Track Categories

The track category is the heading under which your abstract will be reviewed and later published in the conference printed matters if accepted. During the submission process, you will be asked to select one track category for your abstract.

With the assistance of Big Data, it could be workable for digital currency proprietors to end up more mindful of ongoing hacking endeavors. Applications fueled by huge information could likewise take proactive parts by recognizing the components that propose penetration endeavors are in advance or going to happen, at that point prevent programmers from effectively completing their arranged assaults on cryptocurrency holders.

  • Track 1-1Blockchain Technology
  • Track 1-2Cryptocurrency
  • Track 1-3Protecting Against Hacks

Technology is evolving and changing genuinely fast. But what's vital to notice that information science is what maximum of the generation is revolving around. Big data was once “the subsequent massive aspect of the future” like some years back. We’re already dwelling the future and big data is everywhere. There might be some industries which are still not in the awe of the capacity of data science and how it is able to help them however maximum other are creating a great use of this technology. With the sector increasingly tuning right into a “virtual workspace”, data science is clearly the future of everything.

  • Track 2-1Data Analytics in education
  • Track 2-2Data Analytics in healthcare and pharmacy
  • Track 2-3Data Analytics in data management
  • Track 2-4Data Analytics in social media
  • Track 2-5Data Mining and Big Data
  • Track 2-6Helping Buyers Spy Volatility

Data analytics makes use of the data mining approach. The simple calculations in data mining and research form the basis for the developing area of data technology, which includes robotized strategies to look at examples and models for an extensive variety of information, with applications extending from logical revelation to business perception and examination.

  • Track 3-1Neural Networks
  • Track 3-2Cluster Analysis, Genetic Algorithms
  • Track 3-3Decision Trees and Recession rules
  • Track 3-4Data understanding & Data preparation
  • Track 3-5Modelling & Evaluation
  • Track 3-6Deployment

It targets to deliver the education zone, which yields the biggest volumes of records each day, within the data mining region so that this large quantity of data can be pre-processed to retrieve genuine, beneficial and actionable data. While a whole lot of people make contributions to educational content, their authenticity is at stake. This content can be labeled as data and not actual information. So that in order to retrieve data, an expert has to parse through all information, using his very own set of abilities, after which its personal authenticity is confirmed. Data mining strategies can explore data; however, they continually need the professional recommendation to make very last decisions, particularly when the line between data and information is the blur.

  • Track 4-1Student modelling
  • Track 4-2Analysis and visualization of data
  • Track 4-3Developing concept maps
  • Track 4-4Constructing courseware
  • Track 4-5Distillation of data for human judgment

Big data is something that may be used to examine insights which could result in better choice and strategic enterprise actions. Data analytics includes automating insights into certain datasets in addition to supposes the use of queries and data aggregation tactics. Big data is a theoretical concept for outlining issues that rise up to a large size of data in which conventional data managing tools aren't capable enough. While data Analytics is a bunch of tools and strategies to carry out an evaluation of data (big and small). So, if one has a big data trouble, then data analytics is there to clear up those issues.

  • Track 5-1Big Data Characteristics
  • Track 5-2Data Stream Algorithms
  • Track 5-3Hadoop
  • Track 5-4Technology and Tools in Open Science
  • Track 5-5Big Data Analytics and machine learning

It is the science of analytical reasoning supported through interactive visual interfaces. Over the past many years, a massive quantity of automated data evaluation techniques had been evolved. However, the complex nature of many issues makes it crucial to encompass human intelligence at an early level within the data analysis system. Visual Analytic techniques permit decision makers to combine their human flexibility, imagination, and background understanding with the tremendous storage and processing capacities of these day's computer systems to gain a perception of complicated issues. Using superior visual interfaces, people may immediately interact with the data analysis competencies of nowadays computer, letting them make well-knowledgeable decisions in complex situations.

