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Academic Articles & Research Papers
Showing 20 of 85 academic articles
Zihao Qiu, Siyu Chen, Zixin Feng, Ruitong Luo et al. • Frontiers in Physics
(2025)
Futures, as significant financial derivatives, play a crucial role in financial markets by fulfilling price discovery functions and providing efficient risk hedging tools. Against the backdrop of geopolitical conflicts, market risk emerges ...
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Open Access (DOAJ)
DOI: 10.3389/fphy.2025.1674717
Zhadyra Alimbayeva, Chingiz Alimbayev, Kassymbek Ozhikenov, Nurlan Bayanbay et al. • Sensors
(2024)
With cardiovascular diseases (CVD) remaining a leading cause of mortality, wearable devices for monitoring cardiac activity have gained significant, renewed interest among the medical community. This paper introduces an innovative ECG monit...
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Open Access (DOAJ)
DOI: 10.3390/s24134201
Shtwai Alsubai, Mohamed Ayari, Natalia Kryvinska, Ahmad Almadhor et al. • Scientific Reports
(2026)
Abstract Cybersecurity challenges have become increasingly complex and widespread, and the risks associated with these problems are substantial, affecting thousands of individuals and organisations and being crucial to national security. As...
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Open Access (DOAJ)
DOI: 10.1038/s41598-026-40634-z
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
Rafał Kozik, Marek Pawlicki, Michał Choraś, Witold Pedrycz • Complexity
(2019)
Network and information security are regarded as some of the most pressing problems of contemporary economy, affecting both individual citizens and entire societies, making them a highlight for homeland security. Innovative approaches to ha...
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Open Access (DOAJ)
DOI: 10.1155/2019/5826737
Aditya Kuppa, Nhien‐An Le‐Khac • IEEE Transactions on Information Forensics and Security
(2021)
Machine Learning methods are playing a vital role in combating ever-evolving threats in the cybersecurity domain. Explanation methods that shed light on the decision process of black-box classifiers are one of the biggest drivers in the suc...
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DOI: https://doi.org/10.1109/tifs.2021.3117075
Mantas Lukauskas, Vaida Pilinkienė, Jurgita Bruneckienė, Alina Stundžienė et al. • Mathematics
(2022)
The outbreak of war and the earlier and ongoing COVID-19 pandemic determined the need for real-time monitoring of economic activity. The economic activity of a country can be defined in different ways. Most often, the country’s economic act...
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Open Access (DOAJ)
DOI: 10.3390/math10193461
Tianlin Zhang, Annika Marie Schoene, Shaoxiong Ji, Sophia Ananiadou • npj Digital Medicine
(2022)
Mental illness is highly prevalent nowadays, constituting a major cause of distress in people's life with impact on society's health and well-being. Mental illness is a complex multi-factorial disease associated with individual risk factors...
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DOI: https://doi.org/10.1038/s41746-022-00589-7
PMC: 8993841
Seyedmostafa Sheikhalishahi, Riccardo Miotto, Joel T. Dudley, Alberto Lavelli et al. • JMIR Medical Informatics
(2019)
BACKGROUND: Novel approaches that complement and go beyond evidence-based medicine are required in the domain of chronic diseases, given the growing incidence of such conditions on the worldwide population. A promising avenue is the seconda...
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DOI: https://doi.org/10.2196/12239
PMC: 6528438
Xinhui Liu, Jifa Chen, Qiubo Huang • Applied Sciences
(2025)
With the explosion of information, the amount of text data has increased significantly, making text categorization a central area of research in natural language processing (NLP). Traditional machine learning methods are effective, but deep...
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Open Access (DOAJ)
DOI: 10.3390/app15084236
Xia Zhao, Limin Wang, Yufei Zhang, Xuming Han et al. • Artificial Intelligence Review
(2024)
Abstract In computer vision, a series of exemplary advances have been made in several areas involving image classification, semantic segmentation, object detection, and image super-resolution reconstruction with the rapid development of dee...
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DOI: https://doi.org/10.1007/s10462-024-10721-6
Daniel Veltri, Uday Kamath, Amarda Shehu • Bioinformatics
(2018)
Motivation: Bacterial resistance to antibiotics is a growing concern. Antimicrobial peptides (AMPs), natural components of innate immunity, are popular targets for developing new drugs. Machine learning methods are now commonly adopted by w...
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DOI: https://doi.org/10.1093/bioinformatics/bty179
PMC: 6084614
Heang‐Ping Chan, Lubomir M. Hadjiiski, Ravi K. Samala • Medical Physics
(2020)
Computer-aided diagnosis (CAD) has been a major field of research for the past few decades. CAD uses machine learning methods to analyze imaging and/or nonimaging patient data and makes assessment of the patient's condition, which can then ...
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Open Access
DOI: https://doi.org/10.1002/mp.13764
PMC: 7293164
Wan Zhu, Longxiang Xie, Jianye Han, Xiangqian Guo • Cancers
(2020)
Deep learning has been applied to many areas in health care, including imaging diagnosis, digital pathology, prediction of hospital admission, drug design, classification of cancer and stromal cells, doctor assistance, etc. Cancer prognosis...
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DOI: https://doi.org/10.3390/cancers12030603
PMC: 7139576
Phillip M. Cheng, Emmanuel Montagnon, Rikiya Yamashita, Ian Pan et al. • Radiographics
(2021)
Deep learning is a class of machine learning methods that has been successful in computer vision. Unlike traditional machine learning methods that require hand-engineered feature extraction from input images, deep learning methods learn the...
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DOI: https://doi.org/10.1148/rg.2021200210
Niels Justesen, Philip Bontrager, Julian Togelius, Sebastian Risi • IEEE Transactions on Games
(2019)
In this paper, we review recent deep learning advances in the context of how they have been applied to play different types of video games such as first-person shooters, arcade games, and real-time strategy games. We analyze the unique requ...
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Open Access
DOI: https://doi.org/10.1109/tg.2019.2896986
Roger Alves de Oliveira, Math Bollen • Electric Power Systems Research
(2022)
This paper aims to introduce deep learning to the power quality community by reviewing the latest applications and discussing the open challenges of this technology. Publications covering deep learning to power quality are stratified in ter...
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DOI: https://doi.org/10.1016/j.epsr.2022.108887
Shahbaz Rezaei, Xin Liu • IEEE Communications Magazine
(2019)
Traffic classification has been studied for two decades and applied to a wide range of applications from QoS provisioning and billing in ISPs to security-related applications in firewalls and intrusion detection systems. Port-based, data pa...
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Open Access
DOI: https://doi.org/10.1109/mcom.2019.1800819
Andreas Mayr, Günter Klambauer, Thomas Unterthiner, Sepp Hochreiter • Frontiers in Environmental Science
(2016)
The Tox21 Data Challenge has been the largest effort of the scientific community to compare computational methods for toxicity prediction. This challenge comprised 12,000 environmental chemicals and drugs which were measured for 12 differen...
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DOI: https://doi.org/10.3389/fenvs.2015.00080