machine learning

Special Sessions

  • AI and Machine Learning in Multidisciplinary Innovative Application
    • Machine learning is a buzzword for today's technology, and it is growing very rapidly day by day. We are using machine learning in our daily life even without knowing it such as Google Maps, Google Assistant, Alexa, etc. Below are some most trending real-world applications of Machine Learning:
    • Scope/Topics:
      • AI, ML technology
      • Machine learning/ Deep learning applications,
      • Applicability of AI, Machine learning, and deep learning-based intelligent models for big
      • Data handling
      • Machine Learning for Natural Language Processing (NLP)
      • Protocols, Algorithms for real-time health monitoring with Machine Learning(ML)
      • Social data analysis for IoT applications in ML
      • Machine Learning and Artificial Intelligence
      •  Machine learning frameworks
    • Names, Designations, Affiliations, and Email IDs of the Session Chairs:
      1. Dr.Manjushree Nayak
        Associate  Professor
        Department of Computer Science and Engineering, NIST, Berhampur 
        Institute Park, Palur Hills, Berhampur, Odisha,761008, India 
      2. Prof.(Dr.) Bhavana Narain
        Professor
        MATS School of IT, MATS University, 
        Pagaria Complex, Pandari, Raipur, Chhattisgarh, India
      3. Dr. Lalit Garg
        Senior Lecturer
        Institution and department
        Department of Computer Information Systems,
        Computer Information Systems, Level 1 Block A Room 23, ICT Building, University of Malta, Msid
    • Contact Person Details:

 

 

  • Cyber and AI for connected wearables and Big Data Analytics for Cyber-physical Applications
    • Researchers working in cyber security, big data, medical imaging, and healthcare rely on the expertise of clinicians who play a significant role in understanding complex medical data for the diagnosis of diseases. Automation of diagnostic procedures for various healthcare problems may help in improving patient care and overall healthcare. Recently, advanced machine learning methods have shown promising results in biomedical and healthcare applications. Therefore, there is a need to explore novel learning methods, optimization, and inference techniques for processing biomedical and healthcare data to get performance closer to clinical diagnosis. Advances in machine learning can be used to develop sophisticated and novel applications in the field of biomedical and healthcare domains. This will attract healthcare practitioners who have access to interesting sources of data but lack the expertise in using machine learning techniques effectively. Special attention will be devoted to handling feature selection, class imbalance, model robustness, scalability, distributed and heterogeneous data sources, and data fusion in biomedical and healthcare applications.
    • Scope/Topics :
      • Resource-constrained deep learning for wearable IoT
      • Context-aware pervasive wearable health systems based on edge machine learning
      • Machine learning and Data Analytics for sensing, analysis, and interpretation in IoT healthcare
      • AI-driven health & fitness devices, systems, and services
      • Wearable devices with custom hardware for medical deep learning
      • Neuromorphic AI and cognitive computing in smart health
      • Data storage, retrieval, and transfer between wearable devices, gateways, and cloud backend
      • Body-centric wireless communication issues (propagation & transmission), including Ultra wideband, millimeter wave, and Tehra-hertz propagation 
      • Wearable and implantable wireless sensors challenges
      • Small-scale/nano communication
      • Security and privacy issues for wireless healthcare data
      • Knowledge graphs and knowledge representation for smart health and IoT
      • Connected Wearables for Assisted Living
  • Names, Designations, Affiliations, and Email IDs of the Session Chairs:
    1. Dr. Gaurav Gupta

      Associate Professor
      Yogananda School of Artificial Intelligence Computer and Data Sciences Shoolini University Solan 
      Himachal Pradesh India 

  • Contact Person Details:

 

 

  • Technologies that enable Smart Farming, Smart Homes etc. based on IoT, AI and Privacy Preservation Techniques in Big Data Analytics as well as Cloud Computing
    • The growing emphasis on digital transformation is encouraging more organizations to adopt initiatives driven by the Internet of Things (IoT). While such initiatives enable enterprises to enhance customer experiences, create new business channels, or acquire new partner ecosystems, gaining the insights to realize these benefits can prove to be challenging. The sheer volume of data that these devices generate, the variety of data that comes in, and the velocity at which data is collected create their own set of challenges in terms of storage, processing power, and analytics for such enterprises.

      The growth of IoT adoption has been exponential across all industries, but organizations within each industry face a unique set of challenges along this journey. Enterprises leveraging all the big data generated from IoT devices in their machine-learning models are able to use prescriptive and predictive analytics to make well-informed decisions.

      The technologies that are the building blocks of Smart Farming are now advancing at a rapid pace never witnessed before. Smart farming (SF) involves the unification of information and communication technologies into machinery, equipment, and sensors for use in agricultural production systems. New technologies such as the internet of things and cloud computing are expected to advance this development, introducing more robots and artificial intelligence into farming. This topic describes the advances of major core technologies and their applicability to creating a Smart Farm System.

      Nowadays IoT can be a Part of IoRT and AI as well as BIG data. The huge amount of data that is being generated over the network is termed as Big Data.

      Motivation of Special Session: - The growth of IoT and cloud computing adoption has been exponential across all industries, but organizations within each industry face a unique set of challenges along this journey. Enterprises leveraging all the big data generated from IoT devices in their AI and machine learning models are able to use prescriptive and predictive analytics to make well-informed decisions.

    • Scope/Topics :
      •  IOT
      • Cloud Computing
      • Big Data Analytics
      • Artificial Intelligence AI & Machine Learning ML
    • Names, Designations, Affiliations, and Email IDs of the Session Chairs:
      1. Dr. Rashmi Soni
         Professor CSE
         New Horizon College of Engineering
         Bangalore
         [email protected]
         [email protected]
    • Contact Person Details: