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Akademický článek

Pre-trained quantum convolutional neural network for COVID-19 disease classification using computed tomography images

Autor: Nazeh Asadoorian, Shokufeh Yaraghi, Araeek Tahmasian

Publikováno v: PeerJ Computer Science, Vol 10, p e2343 (2024)

Background The COVID-19 pandemic has had a significant influence on economies and healthcare systems around the globe. One of the most important strategies that has proven to be effective in limiting the disease and reducing its rapid spread is early

Externí odkaz: https://doaj.org/article/59a067a04ea8458386be980a6301559b

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Akademický článek

Double MAC on a Cell: A 22-nm 8T-SRAM-Based Analog In-Memory Accelerator for Binary/Ternary Neural Networks Featuring Split Wordline

Autor: Hiroto Tagata, Takashi Sato, Hiromitsu Awano

Publikováno v: IEEE Open Journal of Circuits and Systems, Vol 5, Pp 328-340 (2024)

This paper proposes a novel 8T-SRAM based computing-in-memory (CIM) accelerator for the Binary/Ternary neural networks. The proposed split dual-port 8T-SRAM cell has two input ports, simultaneously performing two binary multiply-and-accumulate (MAC)

Externí odkaz: https://doaj.org/article/55a638030cfb438b9833212a4b3c09e4

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Akademický článek

Network Anomaly Detection Using Quantum Neural Networks on Noisy Quantum Computers

Autor: Alon Kukliansky, Marko Orescanin, Chad Bollmann, Theodore Huffmire

Publikováno v: IEEE Transactions on Quantum Engineering, Vol 5, Pp 1-11 (2024)

The escalating threat and impact of network-based attacks necessitate innovative intrusion detection systems. Machine learning has shown promise, with recent strides in quantum machine learning offering new avenues. However, the potential of quantum

Externí odkaz: https://doaj.org/article/0e6826d858b745c0a451d3210f92a5a8

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Akademický článek

Intrusion detection model using optimized quantum neural network and elliptical curve cryptography for data security

Autor: Heba Kadry, Ahmed Farouk, Elnomery A. Zanaty, Omar Reyad

Publikováno v: Alexandria Engineering Journal, Vol 71, Iss , Pp 491-500 (2023)

Secure data transmission in wireless mesh networks is a necessary attribute for machine learning-based intrusion detection systems (IDS). Numerous attacks may have an adverse effect on the system's computing efficiency. In order to accurately detect

Externí odkaz: https://doaj.org/article/185fb5b2e08a4b8bb1563732a934e25d

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Akademický článek

Fingerprinting-Based Indoor Localization With Hybrid Quantum-Deep Neural Network

Autor: Paulson Eberechukwu N, Minsoo Jeong, Hyunwoo Park, Sang Won Choi, Sunwoo Kim

Publikováno v: IEEE Access, Vol 11, Pp 142276-142291 (2023)

This paper presents an approach for enhancing indoor localization accuracy using a hybrid quantum deep neural network model (H-QDNN). To improve the accuracy of indoor localization based on contemporary techniques, we employ the combined strengths of

Externí odkaz: https://doaj.org/article/512838cbf6bb48e3987c35231b34bd28

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Akademický článek

QuCardio: Application of Quantum Machine Learning for Detection of Cardiovascular Diseases

Autor: Sharanya Prabhu, Shourya Gupta, Gautham Manuru Prabhu, Aarushi Vishal Dhanuka, K. Vivekananda Bhat

Publikováno v: IEEE Access, Vol 11, Pp 136122-136135 (2023)

This research is the first of its kind to leverage the power of Quantum Machine Learning (QML) to perform multi-class classification of Cardiovascular Diseases (CVDs). We propose a novel approach that enables multi-class classification with Pegasos Q

Externí odkaz: https://doaj.org/article/ce9d14a95e064879870a37407aae4df5

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Akademický článek

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Akademický článek

Full-Learning Rotational Quaternion Convolutional Neural Networks and Confluence of Differently Represented Data for PolSAR Land Classification

Autor: Yuya Matsumoto, Ryo Natsuaki, Akira Hirose

Publikováno v: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 2914-2928 (2022)

Quaternion convolutional neural networks (QCNNs) expand the range of their applications in processing optical and polarimetric synthetic aperture radar (PolSAR) images. Conventional real-valued convolutional neural networks (RVCNNs) compress a three-

Externí odkaz: https://doaj.org/article/b4079bdaa30545a592782464bb59adc0

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Akademický článek

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