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Previous Speakers

Vijayan K Asari

Vijayan K Asari

Professor University of Dayton USA

Panos M. Pardalos

Panos M. Pardalos

Distinguished Professor of Industrial and Systems Engineering University of Florida USA

Richard Jiang

Richard Jiang

Faculty of Engineering and Environment Northumbria University UK

Anton Nijholt

Anton Nijholt

Full Professor University of Twente Netherlands

Jingsong Wang

Jingsong Wang

Principle Software Developer Oracle USA

Liz Bacon

Liz Bacon

Professor University of Greenwich UK

Mary Mehrnoosh Eshaghian-Wilner

Mary Mehrnoosh Eshaghian-Wilner

Professor of Engineering Practice USC University of southern california USA

Luis Sousa

Luis Sousa

Professor China University of Mining and Technology China

Machine Learning 2018

About Conference

Conference series team cordially invites all participants across the world to attend the World Machine Learning and Deep Learning Congress (Machine Learning 2018) which is going to be held during August 30-31 in Dubai, UAE. The main theme of the conference is Machine Learning: Discovering the New Era of Intelligence". This conference aimed to expand its coverage in the areas of Machine Learning and Deep Learning where expert talks, young researcher’s presentations will be placed in every session of the meeting will be inspired and keep up your enthusiasm. We feel our expert Organizing Committee is our major asset, however Speakers are what make events stand out. World Machine Learning and Deep Learning Congress is bringing the most innovative minds, practitioners, experts and thinkers to inspire and present to the delegates new innovative ways to work and innovate through their data. Your presence over the venue will add one more feather to the crown of Machine Learning 2018.

Machine Learning is a method of teaching computers how to perform complex tasks that cannot be easily described or processed by humans and to make predictions. It is a combination of Mathematical Optimization and Statics. In the other hand, Deep Learning is the subset of ML that focus even more narrowly like neuron level to solve any problem. Machine Learning 2018 is comprised of the following sessions with 20 tracks designed to offer comprehensive sessions that address current applications, discoveries and issues of Machine Learning and Deep Learning.


Target audience for the conference:

  • Data Engineers/Developers
  • Startup Professionals
  • Scientists/Researchers
  • Professors
  • President/Vice president
  • Chairs/Directors

And last but not the least……….

  • Anyone interested in Machine Learning & thrives to make the future developed and better

Sessions / Tracks

Conference series team cordially invites all the participants from all over the world to attend World Machine Learning and Deep Learning Congress during August 30 - 31, 2018 in Dubai, UAE. This includes prompt keynote presentations, Oral talks, Poster presentations and Exhibitions.

Track: Machine Learning

Machine Learning is a subset of Artificial Intelligence (AI) that provides computers with the ability to learn without being explicitly programmed and to make intelligent decisions. It also enables machines to grow and improve with experiences. It has various applications in science, engineering, finance, healthcare and medicine. 

Advantages of Machine Learning-

  • Useful where large-scale data is available
  • Large-scale deployments of Machine Learning beneficial in terms of improved speed and accuracy
  • Understands non-linearity in the data and generates a function mapping input to output (Supervised Learning)
  • Recommended for solving classification and regression problems
  • Ensures better profiling of customers to understand their needs
  • Helps serve customers better and reduce attrition

And many more………

This Machine Learning Conference is focused on adding more value & knowledge to the revolutionary era of Intelligence.

Track: Deep Learning

Deep Learning is a subset of Machine Learning which deals with deep neural networks. It is based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers, with complex structures or otherwise, composed of multiple non-linear transformations. Machine Learning Conferences has added the topic Deep Learning Conferences which will clear the doubts & will add more knowledge from the most innovative minds through out the globe.

Track: Artificial Intelligence

Artificial Intelligence is a technique which enables computers to mimic human behaviour. In other words, it is the area of computer science that emphasizes the creation of intelligent machines that work and reacts like humans. With increasing world of AI, knowledge transfer is also very much necessary. For that Machine Learning Conferences has added this very important topic of Artificial Intelligence meetup.