  • Track 6-1Software Visualization
  • Track 6-2Computational Visualistics
  • Track 6-3Cartography
  • Track 6-4Visual analytics tools
  • Track 6-5Visual analytics in SAS

We are in a world of more data, and even the most complicated ones, data and big data is changing the way real estate professionals, buyers, sellers and even banks, think about transactions involving property. This info-graphics covers the impact of big data analytics in the real estate industry. The advantages moreover like better and accurate prognosis, business lucidity, identifying prior unknown potential, faster and more comprehensive analyses, faster reactions by management, improved customer service, etc. Data Analytics has helped to change both commercial and residential real estate industry.

  • Track 7-1Understanding Viral Content Marketing
  • Track 7-2The Carbon Budget
  • Track 7-3The key elements
  • Track 7-4Technical illustration
  • Track 7-5Maestro concept

Business Analytics pertains to the exploration of historical data from many source structures through statistical analysis, quantitative evaluation, data mining, predictive modeling and specific technology and strategies to discover developments and recognize the information that could power business exchange and assist sustained successful business practices.

  • Track 8-1Business intelligence
  • Track 8-2Business Analytics and Research (BA&R)
  • Track 8-3Emerging phenomena
  • Track 8-4Technology drives and business analytics
  • Track 8-5Capitalizing on a growing marketing opportunity

In today’s world, analytics are crucial to controlling claims charges. And with pharmacy prices accounting for this sort of massive part of the one's expenses, rising’s prescription monitoring technologies carry a key element to the value-saving equation. Healthcare presents one of the maximum complex types of data of any industry because of its privacy rules and federal/state policies. Yet with the rapid increase of enabling technology gear shooting healthcare records, the volume of available information is increasing. Now businesses are looking to make sense of all of this information and the way best to use it to their enterprise and medical care.

  • Track 9-1Big data in nursing inquiry
  • Track 9-2Methods, tools and processes used with data analytics with relevance to nursing
  • Track 9-3Data Analytics and Nursing Practice
  • Track 9-4Data Mining in Healthcare data
  • Track 9-5Medical Data Mining
  • Track 9-6Eagle Eye – India’s first cloud based digital pharmacy

The primary purpose of AI is to infuse intelligence to machines. This consists of computer vision (to assist dealers to view the world around it), Language Processing (Speech and textual content processing) to assist the agent to recognize human text and speech and additionally to respond to them more clearly. machine mastering facilitates the marketers to learn and enhance their performance much like how human beings do. Data Mining, Data Analytics, all makes use of statistical learning algorithms (machine learning) to extract intelligence from the information. This intelligence might also assist in improving its overall performance.

  • Track 10-1Scientific Computing
  • Track 10-2Computer Graphics
  • Track 10-3Algorithmic Trading
  • Track 10-4Cybernetics
  • Track 10-5Artificial Neural Networks

The bottom-line of data analytics in cloud computing is cloud computing itself. Cloud computing is built around a sequence of hardware and software that may be remotely accessed through any net browser. Typically documents and software are shared and worked on via a couple of customers and all records are remotely centralized in place of being saved on customers’ hard drives. Analytics in cloud computing, including tracking social media engagement and records, is clearly making use of the concepts of analytics to information housed on cloud drives instead of on individual servers or drives.

  • Track 11-1Core Cloud Services
  • Track 11-2Cloud Technologies
  • Track 11-3Computing Models
  • Track 11-4Client-Cloud Computing Challenges
  • Track 11-5High Performance Computing Systems for Medical Applications

Climate informatics can be defined as information-managed analysis, and therefore gives a compliment to present techniques to climate science. Weather datasets mix up strategies from machine learning and data Mining with the greater conventional statistical strategies used by field researchers, and the physics-based simulations utilized in weather modeling. The aim of climate informatics is to inspire collaboration between weather scientists and data scientists, with a view to developing tools to examine complicated and ever-developing quantities of observed and simulated weather records, and thereby bridge the space between data and expertise. Here, latest weather informatics work is presented, together with information on a number of the remaining demanding situations.

  • Track 12-1Climate Science
  • Track 12-2Future Weather Predictions & Analysis
  • Track 12-3Climate Modelling
  • Track 12-4Statistical Techniques & Climate Datasets
  • Track 12-5The Fourth Paradigm

A data analytics method may be used with a purpose to predict energy intake in homes. The different steps of the data evaluation method are performed which will comprehend smart buildings, in which the building control and manage operations along with heating, ventilation, Aircon, lights, and safety are realized robotically by way of miming the needs of the building customers and optimizing sources like electricity and time.

