SCIENTIFIC SESSIONS


  • Session 1: Information fusion

    The Artificial Intelligence 2020 meeting is intended to present within a single forum all of the developments in the field of multi-sensor, multi-source, multi-process information fusion and thereby promote the synergism among the many disciplines that are contributing to its growth. Abstracts are invited on the various topics like Data/Image, Feature, Decision, and Multilevel Fusion, Multi-classifier/Decision Systems, Multi-Look Temporal Fusion, Multi-Sensor, Multi-Source Fusion System Architectures Distributed and Wireless Sensor Networks, Higher Level Fusion Topics Including Situation Awareness And Management, Multi-Sensor Management and Real-Time Applications, Adaptive And Self-Improving Fusion System Architectures, Active, Passive, And Mixed Sensor Suites.


  • Session 2: Neural systems

    Artificial Intelligence 2020 Asia covers information processing in natural and artificial neural systems. The conference presents a fresh, undogmatic attitude towards this multi-disciplinary field, aiming to be a forum for novel ideas and improved understanding of collective and cooperative phenomena in systems with computational capabilities. Abstracts are invited on the broad subject which involves physics, biology, psychology, computer science and engineering.


  • Session 3: Evolutionary Computation

    Artificial Intelligence 2020 would be discussing on the various topics like nature-inspired algorithms, population-based methods, and optimization where selection and variation are integral, and hybrid systems where these paradigms are combined.


  • Session 4: Machine learning and computing

    Artificial Intelligence 2020 aims to promote the integration of machine learning and computing. The focus of the conference would be on state-of-the-art machine learning and computing. 


  • Session 5: Machines and Minds

    The AI 2020 conference would invite abstracts related to the Machines and Mentality. Discussions would be on the Knowledge and Its Representation, Epistemic Aspects of Computer Programming, Connectionist Conceptions, Artificial Intelligence and Epistemology, Computer Methodology, Computational Approaches to Philosophical Issues, Philosophy of Computer Science, Simulation and Modelling and Ethical Aspects of Artificial Intelligence.


  • Session 6: Computer vision and perception

    AI 2020 Conference discusses the trends followed and the progress made, in addition to identifying the major challenges that still ahead. Abstracts are invited on the topics rather than promoting a specific paradigm; discusses topics on contours, shape hierarchies, shape grammars, shape priors, and 3D shape inference; reviews issues relating to surfaces, invariants, parts, multiple views, learning, simplicity, shape constancy and shape illusions; addresses concepts from the historically separate disciplines of computer vision and human vision using the same “language” and methods.


  • Session 7: Virtual Intelligence

    Virtual intelligence is that the term given to AI that exists within a virtual world. Many       virtual worlds have choices for persistent avatars that give information, training, role taking part in, and social interactions. The immersion of virtual worlds provides a unique platform for VI beyond the normal paradigm of past user interfaces (UIs). What Alan Turing established as the benchmark for telling the distinction between human and computerised intelligence was done void of visual influences. With today's VI bots, virtual intelligence has evolved past the constraints of past testing into a brand new level of the machine's ability to demonstrate intelligence. The immersive features of those environments offer non-verbal components that have an effect on the realism provided by virtually intelligent agents.


  • Session 8: Artificial Neural Networks

    The Artificial Neural Network (ANN), or simply neural network, is a machine learning method evolved from the idea of simulating the human brain. The data explosion in trendy drug discovery analysis needs sophisticated analysis strategies to uncover the hidden causal relationships between single or multiple responses and a large set of properties. The ANN is one of many versatile tools to meet the demand in drug discovery modelling. Compared to a traditional regression approach, the ANN is capable of modelling complex nonlinear relationships. The ANN also has glorious fault tolerance and is quick and extremely scalable with parallel processing.


  • Session 9: Robotic Process Automation (RPA)

    RPA is an Independent Intellectual Property. As the first professional vendor of RPA products in China, RPA aims to solve the problem of business process automation for enterprises, greatly reducing the number of people engaged in standard, repetitive, cumbersome and high-volume work tasks. It is the purest form of automation. With its lightweight, efficient and fast performance, RPA has stepped out of the "machine-making" stage and stepped into a new field of "replacement for people to do things."


  • Session 10: Speech Recognition

    Some speech recognition systems need "training" (also known as "enrolment") wherever an individual speaker reads text or isolated vocabulary into the system. The system analyses the person's specific voice and uses it to fine-tune the recognition of that person's speech, resulting in increased accuracy. Systems that don't use coaching are referred to as "speaker independent" systems. Systems that use training are called "speaker dependent.


  • Session 11: Natural Language Processing (NIP)

    As our unconscious mind is a powerful tool, it can also resolve the problems that you are not seeing consciously. The unconscious has the answers that you need to overcome that particular situation. When you become aware of how to overcome a challenge, your whole thought process changes. Your entire being changes. You may find yourself doing things differently and getting a great outcome from it. Now wouldn't that be something that appeals to you.