Types of Artificial Intelligence:

  • Narrow Artificial Intelligence - Narrow artificial intelligence is also known as weak AI. It is an artificial intelligence that mainly focuses on one narrow task. Narrow AI is defined in contrast to either strong AI or artificial general intelligence. All currently existing systems consider artificial intelligence of any sort is weak AI at most. It is commonly used in sales predictions, weather forecasts & playing games. Computer vision & Natural Language Processing (NLP) is also a part of narrow AI. Google translation engine is a good example of narrow Artificial Intelligence
  • Artificial General Intelligence
  • Artificial Super Intelligence

Track: Artificial Neural Networks (ANN) & Chainer

A human brain has neurons that help in adaptability, learning ability & to solve any problem. Unlike Human brain, computer scientists dreamt for computers to solve the perceptual problems that fast. And hence, ANN model came into existence. Artificial Neural Networks is nothing but a biologically inspired computational model that consists of processing elements (neurons) and connections between them, as well as of training and recall algorithmsArtificial Neural Networks (ANN) Conference will help to build relation with the most eminent persons in the field.

Track: Deep Learning Frameworks

Deep Learning is a subset of Machine Learning which deals with deep neural networks. It is based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers, with complex structures or otherwise, composed of multiple non-linear transformations. Deep Learning is able to solve more complex problems and perform greater tasks. Deep Learning Framework is an essential supporting fundamental structure that helps to make complexity of DL little bit easier. 

Track: TensorFlow

The TensorFlow is an open source software library for numerical computation using data flow graphs. It is originally developed by Google Brain Team to conduct machine learning and deep neural networks research. It is a general tool but can be applicable in a wide variety of other domains as well. TensorFlow provides an extensive suite of functions and classes that allow users to build various models from scratch. Because of it’s increasing demand, organizations are conducting Tensorflow Conference in many countries to make people aware of this tool.

Track: The Role of AI & Machine Learning in Medical Science

Machine learning works effectively in the presence of huge data. Medical science is yielding large amount of data daily from research and development (R&D), physicians and clinics, patients, caregivers etc. These data can be used for synchronizing the information and using it to improve healthcare infrastructure and treatments. This has potential to help so many people, to save lives and money. As per a research, big data and machine learning in pharma and medicine could generate a value of up to $100B annually, based on better decision-making, optimized innovation, improved efficiency of research/clinical trials, and new tool creation for physicians, consumers, insurers and regulators. Due to the presence of enormous data in Healthcare, Machine Learning Conferences are adding medical Science topic in their every meetup.

Track: Natural Language Processing (NLP) and Speech Recognition

Natural Language Processing (NLP) is a sub-set of artificial intelligence that focuses on system development that allows computers to communicate with people using everyday language. Natural language generation system converts information from computer database into readable human language and vice versa.

The field of NLP is divided in 2 categories:-

  • Natural Language Understanding (NLU)
  •  Natural Language Generation (NLG)    

Track: Computer Vision and Image Processing

Computer Vision is a sub-branch of Artificial Intelligence whose goal is to give computers the powerful facility for understanding their surrounding by seeing the things more than hearing or feeling, just like humans. It is used for processing, analyzing and understanding digital images to extract information from that. In other words, it transforms the visual images into description of the words. Machine Learning Conference gives a platform for the researchers to come & talk on a common platform.

Track: Pattern Recognition

Pattern Recognition is a classification of Machine Discovering that predominantly concentrates on the acknowledgement of the structure and regularities in detail; however, it is considered almost similar to machine learning. Pattern Recognition has its cause from engineering, and the term is known with regards to Computer vision. Pattern Recognition, for the most part, has a better enthusiasm to formalize, illuminate and picture the pattern and give the last outcome, while machine learning customarily concentrates on expanding the recognition rates before giving the last yield. Pattern Recognition algorithms normally mean to give a reasonable response to every single input and to perform in all probability coordinating of the data sources, taking into charge their statistical variety. There are various uses of Pattern Recognition. 

