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2nd International Conference on Computer Science, Machine Learning and Big Data
From Monday, December 16, 2019
To Tuesday, December 17, 2019
Hits : 50

COMPUTER SCIENCE MEET 2019

The Computer Science Meet 2019 cordially invites all the participants across the globe to attend the 2nd International Conference on Computer Science, Machine Learning and Big Data which is going to be held during December 16-17, 2019 Dubai, UAE to share the ideas in globally trending technologies in Machine learning, Big data, Artificial Intelligence and many more.

Importance and Scope

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. The current era fully rolled out with many new Artificial Intelligence technologies. In such case, more Software companies and industries were newly introduced within the market which obviously shows the market growth of Artificial Intelligence.

While analyzing the revenue growth of Artificial Intelligence, it highly developed from $150 billion USD to $250 billion USD since from 2010-2015. And the annual growth percentage increases from 20-55 percentages, which clearly shows that Software technology contains huge scope in coming years. Machine learning means using predictive analytics and intelligent automation to formulate data-driven predictions. It allows marketers to identify the likelihood of future outcomes based on historical data. In a recent survey of top marketing influencers, 97% said that the future of marketing will be a combination smart people armed with machine learning – in other words, that machine learning is the future of marketing. Want to make sense of the volumes of data you have collected? Need to incorporate data-driven decisions into your process? This Conference provides an overview of machine learning techniques to explore, analyze, and leverage the Bigdata. Machine learning is ideal for exploiting the opportunities hidden in big data.

Artificial Intelligence has witnessed tremendous growth in the recent past due to the necessity for advancement in the areas of machine translation, object perception, and object recognition. The landscape of tools and infrastructure for training and deploying of neural networks via ‘Machine Learning’ is further evolving rapidly. The rapid uptake of artificial intelligence in end-use industries such as retail and business analytics is expected to augment growth over the next few years.

The deep learning & machine learning would cover the major investment area in AI throughout the forecast period. It includes both cognitive applications (i.e. machine learning, searching, tagging, text and rich media analytics, filtering, categorization, clustering, hypothesis generation, question answering, visualization, alerting, and navigation) and AI platforms, which facilitate the development of intelligent, advisory, and cognitively enabled solutions.

Analytics is another major segment expected to witness bullish growth over the coming years and is major because increasing awareness, needs, and adoption of big data analytics among several small and large enterprises. Organizations are increasingly adopting these solutions owing to the growing need to make fact-based strategic business decisions to reduce the risk of failure and excel in this highly competitive environment.

Why to attend?

With members from around the world focused on learning about Machine learning, Artificial Intelligence, and Big data technologies, this is your single best opportunity to reach the largest assemblage of participants from the Global Information Technology Community. Conduct demonstrations, distribute information, acquire knowledge about current and trending global technologies, make a splash with a new research, and receive name recognition at this 2-days event. World-renowned speakers, the most recent techniques, tactics, and the newest updates in the Machine learning, Artificial Intelligence, and Bigdata Analytics are the hallmarks of this conference.

Target Audience

  • Scientists/Researchers
  • President/Vice president
  • Chairman’s/Directors
  • Professors, Data Analysts
  • Data Scientists
  • Experts and Delegates etc.
  • Heads, Deans, and Professors of Computer Science Departments
  • Research Scholar
  • Engineers
  • Consultants
  • Lab technicians 
  • Founders and employees of the related companies

Sessions / Tracks

 

Highlights and Advancements in Computer Science, Machine Learning, and Big Data Analytics

Track 1:  Computer Science and Technology

Computer Science Technology forms the technological infrastructure of recent commerce. Engineering is associate ever-evolving, increasing field. It is the drive of each trade and permeates way of life. It is the flexibility to mix the ability of computing with the management of multimedia system information and is arguably the key to get an ascendancy in any field.

·         Scientific computing

·         Computer graphics

·         Algorithmic trading

·         Simulation

·         Human-Computer Interaction

Track 2:  Machine learning

Machine learning is a kind of computing (Artificial Intelligence) which permit software system applications to become additionally correct in predicting outcomes while not being expressly programmed. The essential plan of machine learning is to compile algorithms which receive input file associate degree and is used in applied mathematical analysis to foresee an output worth among a satisfactory vary.