  • Session 12: Data mining with big data

    Big data concern large-volume, complex, growing info sets with multiple, autonomous sources. With the fast development of networking, info storage, and also the data assortment capability, large info are currently quickly increasing altogether science and engineering domains, as well as physical, biological and biomedical sciences. This paper presents a HACE theorem that characterizes the alternatives of the huge knowledge revolution, and proposes an enormous processing model, from the data mining perspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user interest modelling, and security and privacy issues. We analyse the challenging problems within the data-driven model and also in the massive data revolution.


  • Session 13: Cyber Defence

    Cyber defence could be a network defence mechanism which has response to actions and critical infrastructure protection and data assurance for organizations, government entities and alternative potential networks. Cyber defense focuses on preventing, detection and providing timely responses to attacks or threats thus no infrastructure or data is tampered with. With the growth in volume also as complexity of cyber-attacks, cyber defense is essential for many entities in order protect sensitive information as well as to safeguard assets.


  • Session 14: Cyber Security

    Cyber Security is implausibly necessary as a results of presidency, military, corporate, financial, and medical organizations collect, process, and store new amounts of information on computers and utterly totally different devices. A significant portion of that information can be sensitive data, whether that be intellectual property, monetary information, personal data, or different sorts of information for which unauthorized access or exposure could have negative consequences. Organizations  transmit sensitive information across networks and {to fully to utterly to totally} completely totally different devices among the course of doing businesses, and cyber security describes the discipline dedicated to protecting that knowledge and so the systems used to process or store it.


  • Session 15: Robotics

    Robotic technologies are used to develop machines that can substitute for humans and replicate human actions. Robots may be used in many things and for uncountable purposes, but these days several are employed in dangerous environments (including bomb detection and deactivation), manufacturing processes, or wherever humans cannot survive (e.g. in space, under water, in high heat, and clean up and containment of hazardous materials and radiation). Robots will take on any kind however some are created to resemble humans in appearance. This is aforementioned to assist within the acceptance of a robot in certain replicative behaviors usually performed by folks. Such robots plan to replicate walking, lifting, speech, cognition, and basically anything a human can do.


  • Session 16: Machine Learning

    Machine Learning could be a sub-area of AI, whereby the term refers to the flexibility of IT systems to severally find solutions to issues by recognizing patterns in databases. In different words: Machine Learning allows IT systems to acknowledge patterns on the basis of existing algorithms and information sets and to develop adequate resolution ideas. Therefore, in Machine Learning, artificial information is generated on the premise of expertise. In order to modify the software to independently generate solutions, the previous action of individuals is important.


  • Session 17: Decision Management

    Decision management is described as an "emerging important discipline, due to an increasing need to automate high-volume decisions across the enterprise and to impart precision, consistency, and agility in the decision-making process”. Decision management is implemented "via the utilization of rule-based systems and analytic models for enabling high-volume, automated decision making”. Organizations request to enhance the worth created through every decision by deploying software system solutions (generally developed using BRMS and prophetical analytics technology) that higher manage the trade-offs between exactness or accuracy, consistency, agility, speed or decision latency, and value of decision-making among organizations. The idea of decision yield, for instance, focuses on all five key attributes of decision-making: more targeted decisions (precision); in the same way, over and over again (consistency); while being able to adapt "on-the-fly" (business agility) while reducing value and rising speed, is an overall metric for how well a corporation is creating a specific decision”.


  • Session 18: Artificial Intelligence and Advances

    This volume contains a well-balanced set of applications and theory papers in artificial intelligence advances. The applications papers each discuss a system that is (or is close to being) a fielded system that solves real problems using one or more AI techniques. They cover areas such as education, physics, energy, control, medicine and mechanical engineering. The theory papers, representing recent advances in various theoretical aspects of AI technology, concern themselves with “building block” issues, i.e. theories, algorithms, architectures, and software tools that can or will be used for modules within future systems. The topics covered are: clustering, natural language, adaptive algorithms, distributed processing, knowledge acquisition, and systems programming.


  • Session 19: AI Machine Learning in Health Care & Medical Science

    Machine Learning works effectively within the presence of big information. Medical science is producing a large amount of knowledge each day from analysis and development (R&D), physicians and clinics, patients, caregivers etc. These information is used as synchronizing the data and exploitation it to boost health care infrastructure and treatments. This has the potential to help so many people, to save lives and money. As per a research, big data and machine learning in pharmacy 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.


