AIMC Topic: Cryptosporidium

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Deep learning-based no-reference image quality assessment framework for Cryptosporidium spp. and Giardia spp.

PloS one
Image Quality Assessment (IQA) plays a critical role in image-based decision-making systems, especially in domains requiring high diagnostic precision. Effective feature information is a prerequisite for the high performance of machine learning metho...

Data-driven prediction of daily Cryptosporidium river concentrations for water resource management: Use of catchment-averaged vs spatially distributed features in a Bagging-XGBoost model.

The Science of the total environment
Cryptosporidium is a waterborne pathogen which poses a major challenge to water utilities because of its resistance to chlorination and its infectivity at very low concentrations. The ability to make predictions of Cryptosporidium concentrations in r...

Machine learning and explainable artificial intelligence for the prevention of waterborne cryptosporidiosis and giardiosis.

Water research
Cryptosporidium and Giardia are important parasitic protozoa due to their zoonotic potential and impact on human health, and have often caused waterborne outbreaks of disease. Detection of (oo)cysts in water matrices is challenging and extremely cost...

QSAR and deep learning model for virtual screening of potential inhibitors against Inosine 5' Monophosphate dehydrogenase (IMPDH) of Cryptosporidium parvum.

Journal of molecular graphics & modelling
Cryptosporidium parvum (Cp) causes a gastro-intestinal disease called Cryptosporidiosis. C. parvum Inosine 5' monophosphate dehydrogenase (CpIMPDH) is responsible for the production of guanine nucleotides. In the present study, 37 known urea-based co...

Deep learning-enabled imaging flow cytometry for high-speed Cryptosporidium and Giardia detection.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Imaging flow cytometry has become a popular technology for bioparticle image analysis because of its capability of capturing thousands of images per second. Nevertheless, the vast number of images generated by imaging flow cytometry imposes great cha...

An mRMR-SVM Approach for Opto-Fluidic Microorganism Classification.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The detection of microorganisms is important in numerous applications such as water quality monitoring, blood analysis, and food testing. The conventional detection methods are tedious and labour-intensive. Establish methods involve culturing, counti...