Track 1: Artificial Intelligence

Artificial intelligence is a behaviour based-system concept in robot. Artificial Intelligence brings intelligent behaviour to the robot to be able to provide services to humans in unpredictable and changing environments, such as homes, hospitals, the work place, and all around us Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think. Artificial intelligence is accomplished by studying how human brain thinks and how humans learn, decide, and work while trying to solve a problem, and then using the outcomes of this study as a basis of developing intelligent software and systems. In the real world, the knowledge has some unwelcomed properties.

Track 2: 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 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)

Track: 3 IoT, Autonomous Systems and Agents

By 2020, we can hope to see more than 20 billion dynamic IoT (Internet of Things) gadgets. In addition, 75% of associations will be hindered from accomplishing the full advantages of IoT because of an absence of experts in the data science field.

Autonomous systems use data accumulated from sensors to settle on free choices and afterward follow up on them utilizing relating actuators. An agent is a framework arranged in, and part of, a specialized or regular habitat, which senses in some way that environment, and follows up on it in quest for its own plan.

Analytics & Organizational Culture
Analytics & Strategy

Track 4: Internet of things

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?

Track 5: Big data analytics

Artificial Intelligence (AI), mobile, social and Internet of Things (IoT) are driving information complexness, new forms and sources of knowledge. Big Data analytics is that the use of advanced analytic techniques against terribly giant, numerous information sets that embrace structured, semi-structured and unstructured information, from totally different sources, and in several sizes from terabytes to zettabytes. Analysing huge information permits analysts, researchers, and business users to create higher and quicker selections victimization information that was antecedent inaccessible or unusable. Victimization advanced analytics techniques like text analytics, machine learning, prognostic analytics, data processing, statistics, and language process, businesses will analyse antecedent untapped information sources freelance or at the side of their existing enterprise information to realize new insights leading to higher and quicker selections.

Big data Hadoop
Apache
Scala
Spark

Track 6: Data mining

Data mining is thought of a superset of the many different strategies to extract insights from knowledge. It would involve ancient applied mathematics strategies and machine learning. Data processing applies strategies from many alternative areas to spot antecedent unknown patterns from knowledge. This could embody applied mathematics, algorithms, machine learning, text

analytics, statistical analysis and alternative areas of analytics. Data processing conjointly includes the study and follow the knowledge of storage and data manipulation.

High-performance data mining algorithm
Data Mining in Healthcare data
Medical Data Mining
Advanced Database and Web Application
Data mining and processing in bioinformatics, genomics and biometrics

Track 7: Business Intelligence

The competitive intelligence might be a technology-driven methodology for Analysing data and presenting an unjust information to help executives, managers, and different company end users to produce enlightened businesses selections. Business intelligence will be employed by enterprises to support a large vary of business choices - starting from operational to strategic. Basic operational choices embody product positioning or valuation. Metal encompasses a decent kind of tools, applications, and methodologies that differentiate the corporations to collect information from internal and external sources; prepare it for analysis; develop and activate queries against the data; and build reports, dashboards and knowledge visualizations to make the analytical results on the market to the corporate decision-makers, likewise as operational staff.

Why BI is important?
Types of BI tools
BI trends
BI for Big data

Track 8: Data analytics in cloud

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

Track 9: Cloud computing in E-commerce

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

Track 10: Homeland Security and Data Analysis

The idea of "Homeland Security" extends and recombines responsibilities of government organizations and substances. As indicated by Homeland security examines the U.S. government Homeland Security and Homeland security incorporates 187 administrative offices and departments. Data analysis has numerous aspects and approaches, enveloping assorted procedures under an assortment of names, and is utilized in various business, science, and sociology spaces. In the present business world, data analysis assumes a job in settling on choices increasingly logical and helping organizations work even more successfully.

Border Security
Critical Infrastructure Security
Science and technology

Track 11: Big Data Architecture, Technologies and Applications

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

Few most commonly used technologies of big data are:

The Hadoop Ecosystem

Apache Spark
NoSQL Databases
Hive
Sqoop
PolyBase

Big data applications in present world:

Big Data in Retail
Big Data in Healthcare
Big Data in Education
Big Data in E-commerce
Big Data in Media and Entertainment
Big Data in Finance
Big Data in Travel Industry
Big Data in Telecom
Big Data in Automobile

Track 12: Big Data Analytics and Data Science

Big Data Analytics includes examining, changing, purging and demonstrating information. The Big Data significantly manages handling and breaking down massive measures of information utilizing Hadoop innovation. Data Science includes the execution of various phases of analytics activities, for example, information control, representation and prescient model structure utilizing R programming.

Hadoop: The new enterprise data operating system
Big data lakes
Data Visualization.

Track 13: 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 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.

Track 14: Data Mining Applications in Science, Engineering, Healthcare and Medicine

Data mining is the process of discovering patterns to extract information with an intelligent method from a data set and transform the information into a comprehensible structure for further use. Data mining is the detailed examination step of the "knowledge discovery in #databases" process. These applications relate Data mining structures in genuine cash related business territory examination, Application of data mining in positioning, Data mining and Web Application, Engineering data mining, Data Mining in security, Social Data Mining, Neural Networks and Data Mining, Medical Data Mining, Data Mining in Healthcare

The Modern Algorithmic Toolbox
Query Processing, Data Modeling, and Analysis
Bayesian networks
Case Studies and Implementation
Economic Growth in Renewable Energy Industry
Application of data mining in education
Data mining and processing in bioinformatics, genomics and biometrics
Engineering data mining
Advanced Database and Web Application
Medical Data Mining
Medical Data Mining Data Mining in Healthcare data
Data mining in security

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