Data Analytics and Mining

Prof. Vikram Goyal, Professor, IIIT Delhi
Prof. Mayank Vasta and Prof. Richa Singh, IIT Jodhpur
Prof. Manoj Kumar, Professor, NIT Delhi

The track on data analysis and mining serves as a dynamic and information-rich forum where professionals, researchers, and experts converge to explore the latest developments, challenges, and innovations in the field. The field of data analytics and mining is characterized by constant innovation and adaptation to emerging technologies and data sources. Key trends include the increasing adoption of artificial intelligence and machine learning techniques for more advanced and accurate analysis, the growing importance of real-time analytics for timely insights, and the emphasis on data privacy and security in the wake of evolving regulations. Additionally, the fusion of data analytics with other technologies like the Internet of Things (IoT) and edge computing is opening up new possibilities for data-driven insights in various industries. The topics include, but are not limited to:

    Data retrieval
    Big Data Storage techniques
    Data Mining and warehousing
    Data visualisation
    Modelling structure and storage of data
    Scalability and portability issues of data
    Data privacy and security
    Parallel processing of big data
    Distributed access of data
    Application of big data and related topics
    Web mining, text mining
    Sentiment analysis
    Novel theoretical and computational models

Cryptography, Cyber Security and Network Security

Prof. Harsh K. Verma, Professor, Department of Computer Science Engineering, NIT Jalandhar
Dr. T P Sharma, Associate Professor, NIT Hamirpur
Dr. Chandra Sekhar Obbu, Associate Professor, NIT Delhi

Computer network security consists of measures taken by some organizations or businesses to monitor and avoid unauthorized access from outside attackers. In the initial days of the internet, its use was limited to development purposes. The topics in the track include but are not limited to Design and security analysis of cryptographic primitives and protocols, Novel applications of cryptography, Formal verification of cryptographic security properties, Cryptographic standards, Post-quantum cryptography, Hardware & software implementations. This track also emphasizes emerging research areas in cyber security and privacy. It includes cyber security concepts, threats in cyberspace, security standardization, security and privacy regulations and laws, cyber hacking, digital forensics, Trust management, security and privacy in block chain technologies, cyberspace protection and anti-malware, network traffic analysis, identity and access management in cyber systems, cyber threat intelligence, cryptography trends, steganography, security of cyber-physical systems and IoT, secure cloud and edge computing architectures, security of web-based applications, and cyber harmony and cyber welfare. The track provides an important viewpoint on an established, major research area; support or challenge long-held beliefs in such an area with compelling evidence; or present a convincing, comprehensive new taxonomy of such an area. The topics include, but are not limited to:

    Security and privacy in mobile systems
    Security and privacy in adhoc networks
    Network performance analysis
    Cyber risk and vulnerability assessment
    Intrusion detection and prevention
    Visual analytics for cyber security
    Security and privacy in Grid computing
    Biometric security and privacy
    Security and privacy in Wireless Sensor networks
    Cryptographic aspects of block chains & crypto currencies
    Cryptanalysis, Side-channel attacks and defences
    Trust Management
    Cyber harmony
    Vehicle-to- Everything (V2X) Communications
    Machine-to-Machine(M2M) Communication

Cloud Computing and IoT

Dr. Mashtaq Ahmed, Associate Professor, MNIT Jaipur
Dr. Piyush Kumar, Assistant Professor, NIT Patna
Prof. Jyoteesh Malhotra, Professor, NIT Delhi

The track has a vast expanse in the field Data Analytics, Distributed & Parallel Computing, High Performance Computing, Quantum Computing, Cloud Quality Management & Service level agreement, Cluster, Cloud, & Grid Computing, Mobile Computing and Edge Computing. Complementing AI into the cloud paradigm promises a journey of unlocking business potential and their operations with greater agility and efficiency. Cost savings and enhanced data management are just some of the added bonuses that come with investing in cloud technology. While the benefits are abundant, the obstacles in execution are as detrimental. Integration challenges, data privacy and concerns around connectivity can be major setbacks to your strategy. The topics include, but are not limited to:

    Quantum Computing
    Cloud virtualization and IoT
    Cloud and IoT federation
    Reliability and security
    Inter cloud and multi-cloud.
    Network virtualization
    Fog computing
    Cognitive Computing
    Wireless Sensor Networks
    Unmanned Aerial Vehicles
    Ubiquitous Computing
    Blockchain Technology
    Cloud at the Edges and Mobile cloud
    Cloud security
    Hybrid cloud infrastructure for IoT
    Security in IoT and edge cutting technologies

Artificial Intelligence and Machine Learning

Dr. Shailender Kumar, Associate Professor, DTU, New Delhi
Dr. Yogesh Kumar Meena, Associate Professor, MNIT Jaipur
Dr. Shelly Sachdeva, Associate Professor, NIT Delhi

Artificial Intelligence and Machine learning track deals with aspects of Signal Processing, Audio and Speech Processing, Computer Vision, Natural Language Processing, Supervised and unsupervised learning, Deep Learning, Data Mining, Generative Models, Reinforcement Learning and Optimization algorithm. Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions) and self-correction. Neuromorphic computing mimicking the human brain is one such wave towards AI capabilities. The topics include, but are not limited to:

    Pattern recognition
    Computational intelligence
    Augmented reality and virtual reality
    Signal processing
    Self driving vehicles
    Robotics
    Image processing
    Generative AI use-cases
    Machine Learning for Systems
    DeepFake Technologies
    Recommender systems, computational advertising, multimedia, finance, bioinformatics
    Cognitive Computing
    Audio / Video Recognition
    High reliability and error tolerance in AI
    Time series prediction and forecasting

Digital Innovation in Healthcare and its Application

Dr. Deepak Kumar Jain, Associate Professor Chongqing University of Posts and Telecommunications, China
Dr. Deepak Ranjan Nayak, Assistant Professor, MNIT Jaipur
Dr. Anurag Singh, Associate Professor, NIT Delhi

The goal of accelerating medical innovation while enhancing the effectiveness of patient care becomes a reality in Healthcare 4.0, the fourth healthcare revolution. Utilizing a new generation of information technologies, including the Internet of things (loT), big data, cloud computing, artificial intelligence and computer vision, smart healthcare is able to completely revolutionize the current medical system making it more effective, convenient and individualized. Healthcare professionals are progressively adopting new digital trends, such as wearable gadgets that allow patients to supplement their health data and networked equipment that enable remote monitoring and patient care. This track provides the opportunity to learn the recent research trends and future scope of smart healthcare. The topics include, but are not limited to:

    Health Informatics and Electronic Health Records (EHR)
    Telemedicine and Remote Patient Monitoring
    IoT Applications in Healthcare
    Healthcare Data Privacy and Cybersecurity
    Training and learning algorithms in healthcare systems
    Augmented Reality (AR) and Virtual Reality (VR) in Medical Training and Treatment
    Healthcare Robotics and Automation
    Digital Therapeutics and Health Apps
    Explainable AI in Healthcare Decision-Making
    E-Health and Mobile Health (mHealth) Integration
    Ethical Considerations in AI-Driven Healthcare
    IoT, Fog & Cloud Computing-based Cyber Physical System for Digital Healthcare
    Blockchain Applications in Healthcare
    Precision Medicine and Genomics
    Digital Mental Health Solutions
    Healthcare Gamification for Patient Engagement
    Ethical Considerations in AI-Driven Healthcare