Computer Vision using Yolo-v4 and Data Augmentation techniques

Object Detection : is a computer technology related to Computer vision and Image Processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos.

Classification : is a process related to categorization,the process in which ideas and objects are recognized, differentiated and understood.

Localization : Placing a rectangular box for classified objects.

Entity Recognition on images using OCR

An entity can be a word or series of words that consistently refer to the same thing. Every detected entity is classified into a prelabelled category.
For example, a NER model might detect the word "London" in a text and classify it as a 'Geography'.

But we need to find the entities from the images. So for this purpose, we need to extract text from the images, so for extracting text we are a technique called OCR.

OCR stands for Optical Character Recognition. It is widespread technology to recognize text inside images, such as scanned documents and photos. OCR is used to convert any kind of image containing text like(typed handwritten or printed) into machine-readable text format.

Generating Abstract images using Neural Networks

Artificial neural networks are comprised of node layers containing an input layer, one or more hidden layers, and an output layer. Each node connects to another and has an associated weight and threshold value.

For generating random colored images we will develop an ANN architecture that takes each pixel value as an input. All inputs are modified by weight and summed. For handling the n-dimensional data as an input we can use the techniques mentioned below.

Image and Video colorization using Deep learning and OpenCV

Converting color images to black and white is quite easy using the OpenCV framework,but for converting black and white images to color we need to take a different approach. We need to add colors to black and white pixels using Deep Learning Neural Networks. Let’s first look at how the color information is represented in gray scale images and digital images.

Gray scale image means the value of each pixel represents only the intensity information of the light. Such images commonly display only the darkest black to the brightest white. The image carries only black, white, and gray colors, in which gray has multiple levels.Each pixel typically consists of 8 bits(1byte) for gray scale images and there are 256 possible grayscale colors.

Multi-label classification using Transfer Learning techniques

COVID-19 is a contagious disease that caused thousands of deaths and infected millions worldwide. Using convolutional neural networks and transfer learning techniques we develop a highly accurate model to detect whether patients have been infected with covid-19, some other virus, or nothing. This work mainly focuses on the use of CNN models for classifying chest X-ray images for coronavirus, viral infected, and normal patients.

Finally using fine-tuning VGG16, VGG19, and ResNet50 deep learning techniques in terms of extracting information from the data it was observed that VGG16 performed well in classifying covid, Normal, and viral pneumonia. However, excellence in high performance remained besides VGG16 with high precision and high model accuracy.

Price Predictions for NFT Images

An NFT is an image in the form of a digital token. These tokens are then sold online. AN NFT can be anything in digital form. Every NFT image has its own price and we implement Machine learning techniques to predict the price for the NFT images.

For implementing this problem statement we collected the images and related prices using selenium script around 5000 images from Multiple sources. To extract the information from all the images we used OpenCV and NumPy and we collected the pixel intensities from every image. Hence there are in RGB color space we converted 3-dimensional data into one dimensional which works as independent data for machine learning Algorithms and Prices of each image as a dependent feature.

Summary extraction of an article using experimental NLP techniques

Summary Extraction is the technique for generating meaningful and complete information of the text while focusing on the sections that convey useful information, and without losing the overall meaning of the text. Summary Extraction aims to transform lengthy Text into shortened versions, which makes it easy to understand.

What is Next Word Prediction?

As technology becomes more advanced, we are offered privileges we could not have ever imagined and things that make our daily activities easier. Every single thing that we express has some value that can be extracted and converted into information.

Next word prediction, also referred as Language Modelling, is the process of predicting what word or multiple words come next in an application involving the basic task of typing.

We have been gradually accustomed to our mobile keyboards predicting the next word without even realizing it. Nowadays, emails and text based applications provides users with the ability to integrate this option directly.

Taking it to the next level, the data scientists at Bluetick Consultants have developed a model which predicts the next 5 words in a sentence, whenever you hit the spacebar.

IMDB Movie Review Sentiment Analysis

The act of computationally recognising and categorising opinions contained in a piece of text, especially in order to discern whether the writer has a good, negative, or neutral attitude toward a given topic, product, etc.

Sentiment analysis is a technique for analysing a piece of text to determine the sentiment contained within it. It accomplishes this by combining machine learning and natural language processing (NLP).

This project is about movie reviews sentiment analysis based on Machine Learning, NLP, and LSTM models.

Named Entity Recognition

The goal of Named Entity Recognition is to locate and classify named entities in a sequence. The named entities are pre-defined categories chosen according to the use case such as names of people, organizations, places, codes, time notations, monetary values, etc. Essentially, NER aims to assign a class to each token (usually a single word) in a sequence. Because of this, NER is also referred to as token classification.

Retrieval chatbot using Natural Language Processing

In this Python project, we are going to build a chatbot using deep learning techniques. The chatbot will be trained on the dataset that contains categories (intents), pattern and responses. We use a special recurrent neural network (LSTM) to classify which category the user’s message belongs to and then we will give a random response from the list of responses.