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machine learning model  (CH Instruments)

 
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    CH Instruments machine learning model
    Machine Learning Model, supplied by CH Instruments, 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/deep+learning+(dl)+models/deep+learning+model/pmc10958024-235-14-10
    Average 90 stars, based on 1 article reviews
    machine learning model - by Bioz Stars, 2026-11
    90/100 stars

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    Related Articles

    other:

    Article Title: Dynamic optimisation for graded tissue scaffolds using machine learning techniques
    Article Snippet: Dynamic optimisation for graded tissue scaffolds using machine learning techniques Chi Wu a, Boyang Wan a, Yanan Xu a, D S Abdullah Al Maruf b,c, Kai Cheng d, William T Lewin e,f, i, Jianguang Fang g, Hai Xin b,c, Jeremy M Crook e,f,h,i, Jonathan R Clark b,c,d, Grant P Steven a, Qing Li a,* a School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, NSW 2006, Australia b Integrated Prosthetics and Reconstruction, Department of Head and Neck Surgery, Chris O’Brien Lifehouse, Camperdown, NSW 2050, Australia c Central Clinical School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2050, Australia d Royal Prince Alfred Institute of Academic Surgery, Sydney Local Health District, Camperdown, NSW 2050, Australia e Arto Hardy Family Biomedical Innovation Hub, Chris O`Brien Lifehouse, Camperdown, NSW 2050, Australia f Sarcoma and Surgical Research Centre, Chris O’Brien Lifehouse, Camperdown, Australia g School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia h Intelligent Polymer Research Institute, AIIM Facility, The University of Wollongong, Wollongong, Australia i School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, Australia A R T I C L E I N F O Keywords: Functionally-graded tissue scaffolds Dynamic topology optimisation Machine learning Sheep mandible Bone remodelling Homogenisation A B S T R A C T Tissue scaffolds have emerged as a promising solution for treatment of critical size bone defects, offering significant advantages over conventional strategies.

    Article Title: Detecting cognitive engagement in online course forums: A review of frameworks and methodologies
    Article Snippet: A key aspect of online learning in higher education involves the utilization of course discussion forums.. Assessing the quality of posts, such as cognitive engagement, within online course discussion forums, and determining students’ interest and participation is challenging yet beneficial.. This research investigates existing literature on identifying the cognitive engagement of online learners through the analysis of course discussion forums.

    Article Title: Development of criteria to optimize manual smear review of automated complete blood counts using a machine learning model.
    Article Snippet: Overall, the combined CCG + Machine Learning Model significantly improved the MSR rate from the current practice of reviewing all smears (P <3 × 10−160, Chi- Square test).

    Article Title: Covid‐19 cases prediction using SARIMAX Model by tuning hyperparameter through grid search cross‐validation approach
    Article Snippet: Machine learning has been extensively used in forecasting or predicting future trends (Han & Chi, n.d.).

    Article Title: Distributed ledger based document image extracting and processing within an enterprise system
    Article Snippet: Various systems of this architecture have been described in this disclosure, including SyBR, RECON, PAIDOL, AI-Machine Learning (CHI), etc.

    Diagnostic Assay:

    Article Title: Prediction of the local treatment outcome in patients with oropharyngeal squamous cell carcinoma using deep learning analysis of pretreatment FDG-PET images
    Article Snippet: .. The deep learning model’s diagnostic performance with the highest AUC was subsequently analyzed to compare to T-stage, clinical stage, and a multivariate clinical model. First, this highest AUC value provided by the specific deep learning model was compared to that in T-stage and clinical stage, respectively, using the chi-square test. ..

    Concentration Assay:

    Article Title: Optimized machine learning model for air quality index prediction in major cities in India
    Article Snippet: Rakholia et al. analyzed the air pollution concentration in Ho Chi Minh City using machine learning model. .. Rakholia et al. analyzed the air pollution concentration in Ho Chi Minh City using machine learning model. ..



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    Image Search Results


    Characteristics of the reviewed studies focusing on the utilization of AI technology in the ASEAN region until May 2024

    Journal: BMC Cancer

    Article Title: Artificial intelligence utilization in cancer screening program across ASEAN: a scoping review

    doi: 10.1186/s12885-025-14026-x

    Figure Lengend Snippet: Characteristics of the reviewed studies focusing on the utilization of AI technology in the ASEAN region until May 2024

    Article Snippet: Hamid et al. (2024) [ ] , Malaysia , Breast , , Application of Artificial Intelligence (AI) System in Opportunistic Screening and Diagnostic Population in a Middle-income Nation , , Retrospective cross-sectional study , Deep learning model (DL)- Lunit INSIGHT MMG (version 1.1.7.2, Lunit, South Korea) , Silent trial.

