CARES 2027

Special Session 01: Multimedia Techniques for Intelligent and Accessible E-Health Systems (MTEHS) 

“Multimedia Techniques for Intelligent and Accessible E-Health Systems (MTEHS)”, is dedicated to emerging e-health applications, using the application of multimedia computing, artificial intelligence, machine learning and multimodal data analytics. In the modern healthcare setting, the information created by medical devices is highly heterogeneous, ranging from medical images and clinical videos to physiological signals, electronic health records, data from wearable devices, and text-based clinical information. A comprehensive integration and effective analysis of these modalities can facilitate more precise, more efficient, personalized, and more accessible health care.

The session will give an opportunity for the researchers and practitioners to share latest developments in e-health systems with multiple media and talk about emerging challenges. These feature medical image processing and analysis, medical video understanding, multimodal and cross-modal learning, privacy-aware multimedia analytics, healthcare data fusion, AI-assisted diagnosis, clinical decision-support systems, telemedicine, remote patient monitoring, mobile health, and wearable healthcare technologies, as well as explainable and trustworthy AI for healthcare and healthcare visualization.

Special focus will be placed on smart systems that can combine different health care modalities and assist health care professionals and patients in the real world. Proposals on robustness, interpretability, fairness, privacy, security, resource efficiency and accessibility are particularly welcome.

The intent of this session is to promote interaction between researchers in the fields of multimedia computing, artificial intelligence, computer vision, healthcare informatics, biomedical engineering, and others. It will also suggest the directions and opportunities for the future research of the translation of the computational techniques based on multimedia technologies into usable e-health solutions that could be of benefit to the public.

The session welcomes original research contributions covering, but not limited to:

• • Multimedia processing for healthcare

  • Medical image processing and analysis

  • Medical video analysis and understanding

  • Multimodal and cross-modal learning for healthcare

  • AI and deep learning for e-health

  • Medical image segmentation, classification, and detection

  • Biomedical signal and physiological data processing

  • Healthcare data fusion and representation learning

  • Explainable and trustworthy AI in healthcare

  • Intelligent clinical decision-support systems

  • Telemedicine and multimedia-enabled remote healthcare

  • Mobile health and wearable healthcare systems

  • Remote patient monitoring

  • Healthcare visualization and human-computer interaction

  • Generative AI for healthcare multimedia

  • Privacy-preserving healthcare analytics

  • Secure multimedia communication for e-health

  • Federated and distributed learning for healthcare

  • Edge/fog computing for intelligent healthcare

  • Assistive and accessible healthcare technologies

  • IoT-enabled multimedia healthcare systems

  • Personalized and precision healthcare

  • Real-world deployment of AI-enabled e-health systems 

Submission Link and Guidelines: As per the CARES 2027

Deadlines:

Submission Deadline: 15/11/2026 

Acceptance Notification: 15/12/2026

Camera Ready Submission: 20/12/2026 

Special Session Organizers

Dr. Mohit Dua
National Institute of Technology Kurukshetra
Kurukshetra, Haryana, India
Email: er.mohitdua@nitkkr.ac.in

Dr. Pankaj Verma

National Institute of Technology Kurukshetra

Kurukshetra, Haryana, India

Email: pankaj@nitkkr.ac.in 

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Special Session 02: Data Analytics and Smart Healthcare: Computational Intelligence and Applications (DASH)  

“Data Analytics and Smart Healthcare: Computational Intelligence and Applications (DASH)” focuses on emerging computational approaches for transforming healthcare data into actionable insights, improved decision-making, and personalized care. The rapid growth of electronic health records, medical imaging, wearable devices, Internet of Medical Things (IoMT), genomic data, and other digital health technologies has created significant opportunities for data-driven healthcare while also introducing challenges in processing, integration, interpretation, and utilization of heterogeneous data.

With the increasing adoption of digital health technologies, advanced data analytics, artificial intelligence, machine learning, and computational intelligence are becoming important for early disease detection, risk prediction, clinical decision-making, patient monitoring, healthcare resource optimization, and personalized treatment. DASH aims to bring together recent developments in these areas and explore their applications in addressing contemporary healthcare challenges.

The session emphasizes the integration of data analytics, computational intelligence, and emerging healthcare technologies, connecting methodological advances with practical healthcare applications. It will provide a platform for researchers to present innovative algorithms, computational models, analytical frameworks, and application-oriented solutions, while addressing important aspects such as interpretability, reliability, scalability, privacy, and responsible use of healthcare data.