·         Machine learning algorithms

·         Supervised learning

·         Unsupervised learning

Track 3:  Deep learning

Deep learning is associated with the developments in computing power and special sorts of neural networks to check the advanced patterns in a large amount of knowledge. Deep learning techniques is a square measure, present the state of the art for characteristic objects in pictures and words are in sounds. Researchers currently expect to apply these successes in pattern recognition to a lot of advanced tasks like automatic language translation, medical diagnoses and diverse which are necessary in social and business issues.

·         How to build neural networks

·         Convolutional networks

·         RNNs, LSTM, Adam, Dropout, BatchNorm

·         Xavier/He initialization

Track 4:  Artificial intelligence

AI or computer science is the simulation of human intelligence processes by machines, particularly by PC systems. These processes embrace learning (the acquisition of data and rules for victimization the information), reasoning (using the foundations to achieve approximate or definite conclusions), and self-correction. Applications of AI embrace skilled systemsspeech recognition, and machine vision. Today, it's AN umbrella term that encompasses everything from robotic method automation to actual AI. It's gained prominence is recently due to the in part, to big data, or to the rise in speed, size, style of information and businesses square measure which is currently grouping. AI will perform tasks like distinguishing patterns within the information additional expeditiously than human businesses to realize additional insight out of their information.

·         Robotic process automation

·         Machine vision

·         Natural language processing

·         Robotics

Track 5:  Artificial intelligence applications

A.I. is getting used nowadays by businesses in both huge and tiny. About what proportion of effect will the A.I have on our future and in what ways will it be succeeded in our day-to-day life? Once A.I. really blossoms, what proportion of improvement can it have on the present iterations of this technology?

·         AI in healthcare

·         AI in business

·         AI in education

·         AI in finance

·         AI in manufacturing

Track 6:  Bigdata

Big information may be a term that describes the big volume of information – each structured and unstructured – that inundates a business on a day-after-day basis. However, it’s not the number of information that’s necessary. It is what organizations do with the information that matters. Big Data information will be analyzed for insights that cause higher selections and strategic business moves. The number of information that’s being created and hold on a world level is nearly unthinkable, and it simply keeps growing. Meaning there’s even a lot of potential to harvest key insights from business information nonetheless solely a little share of information is analyzed. What will that mean for businesses? However, they will create higher use of the raw info that flows into their organizations each day.

·         Streaming data

·         Social media data

·         Publicly available sources

·         Data Exploration & Visualization Importance of Big data

·         Applications of Big data

Track 7:  Big data analytics

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. Analyzing huge information permits analysts, researchers, and business users to create higher and quicker selections victimization information that was antecedently inaccessible or unusable. Victimization advanced analytics techniques like text analytics, machine learning, prognostic analytics, data processing, statistics, and language process, businesses will analyze antecedently 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

Track 8:  Data Mining

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 antecedently 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

Track 9:  Cloud computing

The cornerstone of data analytics in cloud computing is cloud computing itself. Cloud Computing is made around a series of hardware and computer code that may be remotely accessed through any web browser. Usually, files and computer code area unit shared and worked on by multiple users and everyone knowledge is remotely centralized rather than being hold on users’ onerous drives.

·         IoT on Cloud Computing

·         Fog Computing

·         Cognitive Computing

·         Mobile Cloud Computing

Track 10:  Data analytics in cloud

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

Track 11:  Cloud computing in E-commerce

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

Track 12:  Business Intelligence

The competitive intelligence might be a technology-driven methodology for Analyzing 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

Track 13:  SAP SAS (Statistical Analysis System)

SAP is an ERP (Enterprise Resource Planning) software while SAS is an analytics package developed by SAS (Statistical Analysis System) institute. It was founded by James Goodnight and several Colleagues in 1976 from North Carolina State University. Currently, it is used as an integration of software products that enables anyone to perform: Data Manipulation, Statistical and mathematical analysis, Planning, forecasting and decision Support, Report Writing and Graphics, Quality Improvement, Applications Development, Web Reporting, Data Entry, Retrieval, and Management, Data Warehousing and Data Mining. SAS runs on both Windows and UNIX platforms. It is used in a wide range of industries such as healthcare, education, financial services, life sciences etc.

