Services
I can do research and work projects in the fields mentioned below or help in writing scientific articles.
IMAGE PROCESSING
Image processing refers to the manipulation and analysis of digital images using algorithms and computational techniques. It involves various operations such as enhancing, filtering, transforming, and analyzing images to extract useful information or to improve their quality for a particular application. Image processing finds applications in various fields including medicine, remote sensing, surveillance, industrial inspection, and entertainment.
COMPUTER VISION
Computer vision is a field of artificial intelligence and computer science that focuses on enabling computers to interpret and understand visual information from the real world, much like human vision. It involves the development of algorithms and techniques to extract meaningful insights and understanding from images and videos. Computer vision systems aim to replicate and augment human visual perception capabilities to solve a wide range of tasks and applications.
MACHINE LEARNING
Machine learning is a subset of artificial intelligence (AI) that focuses on the development of algorithms and statistical models that enable computers to learn and make predictions or decisions based on data, without being explicitly programmed to perform specific tasks. In essence, machine learning algorithms learn from patterns and relationships within data to make informed decisions or predictions.
CONVOLUTIONAL NEURAL NETWORK
A Convolutional Neural Network (CNN) is a type of deep neural network commonly used in tasks involving image analysis and recognition. CNNs are specifically designed to process spatial data, such as images, by leveraging the spatial structure present in the data.
RECURRENT NEURAL NETWORK
Recurrent Neural Networks (RNNs) are a class of neural networks designed to model sequential data by maintaining internal state or memory. Unlike traditional feedforward neural networks, which process inputs independently, RNNs have connections that form directed cycles, allowing them to exhibit temporal dynamics and process sequences of inputs.
GENERATIVE ADVERSARIAL NETWORK
GAN stands for Generative Adversarial Network. It is a type of artificial intelligence framework introduced by Ian Goodfellow and his colleagues in 2014. GANs consist of two neural networks, namely the generator and the discriminator, which are trained simultaneously through a competitive process.