International Conference on Artificial Intelligence is going to be conducted during October 25-26, 2018 at Amsterdam, Netherlands. Artificial Intelligence will focus on the theme “Advancement in Machine Learning”.
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Artificial Intelligence is a way of making a computer, a computer-controlled robot, or software think intelligently as humans think. Research associated with artificial intelligence is highly technical and specialized. The core problems of artificial intelligence include programming computers for certain traits such as Knowledge, Reasoning, Problem solving, Perception, Learning, Planning, Ability to manipulate and move objects.
The purpose of this conference is to promote research in the field of Artificial Intelligence & Scientific exchange among AI researchers, practitioners, scientists, engineers in affiliated disciplines
The Key Points of this conference are listed below:
Amsterdam is the capital and most populous municipality of the Netherlands, known for its artistic heritage, elaborate canal system and narrow houses with gabled facades, legacies of the city’s 17th-century Golden Age. As the commercial capital of the Netherlands and one of the top financial centres in Europe, Amsterdam is considered an alpha world city by the Globalization and World Cities (GaWC) study group. The city is also the cultural capital of the Netherlands. Many large Dutch institutions have their headquarters there, and seven of the world's 500 largest companies, including Philips and ING, are based in the city.
Artificial Intelligence Congress 2018 invites honorable speakers, researchers, PhD. students and exhibitors from all over the world to attend and register for the “International Artificial Intelligence Congress” which is going to be organized from October 25th-26th, 2018 at Amsterdam, Netherlands. We invite you to accompany us at the Artificial Intelligence Congress 2018, where you are assured to have a substantially worthwhile experience with global scholars.
Session-1: Artificial Neural Network
An Artificial Neural Network (ANN) is an information taking care of perspective that is animated by the way natural tangible frameworks, for instance, the cerebrum, process information. The key part of this perspective is the novel structure of the information planning system. It is made out of a broad number of interconnected taking care of segments (neurons) filling in as one to handle specific issues.
ANNs are made out of different center points, which emulate natural neurons of human personality. The neurons are related by associations and they coordinate with each other. The centers can take enter data and perform essential undertakings on the data. The outcome of these exercises is passed to various neurons. The yield at each center point is called its institution or center point regard.
Session-2: K Nearest Neighbor
KNN is a kind of case based learning, or emotionless perceiving, where the most remote point is simply approximated locally and all count is surrendered until delineation.
K-Nearest Neighbors is a champion among the most central and essential portrayal figurings in Machine Learning. It has a place with the controlled learning space and discovers striking application in plot confirmation, data mining and impedance locale.
It is broadly pointless, in fact, conditions since it is non-parametric (it doesn't make any secured assumptions about the course of data). KNN has for the most part high exactness. It is flexible in nature i.e. it is useful for classification or regression.
Session-3: Machine Learning
Machine learning teaches computers to do what comes naturally to humans and animals which they learn from experience. Machine learning algorithms use computational methods to gain information directly from data without relying on a predetermined equation as a model. As the number of samples available for learning increases, the performance of the machine increases.
Machine learning algorithms find natural patterns in data that generate insight and help you make better decisions and predictions. They are every day used to make difficult decisions in medical diagnosis, stock trading, energy load forecasting, and more.
Session-4: Support Vector Machine
Support Vector Machine (SVM) is a classifier technique that performs arrangement of tasks by building hyper planes that isolates instances of various class names. SVM supports regression as well as classification tasks which can handle different types of variables. Support Vector Machines are based on the concept of decision planes that define decision boundaries.
Session-5: Image Processing
Image Processing is a method to perform any operation on an image, either to get an enhanced image or to find out any characteristic about the image. It is kind of signal processing. Here the input is an image and the output can be an image or any feature of an image. Image Processing is basically used for visualization, image retrieval, image enhancement, etc.
Session-6: Data Mining
Data Mining is a process mostly used for converting raw data into some useful information. Data mining is also known as Knowledge Discovery in Data. Data Mining focuses on large data sets & database. Data Mining is widely used in business, science research, security, etc.
A robot is a multitasking controller intended to move materials, devices or specific gadgets through programming to perform variety of tasks. A robot is a system that contains sensors, control frameworks, controllers, control supplies and programming all cooperating together to perform a task. Robots have supplanted humans in performing dreary assignments and perilous errands which people lean toward not to do or can't do.
Session-8: Virtual Intelligence
Virtual Intelligence is a term given to computerized reasoning that exists inside a virtual world. Virtual Intelligence is the crossing point of Virtual Environment and Artificial Intelligence. Virtual Intelligence is a program intended to make PC frameworks less demanding to utilize. Virtual Intelligence is a thoroughly program based machine i.e. they are not mindful.
Watson created by IBM in 2010 as a question answering computing system. As a system, it is described as a heterogeneous ensemble of experts. The supercomputer IBM Watson consumes all the data & gives doctors the relevant information to a particular case, giving them access to better diagnostic information.
Drones also known as unmanned aerial vehicles (UAVs) which has no human pilot onboard, and is either controlled by a person on the ground or autonomously via a computer program. These stealth craft are becoming increasingly popular for war, military purposes, wildlife, atmospheric research to disaster relief and sports photography, archaeological sites, signs of illegal hunting and crop damage.
Session-11: Deep Learning
Deep learning also known as deep structured learning or hierarchical learning is part of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised. ‘Deep Learning’ means using a neural network with several layers of nodes between input and output. The series of layers between input & output do feature identification and processing in a series of stages, just as our brains seem to.
Session-12: Speech & Pattern Recognition
In speech analysis, the voiced-unvoiced decision is performed in conjunction with the pitch analysis. The linking of voiced unvoiced (V-UV) decision to pitch analysis results in unnecessary complexity, as well as makes it difficult for classification of short speech segments which are less than a few pitch periods in duration.
Pattern recognition is a part of machine learning that emphasis on the recognition of patterns. Pattern recognition systems are in many cases trained from supervised learning, but when no data are available other algorithms can be used to discover previously unknown patterns i.e. unsupervised learning.
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 expected 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 reach around USD 58,975.4 million by 2025 from its enabled revenue arenas.
The AI market includes end-use verticals such as healthcare, BSFI, law, retail, advertising & media, automotive and transportation, agriculture, manufacturing, and others. The advertising & media segment dominated the overall market and accounted for over 20% of the total market share in 2016. The AI Market by healthcare application further segregated in to use-cases including, robot assisted surgery, virtual nursing assistants, hospital workflow management, dosage error reduction, clinical trial participant identifier, preliminary diagnosis, and automated image diagnosis. Additionally BFSI applications market further includes, risk assessment, financial analysis/research, investment/portfolio management solicitations.
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Universities in Amsterdam related to Artificial Intelligence:
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