The main conference on machine learning is being held here on November 09-10, 2023 Stockholm, Sweden. We invite you to attend the "10th World Conference on Machine Learning and Deep Learning" with the topic "Intensify the World with Machine Learning and Artificial Intelligence." There will also be an exposition and useful workshops in addition to speaker sessions, symposia, and poster presentations. We truly hope you will attend this amazing event on behalf of the planning team! Machine learning is the process of teaching computers how to execute complicated tasks that are difficult for humans to define or comprehend, as well as how to make predictions. It blends statics with mathematical optimization. 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 2023 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.

Target Audience?

  • Chief Information Officers(CIO)
  • Graphic Controllers(GCIOs)
  • Chief Technical Officers(CTOs)
  • Presidents
  • Vice presidents
  • Chairs
  • Directors
  • Data Scientists
  • Developers
  • Startup Professionals
  • Scientists
  • Researchers
  • Professors
  • Data Analysts
  • Data Architects
  • Cloud Architects
  • Business Intelligence Developers
  • Machine Learning Engineers
  • Data Scientists

Track 1: Deep Learning Frame Work

Through a high- position programming interface, deep literacy( DL) fabrics give the structure blocks for developing, training, and assessing deep neural networks. To give high speed, multi-GPU accelerated training, popular deep literacy fabrics like MX Net, P Arsonist, Tensor Flow, and others calculate on GPU- accelerated libraries like CUDNN, NCCL, and DALI.

Track 2: AI Gaming

Artificial intelligence( AI) is used in videotape games to produce mortal- suchlike intelligence innon-player characters( NPCs) by generating responsive, adaptable, or intelligent conduct. Since the 1950s, when they first appeared, artificial intelligence has played a significant part in videotape games. The field of artificial intelligence (AI) in videotape games is separate from academic AI. In place of machine literacy or decision timber, it enhances the gaming experience. The conception of AI opponents was greatly vulgarized during the florescence of hall videotape games in the form of graduated difficulty settings, distinctive movement patterns, and in- game events that were reliant on player commerce.

Track 3: Big Data Analytics

After looking into retired patterns, correlations, and other perceptivity from a massive volume of data, big data analytics provides a small number of useful data. This eventually results in wiser business opinions, lesser profitability, more effective operations, and eventually satisfied guests also the big data conference enhances its value. The following are some ways that big data analytics benefits an association cost cutting New Products and Services that make opinions more snappily and more. Data collection, data storage, data analysis, hunt, sharing, transfer, visualization, querying, streamlining, information sequestration, and data source are just a many of the big data analysis challenges.

Track 4: Internet of Things (IoT)

The term" Internet of effects"( IoT) refers to the entire network of physical objects, including cabinetwork, appliances, buses , and other particulars bedded with electronics, connectivity, detectors, selectors, software, and other factors. These objects are given an IP address Internet Protocol, which allows them to connect and change data, perfecting effectiveness, delicacy, and profitable benefit while also taking lower mortal commerce. The emulsion of multitudinous technologies, similar as ubiquitous computing, extensively available detectors, sophisticated bedded systems, and machine literacy, has caused the sector to advance. singly and inclusively, the traditional fields of embedded systems, wireless detector networks, control systems, and robotization make Internet of effects bias that support one or further common ecosystems possible.

  • Internet Protocol
  • Embedded System
  • Wireless Sensor Networks
  • Smart Phones
  • Smart Speakers

Track 5: Robotic Process Automation

A type of business process robotization technology called robotic process robotization( RPA) is grounded on digital workers or tropical software robots. It's also known as software robotics at times. Using internal operation programming interfaces or technical scripting languages, a software set of way to automate a process and affiliate to the aft end system is used in traditional workflow robotization results. RPA systems, in discrepancy, produce the action list by observing how the stoner completes the task in the graphical user interface( GUI) of the programmed, and also automate the process by having the stoner repeat the action list within the GUI. By doing this, the hedge to using robotization in products that might not else have APIs for this purpose can be lowered.

Track 6: Artificial Intelligence

In discrepancy to the natural intelligence displayed by creatures, including humans, artificial intelligence demonstrated by robots. Artificial intelligence exploration is the study of intelligent agents which are any systems that can sense their surroundings and take conditioning to increase their chances of success. Preliminarily robots that mimic and parade" mortal" cognitive capacities associated with the mortal mind like and problem- working were appertained to as artificial intelligence.

Track 7: Machine Learning &Deep Learning

Machine learning (ML) is a topic of study focused on comprehending and developing "learning" 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 can be either fully or partially guided.

Track 8: 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.

