Call for Abstract

Scientific Program

World Summit on Big Data, Machine Learning and Artificial Intelligence, will be organized around the theme ““Future Technologies in Data Driven Approaches and Adaption””

Big Data 2021 is comprised of 14 tracks and 2 sessions designed to offer comprehensive sessions that address current issues in Big Data 2021.

Submit your abstract to any of the mentioned tracks. All related abstracts are accepted.

Register now for the conference by choosing an appropriate package suitable to you.

Track 1:Big Data

 

Big Data may be a collection of knowledge that’s huge in volume, yet growing exponentially with time. It is acknowledged with so large size and complexity that none of traditional data management tools can store it or process it efficiently.
 
Big data is additionally a knowledge but with huge size. Big Data is a field that gets ways investigate, methodologically separate data from, or in any case manage informational indexes that are excessively huge or complex to be managed by customary information handling application programming. Information with numerous fields (segments) offer more noteworthy measurable force, while information with higher intricacy (more credits or sections) may prompt a higher bogus disclosure rate. Enormous information investigation challenges incorporate catching information, information stockpiling, information examination, search, sharing, move, representation, questioning, refreshing, data security and information source. Enormous information was initially connected with three key ideas: volume, assortment, and speed. The examination of Big Data presents difficulties in testing, and accordingly beforehand taking into account just perceptions and inspecting. In this manner, huge information frequently incorporates information with sizes that surpass the limit of conventional programming to measure inside an adequate time and worth.

Track 2:Artificial Intelligence

Artificial intelligence as a tutorial discipline was founded in 1956. The goal of this,was to get computers to perform tasks regarded as uniquely human things that required intelligence. At first, researchers worked on problems like playing checkers and solving logic problems.

 

If you checked out the output of one of these checkers playing programs you will see some sort of “artificial intelligence” behind those moves, particularly when the pc beat you. Early successes caused the primary researchers to exhibit almost boundless enthusiasm for the chances of AI, matched only by the extent to which they misjudged just how hard some problems were.

 

Artificial intelligence refers to the output of a computer. The computer is doing something intelligent, so it’s exhibiting intelligence that's artificial. AI doesn’t says anything about how to solve the problems. And one category of techniques started becoming more widely utilized in the 1980s: machine learning.

Track 3:Machine Learning

 

Machine Learning might be a technique for information investigation that computerizes scientific model structure. It is a part of AI upheld the possibility that frameworks can gain from information, recognize examples and settle on choices with negligible human mediation. 
 
The explanation that those early scientists discovered a few issues to be a lot harder is that those issues essentially weren't amiable to the main strategies utilized for AI. Hard-coded calculations or fixed, rule-based frameworks simply didn't turn out okay for things like picture acknowledgment or separating significance from text.
  • Track 3-1Types of Machine Learning
  • Track 3-2Machine Learning Classification

Track 4: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. Artificial Intelligence, 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.

Track 5:AI using Machine Learning


Artificial intelligence may be a technology which enables a machine to simulate human behaviour. Machine learning may be a subset of AI which allows a machine to automatically learn from past data without programming explicitly. The goal of AI is to form a sensible computing system like humans to unravel complex problems.


Track 6:Machine Learning on Big Data

Machine Learning algorithms become more powerful as the size of preparing datasets develops. So when consolidating huge information with AI, the algoritms help us to stay aware of constant information, while the volume and assortment of a similar information takes care of the algorithms and causes them develop. By taking care of huge information to an AI algorithm, we may hope to see characterized and investigated results, as concealed examples and examination that can aid prescient demonstrating. For certain organizations, these algorithm may mechanize measures that were beforehand human-focused. Be that as it may, usually, a partnership will survey the calculation's discoveries and quest them for important bits of knowledge which may manage business activities. Here's the place where individuals return into the image. While AI and information investigation run on PCs that beat people by a colossal edge, they do not have certain dynamic capacities. PCs presently can't seem to copy numerous qualities inalienable to people, as basic reasoning, expectation and in this way the capacity to utilize comprehensive methodologies. Without a specialist to supply the appropriate information, the value of calculation produced results lessens, and without a specialist to decipher its yield, proposals made by a calculation may bargain organization choices.

Track 7:Machine Learning Applications for Big Data

Cloud Networks

A Research firm has a lot of clinical information it needs to consider, yet to do as such on-premises it needs workers, online capacity, systems administration and security resources, all of which amounts to an irrational cost. All things being equal, the firm chooses to put resources into Amazon EMR, a cloud administration that offers information investigation models inside an oversaw system.