  • Track 13-1House Automation System
  • Track 13-2Energy Efficiency & Smart Building Models
  • Track 13-3Smart Building Design & Implementation Principles
  • Track 13-4Challenges Regarding Smart Buildings
  • Track 13-5Smart Operations & Optimizing Sources

IOT is about gadgets, information and connectivity. The actual value of net of factors is about growing smarter merchandise, turning in sensible insights and supplying new business results. As hundreds of thousands of gadgets get linked, internet of things will trigger a big influx of huge data. the key mission is visualizing and uncovering insights from numerous varieties of data (established, unstructured, pictures, contextual, dark data, real-time) and in context of your programs.

  • Track 14-1Medical & Healthcare
  • Track 14-2Transportation
  • Track 14-3Environmental Monitoring
  • Track 14-4Infrastructure Management
  • Track 14-5Enterprise
  • Track 14-6Consumer Application

Data Analytics in Bioinformatics merges the fields of biology, technology, and medicine so that you can present a complete take a look at the emerging information processing programs vital within the field of digital clinical record management. Whole with interdisciplinary research sources, this is an important reference source for researchers, practitioners, and college students inquisitive about the fields of biological computation, database control, and health statistics generation, with a unique focus on the methodologies and equipment to control huge and complicated digital information.

  • Track 15-1Systems for Brain - Machine Interface
  • Track 15-2Data mining & processing in bioinformatics, genomics & biometrics
  • Track 15-3Bio-Surveillance
  • Track 15-4Electronic Health Records
  • Track 15-5Predictive Analytics
  • Track 15-6Real-time Alerting

A social networking internet site collects data associated with user choices, community pursuits and phase consistent with unique standards along with demographics, age or gender. Right analysis exhibits key consumer and customer developments and allows the social community's alignment of content, format and overall strategy.

  • Track 16-1Google Analytics
  • Track 16-2Networks & Relations
  • Track 16-3Development of Social Network Analysis
  • Track 16-4Analyzing Relational Data
  • Track 16-5Dimensions & Displays
  • Track 16-6Positions, Sets & Clusters

The primary purpose to apply data analytics to tackle fraud is due to the fact a number of internal control systems have extreme control weaknesses. in an effort to efficaciously test and monitor inner controls, companies want to examine each transaction that takes place and check them in opposition to installing parameters, throughout programs, across systems, from dissimilar programs and data assets. Most internal control systems really cannot handle this. On the top of that, as we enforce internal systems, some controls are even never turned on. The use of data analytics, one can discover root troubles, discover traits, and offer particular results. With the quantity of transactions flowing through corporations nowadays, the speed of business has increased particularly because scrutiny of individual transactions is quite tough to offer. This lack of scrutiny over individual transactions opens up the gate for people to abuse systems, perpetrate fraud, and materially affect monetary outcomes.

  • Track 17-1Counter Measures to Combat Cyber Terrorism
  • Track 17-2Cyber Security for Critical Infrastructures & High Performance Computing
  • Track 17-3Security/Privacy Technologies
  • Track 17-4Personal Identity Verification
  • Track 17-5Human Activity Recognition

Road and traffic accidents are unsure and unpredictable incidents and their analysis calls for the understanding of the elements affecting them. one of the key targets in accident data evaluation to discover the primary elements related to a road and traffic accident. but, miscellaneous nature of road accidents records makes the analysis task tough. Data segmentation has been used extensively to conquer this heterogeneity of the accident information. The outcomes display that the aggregate of k mode clustering and association rule mining may be very inspiring as it produces crucial information that might stay hidden if no segmentation has been done prior to generating association regulations. in addition, a trend analysis has also been executed for every clusters and EDS accidents which reveals distinctive trends in the different cluster while a positive trend is shown by means of EDS. trend analysis additionally suggests that previous segmentation of accident records may be essential earlier than analysis.

