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cnn based on the matlab® implementation of the alexnet network  (MathWorks Inc)


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    MathWorks Inc cnn based on the matlab® implementation of the alexnet network
    Cnn Based On The Matlab® Implementation Of The Alexnet Network, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/cnn+implementation/pmc07546325-96-10-10
    Average 90 stars, based on 1 article reviews
    cnn based on the matlab® implementation of the alexnet network - by Bioz Stars, 2026-11
    90/100 stars

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    Article Title: Unsupervised learning reveals rapid gait adaptation after leg loss and regrowth in spiders.
    Article Snippet: The reported P-values for linear regression fits were corrected using the Bonferroni–Holm post hoc test for multiple comparisons (Holm, 1979) using the MATLAB implementation ‘Bonferroni-Holm Correction for Multiple Comparisons’ (https://www.mathworks.com/matlabcentral/ fileexchange/28303-bonferroni-holm-correction-for-multiplecomparisons).

    Article Title: Using electromagnetic articulography to investigate tongue, lip and jaw tremor associated with Parkinson's disease.
    Article Snippet: Voice tremor was assessed in sustained vowels using a Matlab implementation of the tremor 3.05 tremor-detection Praat script (Brückl et al., 2018).

    Article Title: Fuzzy logic applied to tunning mutation size in evolutionary algorithms.
    Article Snippet: All CMA-ES, GWO, and PSO algorithms used default parameters for their MATLAB implementation.

    Article Title: Augmenting MRI scan data with real-time predictions of glioblastoma brain tumor evolution using faster exponential time integrators
    Article Snippet: Our MATLAB implementation of the exponential integrators method is faster than the state-of-the-art MATLAB library [24] We also compare our exponential integrators simulator to the implicit dynamics simulation using the unconditionally stable Crank–Nicolson method and finite difference discretization in space.

    Article Title: Unsupervised learning reveals rapid gait adaptation after leg loss and regrowth in spiders
    Article Snippet: The reported P -values for linear regression fits were corrected using the Bonferroni–Holm post hoc test for multiple comparisons ( ) using the MATLAB implementation ‘Bonferroni-Holm Correction for Multiple Comparisons’ ( https://www.mathworks.com/matlabcentral/fileexchange/28303-bonferroni-holm-correction-for-multiple-comparisons ).

    Article Title: Organic electro-scattering antenna: Wireless and multisite probing of electrical potentials with high spatial resolution.
    Article Snippet: Modeling of cardiomyocyte action potential recording with OCEANs The cell- OCEAN interface model was adapted from a MATLAB implementation of the Luo- Rudy model (GitHub permalink: https:// github.com/meeravarshneya1234/ventricular_myocyte_models/ tree/a57aaf7a632b3808fe0fe93355c2a8d9eed16dd0/Luo_ Rudy91_Model).

    Article Title: Cost optimization for Computer Numerical Control machining workshop: A queueing modeling approach using the meta-heuristic techniques.
    Article Snippet: ISA Transactions 161 (2025) 178–199 A 0 a Contents lists available at ScienceDirect ISA Transactions journal homepage: www.elsevier.com/locate/isatrans Research article Cost optimization for Computer Numerical Control machining workshop: A queueing modeling approach using the meta-heuristic techniques Parmeet Kaur Chahal, Kamlesh Kumar ∗ Department Of Mathematics, Central University of Jammu, Samba, Jammu & Kashmir, 181143, India A R T I C L E I N F O Keywords: Computer Numerical Control (CNC) machining workshop Bernoulli’ s phase repairs Recovery policy (Q) Control F -policy Reneging Feedback Particle Swarm Optimization(PSO) Ant Colony Optimization(ACO) Flower Pollination Algorithm(FPA) A B S T R A C T This research paper introduces an innovative approach to optimize repair of failed Computer Numerical Control (CNC) machines within CNC machining workshops by leveraging queueing theory.. The proposed model addresses a spectrum of real-world scenarios encountered in manufacturing environments, including CNC machine failures, robotic server breakdowns, reneging behavior of failed CNC machines, and mechanisms to handle unsatisfactory repairs.. In this system, failed CNC machines are attended by a single robotic server following a first come first served protocol, while also accounting for potential breakdowns of the robotic server during servicing.

    Spectroscopy:

    Article Title: Functional 31 P Magnetic Resonance Spectroscopy at 9.4 T
    Article Snippet: .. For quantification, a MATLAB implementation of the AMARES [ ] algorithm included in the OXSA (Oxford Spectroscopy Analysis) toolbox [ ] was used in a multistep approach. ..



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    Performance of the developed COVID-19 detection models on the unseen dataset.

    Journal: Computers in Biology and Medicine

    Article Title: COVID-19 detection in chest X-ray images using deep boosted hybrid learning

    doi: 10.1016/j.compbiomed.2021.104816

    Figure Lengend Snippet: Performance of the developed COVID-19 detection models on the unseen dataset.

    Article Snippet: To identify the significance of exploitation of deep feature engineering, for comparison purposes, we have used a Softmax classifier-based implementation of existing CNN models as well.

    Techniques:

    Performance comparison of hybrid based DHL and Softmax classifier-based implementation of well-established CNN models.

    Journal: Computers in Biology and Medicine

    Article Title: COVID-19 detection in chest X-ray images using deep boosted hybrid learning

    doi: 10.1016/j.compbiomed.2021.104816

    Figure Lengend Snippet: Performance comparison of hybrid based DHL and Softmax classifier-based implementation of well-established CNN models.

    Article Snippet: To identify the significance of exploitation of deep feature engineering, for comparison purposes, we have used a Softmax classifier-based implementation of existing CNN models as well.

    Techniques: Comparison

    ROC curve for the proposed frameworks (DHL, DBHL), the developed and well-established CNN Models. The square bracket values represent the tolerance or error, calculated at a 95% confidence interval .

    Journal: Computers in Biology and Medicine

    Article Title: COVID-19 detection in chest X-ray images using deep boosted hybrid learning

    doi: 10.1016/j.compbiomed.2021.104816

    Figure Lengend Snippet: ROC curve for the proposed frameworks (DHL, DBHL), the developed and well-established CNN Models. The square bracket values represent the tolerance or error, calculated at a 95% confidence interval .

    Article Snippet: To identify the significance of exploitation of deep feature engineering, for comparison purposes, we have used a Softmax classifier-based implementation of existing CNN models as well.

    Techniques: