Special Session

SS1/ Special Session on Technological Advancements in Machine Learning and Blockchain for Secure IoT Applications (SS_TAMBI)

AIMS & SCOPE

Today’s world is changing with the adoption of the Internet of Things (IoT). IoT is helping in prominently capturing a tremendous amount of data from multiple sources. However, wrapping around the multitude of data coming from countless IoT devices, makes it complex to collect, process, and analyze the data. Realizing the future and full potential of IoT devices will require an investment in new technologies. The convergence of Machine Learning (ML) for IoT can redefine the way industries, business, and economies function.

While IoT deals with devices interacting using the internet, AI makes the devices learn from their data and experience. The traditional client-server architecture yields many significant limitations to meet the security demands of IoT, such as relying on the trusted server, incapability for time-sensitive applications, and high data maintenance cost. Blockchains, like Bitcoin and Ethereum, have achieved great success beyond our expectations. Blockchain is a decentralized platform in which each node stores a copy of the whole ledger. The blockchain is perceived as a promising technique for scaling IoT security. Thus, this special session will mainly focus on the challenges of blockchain and ML techniques for IoT.

The special session on Technological Advancements in Machine Learning and Blockchain for Secure IoT Applications aims to bring together leading academicians, scientists, researchers and scholars to exchange and share their experiences, research results on all aspects of a smart and secure IoT environment. Researchers will present and discuss the most recent innovations, trends, and concerns as well as practical challenges encountered in ML and Blockchain for IoT. This special session is to encourage and assist the professionals engaged in the above fields to maintain the integrity and competence of the profession foster a sense of partnership amongst the international professionals.

Full-length original and unpublished research papers based on theoretical or experimental contributions related to the below mentioned tracks are invited for submission in this special session:

Tracks: (Sub-Themes)

• • AI for IoT-based Wireless sensor networks

  • AI and IoT in the Automotive Industry

  • Applications of AI and IoT in Smart Home Security

  • AI and IoT in Business: Research and Innovation to Market Deployment

  • IoT in Monitoring & Improving Manufacturing Processes

  • Deep learning and Machine Learning Approaches for disease prediction

  • Intelligent Fault Detection and Diagnosis

  • Efficient energy management for the IoT in smart cities

  • M2M (Machine-To-Machine) Wireless Sensor Systems

  • Intelligent Agents and Autonomous Robots in IoT Security

  • Supply Chain and Logistics

  • Modelling and Simulation for Industrial IoT

  • AI for energy efficient cloud operations

  • Secure governance and cyber policies

  • Blockchain architecture for decentralization in IoT security

  • Decentralized consensus for IoT security

  • Smart contract for IoT security

  • Lightweight decentralized protocols for IoT security

  • Blockchain-based security protocol for IoT security

  • Blockchain for secure edge and fog computing security

  • Blockchain for IoT-enabled critical verticals and sectors (energy, transport, health, etc.)

  • Blockchain for Industrial Internet of Things (IIoT) security

  • Blockchain for Cyber-Physical Systems (CPS) security

Organizers

SESSION CHAIR

Dr. A. PRASANTH,

Associate Professor,

Department of Computer Science and Engineering,

Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology,

Chennai, Tamil Nadu, India.

Contact Number: 9159686372.

Email: draprasanthdgl@gmail.com

Website: https://sites.google.com/view/draprasanth/home

Prof. S. JAYACHITRA,

Assistant Professor,

Department of Electronics and Communication Engineering,

PSNA College of Engineering and Technology,

Dindigul, Tamil Nadu, India.

SS2/ Special Session on Revolutionizing Precision Medicine with Next-Generation AI (SS_RPMAI)

AIMS & SCOPE

The special session on "Revolutionizing Precision Medicine with Next-Generation AI” seeks to examine the complex nature of AI models that can create a barrier to trust and acceptance from both medical professionals and patients though it presents exciting possibilities for improving medical diagnosis, treatment, and patient outcomes. With a strong emphasis on XAI in healthcare engineering, this session will highlight how AI can be integrated into various healthcare disciplines while ensuring transparency and building trust. The primary objective of this session is to understand the core principles of XAI making AI models interpretable and adapt models to analyses largescale medical data. It showcases specific XAI methods for different AI techniques, including rule-based learning, deep learning for medical images, natural language processing for clinical text analysis, and reinforcement learning for resource optimization. Additionally, it delves into integrating XAI into clinical workflows, mitigating bias in personalized care, using explainable models for public health initiatives, and evaluating XAI models under ethical considerations. This session bridges that gap by specifically providing a comprehensive overview of XAI which is crucial for building trust and transparency in AI-driven medical decisions.

