BEGIN:VCALENDAR VERSION:2.0 PRODID:icalendar-ruby CALSCALE:GREGORIAN BEGIN:VEVENT DTSTAMP:20240329T074610Z UID:22cec7b0-ef9d-405d-beef-39d84809fb32 DTSTART:20210916T080000 DTEND:20210917T080000 CLASS:PRIVATE DESCRIPTION:
About
\n\n
\n
\nConferenc
e series LLC cordially invites all participants across the globe to attend
the 6th International Conference on Big Data Analytics and Data Mining (B
ig Data 2020) which is going to be held during September 26-27\, 2020 in C
hicago\, USA to share the &ldquo\;Modern Technologies and Challenges in Bi
g Data"\; and technology. The main theme of the conference is &ldquo\;
Modern Technologies and Challenges in Big Data"\;. This conference aim
ed to expand its coverage in the areas of Big Data and Data Mining where e
xpert talks\, young researcher&rsquo\;s presentations will be placed in ev
ery session of the meeting will be inspired and keep up your enthusiasm. W
e feel our expert Organizing Committee is our major asset\, however your p
resence over the venue will add one more feather to the crown of Big Data
2020.
\n
\nData Analytics 2020\, the extraction of hidden predic
tive information from large databases\, is a powerful new technology with
great potential to help companies focus on the most important information
in their data warehouses. Data Analytics 2020 is comprised of the followin
g sessions with 25 tracks and 133 sub sessions designed to offer comprehen
sive sessions that address current issues of Big Data and Data Mining. Dat
a mining tools predict future trends and behaviours\, allowing businesses
to make proactive\, knowledge-driven decisions. The automated\, prospectiv
e analyses offered by data mining move beyond the analyses of past events
provided by retrospective tools typical of decision support systems. Data
mining tools can answer business questions that traditionally were too tim
e-consuming to resolve.
\n
\nTarget audience for the conference:
\n
\nData Base engineers
\nScientists/Researchers
\nP
rofessors
\nPresident/Vice president
\nChairs/Directors
\nD
ata Scientists
\nStudents
\nExperts and Delegates
\n \;
Conference series LLC cordially inv
ites all the participants from all over the world to attend International
Conference on Big Data Analytics &\; Data Mining during September 26-27
\, 2020 in Chicago\, USA. This includes prompt keynote presentations\, Ora
l talks\, Poster presentations and Exhibitions.
\n
\nTra
ck1: Data Analytics
\n
\nBig data is data sets that is
so capacious and composite that outdated data processing application softw
are is inadequate to deal with them. Big data challenges include capturing
data\, data storage\, data analysis\, search\, sharing\, transfer\, visua
lization\, querying\, updating and information privacy. There are three di
mensions to big data known as Volume\, Variety and Velocity
\n
\
nRelated Societies:
\n
\nNational Centre for Data Mining\, Chica
go
\n
\nWeb Analytics Association\, Florida
\n
\nSIAM
society for industrial and applied mathematics\, United States
\n
\nIAENG Society of Data Mining\, Hong Kong
\n
\nTrack2
: Big data technologies
\n
\nHuge information brings op
en doors as well as difficulties. Conventional information process-sing ha
s been not able meet the gigantic continuous interest of huge information\
; we require the new era of data innovation to manage the episode of huge
information
\n
\nRelated Societies:
\n
\nAmerican Stat
istical Association\, United States
\n
\nData Mining Section of
INFORMS\, United States
\n
\nInternational Institute for Analyti
cs\, Oregon
\n
\nThe International Machine Learning Society\, Ge
rmany
\n
\nTrack3: Complexity in data structure and algo
rithm
\n
\nHuge information is information so vast that
it doesn'\;t fit in the fundamental memory of a solitary machine\, and
the need to prepare huge information by productive calculations emerges i
n Internet seeks\, system activity checking\, machine learning\, experimen
tal figuring\, signal handling\, and a few different territories. This cou
rse will cover numerically exhaustive models for increasing such calculati
ons\, and some provable confinements of calculations working in those mode
ls.
