Scientific Sessions

Track 1

Data Mining Applications in Science, Engineering, Healthcare and Medicine

Datamining 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 #WebApplication, Engineering data mining, Data Mining in security, Social Data Mining, #NeuralNetworks and Data Mining, Medical Data Mining, Data Mining in Healthcare

Track 2

Machine Learning

Machine learning is a supplication of artificial intelligence (AI) that provides systems the ability to automatically learn and better from happening without being exact programmed. Machine learning is center of attention the expansion of computer programs that can approach data and use it to learn for individually. The process of learning starts with observations or data, such as examples, shortest experience, or instruction, in order to look for plan in data and make better decisions in the future founded on the examples that we provide. The primary aim is to allow the computers learn automatically without human interfere and adjust actions accordingly.

Track 3

Data Mining Methods and Algorithms

Data mining #structures and calculations an interdisciplinary subfield of #programming building is the #computational arrangement of finding case in information sets including techniques like Big #Data Search and Mining, Data Mining Analytics, High execution information mining figuring's, Methodologies on sweeping scale information mining, Methodologies on expansive scale information mining, Big Data and Analytics, Novel Theoretical Models for Big Data.

Track 4

Big Data in Nursing Research

With advances in technologies, nurse scientists are increasingly generating and using large and complex datasets, sometimes called “BigData,” to promote and improve Health Conditions. New strategies for collecting and detailed examination large datasets will allow us to better understand the biological, genetic, and behavioural underpinnings of health, and to improve the way we prevent and manage illness

Track 5

Big Data Analytics

Big data analytics probe and analyse huge amounts of data to i.e., big data - to uncover hidden patterns, unknown co-relations, market trends, customer preferences and other useful information that can help organizations make more-informed business decisions. Operate and carry by specialized #analytics systems and #software, big data analytics can lay the way to various business benefits, including new revenue opportunities, more effective marketing, improved operational efficiency, competitive advantages and better customer service

Track 6

Data Warehousing and Security

In computing, a #DataWarehouse and Security, also known as an #EnterpriseDataWarehouse (EDW), is a system used for reporting and data analysis and is considered a central component of business #intelligence. Data Warehouse or Enterprise Data Warehouse is a central repository of integrated data from one or more disparate sources.

Track 7

Data Mining Tools and Software

Information mining undertaking can be shown as a data mining request. A data mining request is portrayed similarly as the data mining task first. This track joins complete examination of mining figuring’s, Semantic-based Data Mining and Data Pre-planning, Mining on data streams, Graph and sub-outline mining, Statistical Methods in Data Mining, Data Mining Predictive Analytics. The basic calculations in information mining and investigation shape the theory for the developing field of information science, which incorporates robotized techniques to examine examples and models for a wide range of information, with applications widening from logical revelation to business insight and examination.

Track 8

Big Data Applications, Challenges and Opportunities

Big data has increased the demand of information management so much that most of the world’s big software companies are investing in software firms specializing in data management and analytics. According to one rough calculation, one-third of the globally stored information is in the form of #alphanumeric text and #still image data, which is the format most useful for most big data applications. Since most of the data is directly generated in digital format, we have the opportunity and the challenge both to influence the creation to facilitate later linkage and to automatically link previously created data. There are different phases in the Big Data analysis process and some common challenges that underlie many, and sometimes all, of these phases.

Track 9

Predictive Analytics

Track 10

Data-driven Analytics and Business Management

Track 11

Data Mining Tasks and Processes

Information mining undertaking can be shown as a data mining request. A data mining request is portrayed similarly as the data mining task first. This track joins complete examination of mining figuring’s, Semantic-based Data Mining and Data Pre-planning, Mining on data streams, Graph and sub-outline mining, Statistical Methods in Data Mining, Data Mining Predictive Analytics. The basic calculations in information mining and investigation shape the theory for the developing field of information science, which incorporates robotized techniques to examine examples and models for a wide range of information, with applications widening from logical revelation to business insight and examination.

Track 12

Big Data algorithm

Big data is data of wide range that it does not fit in the main memory of a single machine, and the need to process big data by organised algorithms arises in machinelearning, scientific #computing, #signalprocessing, Internet search, network traffic monitoring and some other areas. Data must be processed with advanced tools (analytics and algorithms) to make meaningful information.

Track 13

Data privacy and ethics

In our #e-world, information #protection and #cyber security have gotten to be respective terms. In this business, we have a commitment to secure our customer's information, which has been acquired as per their permission exclusively for their utilization. That is an all-important point if not promptly obvious. There's been a ton of speak of late about Google's new protection approaches, and the discussion rapidly spreads to other Internet beasts like Facebook and how they likewise handle and treat our own data.