The session encourages interdisciplinary exchange among researchers from computing, engineering, data science, and healthcare, while promoting new collaborations and innovative solutions for the development of efficient, reliable, and sustainable digital health systems.

The session welcomes original research contributions covering, but not limited to:

• • Healthcare Data Analytics and Predictive Modeling

  • Mathematical Modelling for e-health

  • Medical Imaging and Computer Vision for Disease Diagnosis

  • Artificial Intelligence and Machine Learning for Smart Healthcare

  • Data-Driven Clinical Decision Support Systems

  • Natural Language Processing and Generative AI for Clinical Data

  • Federated Learning and Privacy-Preserving Healthcare Analytics

  • Mathematical and Statistical Models for Drug Discovery

  • Internet of Medical Things (IoMT) and Wearable Devices

  • Digital Health Technologies for Rural and Remote Healthcare

  • Cybersecurity, Privacy, and Blockchain for Digital Healthcare

  • Explainable AI and Ethical Healthcare Informatics

  • Digital Twins and Simulation-Based Healthcare Systems

  • Personalized and Precision Healthcare through Data Analytics

  • Healthcare Resource Optimization and Intelligent Decision Support 

Submission Link and Guidelines: As per the CARES 2027

Deadlines:

Submission Deadline: 15/11/2026 

Acceptance Notification: 15/12/2026

Camera Ready Submission: 20/12/2026 

Special Session Organizers

Dr. Sakshi Gupta
Dronacharya College of Engineering, Gurugram
Email: sakshi86.10@gmail.com 

Mr. Hilal Ahmad Shah 

IILM University, Greater Noida 

Email: hilalshah9832@gmail.com 

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Special Session 03: Real-Time Embedded and FPGA-Based Hardware Solutions for Intelligent E-Health Systems (REH-FPGA) 

The rapid convergence of artificial intelligence, biomedical sensing and embedded computing is enabling healthcare systems that can acquire, process and interpret physiological data directly at the point of use. However, practical e-health applications often require real-time response, low latency, low power consumption, reliability and operation under constrained computational resources. Embedded processors, microcontrollers, FPGA, SoC and reconfigurable platforms provide an important pathway for transforming healthcare algorithms into deployable systems.

This special session, “Real-Time Embedded and FPGA-Based Hardware Solutions for Intelligent E-Health Systems (REH-FPGA),” aims to provide a focused forum for researchers and practitioners working on the implementation of intelligent healthcare solutions on physical computing platforms. The session emphasizes hardware prototypes, real-time operation and experimental validation rather than simulation-only studies.

Topics include microcontroller-based healthcare systems, FPGA/SoC implementations, embedded AI and TinyML, real-time biomedical signal processing, wearable and portable health-monitoring devices, IoT-enabled embedded healthcare, hardware–software co-design, FPGA-based AI accelerators, RISC-V and reconfigurable computing, low-power architectures, secure and fault-tolerant healthcare hardware, and real-device prototyping.

The session seeks interdisciplinary contributions from embedded systems, FPGA/VLSI, artificial intelligence, biomedical engineering and healthcare technology. Experimental evaluation of latency, throughput, power, resource utilization, accuracy, reliability and other practical implementation parameters is particularly encouraged. The session will promote discussion on translating intelligent e-health algorithms into efficient, reliable and deployable hardware systems.

The session welcomes original research contributions covering, but not limited to:

• Microcontroller-based e-health systems

• FPGA and SoC-based healthcare systems

• Embedded AI and TinyML for healthcare

• Real-time biomedical signal processing

• Wearable and portable health-monitoring devices

• IoT-enabled embedded healthcare systems

• Hardware–software co-design for e-health

• FPGA-based AI and biomedical accelerators

• RISC-V and reconfigurable computing for healthcare

• Low-power and energy-efficient healthcare devices

• Secure and fault-tolerant embedded healthcare systems

• Real-device prototyping and experimental validation

Submission Link and Guidelines: As per the CARES 2027

Deadlines:

Submission Deadline: 20/11/2026 

Acceptance Notification: 20/12/2026

Camera Ready Submission: 25/12/2026

Special Session Organizers

Dr. Trailokya Nath Sasamal
National Institute of Technology Kurukshetra, Haryana, India
Email: tnsasamal.ece@nitkkr.ac.in

Dr. Ankur Kumar

Indian Institute of Information Technology Una. Saloh, Una, Himachal Pradesh – 177209

Email: ankur@iiitu.ac.in