\n
\nRelated Societies:
\n
\nIEEE Computer Society
\, United States
\n
\nInternational Educational Data Mining Soci
ety\, United States
\n
\nThe Society of Data Miners: The profess
ional body for data analytics\, data science and data mining\, United Stat
es
\n
\nTrack4: Big Data Applications
\n\nTremendous data is an extensive term for data sets so significant or
complex that customary data planning applications are deficient. Employme
nts of gigantic data consolidate Big Data Analytics in Enterprises\, Big D
ata Trends in Retail and Travel Industry\, Current and future circumstance
of Big Data Market\, Financial parts of Big Data Industry\, Big data in c
linical and social protection\, Big data in Regulated Industries\, Big dat
a in Biomedicine\, Multimedia and Personal Data Mining
\n
\nRela
ted Societies:
\n
\nIEEE Computational Intelligence Society\, Un
ited States
\n
\nAction plan on Science in Society related issue
s in Epidemics and Total pandemics\, Europe
\n
\nBig Data in Asi
an Society\, Singapore
\n
\nTrack5: Internet of things
strong>
\n
\nThe Internet of things (IOT) is the network of phys
ical devices\, vehicles\, home appliances\, and other items embedded with
electronics\, software\, sensors\, actuators\, and network connectivity wh
ich enable these objects to connect and exchange data. Each thing is uniqu
ely identifiable through its embedded computing system but is able to inte
r-operate within the existing Internet infrastructure. "\;Things"\
;\, in the IoT sense\, can refer to a wide variety of devices such as hear
t monitoring implants\, biochip transponders on farm animals\, cameras str
eaming live feeds of wild animals in coastal waters\, automobiles with bui
lt-in sensors\, DNA analysis devices for environmental/food/pathogen monit
oring or field operation devices that assist fire fighters in search and r
escue operations
\n
\nRelated Societies:
\n
\nBig Data
Europe Empowering Communities with Data Technologies\, Europe
\n
\nBig Data and Society\, United Kingdom
\n
\nAdvanced Analytic
s Institute\, Australia
\n
\nAmerican Statistical Association\,
United States
\n
\nTrack6: Optimization and Big Data
\n
\nThe period of Big Data is here: information of immens
e sizes is getting to be universal. With this comes the need to take care
of advancement issues of exceptional sizes. Machine learning\, compacted d
etecting\; informal organization science and computational science are som
e of a few noticeable application areas where it is anything but difficult
to plan improvement issues with millions or billions of variables. Tradit
ional improvement calculations are not intended to scale to occasions of t
his size\; new methodologies are required. This workshop expects to unite
analysts chipping away at unique streamlining calculations and codes fit f
or working in the Big Data setting.
\n
\nRelated Societies:
\n
\nThe International Machine Learning Society\, Germany
\n
\nInternational Institute for Business Analysis\, Ontario
\n
\nEuropean Knowledge Discovery Network of Excellence\, Germany
\n
\nNational Centre for Data Mining\, Chicago
\n
\nTrack
7: Data Mining Applications in Science\, Engineering\, Healthcare and Medi
cine
\n
\nInformation Mining Applications in Engineerin
g and Medicine attentions to offer data excavators who wish to apply stand
-out data some help with mining environments. These applications relate Da
ta mining structures in genuine cash related business territory examinatio
n\, Application of data mining in positioning\, Data mining and Web Applic
ation\, Medical Data Mining\, Data Mining in Healthcare\, Engineering data
mining\, Data Mining in security\, Social Data Mining\, Neural Networks a
nd Data Mining\, these are a portion of the jobs of data Mining.
\n\nRelated Societies:
\n
\nData Mining Section of INFORMS\, U
nited States
\n
\nInternational Institute for Analytics\, Oregon
\n
\nThe International Machine Learning Society\, Germany
\n
\nInternational Institute for Business Analysis\, Ontario
\n<
br />\nTrack8: Big Data in Nursing Research
\n
\nWith advances in technologies\, nurse scientists are increasingly genera
ting and using large and complex datasets\, sometimes called &ldquo\;Big D
ata\,&rdquo\; to promote and improve the health of individuals\, families\
, and communities. In recent years\, the National Institutes of Health hav
e placed a great emphasis on enhancing and integrating the data sciences i
nto the health research enterprise. New strategies for collecting and anal
ysing large data sets will allow us to better understand the biological\,
genetic\, and behavioural underpinnings of health\, and to improve the way
we prevent and manage illness.
