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 machine learning part2


Working with Hyperspheres in Machine Learning part2

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Abstract: We consider the reflection of a photon by a two-level system in a quasi-one-dimensional waveguide. This is important in part because it forms the backdrop for more complicated proposals where many emitters are coupled to the waveguide: leading to super and subradiant coupling even when the emitters are distant. The incorporation of chiral effects, for example unidirectional emission of dipole emitters, has already led to rich physics such as dimer coupling. However, chirality is not the only effect of the dipole, as we explore from a phase singularity perspective. We demonstrate that control of the dipole allows a rich variety of control of the phase and amplitude of the scattered light in both directions. This expands the scope for the physics of 1D chains of emitters.


Dealing with Various Cancers using Machine Learning part2(AI Health Care Series)

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Abstract: he paper proposes a novel hybrid discovery Radiomics framework that simultaneously integrates temporal and spatial features extracted from non-thin chest Computed Tomography (CT) slices to predict Lung Adenocarcinoma (LUAC) malignancy with minimum expert involvement. Lung cancer is the leading cause of mortality from cancer worldwide and has various histologic types, among which LUAC has recently been the most prevalent. LUACs are classified as pre-invasive, minimally invasive, and invasive adenocarcinomas. Timely and accurate knowledge of the lung nodules malignancy leads to a proper treatment plan and reduces the risk of unnecessary or late surgeries. Currently, chest CT scan is the primary imaging modality to assess and predict the invasiveness of LUACs.