reliability models under imperfect debugging. This model is a relatively flexible and unified ...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/app122110736
Jing (Selena) He, Meng Han, Shouling Ji, Tianyu Du et al. • Big Data Mining and Analytics
(2019)
Social networks are important media for spreading information, ideas, and influence among individuals. Most existing research focuses on understanding the characteristics of social networks, investigating how information is spread through t...
DOAJ
Open Access (DOAJ)
DOI: 10.26599/BDMA.2018.9020034