\n
\nRelated Societies:
\n<
br />\nBig Data Europe Empowering Communities with Data Technologies\, Eur
ope
\n
\nBig Data and Society\, United Kingdom
\n
\nAd
vanced Analytics Institute\, Australia
\n
\nTrack9: Clou
d computing &\; E-commerce
\n
\nDistributed computin
g is a sort of Internet-based imagining that gives shared handling resourc
es and information to PCs and unlike devices on concentration. It is a typ
ical for authorizing pervasive\, on-interest access to a common pool of co
nfigurable registering assets which can be quickly provisioned and dischar
ged with insignificant administration exertion. Distributed calculating an
d volume preparations supply clients and ventures with different abilities
to store and procedure their info in outsider info trots. It depends on s
haring of assets to accomplish rationality and economy of scale\, like a u
tility over a system.
\n
\nRelated Societies:
\n
\nAme
rican Statistical Association\, United States
\n
\nData Mining S
ection of INFORMS\, United States
\n
\nInternational Institute f
or Analytics\, Oregon
\n
\nTrack10: Data Mining and Mach
ine Learning
\n
\nMachine learning is a field of comput
er knowledge that gives processors the ability to learn without existence
explicitly programmed. Machine learning is closely associated to computati
onal statistics\, which also attentions on prediction-making through the u
se of computers. Within the field of data analytics\, machine learning is
a technique used to devise difficult models and processes that lend themse
lves to expectation in profitable use\, this is known as predictive analyt
ics.
\n
\nRelated Societies:
\n
\nThe International Ma
chine Learning Society\, Germany
\n
\nInternational Institute fo
r Business Analysis\, Ontario
\n
\nEuropean Knowledge Discovery
Network of Excellence\, Germany
\n
\nTrack11: Artificial
Intelligence
\n
\nArtificial Intelligence is a system
of making a computer\, a computer-controlled robot\, or a software think i
ntelligently\, in the similar manner the intelligent humans think. AI is a
ccomplished by studying how human brain thinks and how humans learn\, deci
de\, and work while trying to solve a problem\, and then using the outcome
s of this study as a basis of developing intelligent software and systems.
\n
\nRelated Societies:
\n
\nNational Centre for Data
Mining\, Chicago
\n
\nWeb Analytics Association\, Florida
\n
\nSIAM society for industrial and applied mathematics\, United Sta
tes
\n
\nTrack12: Data Mining Tools and Software
\n
\nInformation Mining gadgets and programming ventures join
Big Data Security and Privacy\, Data Mining and Predictive Analytics in Ma
chine Learning\, Boundary to Database Systems and Software Systems.
\
n
\nRelated Societies:
\n
\nIAENG Society of Data Mining\,
Hong Kong
\n
\nIEEE Computer Society\, United States
\n
\nInternational Educational Data Mining Society\, United States
\n<
br />\nTrack13: Social Media analytics
\n
\nInf
ormal organization investigation (SNA) is the advancement of looking at so
cial structures using system and chart speculations. It describes arranged
structures as far as lumps (individual on-screen characters\, individuals
\, or things inside the system) and the ties or edges (connections or coop
eration&rsquo\;s) that interface them.
\n
\nRelated Societies:\n
\nThe Society of Data Miners: The professional body for data a
nalytics\, data science and data mining\, United States
\n
\nIEE
E Computational Intelligence Society\, United States
\n
\nAction
plan on Science in Society related issues in Epidemics and Total pandemic
s\, Europe
\n
\nBig Data in Asian Society\, Singapore
\n
\nTrack14: Data Mining Tasks and Processes
\n
\nInformation mining undertaking can be shown as a data mining request.