The session will also allow academic and industry researchers, developers, and practitioners to address difficulties, share ideas, and debate future research paths, boosting collaboration and networking on how XAI ensures that AI decisions are not just accurate but also understandable. The session comprises of following segments:

Tracks: (Sub-Themes)

• • Healthcare engineering and role of AI in medical data analysis

  • Explainable Artificial Intelligence (XAI): Principles and Applications

  • Large-Scale Data Analytics for Healthcare: Methods and Challenges

  • Machine Learning Fundamentals for Explainable Healthcare Models

  • Explainable Rule-Based Learning for Healthcare Knowledge Discovery

  • Explainable Deep Learning Architectures for Medical Image Analysis

  • Explainable Natural Language Processing (NLP) for Clinical Text Analysis

  • Counterfactual Reasoning for Causal Inference in Healthcare

  • Explainable Reinforcement Learning for Healthcare Resource Optimization

  • Explainable AI for Clinical Decision Support Systems (CDSS): Improving Trust and Collaboration

  • Explainable AI for Personalized Medicine and Precision Healthcare

  • Explainable AI for Personalized Medicine and Precision Healthcare

  • Evaluation Frameworks for Explainable AI Models in Healthcare

  • Explainable AI for Mental Health Assessment and Intervention

  • Future Directions and Challenges in Explainable AI for Healthcare Engineering

  • Explainable AI for Healthcare Robotics and Surgical Assistance

Organizers

SESSION CHAIR

Dr. Karthika Subbaraj

Associate Professor,

Department of Information Technology

Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Chennai, India.

E-Mail: skarthikadb@gmail.com, skarthika@ssn.edu.in

Dr. Balamurugan Balasamy

Associate Dean- Students,

Shiv Nadar University, Delhi-NCR Campus, Noida, India

E-Mail: kadavulai@gmail.com

SS3/ Special Session on AI-Enhanced Education: Integrating Computing Technologies and Data Analytics (SS_AIEE)

AIMS & SCOPE

The transformative potential of Artificial Intelligence (AI) in education is significant, offering new methodologies to enhance learning experiences and outcomes. This special session will examine the latest advancements and applications of AI in the educational sector, emphasizing the integration of cutting-edge computing technologies and data analytics. Participants will explore how AI can personalize learning, provide real-time feedback, and create adaptive learning environments that cater to the diverse needs of students. By leveraging AI, educators can offer more targeted and effective instruction, thereby improving student engagement and achievement.

In addition to personalization, this session will address the role of data analytics in education. Data-driven decision-making is becoming increasingly critical in educational institutions, from K-12 schools to higher education. The session will showcase how advanced data analytics can uncover insights into student performance, identify at-risk students, and inform curriculum development. Attendees will gain a deeper understanding of how to harness the power of data to enhance educational outcomes and support evidence-based practices. The session will encourage participants to present case studies and real-world examples of successful AI and data analytics implementations in education, providing valuable takeaways for educators, administrators, and policymakers. This session will highlight the ethical considerations and challenges associated with the adoption of AI in education. Issues such as data privacy, algorithmic bias, and the digital divide will be thoroughly examined to ensure that the integration of AI technologies promotes equity and inclusivity. Experts in the field will discuss best practices for mitigating these challenges and fostering a responsible AI framework in education. 

RECOMMENDED TOPICS

Topics to be discussed in this special session include (but are not limited to) the following:

• Personalized Learning Systems Using AI

• AI-Driven Student Performance Analytics

• Adaptive Learning Technologies in Education

• Real-Time Feedback Mechanisms Powered by AI

• Predictive Analytics for Identifying At-Risk Students

• Natural Language Processing (NLP) in Educational Tools

• AI for Curriculum Development and Improvement

• Automated Grading and Assessment Systems

• Ethical Considerations in AI-Enhanced Education

• Mitigating Algorithmic Bias in Educational AI Applications

• AI in Special Education: Tools and Applications

• Virtual and Augmented Reality in AI-Powered Learning

• AI-Based Career Guidance Systems

• Using AI to Enhance Collaborative Learning Environments

• Data Privacy and Security in Educational AI Systems

• AI for Teacher Professional Development and Training

• Intelligent Tutoring Systems and Their Impact

• AI in Remote and Hybrid Learning Models

• Case Studies of Successful AI Implementations in Education

• Future Trends and Innovations in AI-Enhanced Education

Organizers

Session Organizers

Prof. Mathias Fonkam

Associate Professor

 College of Information Sciences and Technology, Penn State University, USA 

Prof. Narasimha Rao Vajjhala, 

Dean and Associate Professor, Senior Member of IEEE and ACM.

Faculty of Engineering and Architecture, University of New York Tirana, Albania

Dr. Vasileios Paliktzoglou

Bahrain Polytechnic, Bahrain. 

Dr. Sandeep Singh Sengar

Senior Lecturer and Head of Computer Vision and Artificial Intelligence

College of Information Sciences and Technology, Cardiff Metropolitan University, United Kingdom.

Dr. Eriona Çela

Assistant Professor

Department of Psychology and Education,  University of New York Tirana, Albania