Suchergebnisse - A hybrid of Quantum Convolutional Autoencoder AND Quantum Orthogonal Classifier with SVM~

QCAE-QOC-SVM: A hybrid quantum machine learning model for DoS and Fuzzy attack detection on autonomous vehicle CAN bus

Autoren: Meghana R Sowmyashree Sakrepatna Ramesha Adwitiya Mukhopadhyay

Quelle: MethodsX, Vol 15, Iss , Pp 103471- (2025)

Schlagwörter: A hybrid of Quantum Convolutional Autoencoder and Quantum Orthogonal Classifier with SVM, Science

Dateibeschreibung: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2215016125003164; https://doaj.org/toc/2215-0161

Zugangs-URL: https://doaj.org/article/5286ad309eb3460d901086c120afb62a

Hybrid Quantum Neural Network Image Anti-Noise Classification Model Combined with Error Mitigation.

Autoren: Ji, Naihua Bao, Rongyi Chen, Zhao et al.

Quelle: Applied Sciences (2076-3417); Feb2024, Vol. 14 Issue 4, p1392, 16p

Schlagwörter: IMAGE recognition (Computer vision), QUANTUM gates, QUANTUM computers, FEATURE extraction, QUANTUM states, IMAGE compression

QSurfNet: a hybrid quantum convolutional neural network for surface defect recognition.

Autoren: Mishra, Shraddha Tsai, Chi-Yi

Quelle: Quantum Information Processing; May2023, Vol. 22 Issue 5, p1-28, 28p

Schlagwörter: CONVOLUTIONAL neural networks, SURFACE defects, DEEP learning, SUPERVISED learning, PRINCIPAL components analysis, IMAGE recognition (Computer vision)

Firma/Körperschaft: NORTHEASTERN University (Boston, Mass.)

A DEEP LEARNING CNN APPROACH WITH UNIFIED FEATURE EXTRACTION FOR BREAST CANCER DETECTION AND CLASSIFICATION.

Autoren: GANDHI, ONGOLE S. N., TIRUMALA RAO KRISHNA PRASAD, MUNAGA H. M.

Quelle: i-Manager's Journal on Image Processing; Jun2025, Vol. 12 Issue 2, p1-15, 15p

Schlagwörter: CONVOLUTIONAL neural networks, DEEP learning, COMPUTER-aided diagnosis, SUPPORT vector machines, BREAST cancer

Network intrusion detection based on variational quantum convolution neural network.

Autoren: Gong, Changqing Guan, Weiqi Zhu, Hongsheng et al.

Quelle: Journal of Supercomputing; Jun2024, Vol. 80 Issue 9, p12743-12770, 28p

Schlagwörter: CONVOLUTIONAL neural networks, MACHINE learning, MEASUREMENT errors, DECISION trees, SUPPORT vector machines, K-nearest neighbor classification

Quantum computing for space applications: a selective review and perspectives.

Autoren: Torta, Pietro Casati, Rebecca Bruni, Stefano et al.

Quelle: EPJ Quantum Technology; 6/5/2025, Vol. 12 Issue 1, p1-76, 76p

Schlagwörter: QUANTUM annealing, IMAGE recognition (Computer vision), QUANTUM computing, SPACE sciences, DISTRIBUTED computing

Quantum Machine Learning: A Review and Case Studies.

Autoren: Zeguendry, Amine Jarir, Zahi Quafafou, Mohamed

Quelle: Entropy; Feb2023, Vol. 25 Issue 2, p287, 41p

Schlagwörter: MACHINE learning, SCIENTIFIC literature, COMPUTERS, CONVOLUTIONAL neural networks, QUANTUM computing, QUANTUM computers

Introducing Machine Learning in Teaching Quantum Mechanics.

Autoren: Pawelkiewicz, M. K. Gatti, Filippo Clouteau, Didier et al.

Quelle: Atoms (2218-2004); Jul2025, Vol. 13 Issue 7, p66, 24p

Schlagwörter: MACHINE learning, QUANTUM mechanics, PYTHON programming language, SCHRODINGER equation, SCIENCE education, ARTIFICIAL neural networks

People: SCHROEDINGER, Erwin, 1887-1961

Entanglement Detection and Quantification Through Machine Learning: A Comprehensive Review.

Autoren: Varela, Joab M. de Palhares Jr, Alberto B. Duarte, Diogo H. G.

Quelle: Brazilian Journal of Physics; Feb2026, Vol. 56 Issue 1, p1-27, 27p

Structured light meets machine intelligence.

Autoren: Zhang, Zilong Kong, Lingyu Zhang, Lianghaoyue et al.

