Be a part of Webinar on Artificial intelligence & Automation

Automotive engineering plays a dynamic role in many industries and increases automation showing information. These technologies help many industries to release large quantity of properties and also make the works to be completed in very short time while comparing to usual manual works. Generally, automation can play a crucial role in many applications, i.e., in banking, shopping, medical and engineering and army etc., nowadays traffic is a big problem to entire world. By improving the working performance of transportation system then increase flexibility and accessibility it delivers environmental benefits, and even increases efficiency and economic growth.

A data model is a strategy by which we can compose and store data. Similarly, as the Dewey Decimal System composes the books in a library, a data model encourages us and arranges data as per administration, access, and use. Big Data is information so huge that it doesn't fit in the primary memory of a solitary machine, and the need to process big data by proficient algorithms emerges in Internet search; organize traffic observing, AI, logical figuring, signal handling, and a few different regions. Few normally utilized algorithms are:

• K-Means Clustering Algorithm
• Association Rule Mining Algorithm
• Linear Regression Algorithms
• Logistic Regression Algorithms
• Support vector machine (SVM)

Machine learning is a part of artificial intelligence based on the idea that systems can learn from data, make decisions and identify designs with insignificant human intervention. Machine learning is a method for making a personal computer, a PC controlled robot, or a product think smartly, and within the comparative way the perceptive people think. They are normally grouped by either learning style or by comparison in method or function. It simplifies the continuous advancement of scheming through introduction to new scenarios, testing and adaptation, while employing pattern and trend detection for improved decisions in succeeding situations. ML gives possible arrangements in every one of these areas, and is set to be a support of our future progress.

The Internet of things (IOT) is the network of physical devices, vehicles, home appliances, and other items embedded with electronics, software, sensors, actuators, and network connectivity which enable these objects to connect and exchange data. Each thing is uniquely identifiable through its embedded computing system but can inter-operate within the existing Internet infrastructure. "Things", in the IoT sense, can refer to a wide variety of devices such as heart monitoring implants, biochip transponders on farm animals, cameras streaming live feeds of wild animals in coastal waters, automobiles with built-in sensors, DNA analysis devices for environmental/food/pathogen monitoring or field operation devices that assist fire fighters in search and rescue operations

• What is the scope of IOT?
• How can IOT help?

Businesses have used data analytics to assist their strategy to maximize profits. Ideally, information analytics helps to eliminate a lot of the estimate concerned in making an attempt to know purchasers, instead systemically following information patterns to best construct business techniques and operations to reduce uncertainty. Not solely will analytics verify what may attract new customers, usually; analytics acknowledges existing patterns in information to assist higher serve existing customers, that is usually less expensive than establishing a replacement business. In an associate degree dynamic business world subject to unnumbered variants, analytics provides firms the sting in recognizing dynamical climates, in order that they will take initiate applicable action to remain competitive. aboard analytics, cloud computing is additionally serving to create business simpler and therefore the consolidation of each cloud and analytics may facilitate businesses store, interpret, and method their massive information is raised to meet their clients’ wants.

• Software as a service (SaaS)
• SaaS examples
• Best uses of Data analytics in cloud
• Future of Data analytics in cloud

Distributed computing may be a style of Internet-based imagining that offers shared handling resources and knowledge to PCs and in contrast to devices on concentration. it's a typical for authorizing pervasive, on-interest access to a typical pool of configurable registering assets which might be quickly provisioned and discharged with insignificant administration travail. Distributed calculative and volume preparations provide shoppers and ventures with totally different skills to store and procedure their data in outsider data trots. It depends on sharing of assets to accomplish rationality and economy of scale, sort of a utility over a system.

• Microsoft Azure Cloud Computing
• Amazon Web Services
• Google Cloud
• Cloud Automation and Optimization
• High-Performance Computing (HPC)
• Emerging Cloud Computing Technology

Machine learning is a part of artificial intelligence based on the idea that systems can learn from data, make decisions and identify designs with insignificant human intervention. Machine learning is a method for making a personal computer, a PC controlled robot, or a product think smartly, and within the comparative way the perceptive people think. They are normally grouped by either learning style or by comparison in method or function. It simplifies the continuous advancement of scheming through introduction to new scenarios, testing and adaptation, while employing pattern and trend detection for improved decisions in succeeding situations. ML gives possible arrangements in every one of these areas, and is set to be a support of our future progress.

Natural Language Processing (NLP) is a subject of software engineering, Computational Linguistics and Artificial Intelligence which is worried about the capacity of a PC program to comprehend human dialects. The means which are utilized in Natural Language Processing incorporate Lexical Analysis, Syntactic Analysis, Semantic Analysis, Discourse Integration, Pragmatic Analysis, and so forth. This point will assist members with understanding how Artificial Intelligence and Natural Language Processing can be used to play out the helpful errand utilizing different human dialects.

Neural networks are a computational approach which is based on a large collection of neural units loosely modeling the way a biological brain solves problems with large clusters of biological neurons connected by axons. Each neural unit relates to many others, and links can be enforcing or inhibitory in their effect on the activation state of connected neural units. Each individual neural unit may have a summation function which combines the values of all its inputs together. The goal of the neural network is to solve problems in the same way that the human brain would, although several neural networks are much more abstract. Modern neural network projects typically work with a few thousand to a few million neural units and millions of connections, which is still several orders of magnitude less complex than the human brain and closer to the computing power of a worm. New brain research often stimulates new patterns in neural networks. One new approach is using connections which span much further and link processing layers rather than always being localized to adjacent neurons.

The future Artificial Intelligence (AI) has the possible to change the world. While from the time when the Turing Analysis was introduced, computers have become so smart. Artificial Intelligence is quick turning into a major economic energy. Definitely it will be an essential part of human life in future. However, an important question remains is that what will occur if the review of robust Artificial Intelligence be successful and an Artificial Intelligence system come to be better than humans. We have confidence in this session will help us to discuss, improve and avoid such possible outcomes in the future.

Deep Learning is a machine learning technique that constructs artificial neural networks to mimic the structure and function of the human brain. In practice, deep learning, also known as deep structured learning or hierarchical learning, uses a large number hidden layers -typically more than 6 but often much higher - of nonlinear processing to extract features from data and transform the data into different levels of abstraction (representations).

There are many definitions for data science, but we like to think of the field as the multidisciplinary approach to unlocking stories and insights from the data being collected on a variety of behaviors, topics, and trends. Data science is everywhere and chances are you’ve already interacted with it today a whole lot. Take Google’s search engine, for example. Its algorithm and site ranking and results are firmly in the realm of data science. If you’ve uploaded a photo on Facebook and the social media platform suggested tagging a friend, you’ve interacted with data science. That Netflix recommendation to continue your binge watching, Amazon’s product recommendations, or targeted advertisements are all the result of data science.