Track: Facial Expression and Emotion Detection

The use of machines in the public has expanded widely in the most recent decades. These days, machines are utilized as a part of a wide range of businesses. As their introduction with people increment, the communication additionally needs to wind up smoother and more characteristic. Keeping in mind the end goal to accomplish this, machines must be given an ability that let them get it the encompassing condition. Exceptionally, the intentions of a person. At the point when machines are eluded, this term includes to computers and robots. Deep Learning conference will talk in depth about facial expression & emotion detection.

Track: Predictive Analytics

Predictive Analytics is the branch of advanced analytics which offers a clear view of the present and deeper insight into the future. It uses different techniques and algorithms from statistics and data mining, to analyze current and historical data to predict the outcome of future events and interactions. Big Data Conference, Artificial Intelligence Conference as well as Machine Learning summit keep Predictive Analytics as its main part because of its vast scope.

Track: Big Data, Data Science and Data Mining

Nowadays, a huge quantity of data is being produced daily. Machine Learning uses those data and provides a noticeable output that can add value to the organization and will help to increase ROI,

Big Data is informational indexes that are so voluminous and complex that conventional data handling application programming is lacking to manage them. Big Data challenges incorporate capturing data, data storagedata analysis, search, sharing, transfer, visualization, querying, and updating and data security. There are three dimensions to Big Data known as Volume, Variety and Velocity.

Data Science manages both structured and unstructured data. It is a field that incorporates everything that is related to the purging, readiness and last investigation of data. Data science consolidates the programming, coherent thinking, arithmetic and statistics. It catches information in the keenest ways and supports the capacity of taking a gander at things with an alternate point of view.

Data mining is essentially the way toward collecting information from gigantic databases that was already immeasurable and obscure and after that utilizing that information to settle on applicable business choices. To put it all the more essential, Data mining is an arrangement of different techniques that are utilized as a part of the procedure of learning disclosure for recognizing the connections and examples that were beforehand obscure. We can thusly term data mining as a juncture of different fields like artificial intelligence, data room virtual base management, pattern recognition, visualization of data, machine learning, and statistical studies and so on.

Track: Big Data Analytics

Big Data Analytics gives a handful of usable data after examining hidden patterns, correlations and other insights from a large amount of data. That as a result, leads to smarter business moves, higher profits, more efficient operations and finally happy customers. And Big Data Conference adds more value to it.

Big Data Analytics adds value to the organization in following ways:

  • Cost reduction
  • Faster, Better decision making
  • New Products and Services

Track: Dimensionality Reduction

In Machine Learning, when machine captures data, they find random data. Then machine learning uses dimensionality reduction or dimension reduction is the process for reducing the number of random variables under consideration by obtaining a set of principal variables. It can be divided into feature selection and feature extraction. 

Track: Model Selection and Boosting

Model Selection is the undertaking of choosing a statistical model from an arrangement of candidate models, given information. In the least difficult cases, a prior arrangement of information is considered. However, the assignment can likewise include the outline of trials with the end goal that the information gathered is appropriate to the problem of model selection. Given candidate models of comparable prescient or illustrative power, the least complex model is well on the way to be the best decision

Boosting is a machine learning ensemble meta-algorithm for essentially lessening inclination, and furthermore changes in supervised learning, and a group of machine learning algorithms which change over weak learners to strong ones. A weak learner is characterized to be a classifier which is just marginally related to the genuine characterization (it can name cases superior to anything irregular speculating). Conversely, a strong learner is a classifier that is subjectively all around connected with the genuine classification. It plays a very important role in Machine Learning Conference.

Track: Object Detection with Digits

Object detection with digits is a piece of Deep Learning. It is a standout amongst the most difficult issues in computer vision and is the initial phase in a several computer vision applications. The objective of an object detection system is to recognize all examples of objects of a known classification in a picture. Because of its important existence, Deep Learning Conferences always include a track on Object Detection with Digits.

