International Collaborations
Tea is one of the most popular drinks in the world, second only to water. Originating in China contains medical properties and health care functions, and is quite effective in enhancing human immunity. Nowadays, the diagnosis of tea leaf diseases relies on the manual method, making results largely subjective and sometimes inaccurate. Machine learning and image processing methods that do not require manual intervention have been widely used in the detection and identification of plant diseases.The goal was to build models to classify tea leaf diseases with Python and Google Colab. The target audience are biological analysts and food industries.
Ask me about this project:
What are the challenges of building something for a stakeholder?
What processes did we follow to set up this Google Colab?
What was my methodology?
What is an ETL?
The Hodgkin–Huxley model was developed from studies in the relatively gigantic axon of a Squid to understand how cells of the nervous system behave. These scientists translated the cell's behavior to mathematical equations used to manipulate parameters and make predictions of not only the intracelullar environment but the interaction between nervous cells. It is challenging to translate these mathematical equations and designing a large network of cells from a small prototype to predict/understand behaviors observed in ex vivo electrophysiology experiments. The goal is to create Python code using these equations strategically to further understand the behavior of neurons represented in different cells - and develop supporting hypotheses to these questions:
How do neural oscillations emerge?
What parameters may influence up and down states in the striatum?
Ask me about this project:
What processes did we follow to set up this Google Colab?