3d bioprinting is an innovative approach that overcomes the limitations of traditional methods for creating cell-laden biomaterials and constructs. It allows for the fabrication of complex and biologically active tiss...
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This paper introduces CompressedMediQ, a novel hybrid quantum-classical machine learning pipeline specifically developed to address the computational challenges associated with high-dimensional multi-class neuroimagin...
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ISBN:
(数字)9798331519315
ISBN:
(纸本)9798331519322
This paper introduces CompressedMediQ, a novel hybrid quantum-classical machine learning pipeline specifically developed to address the computational challenges associated with high-dimensional multi-class neuroimaging data analysis. Standard neuroimaging datasets, such as large-scale MRI data from the Alzheimer’s disease Neuroimaging Initiative and Neuroimaging in Frontotemporal dementia, present significant hurdles due to their vast size and complexity. CompressedMediQ integrates classical HPC nodes for advanced MRI pre-processing and Convolutional Neural Network (CNN)-PCA-based feature extraction and reduction, addressing the limited-qubit availability for quantum data encoding in the NISQ era. This is followed by Quantum Support Vector Machine (QSVM) classification. By utilizing quantum kernel methods, the pipeline optimizes feature mapping and classification, enhancing data separability and outperforming traditional neuroimaging analysis techniques. Experimental results highlight the pipeline’s superior accuracy in dementia staging, validating the practical use of quantum machine learning in clinical diagnostics. despite the limitations of NISQ devices, this proof-of-concept demonstrates the transformative potential of quantum-enhanced learning, paving the way for scalable and precise diagnostic tools in healthcare and signal processing.
This study introduces a novel paradigm for achieving widely tunable many-body Fano quantum interference in low-dimensional semiconducting nanostructures, beyond the conventional requirement of closely matched energy l...
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Global environmental challenges and energy crises have driven researchers to develop multifunctional and highly efficient nanomaterials. This study presents dual-functional NiMoO4 (NMO)/NiO hierarchical microsph.res t...
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Inflammation is an intrinsic protective mechanism against various forms of cellular injuries in humans;however, its undesired activation results in tissue damage and cell death. The onset of chronic inflammation and o...
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In this work, a thorough assessment of the robustness of complementary channel HfO2 ferroelectric FET (FeFET) against total ionizing dose (TId) radiation is conducted, with the goal of determining its suitability for ...
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development of an efficient technique towards the detection of miRNAs is highly significant for the timely diagnosis and prognosis of cancers. In this study, we utilized the chemical vapor deposition (CVd) technique t...
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The emerging contaminant microplastics (MPs) are getting worldwide attention for their ubiquitous occurrence and potential risk in the environment. However, the seasonal influence on freshwater MP pollution remains po...
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Most audio source separation-related studies have been speech/singing separation, and those on musical instruments separation have been relatively scant. In this research, a stacked recurrent neural network was used t...
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ISBN:
(数字)9781665403801
ISBN:
(纸本)9781665447379
Most audio source separation-related studies have been speech/singing separation, and those on musical instruments separation have been relatively scant. In this research, a stacked recurrent neural network was used to train a model to extract electric guitar sound from mixtures of electric guitar and other musical instruments. Objective assessment scores indicate that the proposed method can extract the electric guitar sounds from the musical mixtures successfully.
Environmental pollution of heavy metal(loid)s (HMs) caused adverse impacts, has become one of the emerging concerns and challenges worldwide. Metal(loid)s can pose significant threats to living organisms even when pre...
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