Track 9: Cloud Computing

A model for delivering computing services over the internet is called pall computing. A wide range of products, services, and results can be developed, stationed, and delivered in real- time. It's composed of a number of pieces of tackle and software that may be viewed ever using any web cyber surfer. The on- demand vacuity of computer system coffers, in particular data storehouse and processing power, without direct active supervision by the stoner, is known as pall computing. Functions in large shadows are constantly dispersed over several spots, each of which is a data centre. pall computing relies on resource sharing to negotiate consonance and frequently uses a" pay- as- you- go" approach.

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: AI & Machine Learning in HealthCare & Medical Science

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.

Track 12: Robotic Process Automation

A type of business process automation technology called robotic process automation (RPA) is based on digital workers or metaphorical software robots. It's also known as software robotics at times. Using internal application programming interfaces or specialized scripting languages 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.

Track 13: Natural Language Processing (NLP) and Speech Recognition

A branch of linguistics, computer science, and artificial intelligence called "natural language processing" studies how computers and human language interact, with a focus on how to train computers to process and analyse 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.

  • natural-language understanding
  • natural-language generation
  • Speech Recognition
  • Linguistics

Track 14: 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.

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 on lookers. 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: 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 17: Online Fraud Detection

Relating fraud Using By using a machine literacy( ML) model and a sample dataset of credit card deals, machine literacy trains a model to descry fraud patterns. The model is tone- literacy, allowing it to acclimate to fresh, uncharted fraud trends. The neural networks can completely acclimatize and can learn from patterns of respectable conduct. These can fete patterns of fraudulent deals and acclimate to changes in the geste of typical deals. The neural networks decision- making process is incredibly quick and can take place in real time.

Track 18: Advancement of Cyber Security

Machine learning and AI  key factor in the development of cyber security is artificial intelligence and machine learning. Machine learning is being used to model network behaviour and enhance overall threat detection in order to detect harmful conduct from hackers. By modeling network behaviour and enhancing threat detection machine learning is utilized to recognize the varied behaviour of hackers.

Track 19: Pattern Recognition

In order to automatically identify patterns and discrepancies in data, pattern recognition uses machine literacy styles. This information may take the form of textbook, images, sounds, or other recognizable rudiments. Systems for pattern recognition can snappily and rightly identify well- known patterns also, they're suitable to classify and identify new particulars, identify patterns and objects that are incompletely hidden, and distinguish forms and objects from colorful perspectives. Image processing, speech and point recognition, upstanding print interpretation, optic character recognition in scrutinized documents like contracts and photos, and indeed medical imaging and diagnostics are just a many of the numerous uses for pattern recognition.

Track 20: 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.

Scope and importance of Machine Learning:

Machine learning as an innovation reduces massive amounts of data, facilitating information researchers' tasks in a mechanised cycle, and is gaining a lot of attention and recognition. Artificial intelligence (AI) includes programmed sets of conventional techniques that have replaced traditional factual procedures, changing how information extraction and translation functions. The experimental approach, which has traditionally been used to characterise information exploration, becomes challenging to use when large and diverse informative resources are being discussed. Massive amounts of material were evaluated for being overhyped precisely because of this justification.

Machine Learning Global Market:

The global AI market was valued at $1.4 billion in 2017 and is anticipated to grow at a compound annual growth rate (CAGR) of 43.6% from 2017 to 2022 to reach $8.8 billion. The machine learning programming includes solutions for many applications and phases that work well with heavy processing workloads. The managed service sector is expected to grow at a greater CAGR in the services component, but the competent service segment is expected to contribute more during the projected period. The managed service is expected to expand more quickly because it boosts efficiency within organisations and reduces the expense of managing artificial intelligence administrations that are requested. The complexity of operations and rising use of machine learning are the main indicators of the development of professional services.

Artificial Intelligence: Applications and Global Markets

The global artificial intelligence market is anticipated to rise from $3.5 billion in 2018 to $26.1 billion by 2023 with a compound annual growth rate (CAGR) of 49.1%. The technology-based divisions of the global artificial intelligence market are made up of the machine learning, natural language processing, computer vision, and expert systems categories. Depending on how it is implemented, the global artificial intelligence market is divided into on-premises deployment and cloud deployment. Applications of machine learning in BFSI Investment prediction for risk management and fraud Administration of Sales and Marketing Initiatives Customers are segmented Others' Digital Support.

Machine Learning & Deep Learning Universities in Stockholm and Sweden:

  • Dalarna University Masters
  • KTH Royal Institute of Technology Masters
  • Linnaeus University Masters
  • Malmo University Masters
  • Stockholm University Masters
  • University of Skovde Masters
  • Uppsala University Masters
  • Halmstad University Masters

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

Related Societies:

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.

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Conference Date November 09-10, 2023
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