Expert Systems

Our Expert system uses human expertise to make complex decisions. We simulate reasoning by applying knowledge and interfaces. Expert system was introduced by researchers at Stanford University of the computer science department. The components are 1.) Knowledgebase because knowledge is required to show the intelligence which is gathered with the help of data, information and past experiences. 2) The inference engine is used to drive the knowledge from the knowledge base to reach the required solution. 3) The user interface is a part of natural language processing and provides the interface between the Expert system and User.

  • Information management
  • Hospitals and medical facilities
  • Employee performance evaluation
  • Process monitoring and control
  • Stock market trading analysis
  • Loan analysis


Natural Language Processing

The processing of natural language has always been one of the significant research issues of artificial intelligence. Here we are applying computational techniques in the form of artificial intelligence algorithms for the analysis and synthesis of natural language as well as speech. The analysis and synthesis are of a large amount of natural language data. The analyzed and synthesized natural language or human language is used to draw insights, create advertisements, language modelling, speech recognition etc

  • Text Classification
  • Language Modeling
  • Speech Recognition
  • Caption Generation
  • Machine Translation
  • Question Answering



Application of artificial intelligence in kinematics, dynamics, control, intelligent machines and systems, simulation of robots etc. The visual odometry is used to describe the motion of an object with kinematics without considering the forces applied to the object. Intelligent machines like humanoid robots are part of machine intelligence that can resolve many problems


An artificial neuron or perceptron takes several inputs and performs a weighted summation to produce an output. A simple neural network consists of a node which takes in input or a list of inputs and performs a transformation. The weight of perceptron is determined during the training process and is based on training data. The inputs are weighted and summed. The sum is then passed through a unit step function in case of the binary classification problem. Perception can only learn simple functions by learning weights from examples. The process of learning weights is called training. The training on perception can be done using Gradient-based methods. The output of perceptron will be passed through an activation function or transfer function.


Computer Vision

Application of deep learning in the monitoring of security and surveillance, commercial video motion detection, Intelligence scene monitoring system, Automatic number plate recognition, Optical character recognition, Real-time 3D scanning system, Autonomous vehicle driving, Image and video creation etc.