A data mining request is portrayed similarly as data mining task primitive
s. This track joins Competitive examination of mining figuring&rsquo\;s\,
Semantic-based Data Mining and Data Pre-planning\, Mining on data streams\
, Graph and sub-outline mining\, Scalable data pre-taking care of and clea
ning procedures\, Statistical Methods in Data Mining\, Data Mining Predict
ive Analytics.
\n
\nRelated Societies:
\n
\nBig Data a
nd Society\, United Kingdom
\n
\nAdvanced Analytics Institute\,
Australia
\n
\nAmerican Statistical Association\, United States<
br />\n
\nTrack15: Data Mining Methods and Algorithms
\n
\nData mining structures and calculations an interdisciplin
ary subfield of programming building is the computational arrangement of f
inding case in awesome information sets including techniques like Big Data
Search and Mining\, Novel Theoretical Models for Big Data\, High executio
n information mining figuring'\;s\, Methodologies on sweeping scale inf
ormation mining\, Methodologies on expansive scale information mining\, Bi
g Data Analysis\, Data Mining Analytics\, Big Data and Analytics.
\n<
br />\nRelated Societies:
\n
\nData Mining Section of INFORMS\,
United States
\n
\nInternational Institute for Analytics\, Orego
n
\n
\nThe International Machine Learning Society\, Germany
\n
\nTrack16: Data mining analysis
\n
\nT
he basic calculations in information mining and investigation shape the pr
emise for the developing field of information science\, which incorporates
robotized techniques to examine examples and models for a wide range of i
nformation\, with applications extending from logical revelation to busine
ss insight and examination.
\n
\nRelated Societies:
\n
\nInternational Institute for Business Analysis\, Ontario
\n
\n
European Knowledge Discovery Network of Excellence\, Germany
\n
\nNational Centre for Data Mining\, Chicago
\n
\nTrack17
: Data Mining Cluster Analysis
\n
\nBunching can be vie
wed as the most essential unsupervised learning issue\; along these lines\
, as each other issue of this kind\, it manages finding a structure in a g
athering of unlabelled information. A free meaning of bunching could be th
e way toward sorting out items into gatherings whose individuals are compa
rable somehow.
\n
\nRelated Societies:
\n
\nWeb Analyt
ics Association\, Florida
\n
\nSIAM society for industrial and a
pplied mathematics\, United States
\n
\nIAENG Society of Data Mi
ning\, Hong Kong
\n
\nTrack18: Data Mining for Cyber Sec
urity
\n
\nCybersecurity which is also known as compute
r security is the technology designed to protect computer systems containi
ng programs or data from damage or unauthorized access. This includes prev
entive measures for cyber terrorism with the help of cyber security with h
igh performance computing
\n
\nRelated Societies:
\n
\
nIEEE Computer Society\, United States
\n
\nInternational Educat
ional Data Mining Society\, United States
\n
\nThe Society of Da
ta Miners: The professional body for data analytics\, data science and dat
a mining\, United States
\n
\nTrack19: Business Analytic
s
\n
\nBusiness Analytics is the investigation of infor
mation through factual and operations examination\, the arrangement of pre
scient models\, utilization of enhancement procedures and the corresponden
ce of these outcomes to clients\, business accomplices and associate admin
istrators. It is the convergence of business and information science.
\n
\nRelated Societies:
\n
\nBig Data Europe Empowering Co
mmunities with Data Technologies\, Europe
\n
\nBig Data and Soci
ety\, United Kingdom
\n
\nAdvanced Analytics Institute\, Austral
ia
\n
\nTrack20: Data privacy and ethics
\
n
\nIn our e-world\, information protection and cyber security have g
otten to be typical terms. In our business\, we have a commitment to secur
e our customers'\; information\, which has been acquired per their expr
ess consent exclusively for their utilization. That is an imperative point
if not promptly obvious. There'\;s been a ton of speak of late about G
oogle'\;s new protection approaches\, and the discourse rapidly spreads
to other Internet beasts like Facebook and how they likewise handle and t
reat our own data.
