A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges DOI
Denis Kleyko, Dmitri A. Rachkovskij, Evgeny Osipov

et al.

ACM Computing Surveys, Journal Year: 2022, Volume and Issue: 55(9), P. 1 - 52

Published: Aug. 24, 2022

This is Part II of the two-part comprehensive survey devoted to a computing framework most commonly known under names Hyperdimensional Computing and Vector Symbolic Architectures (HDC/VSA). Both refer family computational models that use high-dimensional distributed representations rely on algebraic properties their key operations incorporate advantages structured symbolic vector representations. Holographic Reduced Representations an influential HDC/VSA model well-known in machine learning domain often used whole family. However, for sake consistency, we field. I this covered foundational aspects field, such as historical context leading development HDC/VSA, elements any model, models, transformation input data various types into vectors suitable HDC/VSA. second part surveys existing applications, role cognitive architectures, well directions future work. Most applications lie within Machine Learning/Artificial Intelligence domain, however, also cover other provide complete picture. The written be useful both newcomers practitioners.

Language: Английский

Review on COVID‐19 diagnosis models based on machine learning and deep learning approaches DOI Open Access
Zaid Abdi Alkareem Alyasseri, Mohammed Azmi Al‐Betar, Iyad Abu Doush

et al.

Expert Systems, Journal Year: 2021, Volume and Issue: 39(3)

Published: July 28, 2021

COVID-19 is the disease evoked by a new breed of coronavirus called severe acute respiratory syndrome 2 (SARS-CoV-2). Recently, has become pandemic infecting more than 152 million people in over 216 countries and territories. The exponential increase number infections rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) machine (ML), which can assist healthcare sector providing quick precise diagnosis. this paper provides comprehensive review most recent DL ML for studies are published from December 2019 until April 2021. In general, includes 200 that been carefully selected publishers, IEEE, Springer Elsevier. We classify research tracks into two categories: present public datasets established extracted different countries. measures used to evaluate methods comparatively analysed proper discussion provided. conclusion, diagnosing outbreak prediction, SVM widely mechanism, CNN mechanism. Accuracy, sensitivity, specificity measurements previous studies. Finally, will guide community on upcoming development inspire their works future development. This

Language: Английский

Citations

141
Advances in medical image analysis with vision Transformers: A comprehensive review DOI
Reza Azad, Amirhossein Kazerouni, Moein Heidari

et al.

Medical Image Analysis, Journal Year: 2023, Volume and Issue: 91, P. 103000 - 103000

Published: Oct. 19, 2023

Language: Английский

Citations

134
HiFuse: Hierarchical multi-scale feature fusion network for medical image classification DOI
Xiangzuo Huo, Gang Sun, Shengwei Tian

et al.

Biomedical Signal Processing and Control, Journal Year: 2023, Volume and Issue: 87, P. 105534 - 105534

Published: Sept. 30, 2023

Language: Английский

Citations

97
A survey on dataset quality in machine learning DOI Creative Commons

Youdi Gong,

Guangzhen Liu,

Yunzhi Xue

et al.

Information and Software Technology, Journal Year: 2023, Volume and Issue: 162, P. 107268 - 107268

Published: June 1, 2023

With the rise of big data, quality datasets has become a crucial factor affecting performance machine learning models. High-quality are essential for realization data value. This survey article summarizes research direction dataset in learning, including definition related concepts, analysis issues and risks, review dimensions metrics throughout lifecycle analyzed from perspective summarized literatures. Furthermore, this introduces comprehensive evaluation process, which includes framework with metrics, computation methods assessment These studies provide valuable guidance evaluating field can help improve accuracy, efficiency, generalization ability models, promote development application artificial intelligence technology.

Language: Английский

Citations

94
A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges DOI
Denis Kleyko, Dmitri A. Rachkovskij, Evgeny Osipov

et al.

ACM Computing Surveys, Journal Year: 2022, Volume and Issue: 55(9), P. 1 - 52

Published: Aug. 24, 2022

This is Part II of the two-part comprehensive survey devoted to a computing framework most commonly known under names Hyperdimensional Computing and Vector Symbolic Architectures (HDC/VSA). Both refer family computational models that use high-dimensional distributed representations rely on algebraic properties their key operations incorporate advantages structured symbolic vector representations. Holographic Reduced Representations an influential HDC/VSA model well-known in machine learning domain often used whole family. However, for sake consistency, we field. I this covered foundational aspects field, such as historical context leading development HDC/VSA, elements any model, models, transformation input data various types into vectors suitable HDC/VSA. second part surveys existing applications, role cognitive architectures, well directions future work. Most applications lie within Machine Learning/Artificial Intelligence domain, however, also cover other provide complete picture. The written be useful both newcomers practitioners.

Language: Английский

Citations

85