Sessions

Dec 07-08, 2022    Chicago, USA

4th International Conference on

Big Data, AI and IoT

Sessions

5G and Networks for Big Data

5G networks will have the capability to provide connectivity to billions of devices, and these devices will generate a huge amount of data. Similarly, the data rates assumed for 5G networks are in the range of 10 Gbps. Hence, 5G networks can be considered as ultra-dense and hyper fast networks.

AI for IoT

AI enabled IoT creates intelligent machines that simulate smart behavior and supports in decision making with little or no human interference. ... While IoT deals with devices interacting using the internet, AI makes the devices learn from their data and experience.

AI and Big Data

AI has ability to figure it out so well with data analytics is that the primary reason why AI and Big Data are now seemingly inseparable.AI, machine learning and deep learning are learning from every data input and using those inputs to get new rules for future business analytics. “Data is the lifeblood of AI.

Big Data Architecture, Technologies and Applications

Big data architecture is intended to deal with the ingestion, handling, and investigation of information that is excessively massive or complex for traditional database frameworks.

Big Data Models and Algorithms

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 vast that it doesn't fit in the primary memory of a solitary machine, and the need to procedure big data by proficient algorithms emerges in Internet search; organize traffic observing, AI, logical figuring, signal behaviour, and a few different regions.

Web of Things

The internet of things, or IoT, is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers (UIDs) and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction.

Advanced Applications of Artificial Intelligence

Artificial Intelligence (AI) is the machine-displayed intelligence that simulates human behavior or thinking and can be trained to solve specific problems. AI is a combination of Machine Learning techniques and Deep Learning. AI models that are trained using vast volumes of data have the ability to make intelligent decisions.

Future Scope of AI

The future Artificial Intelligence (AI) has the possibility to change the world. While from the time when the Turing Analysis was introduced, computers have become so smart. Artificial Intelligence is quickly turning into major economic energy. Definitely, it will be an essential part of human life in the 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 comes 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.

Machine Learning

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 an 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.

Artificial Neural Networks & Deep Learning

An Artificial Neural Network (ANN) is data organizing worldview that is forced by the way organic sensory systems. Artificial Neural Networks perform specific tasks like pattern recognition, clustering, etc. on the computer. They are similar to the human brains, obtain knowledge through learning and their knowledge is stored within interneuron connection strengths. An Artificial Neural Network is designed for a particular application such as design acknowledgment or information arrangement, through a learning procedure.  They are capable of processing and modelling nonlinear dependence between inputs and outputs in parallel. They are characterized by containing flexible weights along paths between neurons that can be tuned by a learning algorithm that learns from observed data in order to improve the model. Deep Learning is a function of artificial intelligence that copies the workings of the human brain in processing data and creating designs for use in decision making. Deep learning is structured learning that is a section of the machine learning method based on learning data description. It uses some form of inclination extraction for training via back propagation. The layers used in deep learning incorporate hidden layers of artificial neural networks and sets of propositional formulas.

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