    Techniques: Imaging, Genome Wide, Methylation, Modification, Diagnostic Assay

    Architectures of (A) U-Net and (B) DeepLab used in this study. Adapted from and , respectively. Training results showing accuracy and loss values for (C) U-Net and (D) DeepLab.

    Journal: Osteology (Basel, Switzerland)

    Article Title: Shoulder Bone Segmentation with DeepLab and U-Net

    doi: 10.3390/osteology4020008

    Figure Lengend Snippet: Architectures of (A) U-Net and (B) DeepLab used in this study. Adapted from and , respectively. Training results showing accuracy and loss values for (C) U-Net and (D) DeepLab.

    Article Snippet: We have implemented two-dimensional (2D) U-Net [ ] and DeepLab v3 [ ] deep learning (DL) models in Matlab with Deep Learning Toolbox (R2021b) to perform the segmentation of shoulder ZTE MR images.

    Techniques:

    Segmentation results on test images. (A, D) Ground truth or manually segmented images of humeral bone and the remaining other tissues shown for comparison. Output segmented images of (B, C) the humeral head (E, F) and the remaining tissues after DL segmentation performed by (B,E) U-Net and (C,F) DeepLab. Qualitatively, U-Net slightly over-estimated area for humeral head while DeepLab slightly under-estimated. (G) Input ZTE MRI image is shown. (H, I) Segmented ZTE images (from the ground truth; A and D) were used to create separate 3D renderings of the (H) humerus and (I) glenoid / scapular bone.

    Journal: Osteology (Basel, Switzerland)

    Article Title: Shoulder Bone Segmentation with DeepLab and U-Net

    doi: 10.3390/osteology4020008

    Figure Lengend Snippet: Segmentation results on test images. (A, D) Ground truth or manually segmented images of humeral bone and the remaining other tissues shown for comparison. Output segmented images of (B, C) the humeral head (E, F) and the remaining tissues after DL segmentation performed by (B,E) U-Net and (C,F) DeepLab. Qualitatively, U-Net slightly over-estimated area for humeral head while DeepLab slightly under-estimated. (G) Input ZTE MRI image is shown. (H, I) Segmented ZTE images (from the ground truth; A and D) were used to create separate 3D renderings of the (H) humerus and (I) glenoid / scapular bone.

    Article Snippet: We have implemented two-dimensional (2D) U-Net [ ] and DeepLab v3 [ ] deep learning (DL) models in Matlab with Deep Learning Toolbox (R2021b) to perform the segmentation of shoulder ZTE MR images.

    Techniques: Comparison

    DL model performances compared. Boxplots of inference accuracy (Dice score, sensitivity, specificity) quantified on the humeral bone (A) and the remaining tissue (B), determined using U-Net (blue) and DeepLab (red) models. Marked differences in the accuracy metrics for the humeral bone was noted.

    Journal: Osteology (Basel, Switzerland)

    Article Title: Shoulder Bone Segmentation with DeepLab and U-Net

    doi: 10.3390/osteology4020008

    Figure Lengend Snippet: DL model performances compared. Boxplots of inference accuracy (Dice score, sensitivity, specificity) quantified on the humeral bone (A) and the remaining tissue (B), determined using U-Net (blue) and DeepLab (red) models. Marked differences in the accuracy metrics for the humeral bone was noted.

    Article Snippet: We have implemented two-dimensional (2D) U-Net [ ] and DeepLab v3 [ ] deep learning (DL) models in Matlab with Deep Learning Toolbox (R2021b) to perform the segmentation of shoulder ZTE MR images.

    Techniques:

    Mean and standard deviation of the Dice scores, sensitivity, and specificity values. P-values from t-tests indicate statistical difference between the mean values obtained using U-Net vs. DeepLab.

    Journal: Osteology (Basel, Switzerland)

    Article Title: Shoulder Bone Segmentation with DeepLab and U-Net

    doi: 10.3390/osteology4020008

    Figure Lengend Snippet: Mean and standard deviation of the Dice scores, sensitivity, and specificity values. P-values from t-tests indicate statistical difference between the mean values obtained using U-Net vs. DeepLab.

    Article Snippet: We have implemented two-dimensional (2D) U-Net [ ] and DeepLab v3 [ ] deep learning (DL) models in Matlab with Deep Learning Toolbox (R2021b) to perform the segmentation of shoulder ZTE MR images.

    Techniques: Standard Deviation