The Ersilia Model Hub, an open-source repository and CLI of AI/ML models for infectious and neglected disease research.

[MICCAI, 2024] (Oral, RU @ MedIA Best Paper Award) Official implementation of the Brain Latent Progression (BrLP) method from "Enhancing Spatiotemporal Disease Progression Models via Latent Diffusion and Prior Knowledge"

Disease Prediction based on Symptoms.

**A end to end project - Powered by Django and Machine Learning** - This project aims to provide a web platform to predict the occurrences of disease on the basis of various symptoms. The user can select various symptoms and can find the diseases and consult to the doctor online.

Plant Disease Identification Using Convulutional Neural Network

Use when a user wants applicant-facing diagnosis and revision advice on a Chinese NSFC (国自然) application draft — asks for 本子把脉, 本子体检, 国自然申请书修改建议, NSFC benzi audit, 帮我看国自然本子, 标书逻辑诊断, 青年/面上/地区基金申请书修改, 对照已中本子, 从中标样本提炼写法规律, 选题撞题核查, or 申请代码选得对不对 — or wants critique of the title, abstract, key scientific questions, rationale, research contents, innovation, feasibility, research basis, 代表作/代表性论著 quality and support, or cross-section consistency. Accepts PDF/DOCX/Markdown/text or extracted draft text. This is applicant revision advice, not formal communication-review opinions; for expert review forms use nsfc-review.

:tropical_drink: A Clojure library for talking to your database.

The major reason for the death in worldwide is the heart disease in high and low developed countries. The data scientist uses distinctive machine learning techniques for modeling health diseases by using authentic dataset efficiently and accurately. The medical analysts are needy for the models or systems to predict the disease in patients before the strike. High cholesterol, unhealthy diet, harmful use of alcohol, high sugar levels, high blood pressure, and smoking are the main symptoms of chances of the heart attack in humans. Data Science is an advanced and enhanced method for the analysis and encapsulation of useful information. The attributes and variable in the dataset discover an unknown and future state of the model using prediction in machine learning. Chest pain, blood pressure, cholesterol, blood sugar, family history of heart disease, obesity, and physical inactivity are the chances that influence the possibility of heart diseases. This project emphasizes to evaluate different algorithms for the diagnosis of heart disease with better accuracies by using the patient’s data set because predictions and descriptions are fundamental objectives of machine learning. Each procedure has unique perspective for the modeling objectives. Algorithms have been implemented for the prediction of heart disease with our Heart patient data set