Collaborating Authors

Indian Currency Notes Classifier -- on cAInvas


Currency notes have identifiers that allow the visually impaired to identify them easily. This is a learned skill. On the other hand, classifying them using images is an easier solution to help the visually impaired identify the currency they are dealing with. Here, we use pictures of different versions of the currency notes taken from different angles, with different backgrounds and covering different proportions. The dataset contains 195 images of 7 categories of Indian Currency Notes -- Tennote, Fiftynote, Twentynote, 2Thousandnote, 2Hundrednote, Hundrednote, 1Hundrednote.

Spoken digit recognition application on cAInvas


The audio dataset used here is a subset of the Tensorflow speech commands dataset. Each sample is 1-second long mono audio recorded at 8000 Hz. The dataset is a balanced one with 2360 samples in each class. There are many ways to represent audio data, like, waveform, MFCCs, Mel spectrograms, spectrograms and many more. Among them all, the Mel scale is a closer representation of the human audio perception than the standard scale.

Fruits Classification using Deep Learning


In this project, we will be classifying a fruit and displaying its name as output from the given photo of the fruit as input. The dataset consists of 33 selected different kinds of fruits. Each folder is named after a fruit and contains over 400 images of that fruit in different angles and lightings. Based on the given image, we need to classify the fruit as one of the 33 categories. Hence, we have trained a sequential model in keras to predict the name of the fruit with an image of a fruit as the input.

Alzheimer Detection Using CNN


Alzheimer's disease is a degenerative condition in which dementia symptoms grow over time. Memory loss is minimal in the early stages of Alzheimer's, but people with late-stage Alzheimer's lose their capacity to converse and respond to their surroundings. In this article, we have explained how Alzheimer's can be detected at an early age using Convolutional Neural Networks. Basically,the data is splitted into 2 types, test and train data. There were 5121 images in training set and 1279 in testing set. The images provided in the set were mostly quite clear and well formatted.



Fishes also known as Ichthyology, accounts for the majority of Sea Life on our planet . They range from small centimetre to many meters .It can be even said that is one of the most diversified species on the planet that we mere humans have not explored .Distinguishing them is a task that even puzzle experts let alone a normal person .But that's Exactly what we will try to accomplish here . We will be making a model to predict the fish breed of the image passed to it .We will be classifying the fish into one of 9 different categories . Our dataset contains 3 channels images of shape 288x384 . We will be using multiple Convolutional layers followed by a Max Pool layer with Dropout layer to keep our model generalized and prevent overfitting .We will be also using Image Augmentation on features such as rescaling,horizontal and vertical flips .