6th Global Summit on Artificial Intelligence and Neural Networks (CSE) AS, Helsinki, maanantai, 15. lokakuu 2018

6th Global Summit on Artificial Intelligence and Neural Networks  

About Artificial Intelligence and Neural Networks Conference
We take the pride to invite all the participants across the globe to attend the 6th Global Summit on Artificial Intelligence and Neural Networks during October 15-16, 2018 at Helsinki, Finland. Artificial Intelligence and Neural Networks include prompt Keynote presentations, Oral talks, Workshops/Special Sessions, Poster presentations, and Exhibitions. Neural Networks 2018 aims at proclaiming knowledge and sharing new ideas amongst the professionals, industrialists, researchers, and students from research area of Artificial Intelligence. This scientific gathering guarantees that offering the thoughts and ideas will enable and secure you the theme “Harnessing the power of Artificial Intelligence”. Artificial Intelligence is the technology which will revolutionize many fields especially in industries like manufacturing, control systems, cloud computing, Data mining, etc. Artificial neural networks are statistical models directly inspired by, and partially modeled on biological neural networks. 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. Importance and Scope Artificial Intelligence (AI) promises to revolutionize our lives diagnose our health problems, drive our cars and lead us into a new future where thinking machines do things that we’re yet to imagine. Fields like Neural Networks, Machine Learning, Robotics, Evolutionary Computation, Vision, Speech Processing, Expert Systems, Planning and Natural Language Processing. The conference organizers aim is to gather the researchers, academicians and scientists from the field of Neural Networks and Artificial Intelligence community to create an approach towards a global exchange of information on technological advances, new scientific innovations, and the effectiveness of various regulatory programs towards Artificial Intelligence. Target Audience
Researchers Scientists Professors Engineers Students Smart Innovators Robotic Technologist Gaming Professionals Automation Industry Leaders Health Care Service Providers Defence Research Professionals Managers & Business Intelligence Experts Advertising and Promotion Agency Executives Professionals in Media Sector
Sessions/Tracks
Track 1: Artificial IntelligenceArtificial Intelligence is a field of study of computer science based on the premise that intelligent thought can be regarded as a form of computation one that can be formalized and ultimately mechanized. AI algorithms can tackle learning, perception, problem-solving, language-understanding and/or logical reasoning. In the modern world, AI can be used in many ways even when it is to control robots. Sensors, actuators and non-AI programming are parts of a larger robotic system.
Track 2: Cognitive Computing
Cognitive Computing refers to the hardware and/or software that helps to improve human decision-making and mimics the functioning of the human brain. It refers to systems that can learn at scale, reason with purpose and interact with humans naturally. It comprises of software libraries and machine learning algorithms for extracting information and knowledge from unstructured data sources. The main motive is to accurate models of how the human brain/mind senses, reasons, and responds to stimulus. High-performance computing infrastructure is powered by processors like multicore CPUs, GPUs, TPUs, and neuromorphic chips. They interact easily with users, mobile computing, and cloud computing services so that those users can define their needs comfortably.
Neural Informatics for Cognitive Computing
Neural Information theory is a multidisciplinary enquiry of the physiological and biological representation of knowledge and information in the brain at the neuron level.
Track 3: Self-Organizing Neural Networks
Pattern recognition is a part of machine learning that focuses on the recognition of patterns and regularities in data by using supervised learning algorithms that create classifiers based on training data from different object classes. Optical character recognition (OCR) faces detection, **** recognition, object detection, and object classification uses supervised pattern recognition. And the unsupervised learning works by finding hidden structures in clustering techniques.
Feature selection or variable selection is the process of selecting a subset of relevant features for use in model construction. They are also used to simplify the models to make them easier to interpret, shorter training times and enhanced generalization by reducing overfitting (reduction of variables). The data contains many features that are either redundant or irrelevant and can be removed without incurring much loss of information.
Track 4: Backpropagation