·         SAS Administrators

·         Customer Intelligence

·         Data Management

·         Risk Management

·         Fraud & Security Intelligence

·         Data Visualization

Track 14:  Iot (Internet of things)

New intelligent things typically constitute 3 categories: robots, drones and autonomous vehicles. Every one of these areas can evolve to impact a bigger section of the market and support a brand-new section of digital business, however, these represent just one aspect of intelligent things. Existing things together with internet of Things (IoT) devices can become intelligent things delivering the facility of AI enabled systems all over together with the house, office, manufacturing plant floor, and medical facility. the forthcoming revolution of the Internet-of-Things (IoT) and ensuring connectedness of sensible home technology for years.

Internet of Things (IoT) is associate degree system which is connected to physical objects that square measure accessible through the web. The ‘thing’ in IoT might be someone with a cardiac monitor or associate degree automobile with built-in-sensors, i.e. objects that are allotted associate degree science address and might collect and transfer knowledge over a network while not manual help or intervention. The embedded technology within the objects helps them to move with internal states or the external surroundings, that successively affects the choices taken. IoT – and therefore the machine-to-machine (M2M) technology behind it – square measure transfers a form of “super visibility” to just about each business. Imagine utilities and telcos that may predict and stop service outages, airlines that may remotely monitor and optimize plane performance, and care organizations that are based on health care on period ordering analysis. The business prospects square measure endless.

·         Why IOT?

·         What is the scope of IOT?

·         How can IOT help?

Track 15:  Augmented reality (AR) and Virtual reality

Virtual reality (VR) associated Augmented reality (AR) rework the way people move with one another and with package systems making an immersive setting. for instance, VR will be used for coaching situations and remote experiences. AR, that allows a mixing of the important and virtual worlds, means that businesses will overlay graphics onto real-world objects, like hidden wires on the image of a wall. Immersive experiences with AR and VR area unit reaching tipping points in terms of value and capability, however, it won't replace different interface models. Over time AR and VR expand on the far side visual immersion to incorporate all human senses. Enterprises ought to rummage around for targeted applications of VR and AR through 2020.

·         Computer-mediated reality

·         Object recognition

·         Virtual fixture

Market Analysis

Market analysis of Machine learning

The global machine learning market is expected to grow from USD 1.41 Billion in 2017 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1%. The main driving factors for the market are a proliferation of data generation and technological advancement. In the services segment, the managed service segment is expected to grow at a higher CAGR, whereas professional service segment is expected to be a larger contributor during the forecast period. The managed service is said to be growing faster, as it helps organizations to increase efficiency and save costs by managing on-demand machine learning services. 

Industry insights of AI

The global artificial intelligence market size was valued at USD 641.9 million in 2016 on the basis of its direct revenue sources and at USD 5,970.0 million in 2016 on the basis on enabled revenue and AI-based gross value addition (GVA) prognoses. The market is projected to reach USD 35,870.0 million by 2025 by its direct revenue sources, growing at a CAGR of 57.2% from 2017 to 2025, whereas it is expected to garner around USD 58,975.4 million by 2025 from its enabled revenue arenas. Considerable improvements in commercial prospects of AI deployment and advancements in dynamic artificial intelligence solutions are driving the industry growth.

The Artificial Intelligence industry is segmented by core technologies into Natural Language Processing(NLP), Machine Learning, Deep Learning, and Machine Vision archetype. Deep Learning technologysegment is anticipated to dominate the AI market; both in terms of revenue and CAGR over the forecast period of 2017 to 2025. ‘Deep Learning’ technology is gaining prominence because of its complex data drivenapplications including voice and image recognition. It offers a huge investment opportunity as it can be leveraged over other technologies to overcome the challenges of high data volumes, high computing power, and improvement in data storage.

Market analysis of Bigdata

The global big data market size was valued at USD 25.67 billion in 2015 and is expected to witness a significant growth over the forecast period. The elevating number of virtual online offices coupled with increasing popularity of social media producing an enormous amount of data is a major factor driving growth. Increased internet penetration owing to the several advantages including unlimited communication, abundant information and resources, easy sharing, and online services generates huge chunks of data in everyday life, which is also anticipated to propel demand over the coming years.

The statistic shows a revenue forecast for the global big data industry from 2011 to 2026. For 2017, the source projects the global big data market size to grow to just under 34 billion U.S. dollars in revenue.

Location Dubai, UAE
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