Collaborating Authors

Malicious URL Detection


A malicious website is a site that attempts to install malware (a general term for anything that will disrupt computer operation, gather your personal information or, in a worst-case scenario, gain total access to your machine) onto your device.So it is necessary to detect malicious websites or URLs and it can be achieved by training a deep learning model to classify Malicious and Non-Malicious URL. While working on cAInvas one of its key features is UseCases gallery. Since the Malicious URL Detection model is also a part of cAInvas gallery we don't have to look for data manually.We can load the data in a dataframe by using pandas library, we just have to enter the following commands: Running the above command will load the data in a dataframe which we will use for model training. To prevent the class imbalance we oversampled our data using the SMOTE module of imblearn library.We also got to know that some features were already extracted from the URL for classification and stored in ourcsv file. Once we are done analyzing our data we will create the trainset and testset which will contain the feature vector along with the labels for our model training.It can be done by executing the following commands: After creating the dataset next step is to pass our training data into our Deep Learning model to learn to learn to classify URLs.

Document Denoising Using Deep Learning


Often while working with pdfs and docs the most common problem faced by all of us are that several pages are not clearly visible or due to any background the texts are not clearly visible. If the Document Denoising deep learning model is coupled with our camera or any pdf capturing application it can prove to be very useful. Not only we can train our deep learning model using Tensorflow,Keras or Pytorch, we can also compile our model with its edge compiler called DeepC to deploy our working model on edge devices for production. The Document Denoising project is also a part of cAInvas gallery. All the dependencies which you will be needing for this project are also pre-installed.

Food Mnist Classification


This data set consists of 10 food categories, with 5,000 images. For each class, 125 manually reviewed test images are provided as well as 375 training images. On purpose, the training images were not cleaned, and thus still contain some amount of noise. This comes mostly in the form of intense colors and sometimes wrong labels. All images were rescaled to have a maximum side length of 512 pixels.

Brain Tumor Detection


We all are aware of the severeness of Cancer.It is estimated that nearly 18,000 adults die due to Brain Tumor and the survival rate tells us that if detected late then the person dies within the span of 5 years.So, it is necessary that we devise a technique for early detection of the brain tumor and in today's Modern World we have the power of AI to help us in the early diagnosis of these tumors. In today's world with the help of deep learning we can develop a Brain Tumor Detection app which can just by looking at your Brain CT scan would let you know the probability of you having Brain Tumor. While working on cAInvas one of its key features is UseCases Gallary.When working on any of its UseCases you don't have to look for data manually.As they have the feature to import the dataset to your workspace when you work on them.To load the data we just have to enter the following commands: Running the above code in your notebook will load the labelled brain tumor data in your workspace. It is generally suggested that while working on image data it is better to introduce some augmentations in the data like flipping the image, rotating the image by some angles, changing the brightness of the image, etc.Since our deep learning model deals these images in the form of pixel values, so our model thinks these augmented data as new set of data and it improves the performance of the model.For image augmentations we can make a function and pass the image data through it and save them in our directory. As the CT scans contain the images of the brain in the center surrounded by the blank area so in this step we would find the region that contains the image of the brain and crop the rest from the image.For this we would create a function.

Hate Speech and Offensive Language Detection


Nowadays we are well aware of the fact that if social media platforms are not handled carefully then they can create chaos in the world. One of the problems faced on these platforms are usage of Hate Speech and Offensive Language. Usage of such Language often results in fights, crimes or sometimes riots at worst. So, Detection of such language is essential and as humans cannot monitor such large volumes of data, we can take help of AI and detect the use of such language and prevent users from using such languages. All the dependencies which you will be needing for this project are also pre-installed.