This is a compilation of my work done at AITS.

The following are notebooks enlisting deep learning applications in IoT.

They were developed on the cAInvas platform and used the deepC compiler to optimize them for EDGE devices.

Use the links below to run the notebooks on the cAInvas platform and use the deepC compiler.

The datasets required are hosted and available through links in the ipynb files.

List of notebooks:

IPYNB file | On cAInvas | Medium article

Transcribing captcha images to text.

IPYNB file | On cAInvas | Medium article

Recognizing the digit spoken from the audio files by extracting Mel spectrogram using CNN

IPYNB file | On cAInvas | Medium article

Identifying casting defects from product images.

IPYNB file | On cAInvas | Medium article

Using continuous EEG brainwave data and Dense layers to predict the emotion experienced.

IPYNB file | On cAInvas | Medium article

Using deep learning to diagnose Covid 19 from lung CT scans.

IPYNB file | On cAInvas | Medium article

Using transfer learning to identify diseases in rice leaves.

IPYNB file | On cAInvas | Medium article

Using deep learning to differentiate between honey bees that are and aren't carrying pollen.

IPYNB file | On cAInvas | Medium article

Using CNN architecture to predict arrythmia on ECG data.

IPYNB file | On cAInvas | Medium article

Detecting parkinson's disease in patients using speech signals.

IPYNB file | On cAInvas | Medium article

Classify different type of garbage based on material (like glass, paper, metal etc.).

IPYNB file | On cAInvas | Medium article

Predicting the gender of the speaker from acoustic features extracted from a voice recording.

IPYNB file | On cAInvas | Medium article

Categorise pomegranates based on their grade/quality into 12 classes.

IPYNB file | On cAInvas | Medium article

Classifying fetal health into 3 categories using features extracted from cardiotocogram exams in order to prevent child and maternal mortality.

IPYNB file | On cAInvas | Medium article

Detect and monitor seizures in patients using EEG brain signals.

IPYNB file | On cAInvas | Medium article

Use sonar data to identify if the transmitted waves bounced off rocks or mines.

IPYNB file | On cAInvas | Medium article

Use deep learning to identify different species of hummingbirds.

IPYNB file | On cAInvas | Medium article

What is this article talking about? Find out with deep learning!

IPYNB file | On cAInvas | Medium article

Finding potential hazardous and non-hazardous near-earth asteroids.

IPYNB file | On cAInvas | Medium article

Differentiating between a mosquito bite and a tick bite using deep learning.

IPYNB file | On cAInvas | Medium article

Training a deep learning model to prescribe a drug based on the patient’s data.

IPYNB file | On cAInvas | Medium article

Training a deep learning model to respond to the presence of certain objects (here, humans) in the frame.

IPYNB file | On cAInvas | Medium article

Detecting fires in images/frames for early detection and prevention.

IPYNB file | On cAInvas | Medium article

Image tagging based on the weather of the scene.

IPYNB file | On cAInvas | Medium article

Determining the language of the written text using a deep learning model.

IPYNB file | On cAInvas | Medium article

Determining the quality of glass using features like thickness, composition, luminosity, and many others.

IPYNB file | On cAInvas | Medium article

Predicting whether a given company is under financial distress or not based on time-based data for different companies.

IPYNB file | On cAInvas | Medium article

Identifying whether the given text is spam or not (ham) using neural networks.