Track: Cloud Computing

Cloud Computing is a delivery model of computing services over the internet. It enables real-time development, deployment and delivery of broad range of products, services and solutions. It is built around a series of hardware and software that can be remotely accessed through any web browser. Generally, documents and programming are shared and dealt with by numerous clients and all information is remotely brought together as opposed to being put away on clients' hard drives. Machine Learning Conferences has included a special talk on Clod Computing.

Track: Robotic Process Automation (RPA)

Robotic Automation lets organizations automate current tasks as if a real person was doing them across applications and systems. RPA is a cost cutter and a quality accelerator. Therefore RPA will directly impact OPEX and customer experience, and benefit to the whole organization and this is why it becomes a main topic to be discussed in Machine Learning Conference.

Market Analysis

World Machine Learning and Deep Learning Congress welcome presenters, exhibitors and attendees to Dubai, UAE during August 30-31, 2018. The organizing committee is preparing for an exciting and informative conference program including lectures, workshops, symposia on a wide variety of topics, poster presentations and various programs for participants from across the world. We invite you to join us at the Machine Learning 2018, where you will be sure to have a meaningful experience with scholars from around the globe. All members of the Machine Learning 2018 organizing committee look forward to meeting you in Dubai, UAE.

Scope and importance:

Previously Machine Learning & Deep Learning was used to construct software from training examples. Its method was also extended to support data mining and knowledge discoveries. Then ML & DL started doing perceptual tasks like deep learning for computer vision, speech recognition etc. After that its main work was automated decision making & anomaly detection (Cyber Security, Fraud Detection and Machine Diagnosis). But the Future of ML & DL is beyond imagination and it can control & work on variety of topics like:

  • Detecting and Correcting for Bias
  • Risk Sensitive Optimization
  • Explanations of Black Box Systems
  • Verification and Validation
  • Integrating ML Components into Larger Software Systems


The market of Machine Learning & Deep Learning is growing exponentially worldwide. According to the research, the global machine learning market is expected to grow from $ 1.41 Billion in 2017 to $ 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% and the Global Machine Learning as Service market is expected to grow from $ 480.94 million in 2015 to reach $ 5,394.87 million by 2022 with a CAGR of 41.2%.    

Machine learning enabled solutions are being significantly adopted by organizations worldwide to enhance customer experience, Return on Investment (ROI), and to gain a competitive edge in business operations. Moreover, in the coming years, applications of machine learning in various industry verticals are expected to rise exponentially. Some of the verticals are:

  • BFSI

o    Applications of machine learning in BFSI

  1. Fraud and Risk Management
  2. Investment Prediction
  3. Sales and Marketing Campaign Management
  4. Customer Segmentation
  5. Digital Assistance
  6. Others (compliance management and credit underwriting)
  • Healthcare and Life Sciences

o    Applications of machine learning in healthcare and life sciences

  1. Disease Identification and Diagnosis
  2. Image Analytics
  3. Drug Discovery/Manufacturing
  4. Personalized Treatment
  5. Others (clinical trial research and epidemic outbreak prediction)
  • Retail

o    Applications of machine learning in retail

  1. Inventory Planning
  2. Upsell and Cross Channel Marketing
  3. Segmentation and Targeting
  4. Recommendation engines
  5. Others (customer ROI and lifetime value, and customization management)
  • Telecommunication

o    Applications of machine learning in telecommunication

  1. Customer Analytics
  2. Network Optimization
  3. Network Security
  4. Others (digital assistance/contact centres analytics and marketing campaign analytics)
  • Government and Defence

o    Applications of machine learning in government and defence

  1. Threat Intelligence
  2. Autonomous Defence system
  3. Others (sustainability and operational analytics)
  • Manufacturing

o    Applications of machine learning in manufacturing

  1. Predictive Maintenance
  2. Demand Forecasting
  3. Revenue Estimation
  4. Supply Chain Management
  5. Others (root cause analysis and telematics)
  • Energy and Utilities

o    Applications of machine learning in energy and utilities

  1. Power/Energy Usage Analytics
  2. Seismic Data Processing
  3. Smart Grid Management
  4. Carbon Emission
  5. Others (customer specific pricing and renewable energy management)
  • Others (Education, Agriculture, Media and Entertainment, and Education)


Why Dubai, UAE:

UAE is the city of world’s best architecture and now it is stepping towards the advanced future of Artificial Intelligence. UAE believes in Smart Smarter Smartest… and therefore the government took AI adoption one step further by installing a minister in-charge of AI.

