Track 1: The Next Generation of Artificial Intelligence

The science of artificial intelligence is rapidly evolving, with many technologies being used that may make it easier for humans to complete various tasks. We've seen that present Artificial Intelligence systems have a number of limitations, including a lack of personalization, adaptability, and self-learning capabilities. With freshly developed features, Smart Agents technology is an AI tool that will reduce the shortage of capacity.

• Neural network compression
• Web technologies
• Machine learning
• Algorithms

Track 2: Smarter Big Data, Data Analytics, and Insights

Using Big Data analytics, you can examine your data and get answers virtually instantly thanks to today's technologies. With more typical business intelligence tools, it intelligently determines vast volumes of data to find hidden patterns, correlations, and other insights. With a large data boom on the horizon, it's more crucial than ever to take control of your health information, whether it's for banking, government, life science, or a variety of other sources.

• Hadoop
• ‎SAP business analytics
• Digital transformation

Track 3: Deep learning System to Track Human Motion

As a result of an ageing population and stroke victims, motion rehabilitation is becoming increasingly important necessitating the use of human motion analysis. In various domains, such as healthcare, sports, video surveillance, and body-based user interfaces human motion is implicitly necessary. Lower layers may recognise edges, while higher layers may identify ideas connected to a human. A deep-learning-based system is proposed to monitor human mobility utilising computer vision based on image and video processing.

• Motion detection
• Human motion tracking
• Imaging sensors

Track 4: Artificial Intelligence in Music

Artificial intelligence is already being employed in a variety of applications. Now it's time to think about how much it will affect how people make and consume music. With the development of multiple protocols and AI tools like audio mastering, tuning, and streaming the music. AI has become an answer to a simple demand: more music is needed than ever before. It may have a significant impact on any musician's creative process, and artists in the future may be required to have in-depth technical knowledge of neurosciences.

Track 5: Artificial Neural Networks

Artificial neural networks are founded on the belief that they can be imitated using silicon and wires as living neurons and dendrites to cooperate with the human brain by making the proper connections. An artificial neural network is a computational model based on biological principles that comprises of processing elements and connections between them, as well as training and recall algorithms. Feed Forward ANN, Feed Back ANN, Learning vector quantization, and many other forms of neural networks exist.

• Networks with the memory model
• Recurrent neural network
• Supervised model
• Physical neural networks

Track 6: AI for Cyber Security Applications

Artificial neural networks are founded on the belief that they can be imitated using silicon and wires as living neurons and dendrites to cooperate with the human brain by making the proper connections. An artificial neural network is a computational model based on biological principles that comprises of processing elements and connections between them as well as training and recall algorithms. Feed Forward ANN, Feed Back ANN, Learning vector quantization, and many other forms of neural networks exist.

• Network security
• Risk & compliance management
• Fraud detection/ anti-fraud
• Intrusion detection

Track 7: Biometric Security Solutions

As new technologies are developed for security purposes, the biometric security system is one of the most recent upgrades, which comprises the design and implementation of the sensor's enabling hardware and software. Biometric security systems have come a long way in terms of technological breakthroughs and general adoption, but there is still a lot of scepticism about the technology around the world. Face recognition and biometric access for attendance in an organisation are examples of how it is utilised.

• Life science
• Adaptive biometric systems
• Multimodal biometric system
• Human dignity

Track 8: Virtual Reality and Augmented Reality

The employment of computer technology in conjunction with an artificial world is known as virtual reality. You are viewing an entirely different reality than the one in front of you when you use virtual reality. Many virtual reality apps use 3D animated environments with audio, images, photos, and other features for real-time simulation and interactivity via multiple sensory channels.

• The collaboration of AI with AR and VR
• Remote assistance
• 5G will Expedite AR and VR evolution
• Eye-Tracking and facial expressions
• Virtual sports events
• Advanced display technology
• Human-computer interaction

Track 9: Robotics and Mechatronics

Robotics and Mechatronics are primarily concerned with the practical application of current systems and control methods. Robotics is concerned with the configuration, development, operation, design, and use of robots such as a robot management system, sensory feedback, and data processing. Robotics, computers, telecommunications, products, and many more fields are covered by Mechatronics which uses a combination of software, electronics, and mechanical design.

• Engineering cybernetics
• Planetary exploration rovers
• Nanorobotics
• Machine vision
• Automation and robotics

Track 10: Artificial Intelligence in Gaming

When it comes to today's AI applications in the gaming business, it's clear that AI is mostly used to improve the in-game experience and design. They have many Artificial Intelligence-powered components and related applications in video gaming features. It also aids in the long-term retention of the player's attention and contentment. Data mining and procedural content generation are two processes used by AI that are not immediately obvious to the user.

