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5th International conference on Artificial Intelligence, will be organized around the theme “Surging into the future of Artificial Intelligence”
Artificial Intelligence 2018 is comprised of keynote and speakers sessions on latest cutting edge research designed to offer comprehensive global discussions that address current issues in Artificial Intelligence 2018
Submit your abstract to any of the mentioned tracks.
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In IT, a system is outlined as a set of connected parts or parts that square measure organized for a typical purpose. As such, though they're generally spoken of in terms of devices, intelligent systems embody not simply intelligent devices however conjointly interconnected collections of such devices, together with networks and different kinds of larger systems. Similarly, intelligent systems can even embody refined AI based computer code systems, like chatbots, knowledgeable systems and different kinds of computer code.
Artificial Intelligence is also being used for making the advanced technologies like VR Systems, Unmanned Autonomous Vehicles, etc.
- Track 1-1Automated Planning & Control
- Track 1-2AI in Marine Sensing Systems
- Track 1-3Intelligent Transportation Systems
- Track 1-4Driverless technology
- Track 1-5Intelligent and Autonomous AI Systems
- Track 1-6Game artificial intelligence
- Track 1-7Unmanned aerial systems
- Track 1-8Autonomous Unmanned Marine Vehicles
- Track 1-9Intelligent Systems and Power Distribution
- Track 1-10UAV Simulations
Machine learning, also alternatively called as Deep Learning, involves the training up of a computer system to perform actions without being explicitly programmed. ANN is one of the ways or procedures for providing machine learning to the computers. It does so by its multiple layered hidden neural layers. Machine Learning is a huge progress in the field of artificial intelligence which can now be utilized in any walk of life. It has been predicted recently that around 80% of the medical practitioner professionals may lose out on their jobs as their position could be taken up by this technology that shall be able to perform all prognosis, diagnosis and deliver suggestions and treatment to the patients.
- Track 2-1user-oriented machine learning
- Track 2-2Machine Learning and smart energy systems
- Track 2-3machine learning algorithms for predictive modeling
- Track 2-4Reinforcement learning
- Track 2-5Deep Learning
- Track 2-6Language Processing
- Track 2-7New Algorithms for Machine Learning
- Track 2-8Machine Learning Systems
- Track 2-9Bayesian Network
- Track 2-10Supervised & Unsupervised Learning
- Track 2-11Robot Learning
- Track 2-12Incentives in machine learning
- Track 2-13Machine Learning Theory
- Track 2-14Inductive Machine Learning Methods
- Track 2-15Machine Learning and Model Interpretability
- Track 2-16Economic Impacts of Machine Learning
Data mining is a part of a bigger framework, referred to as knowledge discovery in databases (KDD) that covers a complex process from data preparation to knowledge modelling. Main data mining task is classification which has main work to assign each record of a database to one of the predefined classes. The next is clustering which works in the way that it finds groups of records instead of only one record that are close to each other according to metrics defined by user. The next task is association which defines implication rules on the basis of that subset of record attributes can be defined. Data mining is the main important step to reach the knowledge discovery. An algorithm in data mining (or machine learning) is a set of heuristics and calculations that creates a model from data. To create a model, the algorithm first analyses the data that has been provided by the user, followed by detailed scanning to mark any repetitive pattern in their nature. The results of this analysis are then processed through an algorithm over multiple iterations to find the optimum parameters for creating the mining model.
- Track 3-1automated data acquisition
- Track 3-2visual data-mining
- Track 3-3genome mining
- Track 3-4Graph data mining
- Track 3-5Text Mining
Artificial Intelligence Applications in Engineering and Medicine centers to offer information earthmovers who wish to apply emerge information some assistance with mining conditions. Robotics, Medical Imaging, and Data Mining are a portion of the jobs of Artificial Intelligence. Applications of Artificial Intelligence in medical, health care and Web Applications like:
- Track 4-1AI in Cyber World
- Track 4-2Cognitive Science
- Track 4-3AI and Medical Simulations
- Track 4-4Big Data and Cyber Security
- Track 4-5Artificial intelligence in clinical informatics
- Track 4-6computational biomedical imaging analysis
- Track 4-7spatio-temporal models
- Track 4-8VLSI-based computational sensors
- Track 4-9Smart Grid Technology
- Track 4-10Robotic Surgery
- Track 4-11Artificial Intelligence and Diagnostics
- Track 4-12Medical Practitioners vs. AI
Data relates to any piece of information that can be processed by a system. Recent times, the size of data is increasing with rapidly. The data which cannot be withheld within the boundary or domain of a single system is referred to as big data. It has many advantages in this era of technical advancements and innovations. Big Data can help in transforming major business processes by proper and correct analysis of available data. It has its applications in marketing, sales, product development, merchandising, etc.