\n
\nRelated Societies:
\n
\nData M
ining Section of INFORMS\, United States
\n
\nInternational Inst
itute for Analytics\, Oregon
\n
\nThe International Machine Lear
ning Society\, Germany
\n
\nTrack21: Statistical Methods
\n
\nIt includes strategies which are utilized as a pa
rt of biostatistics and computing. It incorporates points such as vigorous
techniques in biostatistics\, longitudinal studies\, analysis with defici
ent data\, meta-analysis\, Monte-Carlo strategies\, quantitative issues in
health-risk analysis\, statistical methodologies in genetic studies\, eco
logical statistics and biostatistical routines in epidemiology.
\n
\nRelated Societies:
\n
\nNational Centre for Data Mining\, C
hicago
\n
\nWeb Analytics Association\, Florida
\n
\nS
IAM society for industrial and applied mathematics\, United States
\n
\nTrack22: Biostatistics applications
\n
\nIt is the study of science that deals with the statistical methods for d
escribing and comparing the phenomenon of particular subject which helps i
n managing medical uncertainties. Its applications are wide spread in medi
cine\, health\, biology etc. for interpretation of data based on observati
ons and facts.
AGENDA 2020
\n
\n----------------------
-------------------
\n2020 Upcoming Soon
\n---------------------
--------------------
\n
\nDay 1 September 26\, 2020
\n
\n
\n08:45-09:15 Registrations
\n09:15-09:30 Opening Ceremony\n
\nKeynote Forum
\nTitle: The theory of random sets as fle
xible texture descriptor for biological and
\n09:30-10:05 medical obj
ects and self-similarity as feature descriptors for the description of the
\nappearances of cells and motion
\nPetra Perner\, Institute of
Computer Vision and applied Computer Sciences\, Germany
\n10:05-10:4
0 Title: Algorithms for a minimum sum of squares clustering
\nPierre
Hansen\, GERAD and HEC Montreal\, Canada
\nGroup Photo
\nPanel D
iscussion
\nNetworking and Refreshment Break 10:40-11:00 @ Foyer
\nTitle: Partially exact alternatives to regularization in proportional h
azards regression
\n11:00-11:35 models with monotone likelihood
\nJohn E Kolassa\, Rutgers University\, USA
\n11:35-12:05 Title: Copu
la Gaussian graphical models for functional data
\nBing Li\, Pennsylv
ania State University\, USA
\n12:05-12:35 Title: Disrupting retail an
alysis with Artificial Intelligence-powered advanced analytics
\nErgi
Sener\, IdeaField Incubation Center BV\, Holland
\n12:35-13:05 Title
: Risk visualization: When actuarial science meets visual analytics
\
nNicolas Wesner\, Mazars Actuariat\, France
\nPanel Discussion
\
nLunch Break 13:05-14:00 @ Ontario
\nTitle: Design productivity\, com
pilation\, and acceleration for data analytic applications
\n14:00-14
:35 Deming Chen\, University of Illinois at Urbana-Champaign\, USA
\n
14:35-15:05 Title: Lifelong learning machines: The cutting edge Artificial
Intelligence
\nHava Siegelmann\, DARPA\, USA
\nSessions: Data A
nalytics | Big Data Applications | Internet of things | Data Mining Applic
ations in
\nScience\, Engineering\, Healthcare and Medicine | Cloud c
omputing &\; E-commerce | Data Mining and
\nMachine Learning | Art
ificial Intelligence | Biostatistics Application | Statisticsal Methods |
Data
\nMining analysis | Modern Data Analytics | Clinical Biostatisti
cs | Regression Analysis
\nSession Chair: Subbulakshmi Padmanabhan\,
StubHub\, USA
\n15:05-15:30 Title: Better monitoring of diabetic leve
ls using new control charts
\nGadde Srinivasa Rao\, The University of
Dodoma\, Tanzania
\n15:30-15:55 Title: Estimation of differencing pa
rameter of arima models
\nAbdul Basit\, State Bank of Pakistan\, Paki
stan
\nPanel Discussion
\nNetworking and Refreshment Break 15:55
