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Showing 20 of 1,285 academic articles

Cybersecurity vs. Information Security

Hamed Taherdoost • Procedia Computer Science (2022)

Protection of data assets is a hot trend topic that is attracting considerable interest worldwide. There are different concepts revolving security of data including cybersecurity and information security. Cybersecurity and information secur...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1016/j.procs.2022.12.050

Cyber risk and cybersecurity: a systematic review of data availability

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...

OPENALEX cc-by DOI: https://doi.org/10.1057/s41288-022-00266-6 PMC: 8853293

Explainable artificial intelligence for cybersecurity: a literature survey

Fabien Charmet, Harry Chandra Tanuwidjaja, Solayman Ayoubi, Pierre-François Gimenez et al. • Annals of Telecommunications (2022)

Abstract With the extensive application of deep learning (DL) algorithms in recent years, e.g., for detecting Android malware or vulnerable source code, artificial intelligence (AI) and machine learning (ML) are increasingly becoming essent...

OPENALEX cc-by DOI: https://doi.org/10.1007/s12243-022-00926-7

Quantifying Uncertainties in Natural Language Processing Tasks

Yijun Xiao, William Yang Wang • Proceedings of the AAAI Conference on Artificial Intelligence (2019)

Reliable uncertainty quantification is a first step towards building explainable, transparent, and accountable artificial intelligent systems. Recent progress in Bayesian deep learning has made such quantification realizable. In this paper,...

OPENALEX Open Access DOI: https://doi.org/10.1609/aaai.v33i01.33017322

Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science

Emily M. Bender, Batya Friedman • Transactions of the Association for Computational Linguistics (2018)

In this paper, we propose data statements as a design solution and professional practice for natural language processing technologists, in both research and development. Through the adoption and widespread use of data statements, the field ...

OPENALEX cc-by DOI: https://doi.org/10.1162/tacl_a_00041

Natural Language Processing

Xavier Vasques • Wiley eBooks (2024)

This book chapter focuses on natural language processing (NLP) and covers various applications such as classifying messages as spam or ham, sentiment analysis, and the use of Bidirectional Encoder Representations from Transformers (BERT). T...

OPENALEX DOI: https://doi.org/10.1002/9781394220649.ch4

Fuzzy logic, neural networks, and soft computing

Lotfi A. Zadeh • Communications of the ACM (1994)

article Free Access Share on Fuzzy logic, neural networks, and soft computing Author: Lotfi A. Zadeh Univ. of California, Berkeley Univ. of California, BerkeleyView Profile Authors Info & Claims Communications of the ACMVolume 37Issue 3Marc...

OPENALEX Open Access DOI: https://doi.org/10.1145/175247.175255

Pattern Recognition with Neural Networks in C++

Abhijit S. Pandya, Robert B. Macy • CRC Press eBooks (2021)

From the Publisher: The addition of artificial neural network computing to traditionalpattern recognition has given rise to a new, different, and more powerful methodology that is presented in this interesting book. This is a practical gui...

OPENALEX DOI: https://doi.org/10.1201/9780138744274

Using neural networks in reliability prediction

Nachimuthu Karunanithi, Darrell Whitley, Yashwant K. Malaiya • IEEE Software (1992)

It is shown that neural network reliability growth models have a significant advantage over analytic models in that they require only failure history as input and not assumptions about either the development environment or external paramete...

OPENALEX DOI: https://doi.org/10.1109/52.143107

Neural networks for control

M.T. Hagan, H.B. Demuth • Proceedings of the ... American Control Conference/Proceedings of the American Control Conference (1999)

Provides a quick overview of neural networks and explains how they can be used in control systems. We introduce the multilayer perceptron neural network and describe how it can be used for function approximation. The backpropagation algorit...

OPENALEX DOI: https://doi.org/10.1109/acc.1999.786109

Neural network computing

• Choice Reviews Online (1994)

At present, computers cannot think, make independent decisions, recognize visual and audible patterns, reach reliable conclusions without complete information or learn from their mistakes. The question this book and research into artificial...

OPENALEX DOI: https://doi.org/10.5860/choice.32-2176

Multilayer perceptron and neural networks

Marius-Constantin Popescu, Valentina Emilia Bălaş, Liliana Perescu-Popescu, Nikos E. Mastorakis (2009)

Abstract:- The attempts for solving linear inseparable problems have led to different variations on the number of layers of neurons and activation functions used. The backpropagation algorithm is the most known and used supervised learning ...

OPENALEX DOI: https://doi.org/10.5555/1639537.1639542

Bidirectional recurrent neural networks

Mike Schuster, Kuldip K. Paliwal • IEEE Transactions on Signal Processing (1997)

In the first part of this paper, a regular recurrent neural network (RNN) is extended to a bidirectional recurrent neural network (BRNN). The BRNN can be trained without the limitation of using input information just up to a preset future f...

OPENALEX DOI: https://doi.org/10.1109/78.650093

Understanding of a convolutional neural network

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...

OPENALEX DOI: https://doi.org/10.1109/icengtechnol.2017.8308186

Artificial Neural Networks: An Overview

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 ...

OPENALEX cc-by DOI: https://doi.org/10.58496/mjcsc/2023/015

Understanding Neural Networks as Statistical Tools

Brad Warner, Manavendra Misra • The American Statistician (1996)

Neural networks have received a great deal of attention over the last few years. They are being used in the areas of prediction and classification, areas where regression models and other related statistical techniques have traditionally be...

OPENALEX DOI: https://doi.org/10.1080/00031305.1996.10473554

Neural Networks

• Bloomsbury Publishing Plc eBooks (2000)

Neural Networks, Second Edition provides a complete introduction to neural networks. It describes what they are, what they can do, and how they do it. While some scientific background is assumed, the reader is not expected to have any prior...

OPENALEX DOI: https://doi.org/10.5040/9781350392434

The basic ideas in neural networks

David E. Rumelhart, Bernard Carlos Widrow, Michael A. Lehr • Communications of the ACM (1994)

article Free AccessThe basic ideas in neural networks Authors: David E. Rumelhart Stanford Univ., Stanford, CA Stanford Univ., Stanford, CAView Profile , Bernard Widrow Stanford Univ., Stanford, CA Stanford Univ., Stanford, CAView Profile ,...

OPENALEX Open Access DOI: https://doi.org/10.1145/175247.175256