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Chong Jun Wei Roy

RoyWeiiiii

PhD in Chemical and Environmental Engineering. Major focus in microalgae and AI incorporated research

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Python57%JavaScript14%Jupyter Notebook14%TeX14%

Top Repositories

Repositories

7
RO
RoyWeiiiii/Scope_2_A-cutting-edge-digital-approach-for-rapid-C-phycocyanin-detection-in-Spirulina-platensis

Evaluate the robustness and performance between ML and DL models in predicting the CPC concentration under various image capturing devices, types of input image datasets, and lighting conditions. The findings in our current study can overcome the bottleneck by eliminating the need for laborious manual extraction processes and reducing the time and

Python10Updated 4 weeks ago
c-phycocyanincmykcnn-regressionhslrgbspirulina-platensissvm-regressorxgboost-regressor
RO
RoyWeiiiii/Scope_3_Microalgae_shape_texture_convolution_classification

The goal of this study is to classify microalgae of different species such as Chlorella vulgaris FSP-E, Chlamydomonas reinhardtii, and Spirulina platensis, using machine learning (ML) and deep learning (DL) methods

Python43Updated 4 months ago
adaptivethresholdazure-custom-visionbilateral-filteringcanny-edge-detectionchlamydomonas-reiinhardtiichlorella-vulgarisclassificationdeep-learninggray-scale-imagesimage-processingknn-classificationmachine-learningmicroalgae-recognitionmorphological-featuresreal-time-monitoringsobel-edge-detectorspirulina-platensissvm-classifiertexture-features
RO
RoyWeiiiii/Scope_5_Microalgae-synthetic-image-generation-for-classification

No description provided.

Python10Updated 5 months ago
RO
RoyWeiiiii/Chong_Jun_Wei_Roy.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

JavaScript00Updated 6 months ago
RO
RoyWeiiiii/Scope_4_Microalgae-detection-and-instance-segmentation

This research work introduced various aspects from dataset preparation techniques to image pre-processing, model comparison, and performance analysis on the detection & instance segmentation of three microalgae class namely Chlorella vulgaris FSP-E, Chlamydomonas reinhardtii, and Spirulina platensis.

Jupyter Notebook00Updated 1 year ago
adaptive-equalizationdetectiongrayscalehistogram-equalizationinstance-segmentationmicroalgaeroboflow-datasetyolov5yolov7yolov8
RO
RoyWeiiiii/ChongJunWeiRoy.github.io

No description provided.

TeX00Updated 1 year ago
RO
RoyWeiiiii/Scope_1_Digitalised-prediction-of-blue-pigment-content-from-Spirulina-platensis

The findings in the present study will be a breakthrough for the estimation of CPC concentration from S. platensis solely based on the information provided in the image without the need to perform a prior extraction process and identification of CPC concentration using analytical equipment.

Python01Updated 1 year ago
ann-regessionc-phycocyanincmyk-colorcnn-regressionhsl-colormlp-regressorrgb-colorspirulina-platensissvm-regressor

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