Few actions of UAE Government in the field of AI and Machine Learning in past years are:

  • 2000 – E-Government-the first government to move to e-government system
  • 2013 – Smart Government-launched services to public wherever they are round the clock
  • 2015 – Smart Transformation – Achieved 100% e-government transformation
  • 2017 – Artificial Intelligence-launched AI strategy as part of UAE Centennial 2071


Why to Attend???

ML conference gathers communities to discuss the recent research and application of Algorithms, Tools, and Platforms to solve the hard problems that exist within organizing and analyzing massive and noisy data sets. Machine Learning 2018 invites attendees from around the world focused on learning about ML & DL. This would be one of best opportunity to reach the largest assemblage of participants from the ML community. Conduct demonstrations, distribute information, meet with current and potential customers, make a splash with a new product line, and receive name recognition at this 2-day event. World’s renowned speakers, the most recent techniques, highlights, discoveries and the newest updates in ML & DL fields are hallmarks of this conference.

  • Extraordinary speakers

Discover advances in ML and DL algorithms and methods from the world's leading speakers, researchers and scholars. Learn from industry experts in speech & pattern recognition, neural networks, image analysis and NLP. Explore how Machine Learning and Deep Learning will impact finance, healthcare, manufacturing, search & transportation.


  • Discover emerging trends

The congress will showcase the opportunities of advancing trends in Machine Learning & Deep Learning and their impact along with successful applications in business. It will also focus on the challenges and areas of improvement related to the field of research and applications. Learn the latest technological advancements & industry trends from a global line-up of experts.


  • Expand your network

A unique opportunity to connect with industry leaders, influential technologists, Machine Learning Professionals & founders leading the deep learning revolution. Learn from & interact with various industry innovators sharing best practices to advance the smart artificial intelligence revolution and become a part of it.

Industries Associated with Machine Learning:

World Wide:

  • GOOGLE – For developing a photographic memory
  • IBM – For embedding Watson where it’s needed most
  • BAIDU – For accelerating mobile search with artificial intelligence
  • SOUNDHOUND – For giving digital services the power of human speech
  • ZEBRA MEDICAL VISION – For using deep learning to predict and prevent diseases
  • PRISMA - For making masterpieces out of snapshots
  • IRIS AI - For speeding up scientific research by surfacing relevant data
  • PINTEREST - For serving up a universe of relevant pins to each and every user
  • TRADEMARKVISION - For helping start-ups make their mark without any legal confusion
  • DESCARTES LABS - For preventing food shortages by predicting crop yields

Universities Associated with Machine Learning:

World Wide:

  • Carnegie Mellon University
  • University of Michigan Ann Arbor
  • Cornell
  • Berkeley
  • Stanford
  • Columbia University
  • University of Washington
  •  Georgia Tech
  • University of California San Diego
  • University of Massachusetts Amherst
  •  John Hopkins University
  • University of Illinois Urbana Champaign
  • Penn State University
  • University of North Carolina Chapel Hill
  • California Institute of Technology
  • University of Wisconsin Madison

 Major Societies & Groups Worldwide-

  • The International Machine Learning Society (IMLS)
  • American Statistical Association
  • IEEE Computer Society
  • European Knowledge Discovery Network of Excellence (KDNet)
  • National Centre for Data Mining (NCDM)
  • Pacific Rim International Conferences on Artificial Intelligence
  • Canadian Artificial Intelligence Association
  • The European Coordinating Committee on AI (ECCAI)
  • Special Interest Group on Artificial Intelligence, Computer Society of India
  • Japanese Society of Artificial Intelligence
  • Sociedad Mexicana de Inteligencia Artificial
  • Russian Association for Artificial Intelligence
  • Computing Research Association
  • The Society for the Study of Artificial Intelligence AND Simulation of Behaviour
  • The International Neural Network Society

