PM 2.5

For Lahore Daily AQI Readings

https://twitter.com/Lahore_Air 

For Islamabad Daily AQI Readings

https://twitter.com/Islamabad_Air 

For Karachi Daily AQI Readings

https://twitter.com/Karachi_Air 

Challenges We Faced

We had multiple problems that we faced during the entire project, the solutions of which are discussed below: 

• Dataset Collection and Annotation 

Today fields of ML/DL is very successful as compare other fields of CS. The reason behind the success of this field is only one thing that is data. If we said Data is one of pillar of this field then it is not wrong. Whenever we want machine start learning we need data because machine doesn't have senses like humans. So for starting this project we also need data.

Collecting the dataset was very difficult as we required a dataset that was either of Pakistan or was near to Pakistan in terms of atmosphere and climate and sky colors. We tried to obtain dataset through multiple sources of Pakistan but we couldn’t find any. We opted to use dataset of China that we were able to find. It was appropriate as Chinese climate is similar to ours and source of our dataset is provided in Dataset section.

• Selection of Technique

Now the problem is either we apply machine learning  or Deep learning approaches or both. First we apply regression. For this we extract five features from images which are:

1. Transmission

2. Blue Colour of the Sky

3. Gradient of Sky Region

4. RMS Contrast 

5. Entropy

We extract features on MATLAB and training a linear regression model but this model doesn't predict right on any image from their dataset.

Now we use Deep Learning approach but for this dataset is not enough. So we do argumentation on images and now our dataset contain approximately 18,000 images and technique we used in argumentation are:

• Shear

• Random Zoom

• Flip Left-Right

• Translation

• Random Crop

Now we apply Transfer learning techniques on our model and train that model on our dataset. We achieved good result and deploy this model. 

• Model deployment

The biggest issue we faced was how to port the application on a mobile device as the model is too big to be run on such platforms. Two possible solutions are, either transform the model into another model, a rather lighter one, that can run on a mobile device, or serve the model and get requests from the mobile application and respond accordingly. Both the choices have its pros and cons as discussed below:

1. Port to Android

Our trained model is pretty extensive and big which makes it harder to deploy it on android/iOS platforms. If we deploy the model on mobile, we would need to convert our keras model into tensorflow and then into tensorflow lite model so its flexible and light enough to work on a mobile phone which in turn also reduces the accuracy of model. This would be the finest solution. Doing so will be entirely too time-consuming but it will make the model more efficient response-wise. Following are the steps that one can take to make the model port on mobile(Android/iOS).

i. Convert your model to tensorflow model

Either your trained your whole model into tensorflow or convert your trained keras model into tensorflow. We choose second option because we already train our model in keras but when we convert our model our accuracy drops. That's why this solution is not feasible in our case and we choose second option. To learn more about conversion, learn more meduim.

ii. .tf to .tflite

There are multiple inputs that tensorflow lite accepts like frozen graphs, saved models etc. To understand the concepts thoroughly, learn more from tensorflow website.

2. Serve The Model

The other option is to serve the model but serving it causes too long to respond. To serve the model, following steps can be used:

i. Establish a Server

First, establish a server. For us, we created a server on on of our PCs that has Ubuntu as the OS using many tutorials freely available on the internet. On sever side we use flask framework

ii. Communication 

Once the model is placed on the server, create an android application and communicate with the server using Php or other relative framework. 

Choosing framework as our option, we are able to get the respective result and we Choose Volley framework to connect our android app to server.

Achievements

Dice IET Innovation Event

Our project is selected by Dice Committee for presentation among hundreds of different Projects.

Team Members