This agent simply takes current state as an input and predict the best possible move which can make agent win.
Neural Network is made up of 4 hidden layers including one input layer and one output layer. Each layer is having 26 neurons in it and output layer have 7 neurons. This 7 neurons represents columns of a board and Neural network gives best possible column to play move. Softmax_cross_entropy is used for cost estimation and AdamOptimizer for optimization of cost of neural network while back propagating. Neural Network is trained using random rollout agent played against another one with different walks. Model is trained on 150,000 games which are won by AI agent.
This neural network returns vector of size 7 which contain probability values for each column and we can select highest one to make it closer to win.
We have developed an Artificial Intelligence model which is one of the best applications of AI (Artificial Intelligence) for pharmaceutical companies. This model take drug names as inputs and according to chemical and biological features and examining different types of drugs like enzymes, chemical etc. It also predicts side effects which are associated with new drugs which can be made from two or more drugs. So, let’s see some insight about this model. How this model is working and what are the features and concepts are used to make this model work in an efficient way.
This model is built using deep neural networks which is multi-perceptron model. Here by every neuron we mean some kind of function and activation function for that layer. These neurons are inspired by human brain and learns new things in the same way.
For training our model of neural nets data of 832 Medicines is used where each medicine have 40260 features. We have used two hidden layers in our program which 2200 and 202 neurons in each layer consecutively.
OpenCV is the most popular library for computer vision. Originally written in C/C++, it now provides bindings for Python.
OpenCV uses machine learning algorithms to search for faces within a picture. Because faces are so complicated, there isn’t one simple test that will tell you if it found a face or not. Instead, there are thousands of small patterns and features that must be matched.
Though the theory may sound complicated, in practice it is quite easy. The cascades themselves are just a bunch of XML files that contain OpenCV data used to detect objects. You initialize your code with the cascade you want, and then it does the work for you.
Before we jump into the process of face detection, let us learn some basics about working with OpenCV. In this section we will perform simple operations on images using OpenCV like opening images, drawing simple shapes on images and interacting with images through callbacks. This is necessary to create a foundation before we move towards the advanced stuff.
In my program, I have taken some images for training my model on the images of particular person with different orientations.
I built the program in three stages which are :
A south Indian cable operator service provider wanted to expand his business with digital cable which thier customer can subscribe to and watch TV on mobile.
Zolute provided a streaming solution to the customer by RTMP broadcasting . this allows customer to subscribe the channels and tv shows on their mobile phone and can watch LIve over internet . This gives cable operator a gateway to expand his business on virtual and mobile environment.
Customer Detail
Unlimited India Calling is a calling card service provider in USA. It provides cheap calling to customer to call to India. They have more than 10000 concurrent calls a day.
Business Scenario
UIC required a system to completely automate the system. which can integrate with its site and voip server. such that user can make payment and recharge from the portal and the system should select calling Switch and create account accordingly.
Zolute’s Solution
Zolute built up a cart which has integration with their ASTPP Switch and voip server. Zolute built up the communication API. This helped the customer to achieve full automation.
Epic Research is a stock tip company , which provides stock market tips and consultancy to Indian and middle east customers. They needed a CRM to have realtime inforgraphics of the employees . and make the leads more productive.
Customer
Epic Research is 2007 established company in Indore, Madhya Pradsh with over 400+ employees and 50 Million contacts .
Business Scenario & Solution
Epic Research needed a fully integrated CRM to achieve highest productivity and reporting . Zolute helped with smart CRm system which deeply integrate with its employees and users activity . It tracks calling, email and SMS and generates reports for the company department heads to track its hierarchy. It helped the company to identify possibilities and more opportunity on how grwoth can be done in sales.
Currently , the CRM is managing its billing, accounts, HR and social media . And the best part, it works on lowest carbon footprint.
