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MACHINE LEARNING 2022
- About Conference
- Market Analysis
- List Of Societies and Associations
- Past Conference Report
This is the premier scientific gathering Machine Learning conference. Join us at the “9th World Conference on Machine Learning and Deep learning” scheduled on December 12–13, 2022 in Dublin, Ireland with the theme of “Intensify the World with Machine Learning and Artificial Intelligence”. In addition to Speaker Sessions, Symposia, Poster Presentations, there will also be an exposition and work shops that are pertinent. On behalf of the planning team, we sincerely hope you'll be there for this wonderful event!
The process of teaching computers to perform complex tasks that are challenging for humans to define or comprehend, as well as how to make predictions, is known as Machine Learning. It combines mathematical optimization and statics. On the other hand, Deep Learning is a branch of Machine Learning that focuses on the level of specific neurons to solve any issue. The following talks are a part of Machine Learning 2022 and are meant to give in-depth analyses of the most recent advancements, uses, and issues in Machine Learning and Deep Learning.
Why to Attend?
Looking for a moment to learn something new and need a short break for professional life. Both are possible by attending the Machine Learning 2022 conference. Come and learn about the newest technology at this event to encourage many bright, young minds, professionals, and experts to pick up new skills and discover new methods to use data to accomplish goals. Your participation there will give one more jewel in its crown with talks on Artificial Intelligence, Pattern Recognition, Data Mining, Cloud Computing, as well as other fields. Machine Learning 2022 provides an opportunity to interact with and learn from professionals from all around the world.
- Chief Information Officers(CIO)
- Graphic Controllers(GCIOs)
- Chief Technical Officers(CTOs)
- Vice presidents
- Data Scientists
- Startup Professionals
- Data Analysts
- Data Architects
- Cloud Architects
- Business Intelligence Developers
- Machine Learning Engineers
- Data Scientists
Track 1: Artificial Intelligence
In contrast to the natural intelligence exhibited by animals, including humans, artificial intelligence (AI) is intelligence demonstrated by robots. Artificial intelligence research is the study of intelligent agents, which are any systems that can sense their surroundings and take activities to increase their chances of success. Previously, robots that mimic and exhibit "human" cognitive abilities associated with the human mind, like "learning" and "problem-solving," were referred to as "artificial intelligence."
Track 2: Machine Learning &Deep Learning
Machine learning (ML) is a topic of study focused on comprehending and developing "learning" methods, or methods that use data to enhance performance on a certain set of tasks. It is considered to be a component of artificial intelligence. A larger family of machine learning techniques built on artificial neural networks and representation learning includes deep learning. Learning can be either fully or partially guided.
Track 3: Deep Learning Frame Work
Through a high-level programming interface, deep learning (DL) frameworks provide the building blocks for developing, training, and evaluating deep neural networks. To provide high speed, multi-GPU accelerated training, popular deep learning frameworks like MX Net, P Torch, Tensor Flow, and others rely on GPU-accelerated libraries like CUDNN, NCCL, and DALI.
Track 4: Data Science
Both structured and unstructured data are managed by data science. It is a field that encompasses everything connected to the preparation, final analysis, and cleansing of data. The fields of programming, coherent reasoning, mathematics, and statistics are all combined in data science. It has the best information-gathering abilities and encourages the ability to observe things from a different perspective. Data science is an interdisciplinary field that applies information from data across a wide range of application fields by using scientific methods, procedures, algorithms, and systems to extract knowledge.
- Final Analysis
- Coherent Reasoning
- Mathemetics & Statistics
Track 5: AI Gaming
Artificial intelligence (AI) is used in video games to create human-like intelligence in non-player characters (NPCs) by generating responsive, adaptable, or intelligent actions. Since the 1950s, when they first appeared, artificial intelligence has played a significant role in video games. The field of artificial intelligence (AI) in video games is separate from academic AI. In place of machine learning or decision making, it enhances the gaming experience. The concept of AI opponents was greatly popularized during the heyday of arcade video games in the form of graduated difficulty settings, distinctive movement patterns, and in-game events that were reliant on player interaction.
Track 6: Cloud Computing
A model for delivering computing services over the internet is called cloud computing. A wide range of products, services, and solutions can be developed, deployed, and delivered in real-time. It is composed of a number of pieces of hardware and software that may be viewed remotely using any web browser. The on-demand availability of computer system resources, in particular data storage and processing power, without direct active supervision by the user, is known as cloud computing. Functions in large clouds are frequently dispersed over several sites, each of which is a data centre. Cloud computing relies on resource sharing to accomplish coherence and often uses a "pay-as-you-go" approach.