 

Machine Learning models of this sort incorporate GPU (Graphics Processing Unit)- quickened picture acknowledgment and text characterization. These calculations don't adapt whenever they are sent, so they can be disseminated and upheld by a substance conveyance organization (CDN). Look at Live Ramp's nitty gritty diagram portraying the relocation of a major information climate to the cloud.

 

Web Scraping ]

While web scraping creates an immense measure of information, it's advantageous to take note of that picking the hotspots for this information is the main piece of the interaction. Look at this IT Svit manage for some best information mining rehearses.

 

Mixed-Initiative Systems

Similarly, smart-car makers actualize has huge information and AI in the prescient investigation frameworks that run their items. Tesla cars, for example, communicate with their drivers and respond to externally stimulated by using data to make algorithm-based decisions.

Track 8:Deep Learning

  • Deep Learning is an AI work that emulates the functions of the human mind in handling information for use in distinguishing objects, perceiving discourse, interpreting dialects, and deciding
  • Deep Learning, AI can learn without human oversight, drawing from information that is both unstructured and unlabeled.
  • Deep Learning, a type of AI, can be utilized to help distinguish extortion or illegal tax avoidance, among different capacities.

Track 9:Working of Deep Learning

 

 

Deep learning has advanced connected at the hip with the computerized period, which has achieved a blast of information taking all things together structures and from each area of the world. This information, referred to just as large information, is drawn from sources like web-based media, web search tools, web based business stages, and online films, among others. This colossal measure of information is promptly open and can be shared through various applications like distributed computing.

 

However, the information, which is ordinarily unstructured, is immense to the point that it could require a very long time for people to grasp it and concentrate significant data. Organizations understand the unfathomable potential that can come about because of disentangling this abundance of data and are progressively adjusting to AI frameworks for computerized uphold.

Track 10:AI & Machine Learning in Health Care and Medical Science


Artificial intelligence in healthcare is employed to interpret the complicated medical and healthcare data by the use of machine learning algorithms and AI. Machine learning is employed within the field of radiology to detect and diagnose diseases through computed tomography and resonance imaging. The identification of drug interactions in medical literature is often possible by improving the tongue processing. This electronic health record data helps to spot the symptoms of the patients.


Track 11:Big Data, Data Science and Data Mining


Big Data is a field that gets ways break down, systematically extract data from informational collections that are in predictable to be managed by conventional information handling application programming. Big Data challenges including capturing data, data storage, data analytics, search, representation, questioning, and refreshing the knowledge security.



Data science gives solutions to the programming, intelligent reasoning, number-crunching, and insights. It gets information within the fastest manner and supports the restriction of watching things with a substitute viewpoint.



Data mining is essentially the way towards collecting information from the databases. We can process the data processing as a juncture of various fields like AI, data room virtual base management, pattern recognition, visualization of knowledge, machine learning, and statistical studies and so on.


Track 12:Automated Machine Learning


Automated machine learning belongs to the sector of knowledge science which refers to the method of applying machine learning to real-world problems. It was proposed as an artificial intelligence based solution to the ever-growing challenge of applying machine to utilize AI models and procedures without requiring turning into a specialist within the field first. It makes business in every industry like healthcare, financial markets, marketing, sports, manufacturing and lots of others applications.


Track 13:Benefits of Big Data

Big Data provides some benefits to all kinds of businesses across the globe. From the education sector to the healthcare industry, almost every industry is now sure to Big Data Analytics in some or the opposite way. That’s why Data Flair has come with the 6 amazing benefits of big data that you must know.

 

Below are the highest advantages of using big data in business –

 

• Better decision making

• Greater innovations

• Improvement in education sector

• Product price optimization

• Recommendation engines

• Life-Saving application in the healthcare industry

Track 14:Big Data Tools


Big data is just overlarge and sophisticated data that can't be addressed using traditional processing methods. Big Data requires a group of tools and techniques for analysis to realize insights from it.There is variety of massive data tools available within the market like Hadoop which helps in storing and processing large data, Spark helps in-memory calculation, Storm helps in faster processing of unbounded data, Apache Cassandra provides high availability and scalability of a database, MongoDB provides cross-platform capabilities, so there are different functions of every Big Data tool..

 

Here is that the list of top 10 big data tools –

 

• Apache Hadoop

 

 

• Apache Spark

 

 

• Apache Storm

 

 

• Apache Cassandra

 

 

• MongoDB

 

 

• Kafka

 

 

• Tableau