Track Categories
The track category is the heading under which your abstract will be reviewed and later published in the conference printed matters if accepted. During the submission process, you will be asked to select one track category for your abstract.
Neural Networks is one of the important topics in machine learning. Artificial neural networks are brain-inspired systems which are deliberated to reproduce the way that the human brain works.
Neural Network s consists of input and output layers, as well as (in most cases) a hidden layer consisting of units that modify the input into something that the output layer can use. They are excellent tools for finding patterns which are far too countless or complex for a human programmer to remove and teach the machine to recognize.
Neural networks allow deep learning in the computer system. As mentioned, neural networks are computer systems modelled after neural connections in the human brain. Neural networks learn by processing training examples. The best examples come in the form of large data sets. By processing the many inputs, the machine can produce a single output.
This process analyses data many times to find associations and give meaning to previously undefined data. Through different learning models, like positive reinforcement, the machine is taught it has successfully identified the object.
The information has intrinsic value, but it’s having no use until we discovered the value of it. The amount of information on planet earth is developing exponentially for many reasons. Different sources and our everyday activities create bunches of information. The information development rate has expanded rapidly. We would now be able to quantify and oversee enormous measures of data with exceptional exactness. Adaptability and readiness are two perspectives valuable in managing Big Data. Effectively exploiting the value of Big Data requires experimentation and investigation. In this session, the researcher will be able to discuss Big data driving factors, Characteristics, Types, Challenges, Applications, Hadoop in Big data.
Machine learning is the application of Artificial Intelligence that dispenses systems the ability to automatically learn, think and improve from experience without being certainly programmed. Machine learning kingpins on the development of computer programs that can outbursts data and use it learn for themselves.
Machine Learning brutalize analytical model building. It uses methods from Statistics, operation research and physics and neural networks to find hidden insights in data without explicitly being programmed for what to look or where to conclude. The main aim is to allow computers to learn automatically without human intervention and adjust actions accordingly.
Machine learning warrants survey of massive quantities of data. While it generally delivers faster, more exact results in order to recognize dangerous risk or beneficial opportunities, it may also require additional time and resources to train it properly. Merging machine learning with cognitive technologies and AI can make it even more productive in managing large volumes of information.
Artificial Intelligence is a concept where intelligence and data processing is done locally on small devices, instead of the cloud. The edge is where a lot of money is being invested right now and putting Artificial Intelligence on the edge will continue to have tremendous value and impact. The AI neural network technology behind computer science has reworked everything from email to drug discovery with its more and more powerful ability to be learned from and establish patterns in information. The very complex quality that lets modern deep-learning networks teach successfully is a way to drive cars and spot insurance fraud. The session concludes AI- Intelligent system, Research areas, agents and environments, search algorithms, Fuzzy logic system, Experts systems, Neural network, AI- Issues and many more.
Deep learning is a batch of machine learning that hires artificial neural networks that learn by processing data. Artificial neural networks impersonate the biological neural networks in the human brain.
Multiple layers of artificial neural networks work together to regulate a single output from many inputs, for example, identifying the image of a face from a patchwork of tiles. The machines learn through negative and positive reinforcement of the tasks they carry out, which requires constant reinforcement and processing to progress.
Data visualization is a graphical representation of information and data. Data visualization enables researchers or decision-maker to understand data analytics presented graphically, so they can grab difficult concepts or distinguish new patterns.
It’s a term that describes any effort to assist individuals to understand the importance of data by inserting it in a visual context. With interactive visualization, researchers can take the theory a step further by using technology to drill down into charts and graphs for details, interactively changing what information they see and how it’s processed. By using visual components like charts, graphs, and maps, data visualization tools offer an accessible way to see and understand trends, outliers, and patterns in data. In the world of massive information, data visualization tools and technologies are essential to analyse huge amounts of data and create data-driven decisions.
Data management is practices, procedures of organizing and maintaining information processes. It’s also a large range of incidental systems that provide an organization to gain control of its data resources.
Data Management as an overall practise is involved with the complete lifecycle of a data asset from its original creation point to its final retirement, how it progresses and changes throughout its time through the internal and external data streams of an enterprise.
Data management methods have roots in statistics, accounting, planning and alternative disciplines that predate the emergence of company computing. Organizations are creating use of huge data to inform business decisions and gain deep insights into client mindsets, trends, and opportunities for making extraordinary client experiences.
Cloud computing provides different kind of computing services like networking, storages, databases, servers, software, analytics, etc over the internet. We can say that Cloud computing could be a general term for any price that involves delivering hosted services over the internet. These services are generally divided in Infrastructure-as-a-Service, Platform-as-a-Service and Software-as-a-Service. Cloud computing depends on sharing of resources to realize coordination and economies of scale, sort of a utility. Corporations providing these computing services are known as cloud suppliers and usually, they charge for cloud computing services based on the usage of customers. The name cloud computing was impressed by the cloud symbol that is usually used to represent the Internet in diagrams and flowcharts.
Data mining, which is used extensively is gaining popularity in sciences. Due to the tremendous amount of complex data generated by transactions involving sciences companies, processing and analysing these data is becoming more and more traditional.
The process of discovering useful information from huge data and application of information mining techniques are referred to as knowledge Discovery in Databases (KDD). KDD consists of varied application domains like computer science, pattern recognition, machine learning and data visualization. People from different domains can provide their research work on Data Mining- Tasks, Issues, Evaluation, Knowledge Discovery, Systems, Classification, Cluster Analysis, Application & Trends, Mining Themes, Mining text data, Mining world wide web.