Related Societies: 

USA: ML SocietyThe International Machine Learning Society; The Association For The Advancement Of Artificial Intelligence; The Artificial Intelligence Society; AI & Society; The Association for Uncertainty in Artificial Intelligence; Society for Artificial Intelligence; Conference on Uncertainty in Artificial Intelligence; Artificial Intelligence International; Slovenian Pattern Recognition Society; International Association of Computer Science and Information Technology; Global Cleantech Cluster Association; Czechoslovak Pattern Recognition Society; Bulgarian Association for Pattern Recognition; The Swiss Association for Pattern Recognition; The British Machine Vision Association and Society for Pattern Recognition;

Europe: European Association for Artificial Intelligence; Conexus Deep Learning Society; Deep Learning Society – Atlantic Rim Collaboratory;  the Hellenic Artificial Intelligence Society; the European Coordinating Committee for Artificial Intelligence; Artificial Intelligence International; The Association for Uncertainty in Artificial Intelligence; Special Interest Group of the Brazilian Computer SocietyFrench Association for Pattern Recognition and Interpretation; National Committee of the Russian Academy of Sciences for Pattern Recognition and Image Analysis; Mexican Association for Computer Vision, Neurocomputing and Robotics, Nederlandse Vereniging voor Patroonherkenning en Beeldverwerking; Italian Association for Pattern Recognition

Asia Pacific & Middle East: Indian Unit for Pattern Recognition and Artificial Intelligence; Artificial Intelligence Society of Hong Kong; Pattern Recognition and Machine Intelligence Committee of the Chinese Association of Automation; Artificial Intelligence Association of India; The Society for the Study of Artificial Intelligence and Simulation of Behaviour; Australian Pattern Recognition Society; Hong Kong Society for Multimedia and Image Computing; Pattern Recognition and Machine Intelligence Committee of the Chinese Association of Automation; The Macau Society for Pattern Recognition and Image ProcessingPakistani Pattern Recognition Society (PRRS); Computer Vision and Pattern Recognition Group of The Korean Institute of Information Scientists and Engineers

Related Conferences:

What will you learn???

Knowledge is everywhere; you can learn whatever you want from any source. But Machine Learning 2018 has made your search easy & compiled 19 sessions on emerging topics at a single place. Those sessions are:

We feel our expert Organizing Committee is our major asset, however, Speakers are what make events stand out. World Machine Learning and Deep Learning Congress is bringing most innovative minds, practitioners, experts, thinkers, eminent Researchers, Scientists, Professors, Developers, Analysts and Newbies under a solitary rooftop to inspire and present to the delegates new innovative ways to work and innovate through their data. Your presence over the venue will add one more feather to the crown of Machine Learning 2018.

Past Conference Report

Automation and Robotics 2017 Report

Thanks to all our wonderful speakers, conference attendees, Automation and Robotics-2016 Conference was the best!

The 2nd World Congress on Automation and Robotics, hosted by the Conference Series LLC was held during June 13-15, 2016 at DoubleTree by Hilton Philadelphia Airport, Philadelphia, USA with the theme Automation and Robotics for a Sustainable Future". Benevolent response and active participation was received from the Editorial Board Members of OMICS Group Journals as well as from the scientists, engineers, researchers, students and leaders from the fields of Automation and Robotics, who made this event successful.