Artificial neural network uses backpropagation method to calculate the error contribution of each neuron after a batch of data (in image recognition) is processed. It is a special case of an older and more general technique called automatic differentiation. It is commonly used by the gradient descent optimization algorithm to adjust the weight of neurons by calculating the gradient of the loss function. This technique is also sometimes called backward propagation of errors because the error is calculated at the output and distributed back through the neural network layers.
Track 5: Computational Creativity
Machine learning is the field of computer science which teaches machines to detect different patterns and to adapt to new circumstances. Machine Learning can be both experienced and explanation-based learning. In the field of robotics machine learning plays a vital role, it helps in taking an optimized decision for the machine which eventually increases the efficiency of the machine and more organized way of performing a particular task. It is employed in a range of computing tasks where designing and programming with good performance is difficult or infeasible for example email filtering, network detection or malicious insiders working towards a data breach optical character recognition (OCR) and computer vision.
Data mining is an extraction process of useful patterns and information from huge data. It is also called as knowledge discovery process, knowledge mining from data, knowledge extraction or data /pattern analysis. It is a logical process that is used to search through a large amount of data in order to find useful data. The goal of this technique is to find patterns that were previously unknown.
Track 6: Artificial Neural Networks
Artificial Neural Networks are the simulations which perform specific tasks like pattern recognition, clustering etc. on the computer.
Artificial neural networks are mathematical models inspired by the organization and functioning of biological neurons. They are similar to the human brains, acquire knowledge through learning and their knowledge is stored within interneuron connections strengths known as synaptic weights.
Architecture of ANN
A large number of artificial neurons called units arranged in a series of layers are contained by neural networks. Different layers are Input layer, Output layer, and Hidden layer.
Input layer contains those units which receive input from outside world on which network will learn and recognize the process.
Output layer contains units that respond to the information about how it’s learned any task.
Hidden layers are in between input and output layers. It transforms the input into something that output unit can use in some ways.
Track 7: Deep Learning
Deep learning is also known as deep structured learning is a part of machine learning process based on learning data representation. It uses some form of gradient descent for training via backpropagation.
The layers used in deep learning include hidden layers of artificial neural networks and sets of propositional formulas.
Deep Neural Networks
DNN is an ANN with multiple hidden layers between the input and output layers. DNN architectures generate compositional models where the object is expressed as a layered composition of primitive. They are typically fed forward neural networks in which data flows from the input layer to the output layer without looping back.
Applications
Automatic speech recognition Image recognition Visual art processing Natural language processing
Track 8: Ambient IntelligenceAmbient intelligence (AmI) deals with the computing devices, where physical environments interact intelligently and conservatively with people. These environments should be aware of people's needs, customizing requirements and forecasting behaviors. It can be diverse, such as homes, meeting rooms, offices, hospitals, schools, control centers, vehicles, etc. Artificial Intelligence research aims to include more intelligence in AmI environments, allowing better support for humans and access to the essential knowledge for making better decisions when interacting with these environments.
Track 9: Perceptrons
Perceptron is a machine learning algorithm that helps to provide classified outcomes for computing. It is a kind of a single-layer artificial network with only one neuron and a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector.
Multilayer Perceptron
Multilayer Perceptron is a class of feedforward artificial neural networks. And the layered feedforward networks are trained by using the static back-propagation training algorithm. For designing and training an MLP perceptron several issues are involved:
A number of hidden layers are selected to use in the neural network.
A solution that avoids local minima is globally searched.
Neural networks are validated to test for overfitting.
Converging to an optimal solution in a reasonable period of time.