  • Session 20: Big Data Algorithms

    A simple, unsupervised learning algorithm that is often used with big data sets, often as a way of pre-clustering or classifying into larger categories that other algorithms can further refine. It has some other inherent problems that make it best suited to large-scale, high-level clustering. Big knowledge challenges embody capturing knowledge, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. Big knowledge was originally related to 3 key concepts: volume, variety, and speed.


  • Session 21: Big Data Analysis

    The construct of massive knowledge has been around for years; most organizations currently perceive that if they capture all the information that streams into their businesses, they will apply analytics and get significant value from it. But even within the Nineteen Fifties, decades before anyone verbalised the term “big knowledge”, businesses were using basic analytics (essentially numbers during a computer programme that were manually examined) to uncover insights and trends. The new advantages that massive knowledge analytics brings to the table, however, are speed and potency. Whereas some years past a business would have gathered data, run analytics and unearthed data that would be used for future decisions, these days that business will determine insights for immediate decisions. The ability to work quicker – and keep agile – offers organizations a competitive edge they didn’t have before.


  • Session 22: Data Mining

    Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for further use. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. Aside from the raw analysis step, it also involves database and data management aspects, data pre- processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, e.g., analysing the effectiveness of a marketing campaign, regardless of the amount of data; in contrast, data mining uses machine-learning and statistical models to uncover clandestine or hidden patterns in a large volume of data.


  • Session 23: Computer Vision

    Computer vision is Associate in Nursing content scientific field that deals with but computers is formed to attain high-level understanding from digital photos or videos. From the attitude of engineering, it seeks to automatize tasks that the human visual system will do. Computervision tasks include methods for acquiring, processing, analysing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g., in the forms of decisions. Understanding during this context means that the transformation of visual pictures (the input of the retina) into descriptions of the globe that may interface with different thought processes and elicit applicable action. This image understanding is seen as the disentangling of symbolic info from image information victimization models made with the aid of pure mathematics, physics, statistics, and learning theory. 


  • Session 24: Image Processing

    In applied science, digital image methodology is that the utilization of laptop algorithms to perform image methodology on digital photos. As a subcategory or field of digital signal processing, digital image processing has several benefits over analogy image process. It allows a way wider range of algorithms to be applied to the computer file and may avoid issues like the build-up of noise and signal distortion throughout process. Since pictures are defined over 2 dimensions (perhaps more) digital image process is also modelled within the form of multidimensional systems.


  • Session 25: Perception

    Perception (from the Latin perception) is the organization, identification, and interpretation of sensory information in order to represent and understand the presented information, or the environment. All perception involves signals that go through the systema nervosum, that in turn result from physical or chemical stimulation of the sensory system. For example, visioninvolves lightweight hanging the tissue layer of the eye, smell is mediate by odour molecules, and hearing involves pressure waves.Perception isn't only the passive receipt of these signals, however it is also formed by the recipient's learning, memory, expectation, and a spotlight.


  • Session 26: Neural System

    The neural systems are structures that build, support, and memorise the inner world through natural computing wherever they facilitate and organize the growing complexness of sensorimotor transmission of data. Neural systems are consistent and primarily based in specific parts classified by location, connections, and performance. In several animals, significantly mice and rats, brain elements known as barrels are directly related to specific body components (whiskers) and are visible in brain sections with standard and special strategies.


  • Session 27: Cloud Computing

    Cloud computing is that the on-demand handiness of computer system resources, particularly info storage and computing power, without direct active management by the user. The term is mostly used to describe ‘information centres’ available to several users over the web. Large clouds, predominant nowadays, typically have functions distributed over multiple locations from central servers. If the connection to the user is comparatively close, it should be designated an edge server. Clouds is also limited to one organization (enterprise clouds), be available to several organizations (public cloud), or a mix of each (hybrid cloud). Cloud computing depends on sharing of resources to attain coherence and economies of scale.


  • Session 28: Hadoop map reducing for analysing Information

    Hadoop Mapreduce may be a framework for process massive information sets in parallel across a Hadoop cluster. Data analysis uses a two-step map and reduce method. The job configuration supplies map and reduce analysis functions and also the Hadoopframework provides the scheduling, distribution, and parallelization services. The top level unit of labor in Map reduce may be a job. A job usually has a map and a reduce phase, though the reduce phase can be omitted. For example, consider a Map reduce job that counts the number of times every word is used across a group of documents. The map section counts the words in every document, then the reduce section aggregates the per-document information into word counts spanning the whole collection.


  • Session 29: Internet of Things

    The Internet of Things is simply "A network of Internet connected objects able to collect and exchange data." It is commonly abbreviated as loT. The word "Internet of Things" has 2 main parts; web being the backbone of property, and Things which means objects / devices. Consumer connected devices include good TVs, good speakers, toys, wearable’s and smart appliances. Smart meters, industrial security systems and good town technologies -- like those wont to monitor traffic and climate -- are samples of industrial and enterprise web of Things devices.


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