The meeting was carried out through various sessions, in which the discussions were held on the following major scientific tracks:

  • Industrial Automation
  • Automation Tools and Technologies
  • Control and Mechatronic Systems
  • Robotics and Applications
  • Manufacturing Automation
  • Internet of Things
  • Process and Energy Automation
  • Security in Manufacturing Industries
  • Automation systems
  • Automation solutions

The conference was initiated with a series of lectures delivered by both Honorable Guestsand members of the Keynote forum. The list included:

Eduard Babulak, The Institute of Technology and Business in Ceske Budejovice, Czech Republic
Farrokh Janabi Sharifi, Ryerson University, Canada

Petter Falkman, Chalmers University of Technology Sweden

James P Gunderson, GunderFish LLC, USA

Asim ur Rehman Khan, National University of Computer & Emerging Sciences, Pakistan

Jerry Vinther, Lillebaelt Academy University of Applied Sciences, Denmark

Conference Series LLC offers its heartfelt appreciation to the Organizing Committee Members, adepts of field, various outside experts, company representatives and other eminent personalities who supported the conference by facilitating the discussion forums. Conference Series LLC also took privilege to felicitate the Organizing Committee Members and Editorial Board Members who supported this event.

Conference Series LLC is proud to announce the "5th World Machine Learning & Deep Learning Congress" to be held during August 30-31, 2018 Dubai, UAE.

For More details visit:

Let us meet Again @ Machine Learning 2018


To Collaborate Scientific Professionals around the World

Conference Date August 30-31, 2018

Speaker Opportunity

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What People Say....

Automation and Robotics 2016 was a wonderful experience. I had a chance of meeting leading Academicians, Entrepreneurs, and Inventors. I am personally very grateful to you for your encouragement and support. The event was very well organized, and there was plenty of time for networking. The onsite team was also very helpful. I do not see any area that needs improvement. I am extremely grateful for your guidance.


Asim ur Rehman Khan National University of Computer & Emerging Sciences, Pakistan

I am pleased to inform you that “Automation and Robotics 2016” Congress was successful and that we had a very good talks and discussions. I remain most grateful and look froward to working with you on the 3rd Congress.


Eduard Babulak The Institute of Technology and Business in Ceske Budejovice, Czech Republic

Most of the presentations were good.


Herbert Pichlik SYSTEC GmbH, Germany

I had a great time at Multimedia 2016. The staff was kind, the venue was appropriate and I was able to interact with researchers from all around the world. Overall the conference was excellent.


Leonardo Sacht, Federal University of Santa Catarina, Brazil

Thank you for many things. It was a good hotel and presentations were exciting. I think the conference was in a friendly atmosphere and I appreciate your preparation.


Takashi Nakamura, Niigata Unviersity, Japan

I enjoyed talking on "the complexity model of communication with computer images". Thanks to Multimedia & Applications for the invitation.


Toshie Takahashi, Waseda University, Japan

Enjoyed invited keynote speech for the international conference


Yoichiro Kawaguchi, Japaneese Computer Graphic Artist, University of Tokyo, Japan

Thanks a lot for you to bring sparking talk to me in the past two days.


Xintao Ding, Anhui Normal University, China

This is an excellent conference for young researchers, getting the opportunity of meeting some senior/experienced researchers and practitioners, learning from them and getting an exposure to various types of applications. The hotel was well chosen, and the facilities and environment are superb for this conference.


Ching Y Suen, Concordia University, Canada

Many thanks for your email, it was a great pleasure to join Multimedia 2016 conference in UK last month with Dr. Suen and other experts.


