10^th International Conference on Data Science and Machine Learning Applications August 08-09, 2022 Dublin, Ireland

Track 01:  DATA SCIENCE 

Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data science practitioners apply machine learning algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence (AI) systems to perform tasks that ordinarily require human intelligence. In turn, these systems generate insights which analysts and business users can translate into tangible business value.

• ·       Capture: Data acquisition, data entry, signal reception, data extraction
• ·       Maintain: Data warehousing, data cleansing, data staging, data processing, data architecture
• ·       Process: Data mining, clustering/classification, data modelling, data summarization
• ·       Communicate: Data reporting, data visualization, business intelligence, decision making
• ·       Analyze: Exploratory/confirmatory, predictive analysis, regression, text mining, qualitative analysis 

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Track 02:   MACHINE LEARNING   

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

• ·        ML – Applications
• ·       Python libraries for Machine Learning
• ·       Web Search Engine
• ·       Database Mining

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Track 03:   DATA INTEGRATION

Data integration involves combining data residing in different sources and providing users with a unified view of them. This process becomes significant in a variety of situations, which include both commercial such as when two similar companies need to merge their databases and scientific combining research results from different bioinformatics repositories, for example domains. Data integration appears with increasing frequency as the volume that is, big data and the need to share existing data explodes. It has become the focus of extensive theoretical work, and numerous open problems remain unsolved. Data integration encourages collaboration between internal as well as external users. The data being integrated must be received from a heterogeneous database system and transformed to a single coherent data store that provides synchronous data across a network of files for clients.  Core data integration

• ·       Data blending
• ·       Data curation
• ·       Data fusion
• ·       Data wrangling
• ·       Database model
• ·       Geoscientific Data Integration
• ·       Web data integration

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Track 04:  ARTIFICIAL INTELLIGENCE    

Artificial intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience. Since the development of the digital computer in the 1940s, it has been demonstrated that computers can be programmed to carry out very complex tasks—as, for example, discovering proofs for mathematical theorems or playing chess—with great proficiency

• ·       Artificial neural networks
• ·       Evaluating approaches to AI
• ·       Algorithmic bias.
• ·       Symbolic AI
• ·       Soft vs. Hard computing
• ·       Machine consciousness, sentience and mind Super intelligence

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Track 05:  SCIENTIFIC COMPUTING

The term computational scientist is used to describe someone skilled in scientific computing. Such a person is usually a scientist, an engineer, or an applied mathematician who applies high-performance computing in different ways to advance the state-of-the-art in their respective applied disciplines in physics, chemistry, or engineering.

Computational science is now commonly considered a third mode of science complementing and adding to experimentation/observation and theory (see image on the right). Here, one defines a system as a potential source of data, an experiment as a process of extracting data from a system by exerting it through its inputs and a model for a system

• ·       Recognizing complex problems
• ·       Computer algebra
• ·       Numerical analysis
• ·       Methods of integration
• ·       Molecular dynamics
• ·       Numerical algorithms.

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Track 06:  NEURAL NETWORKS

A biological neural network is composed of a groups of chemically connected or functionally associated neurons. A single neuron may be connected to many other neurons and the total number of neurons and connections in a network may be extensive. Connections, called synapses, are usually formed from axons to dendrites, though dendrodendritic synapses and other connections are possible. Apart from the electrical signalling, there are other forms of signalling that arise from neurotransmitter diffusion.

Artificial intelligence, cognitive modelling, and neural networks are information processing paradigms inspired by the way biological neural systems process data. Artificial intelligence and cognitive modelling try to simulate some properties of biological neural networks. In the artificial intelligence field, artificial neural networks have been applied successfully to speech recognition, image analysis and adaptive control, in order to construct software agents (in computer and video games) or autonomous robots.

Historically, digital computers evolved from the von Neumann model, and operate via the execution of explicit instructions via access to memory by a number of processors. On the other hand, the origins of neural networks are based on efforts to model information processing in biological systems. Unlike the von Neumann model, neural network computing does not separate memory and processing.