• Computer simulations of board games
• Monte Carlo tree search method

Track 11: Machine Learning

Machine learning is one of the most interesting technologies because it involves computers learning how to accomplish jobs without being explicitly taught and improving naturally as a result of their experience with algorithms. It is a branch of artificial intelligence that is concerned with computational statistics. Although it focuses on using computers to make predictions, not all machine learning is statistical learning.

• Data mining
• Reinforcement learning
• Mathematical optimization
• Exploratory data analysis

Track 12: Blockchain for IoT Security

Blockchain is a fascinating solution for IoT security and is advantageous for a web of things security for similar reasons such as data tampering, access control and money. IoT security needs to be modified as a result of data streaming from sensors and embedded processors. Blockchain IoT security necessitates a significant amount of computing power.

• Cryptography
• Streamlines accounting
• A more efficient supply chain
• Cyber security

Track 13: Convolutional Neural Networks

Convolutional neural networks (CNN) are regularised versions of multilayer perceptron, which are fully connected networks to each neuron in one layer that are fixed to all neurons in the following layer, and are most typically used in deep learning to analyse visual images. Image and video recognition, recommender systems, image classification, medical image analysis, natural language processing and financial time series are just a few of CNN's uses.

• Receptive fields in the visual cortex
• Neural abstraction pyramid
• Electromyography (EMG) recognition

Track 14: Enterprise Artificial Intelligence

Enterprise AI is a subset of enterprise software that uses new technology and artificial intelligence techniques to help companies alter their businesses. It necessitates the development and deployment of a new technology stack for Enterprise AI at scale, as well as a shift in the business environment in several industries, bringing new opportunities through automated, intelligent goods. Machine learning, deep learning, cognitive computing, and natural language processing are examples of intelligent automation processes (NLP).

Track 15: Natural Language Processing

Natural Language Processing focuses on system development that allows computers to communicate with people using everyday language. It is divided into two categories are Natural Language Understanding and Natural Language Generation. This natural language generation is used to converts information from the computer database into readable human language and vice versa.

• Speech recognition
• Neural networks
• Morphological analysis
• Higher-level NLP applications

Track 16: ML Platforms with Cloud Services

Machine Learning is a platform that allows businesses to advance in their business lifecycle by utilising predictive apps that can process large amounts of data using machine learning techniques. Amazon, Google, Microsoft and IBM Cloud are just a few of the cloud computing platforms that offer machine learning web services. This platform required a lot of infrastructure programmers who were experienced with machine learning and data analysis was costly.

• Client-Cloud computing challenges
• Secure data management
• Machine learning workbench and services
• Analytics and security for machine learning

Track 17: Deep Reinforcement Learning

Deep reinforcement learning, where machines can educate themselves based on the outcomes of their own activities is one of the most fascinating subjects of artificial intelligence in this modern technology. Deep reinforcement learning (DRL) combines deep learning and reinforcement learning ideas to produce efficient algorithms. As one of the subgroups of machine learning in AI and Deep Learning, it has networks capable of learning unsupervised from unstructured input.

• Deep learning frameworks
• Business management
• Automated reasoning
• Deep neural networks
• Dynamic programming techniques

Track 18: Autonomous Robots

Autonomous Robots are capable of making their own decisions and acting on them without the need for explicit human direction. Autonomous robots are intelligent machines that are very useful in domains including spaceflight home maintenance such as cleaning, waste water treatment, and delivering goods and services. A subfield of autonomous robotics is artificial intelligence, robotics and information engineering.

• Autonomous navigation
• Self-maintenance
• Sensing the environment
• Research and education mobile robots

Track 19: Data Mining for Robotics and Intelligence Software Agents

The goal of data mining in robotics is to improve algorithms for Linked multicomponent robotic systems, Single robot hose transport, and Reinforcement learning. Robots according to data mining operate using control loops and reactionary mechanisms, as well as machine learning. An intelligent software agent is a self-contained software package with enough intelligence to operate as your personal helper for assigned communications duties utilising artificial intelligence.

• Humanoid robots
• Human-robot interaction
• Robotic sensor data analysis using stream data mining
• Applications of data mining in robotics
• Artificial intelligence and robotics
• Social robotics

Track 20: Support Vector Machines

Many people favour the support vector machine because it achieves great accuracy while using less computing power. It is a basic technique that analyses data for classification and regression analysis utilising a machine learning model and associated learning algorithms. The SVM is primarily concerned with locating a hyper plane in N-dimensional space (N — the number of features) that distinguishes between data points.

• Support-vector clustering
• Linear classification