Mohamed Naiel, Concordia University, Canada


  • ACID Test
  • Adaptive Resonance
  • Agents
  • Aggregation
  • Algorithm
  • Ambient Intelligence
  • Anonymization
  • Argumentation Models
  • Artificial Intelligence
  • Artificial Neural Network (ANN)
  • Artificial Neural Networks
  • Automatic Identification And Capture (AIDC)
  • Automation
  • Autonomous Vehicle Navigation
  • Avro
  • Backpropagation
  • Bayesian Network
  • Behavioral Analytics
  • Big Data
  • Big Data Scientist
  • Bioinformatics
  • Brain Machine Interfaces
  • Business Intelligence (BI)
  • Calibration And Identification
  • Call Detail Record (CDR) Analysis
  • Cascading
  • Cassandra
  • Chainer
  • Chatbot
  • Chatbots
  • Chukwa
  • Clickstream Analytics
  • Clojure
  • Cloud Computing
  • Clustering
  • Cluttered Environment
  • Cognitive Architecture
  • Cognitive Computing
  • Cognitive Science
  • Cold Data Storage
  • Combinatorial Explosion
  • Computational Creativity
  • Computer Science
  • Computer Vision
  • Consciousness
  • Cooperating Robots
  • Data
  • Data Architecture And Design
  • Data Center
  • Data Integration
  • Data Integrity
  • Data Mining
  • Data Science
  • Data Security
  • Data Visualization
  • Data Warehousing
  • Database
  • Database As A Service (DaaS)
  • Database Management System (DBMS)
  • Decision Model
  • Deep Blue
  • Deep Blue (chess Playing AI)
  • Deep Learning
  • Deep Learning In Robotics And Automation
  • Deep Thunder
  • Demographic Data
  • Descriptive Model
  • Design
  • Dimensionality Reduction
  • Distributed Processing
  • Document Store Databases
  • Earning And Adaptive Systems
  • Embodied AI
  • Emotion
  • Emotion Detection
  • Equilibrium
  • Evolutionary Computation
  • Expert Systems
  • Extreme Learning Machine
  • Facial Expression Detection
  • Feature Learning
  • Force And Tactile Sensing
  • Forecast
  • Fuzzy Logic
  • Fuzzy Systems
  • Genetic Algorithm
  • Genetic Programming
  • Hadoop
  • HBase
  • Hive
  • Hue
  • Human Computing Interface
  • Image Processing
  • Image Recognition
  • Impala
  • Implement
  • In-database Analytics
  • Inductive Logic Programming (ILP)
  • Inductive Reasoning
  • Informative Extraction
  • Input/Output
  • Integrated Systems
  • Intelligent Robots
  • Intelligent Transportation Systems
  • Internet Of Things
  • Learning From Experience
  • Logic Programming
  • Logical AI
  • Machine Learning
  • Machine Vision
  • Manipulation Planning
  • MapReduce
  • Marine Robotics
  • Mashup
  • Metadata
  • Micro/Nano Robots
  • Model Selection & Boosting
  • Natural Language Generation (NLG)
  • Natural Language Processing (NLP)
  • Networked Robots
  • Neural Language Generation (NLG)
  • Neural Nets
  • Neural Networks
  • Neurorobotics
  • NewSQL
  • NoSQL
  • Nural Language Processing (NLP)
  • Object Detection With Digits
  • Oozie
  • Optical Character Recognition (OCR)
  • Optimization
  • Overfitting
  • Parallel Processing
  • Parameter
  • Pattern Discovery
  • Pattern Recognition
  • Perception For Grasping And Manipulation
  • Perceptrons
  • Pig
  • Planning Under Uncertainty
  • Predictive Analytics
  • Predictive Model
  • Predictive Modeling
  • Process Control
  • Pruning
  • Python
  • R Programming Language
  • Real-time Data
  • Reasoning
  • Recognition
  • Recurrent Neural Network
  • Regression
  • Reinforcement Learning
  • Resource Allocation
  • Risk Analysis
  • Robotic Process Automation (RPA)
  • Schema
  • Self-Organnising Neural Network
  • Sensor Networks
  • Sequential Analysis
  • Server
  • Simulation And Animation
  • Software
  • Software As A Service (SaaS)
  • Space Robotics And Automation
  • Speech Recognition
  • Speech To Text
  • SQL
  • Statical Data
  • Statistical Data Analysis Technique
  • Strong AI
  • Structured Data
  • Supervised Learning
  • Support Vector Machines
  • Surveillance Systems
  • Swarm Intelligence
  • Swarm Robots
  • Target Function
  • Technology
  • TensorFlow
  • Tentative Hypothesis
  • Test Data Set
  • Text Analytics
  • Training Data Set
  • Turing Test
  • Ubiquitous Computing
  • Unsupervised Learning
  • Validation Data Set
  • Virtual Reality And Interfaces
  • Vision
  • Visual Learning
  • Visual Tracking
  • Weak AI
  • Wearable Technology
  • WebHDFS Apache Hadoop
  • XML Databases