- Track 5-1Data Clustering
- Track 5-2AI and crowdsourcing
- Track 5-3large scale scientific computing
- Track 6-1robotics and control systems
- Track 6-2Navigation and mapping for autonomous mobile robots
- Track 6-3Printable Robot
- Track 6-4Bio-inspired Robotics
- Track 6-5Robotics and legged dynamics
- Track 6-6robot safety
- Track 6-7robotic manipulation
- Track 6-8Robots for Outer Space Exploration
- Track 6-9Robot learning
- Track 6-10Robot Motion Planning
- Track 6-11Multi-robot systems
- Track 6-12AI and Humanoids
- Track 6-13software robot
- Track 6-14Robotic Process Automation
- Track 6-15Robotic Process Automation
- Track 6-16simulations and robotic control
- Track 6-17Biomimetic ocean robots and sensors
An agent is a system of computation or computer system that inhabits and performs a task in a dynamic environment and has the capacity to take free actions and decisions on behalf of the user or owner from its own free-will without being fed with individual step strategies on every consecutive level by the user. In a multi-agent system, multiple agents work together by cooperating and staying interconnected and works on behalf of the user in achieving a task. Generally, the different agents are found to perform different tasks and goals on behalf of the user. But their common pathis to successfully communicate, for which they need to possess the qualities of cooperation, coordination and negotiation amongst themselves.
- Track 7-1agent-based supply chain management
ANN may be regarded as an alternative to the standard physical models, particularly in cases where the underlying physics of the system is too complex to analyse. In essence, it is a “black box” model, which mimics the information processing functions of the human neural system. ANN accepts any standard input vector and produces the desired output by processing the input through a series–parallel combination of functional elements, commonly referred as “neurons” or “nodes”. Multilayer perceptron (MLP) neural network is the most widely used ANN architecture. ANN has created newer and massive strides in the field of science. It has been found to be useful in predicting the survival rate, length of stay in hospitals of patients suffering from trauma or in the intensive care units. ANN being a powerful tool in predicting bivariate models; with recent prediction of the occurrence of heart block and death in patients with myocardial infarctions simultaneously by the use of hybrid models referred to as hybrid ANN-Genetic Algorithm (ANN-GA). ANN has also been successfully used in temperature tracking, constraints and limitations of different products used in summer and winter.
- Track 8-1Neural Networks
- Track 8-2Deep Neural Network
- Track 8-3Recurrent Neural Network
- Track 8-4dynamic biological networks
- Track 8-5adaptive neural processing
Natural language processing (NLP) is a field of software engineering, man made brainpower and computational etymology worried about the collaborations amongst PCs and human characteristic dialects, and, specifically, worried about programming PCs to productively process vast normal dialect corpora. Difficulties in regular dialect preparing oftentimes include discourse acknowledgment, normal dialect understanding, characteristic dialect age every now and again from formal, machine-lucid sensible structures, associating dialect and machine discernment, exchange frameworks, or some blend thereof.
AI applications are far reaching and different and incorporate therapeutic analysis, booking plant forms, robots for dangerous situations, diversion playing, self-ruling vehicles in space, normal dialect interpretation frameworks, and mentoring frameworks. As opposed to treating every application independently, we dynamic the fundamental highlights of such applications to enable us to ponder the standards behind astute thinking and activity.
This area plots four application spaces that will be produced in cases all through the book. Despite the fact that the specific illustrations exhibited are basic - else they would not fit into the book - the application spaces are illustrative of the scope of areas in which AI strategies can be, and are being, utilized.
- Track 10-1software frameworks
- Track 10-2intelligent tutors
- Track 10-3Optical Character Recognition
- Track 10-4Navigation models
- Track 10-5Simulations
- Track 10-6Machine Vision
- Track 10-7Image processing
- Track 10-8Pattern recognition
- Track 10-9biometrics
- Track 10-10computer vision
- Track 10-11AI-optimized Hardware
- Track 10-12Speech Recognition
- Track 10-13AI planning and scheduling
This includes the morals and ethics with which the artificially intelligent devices are constructed by the developer, which as a consequence reflect that in the operation of the AI devices or beings. It is also applicable to Robots.
- Track 11-1Ethics in Computing
- Track 12-1statistical modeling
- Track 12-2probabilistic modeling
- Track 12-3probabilistic and symbolic planning
- Track 12-4statistical machine learning systems
- Track 12-5mathematical foundations of artificial intelligence
- Track 12-6statistical machine learning tools
- Track 12-7role of geometry & compressibilty in deep learning
Affective computing is the examination and advancement of frameworks and gadgets that can perceive, translate, process, and mimic human effects. It is an interdisciplinary field spreading over software engineering, brain research, and subjective science. While the sources of the field might be followed as far back as to early philosophical investigation into feeling, the more present day branch of software engineering began with Rosalind Picard's 1995 paper on emotional figuring. An inspiration for the examination is the capacity to reenact sympathy. The machine ought to decipher the enthusiastic condition of people and adjust its conduct to them, giving a suitable reaction to those feelings.