  • Track 18-1K-Mode Clustering & Data Mining
  • Track 18-2Data Segmentation
  • Track 18-3Road Crash Data
  • Track 18-4Road Accident Analysis & Data Mining
  • Track 18-5Factor Identification & Predictions

Gathering, storing, merging and sorting huge amounts of data had been the main challenge for software and hardware centers. Growing variety of businesses and establishments has solved and evolved tools for saving and storing tables, documents or multimedia information. Database systems are a chief tool in triumphing applications. those systems have regular hundreds or hundreds of thousands of entries. The goals of analytical equipment are obtaining necessary and beneficial information from gathered records and therefore using them for active control and selection making. The main purpose of this contribution is to offer a few possibilities and tools of data analysis as regards to availability of very last users.

  • Track 19-1Big Data Security & Privacy
  • Track 19-2Medical Informatics
  • Track 19-3E-Commerce & Web Services
  • Track 19-4Visualization Analytics for Big Data
  • Track 19-5Predictive Analytics In Machine Learning & Data Mining
  • Track 19-6Interface to Database Systems & Software System

The technology of data analytics attains a huge boon to both people and companies bringing personalized service, detection of fraud and abuse, efficient use of sources and prevention of failure or accident. Current improvements in analytics and big data era have widened the space between what's viable and what's legally allowed, converting the stability of power among individuals and the data collectors. Inside this gap are new possibilities along the dangers of public relations failures and accidental results. And it's miles inside this gap where the ethical questions around what is appropriate are raised.

  • Track 20-1Data Encryption
  • Track 20-2Data Hiding
  • Track 20-3Public Key Cryptography
  • Track 20-4Quantum Cryptography
  • Track 20-5Convolution
  • Track 20-6Hashing

Some of the predictions that emerge as we see the present scenario of big data and data analytics can likely be that, Data volumes will continue to grow, Ways to analyse data will improve, More tools for analysis (without the analyst) will emerge, Prescriptive analytics will be built in to business analytics software, Autonomous agents and things  will continue to be a huge trend, and some more additional changes would be noticed in further future.

  • Track 21-1Business Analytics
  • Track 21-2Optimal Data-Dependent Computation
  • Track 21-3Delegating Computations
  • Track 21-4Convergence of Fast Computations
  • Track 21-5Optimal Error Rates
  • Track 21-6Approximation & Optimization Schemes
  • Track 21-7Randomized Compositions

In figures, a facts movement concentrate, by and big, known as an enterprise data stockroom (EDW), is a structure developed for reporting and facts inspection. Statistics Warehousing is focal narratives of encouraging information from at least one awesome resource. This statistics warehousing merges statistics Warehouse Architectures, Case examines: statistics Warehousing structures, information warehousing in enterprise Intelligence, the position of Hadoop in business Intelligence and facts Warehousing, the business makes use of data Warehousing, Computational EDA (Exploratory statistics evaluation) strategies, system studying and facts Mining.

  • Track 22-1Data Warehouse Architectures
  • Track 22-2Case studies: Data Warehousing Systems
  • Track 22-3Data warehousing in Business Intelligence
  • Track 22-4Role of Hadoop in Business Intelligence and Data Warehousing
  • Track 22-5Commercial applications of Data Warehousing
  • Track 22-6Computational EDA (Exploratory Data Analysis) Techniques

Open data is the feeling that some data need to be unreservedly available to all of us to make use of and republish as they wish, without confinements from proper, licenses or different systems of control. The targets of the open data improvement are like the ones of different "open" traits, as an example, open premise, open device, open fulfilled, and open access.

  • Track 23-1Open Data, Government and Governance
  • Track 23-2Open Development and Sustainability
  • Track 23-3Open Science and Research
  • Track 23-4Technology, Tools and Business

A frequent pattern mining is an example that occurs as often as viable in a data set. Initially proposed by [AIS93] on the subject of regular issue units and affiliation guideline digging for commercial enterprise sector crate research. Stretched out to a huge variety of issues like chart mining, consecutive instance mining, instances association design mining, content material mining.

  • Track 24-1Frequent item sets and association
  • Track 24-2Item Set Mining Algorithms
  • Track 24-3Graph Pattern Mining
  • Track 24-4Pattern and Role Assessment