Track 7: Big Data Analytics
After looking into hidden patterns, correlations, and other insights from a massive volume of data, big data analytics provides a small number of useful data. This ultimately results in wiser business decisions, greater profitability, more effective operations, and ultimately satisfied clients. Additionally, the Big Data Conference enhances its value. The following are some ways that big data analytics benefits an organization cost cutting New Products and Services that Make Decisions More Quickly and Better. Data collection, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy, and data source are just a few of the big data analysis challenges.
Track 8: Data Mining
Data mining is basically the process of gathering information from enormous databases that was previously unknowable and obscure and then using that information to make sensible business decisions. Data mining, to put it simply, is the process of identifying patterns in huge data sets using techniques that combine machine learning, statistics, and database systems. Data mining is an interdisciplinary field that combines statistics and computer science. Its main objective is to extract information from a data set and organize it in a way that may be used later.
Track 9: Internet of Things (IoT)
The term "Internet of things" (IoT) refers to the entire network of physical objects, including furniture, appliances, cars, and other items embedded with electronics, connectivity, sensors, actuators, software, and other components. These objects are given an IP address (Internet Protocol), which allows them to connect and exchange data, improving efficiency, accuracy, and economic benefit while also requiring less human interaction. The fusion of numerous technologies, such as ubiquitous computing, widely available sensors, sophisticated embedded systems, and machine learning, has caused the sector to advance. Independently and collectively, the traditional fields of embedded systems, wireless sensor networks, control systems, and automation make Internet of Things devices that support one or more common ecosystems possible.
Track 10: Computer Vision and Image Processing
Computing vision processing the raw input images to enhance them or get them ready for subsequent activities is the fundamental goal of image processing. The goal of computer vision is to properly analyze the incoming images or videos and extract information from them in order to anticipate the visual input, much like the human brain. As well as performing segmentation and labeling recognized items, image processing is crucial in preparing images for computer vision models. In general, the technologies that enable computers to comprehend images are referred to as computer vision.
Track 11: Robotic Process Automation
A type of business process automation technology called robotic process automation (RPA) is based on digital workers or metaphorical software robots (bots). It's also known as software robotics at times. Using internal application programming interfaces (APIs) or specialized scripting languages, a software set of steps to automate a process and interface to the back end system is used in traditional workflow automation solutions. RPA systems, in contrast, create the action list by observing how the user completes the task in the graphical user interface (GUI) of the programmed, and then automate the process by having the user repeat the action list within the GUI. By doing this, the barrier to using automation in products that might not otherwise have APIs for this purpose can be lowered.
The use of machine-learning algorithms and software, or artificial intelligence (AI), to imitate human cognition in the analysis, display, and comprehension of complicated medical and health care data, is referred to as artificial intelligence in healthcare. AI specifically refers to computer algorithms capacity to make approximations of conclusions based only on input data. Analyzing connections between clinical practices and patient outcomes is the main goal of applications of artificial intelligence in the field of health.
- Drug Development
- Personalized Medicine
- Patient Monitoring
- Treatment Protocol Development
A branch of linguistics, computer science, and artificial intelligence called "natural language processing" (NLP) studies how computers and human language interact, with a focus on how to train computers to process and analyze massive volumes of natural language data. The ultimate goal is to create a machine that is able to "understand" the contents of documents, including the subtle subtleties of language used in different contexts. Once the information and insights are accurately extracted from the documents, the technology can classify and arrange the documents themselves. Speech recognition, natural language interpretation, and natural language synthesis are commonly difficult tasks in natural language processing.
Track 14: Pattern Recognition
In order to automatically identify patterns and regularities in data, pattern recognition uses machine learning methods. This information may take the form of text, images, sounds, or other recognizable elements. Systems for pattern recognition can quickly and correctly identify well-known patterns. Additionally, they are able to classify and identify novel items, identify patterns and objects that are partially hidden, and distinguish forms and objects from various perspectives. Image processing, speech and fingerprint recognition, aerial photo interpretation, optical character recognition in scanned documents like contracts and photographs, and even medical imaging and diagnostics are just a few of the many uses for pattern recognition.