- Track 13-1evolutionary computing
- Track 13-2Data-Intensive Computing
- Track 13-3Mobile and Pervasive Computing
- Track 13-4Turing Computability
- Track 13-5quantum computing
- Track 13-6Neuromorphic computing
- Track 13-7High-performance computing
- Track 13-8computing frameworks
- Track 13-9parallel computing
- Track 13-10wearable computing
- Track 13-11Ubiquitous computing
It refers to the interdisciplinary approach of developing human level or near to that creativity within the range of functions of computers. It is a topic that lies at the juncture of subjects like artificial intelligence, philosophy, cognitive science, psychology. It is one of the wonders of technology where it thrives to create a program, application etc. that has the capability of manipulating and generating creativity found in human beings after understanding comprehensively the different perspectives that acts as major driving force behind this human creativity.
- Track 14-1Computational Neuroscience
- Track 14-2Computational Biology
- Track 14-3Green Computing
- Track 14-4AI in Acoustics
- Track 15-1Systems and Controls
- Track 15-2Expert Systems
- Track 15-3Predictive Analysis
- Track 15-4neuro-controls
- Track 15-5adaptive problem solving systems
- Track 15-6Reliable AI Systems
- Track 15-7cognitive modeling
Regression analysis is used for predicting forecasting with substantial overlap in relation to respective environments. It can be used to decipher the causal relationship between the independent and dependent variables. The application domain includes practically anything and everything starting from business predictions to computational domain to scientific research. Regression involving correlated responses (time series and growth curves); regression in which the response variations are curves, images, multi-dimensional profiles and other complex data objects; methods accommodating missing data, non-parametric and Bayesian methods etc. maybe referred to as upcoming topics in the field of regression analysis research. Variety of a commercial software packages are available for performing regression analysis. Majority of the packages works on the principle of least square error. However many other packages are available that can perform various non-parametric and robust regression specialized software has also been developed for survey analysis.
Cloud computing is the delivery of computational tasks or services or applications over the internet. The applications used to send mail, receive data, watch movies and videos, edit a document on the network are all made possible by the technology of cloud computing acting behind them. It helps in obtaining the predictive values of particular operations, buffering of audio or video on the web networks, creating new applications and follow up changes in them, store data and also provide back up for their retrieval in emergencies, etc.
The primary objective of ambient intelligence is to work in proper coordination in a network consisting of other agents in the environment and result in the achievement of a flexible and effective goal. It majorly is dependent on the collective contributions by ubiquitous computing, ubiquitous communication and intelligent user interface. The commonly existing factors or agents in the environment of functional ambient knowledge may include transportation, home, work, commerce, leisure, education etc. when an environment of regular human paradigm is concerned. Ambient intelligence can not only provide huge scope in development of smart schools, smart treatment rooms at home for patients where they can be still under the observation of doctors and nurses through the amazing innovations of artificial intelligence. Recently an ambient intelligence system has also been launched to help the blind people in their domestic environment and also to carry out other bountiful tasks. It involves various kinds of sensor mechanisms; for example attendance of incoming students in a smart class can be automatically collected without involving the traditional process, checking home-works, setting up tests can all be related. Even in smart houses it is indeed a galloping development where after an entire day’s work the home features starting from opening the door to automated control of every home feature could be done.
- Track 18-1Human-Computer Interaction
- Track 18-2computational intelligence
- Track 18-3Swarm intelligence
- Track 18-4Fuzzy Logic
- Track 18-5Data Science
- Track 18-6Data Analytics
- Track 18-7Inductive Logical Programming
- Track 18-8Case Based Reasoning
According to John McCarthy (the father of Artificial Intelligence), AI is “The science and engineering of making intelligent machines, especially intelligent computer programs”. Its advent happened in order to create a similar level of intelligence in computational machines that is already there in humans. The primary objectives of AI are to create expert systems and to implement the human intelligence in functionalities of machines. A major focus of AI systems is to develop computer programs reflective human intelligence traits such as reasoning, learning, problem solving, etc.
- Track 19-1Machine Intelligence
- Track 19-2Innovative AI technology
- Track 19-3Automated decision-making
- Track 19-4Innovative AI Technologies
- Track 19-5Hybrid Controls
- Track 19-6Artificial intelligence in military