• ·       Biological cybernetics
• ·       Digital morphogenesis
• ·       Evolutionary algorithm
• ·       Neural network software
• ·       Radial basis function network
• ·       Genetic algorithm
• ·       Neural computing

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Track 07:  DATA STRUCTURES & ALGORITHMS

Data Structure is a way of collecting and organising data in such a way that we can perform operations on these data in an effective way. Data Structures is about rendering data elements in terms of some relationship, for better organization and storage. In simple language, Data Structures are structures programmed to store ordered data, so that various operations can be performed on it easily. It represents the knowledge of data to be organized in memory. It should be designed and implemented in such a way that it reduces the complexity and increases the efficiency. 

An algorithm is a finite set of instructions or logic, written in order, to accomplish a certain predefined task. Algorithm is not the complete code or program, it is just the core logic(solution) of a problem, which can be expressed either as an informal high level description as pseudocode or using a flowchart.

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Track 08:   INFORMATION SCIENCE

Information science (also known as information studies) is an academic field which is primarily concerned with analysis, collection, classification, manipulation, storage, retrieval, movement, dissemination, and protection of information. 

Practitioners within and outside the field study the application and the usage of knowledge in organizations in addition to the interaction between people, organizations, and any existing information systems with the aim of creating, replacing, improving, or understanding information systems.

• ·       Information scientist
• ·       Systems analyst
• ·       Information architecture
• ·       Search engines,
• ·       Hyperlinks

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Track 09:   INFORMATION TECHNOLOGY

Information technology (IT) is the use of computers to create, process, store, retrieve, and exchange all kinds of electronic data and information. IT is typically used within the context of business operations as opposed to personal or entertainment technologies IT is considered to be a subset of information and communications technology (ICT). An information technology system (IT system) is generally an information system, a communications system, or, more specifically speaking, a computer system — including all hardware, software, and peripheral equipment.

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Track 10:   DATA INTEGRATION

Data integration involves combining data residing in different sources and providing users with a unified view of them. This process becomes significant in a variety of situations, which include both commercial (such as when two similar companies need to merge their databases) and scientific domains. Data integration appears with increasing frequency as the volume and the need to share existing data explodes. It has become the focus of extensive theoretical work, and numerous open problems remain unsolved. Data integration encourages collaboration between internal as well as external users. The data being integrated must be received from a heterogeneous database system and transformed to a single coherent data store that provides synchronous data across a network of files for clients.^[3] A common use of data integration is in data mining when analysing and extracting information from existing databases that can be useful for Business information.

• ·       Core data integration
• ·       Customer data integration
• ·       Cyber infrastructure
• ·       Data blending
• ·       Data fusion
• ·       Data mapping
• ·       Data wrangling
• ·       Database model
• ·       Geoscientific Data Integration

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Track 11:   DATA SCIENCE AND ROBOTICS

The field of robotics has definitely improved to a great extent. During the initial days of development, scientists were faced with two major challenges -one, predicting every action of a robot, and two, reducing the computational complexity in real-time vision tasks.

While robots could perform specific functions, it was impossible for scientists to predict their next move. For every new functionality, a robot would have to be reprogrammed every time, which made the task a tedious one. Another major obstacle with robots is that unlike humans who use their unique sense of vision to make sense of the world around them, robots can only visualize the world in a series of zeros and ones. Thus, accomplishing real-time vision tasks for robots would mean a fresh set of zeros and ones every time a new trend emerges, thereby increasing the computational complexity.

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Track 12:   CODING AND DATA SCIENCE

Coding and data Science  can be used for building websites, software applications, data analysis, machine learning, building data pipelines, visualization, and much more. ... As an aspiring data scientist, your goal with learning to code will be, Read and write data from different sources. Work on different data types Coding, sometimes called computer programming, is how we communicate with computers. Code tells a computer what actions to take, and writing code is like creating a set of instructions. By learning to write code, you can tell computers what to do or how to behave in a much faster way. You can use this skill to make websites and apps, process data, and do lots of other cool things.

• ·        Domain of Artificial Intelligence
• ·        Building Chatbots 
• ·       Artificial Intelligence.
• ·        Programming languages

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Track 13:   COMPUTER VISION

Computer Vision, often abbreviated as CV, is defined as a field of study that seeks to develop techniques to help computers and understand the content of digital images such as photographs and videos.