Quelle: eLight; 9/29/2025, Vol. 5 Issue 1, p1-35, 35p

Schlagwörter: ARTIFICIAL intelligence, OPTICAL devices, DETECTION algorithms, QUANTUM theory, IMAGING systems

Quantum Machine Learning: Exploring the Role of Data Encoding Techniques, Challenges, and Future Directions.

Autoren: Ranga, Deepak Rana, Aryan Prajapat, Sunil et al.

Quelle: Mathematics (2227-7390); Nov2024, Vol. 12 Issue 21, p3318, 32p

Schlagwörter: QUANTUM computing, MACHINE learning, RESEARCH personnel, ENCODING, SCALABILITY

Optimizing network bandwidth slicing identification: NADAM-enhanced CNN and VAE data preprocessing for enhanced interpretability.

Autoren: Islam, Md. Fahim Ul Hossain, Shahriar Alam, Md. Golam Rabiul et al.

Quelle: PLoS ONE; 10/21/2025, Vol. 20 Issue 10, p1-38, 38p

Schlagwörter: ARTIFICIAL intelligence, BANDWIDTH allocation, ELECTRONIC data processing, CONVOLUTIONAL neural networks, QUALITY of service, OPTIMIZATION algorithms, LATENT variables

Intelligent understanding of spectra: from structural elucidation to property design.

Autoren: Feng, Shuo Huang, Meng Li, Yanbo et al.

Quelle: Chemical Society Reviews; 9/21/2025, Vol. 54 Issue 18, p8243-8286, 44p

Schlagwörter: ARTIFICIAL intelligence, SPECTRUM analysis, CHEMICAL fingerprinting, SPECTROMETRY, PREDICTION models, QUANTUM chemistry, REVERSE engineering

A Robust Rolling Bearing Fault Diagnosis Approach Using Hybrid Red Deer Optimization Based 1D-CNN.

Autoren: Joseph, Thomas U., Sudeep K., Keerthi Krishnan et al.

Quelle: International Journal of Acoustics & Vibration; Sep2025, Vol. 30 Issue 3, p272-283, 12p

Schlagwörter: ROLLER bearings, FAULT diagnosis, SIGNAL separation, SIGNAL processing, METAHEURISTIC algorithms, SIGNAL denoising, FRACTAL analysis, CONVOLUTIONAL neural networks

Advancements in brain tumor analysis: a comprehensive review of machine learning, hybrid deep learning, and transfer learning approaches for MRI-based classification and segmentation.

Autoren: Das, Surajit Goswami, Rajat Subhra

Quelle: Multimedia Tools & Applications; Jul2025, Vol. 84 Issue 23, p26645-26682, 38p

Schlagwörter: BRAIN tumors, MACHINE learning, DEEP learning, MAGNETIC resonance imaging, ARTIFICIAL neural networks, IMAGE segmentation, COMPUTER-assisted image analysis (Medicine), TRANSFER of training

ABO optimized hybrid Trans-CNN-Bi-GRU approach for intrusion detection in IoT networks: a privacy-preserving solution.

Autoren: Guru, R. Pavithra Chen, Thomas M. Sathiyanarayanan, Mithileysh

Quelle: Cluster Computing; Feb2026, Vol. 29 Issue 1, p1-31, 31p

Dimensionality Reduction for Data Analysis With Quantum Feature Learning.

Autoren: Sihare, Shyam R.

Quelle: WIREs: Data Mining & Knowledge Discovery; Mar2025, Vol. 15 Issue 1, p1-61, 61p

Schlagwörter: DIMENSIONAL reduction algorithms, DATA analysis, PATTERN perception, COMPARATIVE studies, QUANTUM computing, QUANTUM information science, DATA quality

AI-driven multi-omics integration in precision oncology: bridging the data deluge to clinical decisions.

Autoren: Hsu, Chou-Yi Askar, Shavan Alshkarchy, Samer Saleem et al.

Quelle: Clinical & Experimental Medicine; 11/21/2025, Vol. 26 Issue 1, p1-22, 22p

Schlagwörter: MULTIOMICS, DATA integration, CLINICAL decision support systems, ONCOLOGY, DEEP learning, CANCER diagnosis, INDIVIDUALIZED medicine, ARTIFICIAL intelligence

A brief review on quantum computing based drug design.

Autoren: Das, Poulami Ray, Avishek Bhattacharyya, Siddhartha et al.

Quelle: WIREs: Data Mining & Knowledge Discovery; Nov2024, Vol. 14 Issue 6, p1-36, 36p

Schlagwörter: DRUG development, DRUG design, OPTIMIZATION algorithms, QUANTUM computing, COMPUTER-aided design

Climate Change Through Quantum Lens: Computing and Machine Learning.

Autoren: Rahman, Syed Masiur Alkhalaf, Omar Hamad Alam, Md Shafiul et al.

Quelle: Earth Systems & Environment; Sep2024, Vol. 8 Issue 3, p705-722, 18p