10^th World Machine Learning and Deep learning Conference November 09-10, 2023 Stockholm, Sweden

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.

• GPU-accelerated
• Multi-GPU accelerated
• MX NET
• P Torch

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.

• Path Finding
• Decision Trees
• Data Mining
• Robotics
• Computer Graphics

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.

• Volume, Density
• Cost Reduction
• Faster, Better Decision Making
• New Products and Services

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.

• Workflow automation tools
• Software developer
• Graphical user interface
• Robot Software

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.

• Web Search Engines
• Recommendation System
• Automated Decision Making
• Strategic Game System

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.

• Data Mining
• Computational Statistics
• Computer Vision
• Natural Language Processing
• Drug Design

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.

• Final Analysis
• Coherent Reasoning
• Mathematics & Statistics
• Algorithms

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.

• Network Infrastructure
• Mobile Agents
• Time Sharing

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.

• Drug Development
• Personalized Medicine
• Patient Monitoring
• Diagnostics
• Treatment Protocol Development

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.

• Workflow automation tools
• Software developer
• Graphical user interface
• Robot Software

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.

• Fingerprints Recognition
• Facial, Voice Recognition
• Iris Recognition
• Palm or finger vein patterns 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 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.

• Mammals
• Animal Species
• Humans
• Knowledge Base Techniques
• Facial Muscle Posture

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.

• Credit Card Transactions
• Make Decisions
• Logistic Regression
• Learning Algorithms

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.

•  Automotive Hacking
• Potential of Artificial Intelligence
• Mobile is the New Target
• Cloud is Also Potentially Vulnerable
• Data Breaches

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.

• Image Processing
• Speech and Finger Print Recognition
• Regularities in Data

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.

• Computing Power
• Data Center
• Virtual Power Network

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.