The problem of computer vision appears simple because it is trivially solved by people, even very young children. Nevertheless, it largely remains an unsolved problem based both on the limited understanding of  biological vision and because of the complexity of vision perception in a dynamic and nearly infinitely varying physical world.

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Track 14:   DATA VISUALIZATION                 

Data visualization is defined as a graphical representation that contains the information and the data. By using visual elements like charts, graphs, and maps, data visualization techniques provide an accessible way to see and understand trends, outliers, and patterns in data. In modern days we have a lot of data in our hands i.e., in the world of Big Data, data visualization tools, and technologies are crucial to analyse massive amounts of information and make data-driven decisions. To model complex events. Visualize phenomenon that cannot be observed directly, such as weather patterns, medical conditions, or mathematical relationships.

• ·       Line Plot
• ·       Bar Plot
• ·       Scatter Plot
• ·       Violin Plot
• ·       Box and Whisker Plot
• ·       Distribution Plot

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Track 15:   BIOINFORMATICS

 “Bioinformatics” is processing and analysing large-scale genomics and other biological datasets to develop biological insights. As such, other words are sometimes used as well, such as “computational genomics” and “genomic data science. “Data science is a little broader, largely a broader term whose definition is similar to that of bioinformatics without the biological focus processing and analyzing large-scale datasets to develop insights. And the essential skills of a data scientist include programming, machine learning, statistics, data wrangling, data visualization and communication, and data intuition bioinformatics careers is domain specific data processing and quality checking, general data transformation and filtering, applied statistics and machine learning, domain-specific statistical tools and data visualization and integration, ability to write code (programming), ability to communicate data-driven insights.

• ·       Domain specific data processing and quality check
• ·       General data transformation and filtering
• ·       Applied statistics and machine-learning
• ·       Domain specific statistical tools and data visualization

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Track 16:   LINEAR REGRESSION, LOGISTIC REGRESSION

Linear Regression is a commonly used supervised Machine Learning algorithm that predicts continuous values. Linear Regression assumes that there is a linear relationship present between dependent and independent variables. In simple words, it finds the best fitting line/plane that describes two or more variables. On the other hand, Logistic Regression is another supervised Machine Learning algorithm that helps fundamentally in binary classification (separating discreet values).

Although the usage of Linear Regression and Logistic Regression algorithm is completely different, mathematically we can observe that with an additional step we can convert Linear Regression into Logistic Regression.

• ·       Binary logistic regression
• ·       Multinomial logistic regression
• ·       Ordinal logistic regression

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Track 17:  ENSEMBLE LEARNING, SUPERVISED LEARNING

In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Unlike a statistical ensemble in statistical mechanics, which is usually infinite, a machine learning ensemble consists of only a concrete finite set of alternative models, but typically allows for much more flexible structure to exist among those alternatives.

Supervised learning algorithms perform the task of searching through a hypothesis space to find a suitable hypothesis that will make good predictions with a particular problem. Even if the hypothesis space contains hypotheses that are very well-suited for a particular problem, it may be very difficult to find a good one. Ensembles combine multiple hypotheses to form a (hopefully) better hypothesis. The term ensemble is usually reserved for methods that generate multiple hypotheses using the same base learner. The broader term of multiple classifier systems also covers hybridization of hypotheses that are not induced by the same base learner

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Track 18:  META-LEARNING

Meta-learning in machine learning refers to learning algorithms that learn from other learning algorithms. Meta-learning algorithms typically refer to ensemble learning algorithms like stacking that learn how to combine the predictions from other machine learning algorithms in the field of ensemble learning. Nevertheless, meta-learning might also refer to the manual process of model selecting and algorithm tuning performed by a practitioner on a machine learning project that modern automl algorithms seek to automate. It also refers to learning across multiple related predictive modelling tasks, called multi-task learning, where meta-learning algorithms learn how to learn.