Track 10: Cloud Computing
Cloud computing is a branch of information technology which grants universal access to shared pools of virtualized computer resources. A cloud can host different workloads, allows workloads to be scaled/deployed-out on-demand by rapid provisioning of physical or virtual machines, self-recovering, supports redundant, and highly-scalable programming models and allows workloads to recover from hardware/software rebalance and failures allocations.
Artificial Intelligence technology plays a very important role in Making resources available, Distribution transparency and Openness Scalability especially for Cloud Computing Application. Artificial intelligence and cloud computing will have an important impact on the development of information technology by mutually collaborating.

Track 11: Autonomous Robots
Autonomous robots are the intelligently capable machines which can perform the task under the control of a computer program. They are independent of any human controller and can act on their own. The basic idea is to program the robot to respond a certain way to outside stimuli. The combined study of neuroscience, robotics, and artificial intelligence is called neurorobotics.
Application and their classification-
· The autonomous robots are classified into four types
Programmable: Swarm Robotics, mobile robots, industrial controlling and spacecraft.
Non- Programmable: Path guiders and medical products carriers.
Intelligent: Robotics in Medical military applications and home appliance control systems.
Adaptive: Robotic gripper, spraying and welding systems.
Track 12: Support Vector Machines
Support Vector Machines are the set of related supervised machine learning algorithm capable of delivering higher performance in terms of classification and regression accuracy. In this algorithm, each data item is plotted as a point in n-dimensional space (where n is the number of features you have) with the value of each feature being the value of a particular coordinate. SVM utilizes an optimum linear separating hyperplane to separate two data sets in a feature space. This optimum hyperplane is produced by maximizing minimum margin between the two sets. Therefore the resulting hyperplane will only be depended on border training patterns called support vectors.
Track 13: Parallel Processing
Parallel Processing reduces processing time by simultaneously breaking up and running program tasks on multiple microprocessors. There are more engines (CPUs) running, which makes the program run faster. It is particularly useful when running programs that perform complex computations, and it provides a viable option to the quest for cheaper computing alternatives. Supercomputers commonly have hundreds of thousands of microprocessors for this purpose. Parallel programming is an evolution of serial computing where the jobs are broken into discrete parts that can be executed concurrently. It is further broken down into a series of instructions and the instructions from each part execute simultaneously on different CPUs.
Track 14: Bioinformatics
Bioinformatics is a multidisciplinary research field that combines computer science, biology, science, statistics and mathematics into a broad-based field that will have profound impacts on all fields of biology. It is the application of computer technology to the management of biological information.
Biocomputing is the computing which designs and constructs the computer containing biological components.
Relation to Artificial Intelligence
Artificial Intelligence has continuously gained attention in bioinformatics
AI algorithms to be used for keeping records. Helping Interpret Large amount of data by using computer technology. Choosing a particular method for analyzing data.
Track 15: Ubiquitous Computing
Ubiquitous computing is a branch of computing in computer science and software engineering where computing is made easier so that they can appear anytime and everywhere. It can occur using any device, in any location, and in any format.
Key features include:
Use of Inexpensive processors which reduces the storage and memory requirements.
Totally connected and constantly available computing devices and capturing of real-time attributes.
Focus on many-to-many relationships, instead of one-to-one, many-to-one or one-to-many in the environment, along with the idea of technology, which is constantly present.
Relies on wireless technology, converging Internet and advanced electronics.
Track 16: Natural Language Processing
Natural language processing (NLP) is a subset of Artificial Intelligence. Its ability is to interpret and understand human language the way it’s spoken or written and to make the machines/computer as intelligent as human beings in understanding language.
Natural language generation (NLG) and Natural language understanding (NLU) are the two main components of NLP. NLU understanding involves mapping the given input in natural language into useful representations and NLG is the process of producing meaningful phrases and sentences in the form of language.
Track 17: Entrepreneurs Investment Meet
Neural Networks 2018 facilitates a unique platform for transforming potential ideas and research into great business. The meeting aims to gather the researchers, academicians and scientists from the field of Neural Networks and Artificial Intelligence community to create an approach towards a global exchange of information on technological advances, new scientific innovations, and the effectiveness of various regulatory programs towards Artificial Intelligence. It's allied sciences to develop and facilitate the most optimized and viable business for engaging people in to constructive discussions, evaluation, and execution of promising business.


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6th Global Summit on Artificial Intelligence and Neural Networks (CSE) AS

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