Pattern recognition is one the vital field in information science. Machine learning is developed in technology. However, those subjects are viewed as two facts of constant field. each in along has well-versed a speedy development over the past years. theorem ways have big from skilled keyword to become thought, whereas graphical models have emerged as a general framework for describing and applying probabilistic models. The attainable relevance of those ways has been improved through the event of a scale of approximate reasoning algorithms like variational Bayes and expectation propagation. New models supported components have had a notable impact on each algorithm and applications.
Data science is a Huge diverted field. Different kind of algorithms, scientific techniques, processes and systems are used in data science to pull out knowledge and insights from structured and unstructured data. It’s a concept to data analysis, unify statistics, machine learning and their related methods.
Data science use the most powerful Hardware equipment, the most powerful programming systems, and the most efficient algorithms to solve different problems. In data science, there has a huge role in mathematics, statistics, computer science, and information science. In 2015, the American Statistical Association recognised database management, machine learning, and distributed and parallel Systems as the three developing fundamental expert networks.
Machine learning algorithms can predict potential network problems before happening. They can pinpoint capacity requirements early. These algorithms can also identify user network problems and make recommendations to fix them. Machine learning takes the questioning out of the network management game and instead allows managers to get to the bottom of network issues fast, without pointing fingers.
Machine learning uses the data that is already running throughout your network, without the need to use extra servers or software. ML requires large amounts of data to be fed through its system to work properly. In some industries, this is incredibly difficult. Within network management, data is already abundant, making it easy for a machine learning solution to be utilized daily.
Computational intelligence is the field where we study the design of intelligent agents. It’s the ability of a computer to learn a particular task from data or experimental observation. Computational intelligence is something that acts according to the environment by understanding data. An intelligent system does work appropriately for its circumstances and its goal and It’s also flexible to changing environments and changing goals. Computational intelligence learns from its experience and it makes appropriate decisions given perceptual limitations and finite computation. The central scientific goal of computational intelligence is to know the principles that build intelligent behaviour possible, in natural or artificial systems. The main hypothesis is that reasoning is computation. The central engineering goal is to specify strategies for the design of useful, intelligent artifacts.
In the field of Human-computer interaction, we research about how Human interacts with computers and understanding complexity to develop successful human-computer interaction.
Human-computer interaction (HCI) is a multidisciplinary field of study where focusing on design and improving computer technologies for better interaction with humans. Now many major corporations and academic institutions put a focus on human-computer interaction. Historically computer developers have not paid much attention to this topic but because of development in the field of machine learning and artificial intelligence, human-computer distance got reduced so now it’s necessary to develop human-computer Interaction.
Learning is a process of transforming information as well as experience into knowledge and skills. To make up the wide gap between the demand for increasing higher education and comparatively limited resources, more and more educational institutes are looking into instructional technology. The use of online resources not only reduces the cost of education but also meets the needs of society. Intelligent e-learning has become necessary channels to reach out to students exceeding geographic boundaries. Intelligent e-learning systems can reach out to the students easily with the help of data science and machine learning. Nowadays one of the very popular IT applications is online teaching and learning. Scientists are doing researches for a better intelligent online e-learning architecture system to provide a better experience in online classrooms. Data science can help to reach out to the right customers with the right content.
Defense organizations as well as other industries focusing on the autonomous system even in this time there is a rapid growth of computerized autonomy. Defense and other industries are impacted by these factors and require a distinct perspective on the potential for Machine learning. The introduction of autonomous and machine learning systems into defence organizations can produce automatic solutions for the prevailing problem of how to get more combat support out of fewer people. Some of the tasks currently performed by humans can be supported by machine learning or done better by autonomous systems. Data science is very important in a combat environment where information can help to make more useful strategies. With the help of data science, combat personnel and combat forces can observe logistics actions like ordering ammunition, food supply, medical treatment.
International healthcare spending is projected to increase at an annual rate of 5.4 percent in 2017-2022. Health-related data enable monitoring of patient health. Data science assists medical practitioners as well as tracking the disease progression during treatment. Secondly, health information science helps to connect with operational processes – system directors will analyze and optimize virtually every operational aspect of the health system. Finally, health informatics can support the systematic measurement of patient-reported outcomes. It concludes the Social and technical context of health informatics, Leading Change in health informatics, The outcome and interventions of health informatics, The data science of health informatics, Clinical Informatics, Nursing Informatics, Public health informatics, Trends, and Developments.
Internet security is a part of the computer security related to the internet, browser security and the World Wide web also network security as it applies to applications or operating systems. Internet security also covers security for transactions made over the internet. The objective of Internet security is to establish rules and measures to use against attacks over the internet. Internet security encompasses the safety of data entered through a web form, and overall protection of information sent via Internet Protocol. The internet represents an insecure channel for exchanging data, that leads to a high risk of fraud, data loss, viruses etc. Several methods are used to protect the transfer of information, including encryption and from-the-ground-up engineering. The present focus is on prevention as much as on real-time protection against well-known and new threats.
Social Media is an immense platform nowadays. Whenever we are uploading or posting something on social media, data will be generated. Also, data are being created by customer's comments, sentiment, likes, shares, retweets, etc.
Social media analytics is the method of assembling hidden insights from social knowledge - structured and unstructured - to modify upon deciding. It can also analyse online media channels such as news websites, blogs, and forums. Social media analytics provides us access to one of the world's largest focus groups.
Sum All is a cross-platform tool that gains insights from social media, e-commerce websites. The sessions on social Media Analytics discuss Facebook, Twitter, LinkedIn, Instagram, YouTube, Pinterest Analytics including other social media platforms. E-commerce markets are also growing very fast. E-commerce sector also using Social media sites for promoting their market.