-16:15 @ Foyer
\n16:15-16:40
\nTitle: Exact inference for the Yo
uden index to discriminate individuals using twoparameter
\nexponenti
ally-distributed pooled samples
\nSumith Gunasekera\, The University
of Tennessee at Chattanooga\, USA
\nPoster
\n16:40-17:00
\n
Title: Classification of multi-channel electroencephalogram time series wi
th linear
\ndiscriminant analysis
\nYongxiang Gao\, Sun yat-sen
University\, China
\nNetworking 17:00-18:00
\n
\nDay 2 Sept
ember 27\, 2020
Keynote Forum
\n09:00-09:35 Title: Improve
business operation efficient through the use of automatic intelligent
\nmodel building system
\nMorgan C Wang\, University of Central Flor
ida\, USA
\n09:35-10:05 Title: Computational Graphology Systems that
reveal people'\;s character from their
\nhandwritten data
\nC
hing Y Suen\, Concordia University\, Canada
\n10:05-10:35 Title: High
-performance data analytics: Platforms\, resource management\, and middlew
are
\nShikharesh Majumdar\, Carleton University\, Canada
\nPanel
Discussion
\nNetworking and Refreshment Break 10:35-10:55 @ Foyer
\n10:55-11:25 Title: Data-driven management of infrastructure systems i
n smart communities
\nGurdip Singh\, Syracuse University\, USA
\
n11:25-11:55 Title: Martingale difference divergence and its applications
to contemporary statistics
\nXiaofeng Shao\, University of Illinois a
t Urbana-Champaign\, USA
\n11:55-12:25 Title: Deep learning in biomed
ical image processing\, analysis\, and diagnosis
\nKenji Suzuki\, Tok
yo Institute of Technology\, Japan
\n12:25-12:55 Title: Biostatistics
and personalized medicine
\nChap T Le\, University of Minnesota\, US
A
\nPanel Discussion
\nLunch Break 12:55-13:50 @ Superior B
\nSessions: Data Analytics | Big Data Applications | Internet of things |
Data Mining Applications in
\nScience\, Engineering\, Healthcare and
Medicine | Cloud computing &\; E-commerce | Data Mining and
\nMac
hine Learning | Artificial Intelligence
\nSession Chair: Subbulakshmi
Padmanabhan\, StubHub\, USA
\n13:50-14:15 Title: Data mining via ent
ropy and time series analysis
\nAsif Ali\, State Bank of Pakistan\, P
akistan
\n14:15-14:40 Title: Effect of competing for risk among breas
t cancer
\nOlawale Abolade O\, Federal Polytechnic\, Nigeria
\n1
4:40-15:05
\nTitle: Application of Artificial Intelligence techniques
to formulate a mathematical
\nequation for the uniaxial compressive
strength of fly ash concrete
\nTamer Elsayed Ahmed Said\, National Re
search Centre\, Egypt
\nWorkshop
\n15:05-16:00
\nTitle: Dis
rupting retail analysis with AI powered advanced analytics solution - Idea
\nField BV
\nErgi Sener\, IdeaField Incubation Center BV\, Holl
and
\nPanel Discussion
\nNetworking and Refreshment Break 16:00-
16:20 @ Foyer
\nNetworking 16:20-17:20
\nAward and Closing Cerem
ony
Please contact the event manager Marilyn (marilyn.b.turner(a
t)nyeventslist.com ) below for:
\n- Multiple participant discounts
\n- Price quotations or visa invitation letters
\n- Payment by alt
ernate channels (PayPal\, check\, Western Union\, wire transfers etc)
\n- Event sponsorship
\n
\nNO REFUNDS ALLOWED ON REGISTRATIONS<
br />\nPrices may go up any time. Service fees included in pricing.
\
n-----------------------------------------------------------------
\n
This event is brought to you by:
\nConference Series - NewYorkEventsL
ist
\nhttp://www.NyEventsList.com
\nhttp://www.BostonEventsList.
com
\nhttp://www.SFBayEventsList.com
\n-------------------------
----------------------------------------
VIO181020CRE
\n
\nReference:
\n
\nhttps://www.eventbrite.com/e/8th-internatio
nal-conference-on-big-data-analytics-and-data-mining-cse-a-tickets-5161475
3116