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Food Mnist Classification

#artificialintelligence

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.


Online Shopper's Intention Prediction -- on cAInvas

#artificialintelligence

How do we know if a customer is going to shop or walk away? Understanding the customers is crucial to any seller/store/online platform. This understanding can be important in convincing a customer who is just browsing to buy a product. In offline stores, the inferences derived influence the placement of objects in the store. When the same experience is translated to an online store, the sequence of web pages browsed to reach a product becomes important.


Malicious URL Detection

#artificialintelligence

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.


Fuel consumption prediction -- on cAInvas

#artificialintelligence

Predict the quantity of fuel consumed during drives. The mileage of a vehicle is defined as the average distance traveled on a specified amount of fuel. But distance is not the only factor that affects fuel consumption. Here, we take into account multiple factors like speed, temperatures inside and outside, AC, and other weather conditions like rain or sun besides distance to predict the consumption of different types of fuels during drives. Predicting the fuel consumption given distance and other factors vice versa (predicting distance given fuel) can prove useful in planning trips as well as performing real-time predictions during driving.


Document Denoising Using Deep Learning

#artificialintelligence

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.