Track 15: Facial Expression and Emotion Detection
The process of identifying human emotion is known as emotion recognition. The precision with which people can gauge the emotions of others varies greatly. The use of technology to assist humans in recognizing emotions is a relatively new area of study. In general, the technology performs best when it integrates several modalities into the context. A facial expression is made up of one or more movements or facial muscle postures. One disputed theory claims that these movements reveal an individual's emotional condition to onlookers. Nonverbal communication can also take the shape of facial expressions. In addition to humans, most other mammals and several other animal species also use them as a key method of social communication.
Track 16: Virtual Reality and Augmented Reality
The technology behind virtual reality allows for the computer to manipulate and interact with the user while presenting complex information. It is an interactive, three-dimensional world that was created by a computer to mimic reality. The ability to display information in 3D, attach noises, and use touch technology greatly improves data comprehension. It has gained popularity as a medical toy with "Helmet-glove" equipment that was intended for a large audience. In augmented reality, a person views a real scene while simultaneously viewing a virtual scene created by a computer that adds more details to the real picture. By superimposing virtual visuals over the real world, it improves it by adding graphics, sounds, and smell.
Track 17: Object Data Detection with Digits
Detecting instances of semantic objects of a specific class in digital photos and videos is the goal of object detection, a field of computer vision and image processing. Face and pedestrian detection are two well-studied object detection areas. Numerous computer vision fields, such as image retrieval and video surveillance, use object detection. It is frequently used in computer vision applications like picture annotation, vehicle counting, activity recognition, face detection, and co-segmentation of moving objects in videos. Additionally, it is used to track moving items, such as a cricket bat, a ball during a football game, or a person in a film.
- Face Detection
- Image Annotation
- Video object co-segmentation
- Video Surveillance
Track 18: Online Fraud Detection
Identifying fraud Using By using a machine learning (ML) model and a sample dataset of credit card transactions, machine learning trains a model to detect fraud patterns. The model is self-learning, allowing it to adjust to fresh, uncharted fraud trends. The neural networks can fully adapt and can learn from patterns of acceptable conduct. These can recognize patterns of fraudulent transactions and adjust to changes in the behavior of typical transactions. The neural networks decision-making process is incredibly quick and can take place in real time.
Track 19: Biometric Security Solutions
The systematic study of scientific methods that provide a system the ability to mimic human learning processes without being explicitly programmed is known as machine learning. The biometric topographies are also studied by machine learning in order to mimic an individual's identification learning processes. It safeguards priceless items and delicate documents. It keeps track of everyone's own biometric identity. Passwords and PINs are not required of users, and their accounts cannot be shared. Even when the data is encrypted, it is still preferable to store biometric information like as Touch ID and Face ID rather than having the service provider store it.
- Fingerprints Recognition
- Facial, Voice Recognition
- Iris Recognition
- Palm or finger vein patterns Recognition
Track 20: Advancement of Cyber Security
Machine learning and AI. A key factor in the development of cyber security is artificial intelligence and machine learning. Machine learning is being used to model network behavior and enhance overall threat detection in order to detect harmful conduct from hackers. By modeling network behavior and enhancing threat detection, machine learning is utilized to recognize the varied behavior of hackers.
Machine Learning Global Market:
Machine learning is expected to have a global market worth $17.1 billion in 2021 and $90.1 billion in 2026, with a compound annual growth rate (CAGR) of 39.4 percent during that time frame. The research gives a summary of the machine learning market globally and examines market trends. The research contains estimated market data for the forecast period, 2021 to 2026, using 2020 as the base year. Platforms and related services for machine learning are included in the study's scope. However, physical services for repairing and maintaining IT Infrastructure, money earned for B2C adoption, and third-party vendors are not included in the scope. Examples of such hardware include data servers, GPUs, and other hardware devices.
Global Artificial Intelligence in Health Market:
The market for artificial intelligence in healthcare, which was estimated to be worth USD 10.4 billion in 2021, is anticipated to increase at a CAGR of 38.4 percent from 2022 to 2030. Some of the key factors propelling the market's expansion are the expanding datasets of digital patient health information, rising desire for individualized treatment, and rising demand for lowering healthcare costs. The rise in demand for early disease diagnosis and better understanding is mostly due to the growing elderly population worldwide, changing lifestyles, and rising prevalence of chronic diseases. Additionally, early patient diagnosis of underlying health issues is made possible by deep learning technology, predictive analytics, content analytics, and Natural Language Processing (NLP) tools.
