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Call for Abstract

Date

February 23, 2021 at 01:00 PM GMT  
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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.

 

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)

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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.

 

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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