ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING PROJECTS
1. DETECTION OF MEASUREMENT ANOMALIES FROM REAL-TIME SENSORS AND MACHINES
EUROPEAN PROJECT: ONLINE ARTIFICIAL INTELLIGENCE FOR IT OPERATIONS CODE: NORTE-01-0247-FEDER-070104
The Project intends to develop methods of collecting and processing in real time large volumes of events generated by infrastructures and applications, allowing uses as diverse as analysing and predicting anomalies, isolating relevant events, identifying interactions between components, automating routine tasks according to identified situations, detect and diagnose application performance problems, among many others.
(2) A QUICK VIDEO REPRESENTATION:
WATER POTABLE OR NOT?
Using Data Science concepts, trained and tested the model by using a machine learning algorithm namely, Linear regression to predict if the water is potable or not.
Access to safe drinking water is essential to health, a basic human right, and a component of effective policy for health protection. This is important as a health and development issue at a national, regional, and local level. In some regions, it has been shown that investments in water supply and sanitation can yield a net economic benefit, since the reductions in adverse health effects and health care costs outweigh the costs of undertaking the interventions.
Content
The water_potability.csv file contains water quality metrics for 3276 different water bodies.
(3) SUPERVISED MACHINE LEARNING MODEL TO PREDICT WHETHER THE PATIENTS HAVE DIABETES OR NOT?
Using Data Science concepts, trained and tested the model by using a machine learning algorithm namely, Logistic regression to predict if the water is potable or not (100 % ACCURACY) ACHIEVED
(4) WHICH CUSTOMER GROUP TO TARGET FOR THE BUSINESS?
USING K-MEANS ALGORITHM
K-Means Clustering is an unsupervised learning algorithm that is used to solve clustering problems in machine learning or data science. Here we use the data collected from the Shopping Mall to predict which group of people should be targetted for the business growth depending upon the people's income and expenditure.
(5) USING MULTI LINEAR REGRESSION FOR HOUSE PREDICTION
Welcome to the House Price Prediction, you will test your regression skills by designing an algorithm to accurately predict the house prices in India. Accurately predicting house prices can be a daunting task. The buyers are just not concerned about the size(square feet) of the house and there are various other factors that play a key role to decide the price of a house/property. It can be extremely difficult to figure out the right set of attributes that are contributing to understanding the buyer's behavior as such. This dataset has been collected across various property aggregators across India. In this provided the 12 influencing factors my role as a data scientist is to predict the prices as accurately as possible.
(6) USING SUPPORT VECTOR MACHINE ALGORITHM FOR DETECTING THE TYPE OF SPECIES
ROBOTICS AND CONTROL
(7) MEDICAL ROBOTICS PROJECT
The Aim of the Project is to integrate a new endoscopic 3 finger arm in the Da Vinci Surgical System and perform simulations to open and close the new endoscopic arm aimed to move the tissues aside which obstructs during the minimal invasive surgery. Simulations performed are done in Virtual Robotic Experimentation Platform (V-REP) and simulated using the ROS (Robot Operating System) as an interface to simulate the Robot(Da Vinci Research Kit).
(8)OBJECT DETECTION AND VIDEO CLASSIFICATION USING NEURAL NETWORKS
Computer vision is an interdisciplinary scientific field that deals with how computers, robots, autonomous vehicles can gain high-level understanding from digital images or videos. With the applications like Mask_RCNN and other deep learning application designed to improve vision, our project develops the chance of improving the way the machines will learn how to view, perceive and interact with the world around them.
(9) NEURAL NETWORKS
Differently from other collaborative models, the proposed
the architecture allows an exchange of information between the involved
FLAFs based on an order of priorities.
The resulting model is efficiently capable of modelling nonlinearities
regardless of their nature and the unknown system to identify.
(10) MODELING AND VISION BASED CONTROL OF A SOFT ROBOT: ARTIFICIAL CILIUM
The project focuses on model and control of a soft robot, namely an artificial cilium which is composed of a discrete number of soft inflatable bending segments. The cilium submerges in a liquid medium placed inside a rectangular glass box, as shown in the video where it follows the optimal kinematics to generate a net flow in forward and backward direction.
Applications: Minimally Invasive Surgery, Aquatic Robot Locomotion.
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(11) HUMAN ROBOT INTERACTION
Sign language for communication is efficacious for humans, and vital research is in progress in computer vision systems. This project deals with robust modelling of number signs in the context of sign language recognition using deep learning-based convolutional neural networks (CNN).
(12) LEARNING IN AUTONOMOUS SYSTEM: PING PONG
A ping pong game is where there are two paddles and a ball in between, have to try and get ball pass between the paddles, this game has been build from scratch and trains a Neural Network to get it better over time.
Deep Q Network is built to read it in pixel data from the game of pong .where Deep mind is to read in the pixel data and the score, these two are its inputs to the DQ Network, based on those two it gets better over time, reinforcement learning.
(13) NATURAL LANGUAGE PROCESSING
Chat bot : John
In the present world of machine learning and AI there are many different kinds of approaches, for the increase of human comfort with the positive increase in technology, Chatbots which is a conversational technology to increase customer service in many fields. The chatbot here uses neural networks and deep learning so it is slightly intelligent. It is a chatbot to answer trivia questions like for a specific purpose. A deep learning chatbot learns right from scratch through a process called “Deep Learning.” In this process, the chatbot is created using machine learning algorithms. A deep learning chatbot learns everything from its data and human-to-human dialogue. The chatbot here is called JOHN, and he answers to some questions to students about the diag school.