MainReads

Discover Books. Explore Knowledge.

Home » Academic Articles & Research Papers

Academic Articles & Research Papers

Search and discover peer-reviewed scholarly literature, open-access articles, and research citations across DOAJ, PubMed Central, and OpenAlex.

Reset
Showing 20 of 423 academic articles

Smart Substation Communications and Cybersecurity: A Comprehensive Survey

José Gaspar, Tiago Cruz, Chan–Tong Lam, Paulo Simões • IEEE Communications Surveys & Tutorials (2023)

Electrical grids generate, transport, distribute and deliver electrical power to consumers through a complex Critical Infrastructure which progressively shifted from an air-gaped to a connected architecture. Specifically, Smart Substations ...

OPENALEX cc-by DOI: https://doi.org/10.1109/comst.2023.3305468

Graph Mining for Cybersecurity: A Survey

Bo Yan, Cheng Hong Yang, Chuan Shi, Yong Fang et al. • ACM Transactions on Knowledge Discovery from Data (2023)

The explosive growth of cyber attacks today, such as malware, spam, and intrusions, has caused severe consequences on society. Securing cyberspace has become a great concern for organizations and governments. Traditional machine learning ba...

OPENALEX Open Access DOI: https://doi.org/10.1145/3610228

Deep Learning in Cybersecurity

Yadigar İmamverdiyev, Fargana J. Abdullayeva • International Journal of Cyber Warfare and Terrorism (2020)

In this article, a review and summarization of the emerging scientific approaches of deep learning (DL) on cybersecurity are provided, a structured and comprehensive overview of the various cyberattack detection methods is conducted, existi...

OPENALEX DOI: https://doi.org/10.4018/ijcwt.2020040105

Generative AI in cybersecurity: A comprehensive review of LLM applications and vulnerabilities

Mohamed Amine Ferrag, Fatima Alwahedi, Ammar Ayman Battah, Bilel Cherif et al. • Internet of Things and Cyber-Physical Systems (2025)

This paper provides a comprehensive review of the future of cybersecurity through Generative AI and Large Language Models (LLMs). We explore LLM applications across various domains, including hardware design security, intrusion detection, s...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1016/j.iotcps.2025.01.001

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

Natural Language Processing

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...

OPENALEX DOI: https://doi.org/10.1007/978-1-4842-5121-8_5

Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

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...

OPENALEX Open Access DOI: https://doi.org/10.48550/arxiv.1602.02830

Spectrally-normalized margin bounds for neural networks

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...

OPENALEX Open Access DOI: https://doi.org/10.48550/arxiv.1706.08498

A neural network approach to ordinal regression

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...

OPENALEX Open Access DOI: https://doi.org/10.1109/ijcnn.2008.4633963

Bayesian Nonparametric Federated Learning of Neural Networks

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...

OPENALEX Open Access DOI: https://doi.org/10.48550/arxiv.1905.12022

Neural Networks and Computing

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...

OPENALEX DOI: https://doi.org/10.1142/p487

Heterogeneous Graph Neural Network

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...

OPENALEX Open Access DOI: https://doi.org/10.1145/3292500.3330961

Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations

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 ...

OPENALEX Open Access DOI: https://doi.org/10.48550/arxiv.1609.07061

Sentiment analysis for software engineering

Bin Lin, Fiorella Zampetti, Gabriele Bavota, Massimiliano Di Penta et al. • ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) (2018)

Sentiment analysis has been applied to various software engineering (SE) tasks, such as evaluating app reviews or analyzing developers' emotions in commit messages. Studies indicate that sentiment analysis tools provide unreliable results w...

OPENALEX DOI: https://doi.org/10.1145/3180155.3180195

D-brane and exponential potentials inspire warm inflation and swampland conjecture

Shama Sadiq, Nadeem Azhar, N. Myrzakulov, S. Toktarbay et al. • Nuclear Physics B (2025)

This research explores the rapid expansion period of the early universe by applying the Chaplygin gas model, an alternative cosmological framework, to analyze the dynamics of inflationary processes. This study assesses the compatibility of ...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.nuclphysb.2025.116981

District Heating Tariffs, Economic Optimisation and Local Strategies during Radical Technological Change

Søren Djørup, Karl Sperling, Steffen Nielsen, Poul Alborg Østergaard et al. • Energies (2020)

This paper addresses economic aspects of heat savings in the context of strategic heat planning. The analysis uses the city of Aalborg, Denmark, as a case where municipalisation through a recent acquisition of a coal-fired cogeneration of h...

DOAJ Open Access (DOAJ) DOI: 10.3390/en13051172