Machine Learning: Applications and Global Market
With a compound annual growth rate (CAGR) of 49.1% from 2018 to 2023, the global artificial intelligence market is expected to increase from $3.5 billion in 2018 to $26.1 billion by 2023. The machine learning, natural language processing, computer vision, and expert systems categories make up the technology-based segments of the global artificial intelligence market. The worldwide artificial intelligence market is split into on-premises deployment and cloud deployment based on the manner of implementation. Machine learning applications in BFSI Fraud and Risk Management investment forecast Management of Sales and Marketing Campaigns Customer Segmentation Digital Support for Others.
List Of Societies and Associations
Machine Learning & Deep Learning Universities in Dublin, Ireland:
- Code Institute
- DDLETB Tallaght Training Centre
- Kids Comp
- ISM Training Centre
- CDETB Ballyfermot Training Centre
- CCT College Dublin
- Zazen Academy of Technology
- Dun Laoghaire Further Education Institute
- Dublin Business School
- Griffith College
- UCD School of Computer Science
- Technological University Dublin
Machine Learning & Deep Learning Universities All over the World:
- Harvard University
- Stanford University
- Massachusetts Institute of Technology
- Max Planck Society
- University of Oxford
- University of Cambridge
- Chinese Academy of Sciences
- Columbia University in the City of New York
- National Institutes of Health
- New York University
- University of Washington
- Princeton University
- Swiss Federal Institute of Technology Zurich
- Helmholtz Association of German Research Centers
- The University of Tokyo
- T singhua University
- McGill University
United States: Algorithmic Justice League, National Center for Applied Data Analytics,
Center for Human-Compatible Artificial Intelligence, Center for Security and Emerging Technology, Global Clean tech Cluster Association, Czechoslovak Pattern Recognition Society, Bulgarian Association for Pattern Recognition, The Swiss Association for Pattern Recognition, The British Machine Vision Association and Society for Pattern Recognition, Confederation of Laboratories for Artificial Intelligence Research, Association for the Advancement of Artificial Intelligence
Europe: European Laboratory for Learning and Intelligent Systems, European Neural Network Society, European Association for Artificial Intelligence, Global Partnership on Artificial Intelligence, Innovation Center for Artificial Intelligence, Kestrel Institute, Knowledge Engineering and Machine Learning Group, French Association for Pattern Recognition and Interpretation, National Committee of the Russian Academy of Sciences for Pattern Recognition and Image Analysis
Asia Pacific: Asia Pacific Artificial Intelligence Association, NUS (National University of Singapore), Conclusion of APAC AI Research Universities, SUTD (Singapore University of Technology and Design) Indian Unit for Pattern Recognition and Artificial Intelligence, Artificial Intelligence Society of Hong Kong, Pattern Recognition and Machine Intelligence Committee of the Chinese Association of Automation, Artificial Intelligence Association of India,The Australian Artificial Intelligence Institute, UBTECH Sydney Artificial Intelligence Centre
Middle East: Hong Kong Society for Multimedia and Image Computing, Pattern Recognition and Machine Intelligence Committee of the Chinese Association of Automation, The Macau Society for Pattern Recognition and Image Processing, Pakistani Pattern Recognition Society (PRRS), Computer Vision and Pattern Recognition Group of The Korean Institute of Information Scientists and Engineers.
Past Conference Report
For making the Machine Learning 2021 Conference the best conference yet, we sincerely appreciate all of our wonderful Keynote Speakers, Speakers, Conference Attendees, Students, Organizing Committee Members, Associations, and Media Partners. Scientific Meetings conference series for 2020–2021 is unparalleled and an irresistible chance to immerse oneself in the present era's pulsating information and communication technology.
By equating the speed of science, technology, and business fields, conference series International Engineering Conventions 2015-2016 enables greater understanding of the technical improvements and scientific advancements around the world. The Conference Series LLC Ltd presented the 8th World Machine Learning and Deep Learning Conference on November 16, 2021 in Barcelona, Spain, with the topic "Intensify the World with Machine Learning"
Our sincere thanks to Organizing Committee Members for their gracious presence, support and assistance towards conference, and with enormous feedback from the participants and supporters of Machine Learning 2022, Conference series LLC Ltd is glad to announce 9thWorld Machine Learning and Deep Learning Conference Dublin, Ireland. Let us meet again @ Machine Learning 2022