## Inspiration

COVID-19 has drastically transformed education from in-person to online. While being more accessible, e-learning imposes challenges in terms of attention for both educators and students. Attention is key to any learning experience, and it could normally be assessed approximately by the instructor from the physical feedback of students. However, it is not feasible for instructors to assess the attention levels of students in a remote environment. Therefore, we aim to build a web app that could assess attention based on eye-tracking, body-gesture, and facial expression using the Microsoft Azure Face API.

## What it does

C.L.A.A.S takes the video recordings of students watching lectures (with explicit consent and ethics approval) and process them using Microsoft Azure Face API. Three features including eye-tracking, body posture, and facial expression with sub-metrics will be extracted from the output of the API and analyzed to determine the attention level of the student during specific periods of time. An attention average score will be assigned to each learner at different time intervals based on the evaluation of these three features, and the class attention average score will be calculated and displayed across time on our web app. The results would better inform instructors on sections of the lecture that gain attraction and lose attention in order for more innovative and engaging curriculum design.

## How we built it

1. The front end of the web app is developed using Python and the Microsoft Azure Face API. Video streaming decomposes the video into individual frames from which key features are extracted using the Microsoft Azure Face API.
2. The back end of the web app is also written with Python. With literature review, we created an algorithm which assesses attention based on three metrics (blink frequency, head position, leaning) from two of the above-mentioned features (eye-tracking and body gesture). Finally, we output the attention scores averaged across all students with respect to time on our web app.

## Challenges we ran into

1. Lack of online datasets and limitation on time prevents us from collecting our own data or using machine learning models to classify attention.
2. Insufficient literature to provide quantitative measure for the criteria of each metric.
3. Decomposing a video into frames of image on a web app.
4. Lag during data collection.

## Accomplishments that we're proud of

1. Relevance of the project for education
2. Successfully extracting features from video data using the Microsoft Azure Face API
3. Web design

## What we learned

1. Utilizing the Face API to obtain different facial data
2. Computer vision features that could be used to classify attention

## What's next for C.L.A.A.S.

1. Machine learning model after collection of accurate and labelled baseline data from a larger sample size.
2. Address the subjectiveness of the classification algorithm by considering more scenarios and doing more lit review
3. Test the validity of the algorithm with more students
4. Improve web design, functionalities
5. Address limitations of the program from UX standpoint, such as lower resolution camera, position of their webcam relative to their face
## Inspiration

The inspiration behind our machine learning app that can diagnose blood diseases runs deep within us. Both of us, as teammates, have been touched by the impact of blood diseases within our own families. Witnessing our loved ones facing the challenges posed by these conditions ignited a passion to create a tool that could potentially alleviate the suffering of others. Our personal connections served as a powerful driving force, propelling us to combine our technical expertise with our heartfelt motivations. Through this app, we aim to provide timely and accurate diagnoses, ultimately contributing to better healthcare outcomes for those at risk and underscoring the importance of empathy-driven innovation.

## What it does

The web application prompts the user to upload an image of their blood cells. The application will then utilize machine learning to identify possible diseases and inform the user of their diagnosis. The possible diagnoses include sickle cell disease, thalassemia, and leukemia. If there were no distinguishable features of a listed disease, the application will inform the user that their blood cells are healthy. It also includes a very brief explanation of the diseases and their symptoms.

## How we built it

First, we used fast.ai libraries to create a machine-learning model built within Kaggle. This first step uses ResNet-18 as a neural network to train our specified model. ResNet-18 is a convolutional neural network that holds millions of reference photos in thousands of different categories and works to identify objects. Next, we trained our specific model using 8000 images of the diseases and healthy blood cells with various conditions and edge cases. This model was then implemented into a second file that uses our pre-trained model to apply to our web application. To create the web application, we used Gradio to locally host a website that could apply the machine learning model. We then refined the UI and added text to guide the user through the process.

## Challenges we ran into

One of the biggest challenges that we ran into was learning how to implement our model into Gradio. Having never applied a model to Gradio in the past, we were tasked with learning the development process and application of Gradio. Eventually, we were able to overcome the difficulties by lots of trial and error and various video tutorials regarding model application and the syntax of Gradio in python.

## Accomplishments that we're proud of

We are extremely proud of the accuracy rate yielded by our model and the intuitive nature of the web application. Having yielded an approximate 95% accuracy from our test trial images, we are thrilled with the high rate of correctness that our machine learning app has achieved. The app's user-friendly interface is designed with accessibility in mind, ensuring that individuals, medical professionals, and caregivers can navigate it with ease. Seeing our project come to fruition has deepened our conviction in the potential of technology to bridge gaps in healthcare, and it reinforces our commitment to applying our skills to causes that hold personal significance.

## What we learned

As a whole, we expanded our knowledge regarding maching learning models and web application development as a whole. Having gone through the process of creating a functioning application required the use of new concepts such as using Gradio and increasing error rate percentage rates within the model.

## What's next for Blood Cell Disease Identifier using Machine Learning

We hope to expand the web application in the future. While easy and simple to use as of now, we hope to add more to the app to increase accuracy, information retention, and advice. One of the biggest improvements to be made is increasing the variety of blood diseases detectable by our model. With more time available after this event, we will be able to advance our front regarding the number of detectable diseases.
## Inspiration

Disasters can strike quickly and without notice. Most people are unprepared for situations such as earthquakes which occur with alarming frequency along the Pacific Rim. When wifi and cell service are unavailable, medical aid, food, water, and shelter struggle to be shared as the community can only communicate and connect in person.

## What it does

In disaster situations, Rebuild allows users to share and receive information about nearby resources and dangers by placing icons on a map. Rebuild uses a mesh network to automatically transfer data between nearby devices, ensuring that users have the most recent information in their area. What makes Rebuild a unique and effective app is that it does not require WIFI to share and receive data.

## How we built it

We built it with Android and the Nearby Connections API, a built-in Android library which manages the

## Challenges we ran into

The main challenges we faced while making this project were updating the device location so that the markers are placed accurately, and establishing a reliable mesh-network connection between the app users. While these features still aren't perfect, after a long night we managed to reach something we are satisfied with.

## Accomplishments that we're proud of

WORKING MESH NETWORK! (If you heard the scream of joy last night I apologize.)

## What we learned

## What's next for Rebuild