• ·       Data Mining: Practical Machine Learning Tools and Techniques
• ·       Meta-Algorithm
• ·       Meta-Classifierm
• ·       Meta-Regression
• ·       Meta-Model
• ·       Stacking
• ·       Neural Networks
• ·       Multi-task learning

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Track 19:   QUANTUM MACHINE LEARNING

Quantum machine learning is the integration of quantum algorithms within machine learning programs. The most common use of the term refers to machine learning algorithms for the analysis of classical data executed on a quantum computer, i.e. Quantum-enhanced machine learning.  While machine learning algorithms are used to compute immense quantities of data, quantum machine learning utilizes qubits and quantum operations or specialized quantum systems to improve computational speed and data storage done by algorithms in a program. This includes hybrid methods that involve both classical and quantum processing, where computationally difficult subroutines are outsourced to a quantum device. These routines can be more complex in nature and executed faster on a quantum computer. Furthermore, quantum algorithms can be used to analyse quantum states instead of classical data. Beyond quantum computing, the term "quantum machine learning" is also associated with classical machine learning methods applied to data generated from quantum experiments

• ·       Machine learning with quantum computers
• ·       Linear algebra simulation with quantum amplitudes
• ·       Quantum machine learning algorithms
• ·       Quantum-enhanced reinforcement learning
• ·       Quantum annealing
• ·       Quantum sampling techniques
• ·       Quantum neural networks
• ·       Hidden Quantum
• ·       Fully quantum

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Benefits of participation:

Advantages of Participating at our Conference

· The advantages of the Speaker and abstract pages are created in Google on your profile under your name would get worldwide visibility.

· Our comprehensive online advertising attracts 30000+ users and 50000+ views to our Library of Abstracts, which takes researchers and speakers to our conference.

· Meet with hundreds of like-minded experts who are pioneers in Data Science and Advances in Data Science and share ideas.

· All participants in the conference would have a different reason to participate with eminent speakers and renowned keynote speakers in one-to-one meetings.

· A rare opportunity to listen what the world's experts are learning about from the world's most influential researchers in the area of Data Science at our Keynote sessions.

· World Data Science Meet 2022 intensive conference schedule, you will acquire experience and expertise in strategic gift preparation that is worth its weight golf, forming an impressive array of recognised professionals.

· Best Poster Award nominations.

· Award for Outstanding Young Researcher.

· Group Registration Advantages.

Benefits of Participation for Speaker

· Worldwide appreciation of the profile of Researchers.

· Obtain credits for professional growth.

· Explore the latest of cutting-edge analysis.

· Make long-term bonds at social and networking activities.

· An ability to advertise one page in the distribution of abstract books and flyers that ultimately gets 1 million views and adds great value to your research profile.

· Learn a transition beyond your area of interest to learn more about new subjects and studies away from your core subject of Data Science.

· We have distinctive networking, learning and enjoyable integration into a single package.

Benefits of Participation for Delegate

· Professional Development-Improve understanding and knowledge.

· Attendance at conferences supports rejuvenates and energises delegates.

· Your involvement in our conference will help with a new methodology and ideology that can be used to broaden the outcomes of businesses or industries.

· Opportunities for Data Science Summit researchers and experts in the same field to meet and exchange new ideas through an physical conference.

Benefit of Participation for Sponsor

· Exposure to the international environment would increase the possibility of new companies.

· Opportunity to demonstrate your company's latest technologies, new products, or service your business to a wide range of international participants.

· Increase business by our conference participants through lead generation.

· It takes a lot of time, effort and drive to create a successful company, so it's always nice to have a network of colleagues and associates to draw energy from individuals who share a common drive and objective.

· Conferences in Data Science provide opportunities for more attention and contemplation that could help you move your company to the next stage.

· Benchmarking main organization plans and moving it forward.

· Get feedback from trustworthy people at our conference to your company questions and challenges.

· On our conference banner, website and other proceedings, branding and marketing content, the advertising logo of your company.

Benefit of Association for Collaborators

· Nobody has these massive visitors to Data Science in the world this is the best forum to highlight society.

· Creating long-lasting peer relationships.

· In our conference banner, website and other proceedings, branding and marketing material, promotional content and your Organization logo will increase your number of subscribers/members by 40%.

· The exposure of our event to your Company listing in the Global Business forum will have a great effect on your association.

· Your representatives can network to update their knowledge and understanding of your organisation and services with key conference delegates.

Data Science advertising materials such as posters, brochures, pamphlets, services that will be circulated to hospitals, universities, society and researchers will be integrated with information