 

Track 14

Big data technologies

 

Big Data is the name given to huge amounts of data. As the data comes in from a variety of sources, it could be too diverse and too massive for conventional technologies to handle. This makes it very important to have the skills and infrastructure to handle it intelligently. There are many of the big data solutions that are particularly popular right now fit for the use

Track 15

Data Mining analysis

Data mining can be defined as processing and analyzing data from different perspectives and finally concluding it as useful information available for clients. Neural network analyses, classification and regression trees, generalized linear models are some of the techniques used for data mining.

Track 16

Cloud computing

Cloud computing is the delivery of computing services—servers, storage, databases, #networking, software, analytics, and more—over the Internet (“#the cloud”).  Cloud computing relies on sharing of resources to achieve coordination and economies of scale, similar to a public utility. Companies offering these computing services are called #cloud providers and typically charge for cloud computing services based on usage.

Track 17

Social network analysis

#Social network analysis (SNA) is the advancement process of looking at #social structures through the use of networks and graph theory. It characterizes networked structures in terms of #lumps (individual actors, people, or things within the network) and the #ties, #edges, or #links (relationships or interactions) that connect them.

Track 18

IoT and edge computing applications

Track 19

Clustering

#Cluster analysis or #clustering is the task of organizing a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups.

Track 20

Frequent pattern mining

#Frequent pattern mining (or) #Pattern mining consists of using/developing data mining algorithms to discover interesting, unpredicted and useful patterns in databases. Pattern mining algorithms can be applied on different types of data such as #sequence databases, #transaction databases, #streams, #strings, #spatial data, and #graphs. Pattern mining algorithms can be designed to discover various types of patterns such as #subgraphs, #associations, #sequential rules, #lattices, #sequential patterns, #indirect associations, #trends, #periodic patterns and #high-utility patterns.

Track 21

Search and data mining

In the course of recent times, there has been an immense increase in the measure of information being put away in databases and the number of database applications in business and the #investigative space. This blast in the measure of electronically put away information was accelerated by the achievement of the social model for putting away information and the improvement and developing of information recovery and control innovations.

Track 22

New visualization techniques

Information representation is seen by numerous orders as a present likeness #visual correspondence. It is not held by any one field, yet rather discovers translation crosswise over numerous. It covers the arrangement and investigation of the #visual representation of information, indicating "#data that has been dreamy in some schematic structure, including attributes or variables for the units of data".

Track 23

Forecasting from Big Data

Big Data is a revolutionary phenomenon has recently gained some attention in response to the availability of unprecedented amounts of data and increasingly sophisticated algorithmic analytic techniques. Big data play a critical role in reshaping the key aspects of forecasting by identifying and reviewing the problems, potential, better predictions, challenges and most importantly the related applications.

Track 24

Optimization and Big Data

The term Big Data is here: information of immense sizes is getting to be universal. With this, there is a need to take care of advancement issues of exceptional sizes. Machine learning, compacted detecting, informal organization science and computational science are some of the meager clear #application areas where it is anything but difficult to plan improvement issues with millions or billions of variables. The #long-established advance calculations are not intended to scale to occasions of this size, new methodologies are required.

Track 25

Open data

Information representation is seen by numerous orders as a present likeness #visual correspondence. It is not held by any one field, yet rather discovers translation crosswise over numerous. It covers the arrangement and investigation of the #visual representation of information, indicating "#data that has been dreamy in some schematic structure, including attributes or variables for the units of data".

Track 26

Business Analytics

#Business analytics refers to the skills, technologies, practices for continuous rerun exploration and investigation of past business performance to #gain insight and #drive business planning. Business analytics is used by companies enact #data-driven decision-making.

Track 27

Complexity and algorithms

The uncertainty of a calculation indicates the aggregate time required by the system to rush to finish. The many-sided quality of calculations is most generally communicated using the enormous O documentation. Many-sided quality is most usually assessed by tallying the number of basic capacities performed by the #calculation. What's more, since the calculation's execution may change with various sorts of info information, subsequently for a calculation we normally use the most pessimistic scenario multifaceted nature of a calculation since that is the extended time taken for any information size.

Track 28

Applied Linguistics

Track 29

Sociolinguistics

Track 30

Language

Track 31

Society

Track 32

Identity

Track 33

Theoretical

Track 34

Empirical

Track 35

Discourse

Track 36

Culture

Track 37

Cultural Linguistic

Track 38

Theory

Track 39

Metalanguage

Track 40

Sociology

Track 41

History

Track 42

Arabic Linguistics

Track 43

English Linguistics

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