ABOUT THE CONFERENCE

The IEEE International Conference on Computational Intelligence Systems and Devices (ICCISD -2026), technically sponsored by IEEE Uttar Pradesh Section and hosted by Sharda University, Greater Noida will be held on 23rd - 24th July 2026. The conference offers a comprehensive platform for researchers, practitioners, and industry professionals to explore advancements in Computer Science, Information Technology, and Computational Intelligence. The conference focuses on leveraging emerging technologies to address global challenges in sustainability. It covers a broad spectrum of cutting-edge topics and applications, emphasizing the integration of intelligent systems and sustainable practices.It is planned to submit the peer reviewed and selected papers of conference as proceedings for possible inclusion in IEEE Xplore.

THE OBJECTIVE OF THE CONFERENCE

The conference will provide an opportunity to the students, scholars, practicing engineers, academicians, and researchers to meet in a forum to discuss various issues and its future direction of various emerging areas of science and technologies and impacts on sustainable development. The objectives of the conference are as follows.

    To bring together researchers, scientists, engineers, policymakers, and industry experts to present and discuss innovative solutions leveraging computational intelligence and evolutionary computation.
    Foster the development of intelligent, adaptive, and energy-efficient devices and embedded systems that enhance IoT scalability, security, and sustainability.
    Support the integration of AI and computational intelligence into sustainable systems including renewable energy, smart grids, climate modelling, and sustainable urban infrastructure.
    To provide a platform for showcasing successful case studies of smart cities contributing to environmental, economic, and social sustainability.
    Encourage breakthroughs in signal and image processing using AI to improve healthcare, remote sensing, multimedia, and real-time analytics applications.
    Facilitate interdisciplinary collaboration among academia, industry, and government to accelerate the translation of intelligent technologies into real-world sustainable solutions.
    Promote ethical, explainable, and responsible AI practices to build trust, transparency, and societal acceptance in emerging intelligent systems and technologies.
    Promote cutting-edge robotics, automation, and human–machine interaction technologies aimed at improving industrial efficiency, environmental conservation, and safety.

The conference will cover a wide range of topics related to Smart Cities and Urban Development, Artificial Intelligence and Machine Learning, Advance Data Communication and Edge Computing, Cyber Security and Privacy in Sustainable Systems, Renewable Energy and Smart Grids, Robotics Automation and Networking, Digital Health and Smart Health Informatics. The conference invites original research papers (not being considered for publication elsewhere) of 5 pages in standard IEEE conference template in one of the following tracks (but are not limited to):

Track 1: Neural Networks, Deep Learning, and Reinforcement Learning

    Explainable AI and Interpretable Deep Models
    Foundation Models and Large Language Models (LLMs)
    Prompt Engineering and Retrieval Augmented Generation
    Neural Architecture Search and AutoML for Devices
    Federated and Distributed Deep Learning on Edge Devices
    Adversarial Robustness in Deep Learning Systems
    Continual and Lifelong Learning Algorithms
    Reinforcement Learning in Robotics and Control Systems
    Generative AI for Realistic Data and Simulation
    Multi-Modal Deep Learning (vision, speech, text, sensors)

Track 2: Fuzzy Systems, Evolutionary Computation, and Hybrid Intelligence

    Adaptive Neuro-Fuzzy Inference Systems (ANFIS)
    Fuzzy Logic for Real-Time Decision-Making
    Swarm Intelligence and Collective Behavior Models
    Genetic Algorithms and Evolutionary Strategies
    Multi-Objective Evolutionary Optimization
    Bio-Inspired Algorithms (PSO, ACO, GWO, DE, Firefly, etc.)
    Hybrid Evolutionary-Deep Learning Models
    Evolutionary Computation for IoT Device Optimization
    Fuzzy Systems for Uncertainty in Healthcare and Robotics
    Evolutionary Game Theory for Smart Systems

Track 3: Intelligent Devices, Embedded Systems, and IoT Applications

    AI-Powered Edge and Embedded Systems
    Neuromorphic and Low-Power AI Chips
    IoT Protocols for Scalability and Sustainability
    Digital Twins for IoT Devices and Systems
    Blockchain-Enabled Secure IoT Applications
    Intelligent Wearable and Implantable Devices
    Embedded Systems for Autonomous Vehicles and Drones
    AI in Predictive Maintenance of IoT Devices
    Cloud-Edge-IoT Integration Architectures
    AI-Driven Energy-Efficient Smart Devices

Track 4: Robotics, Automation, and Human–Machine Interaction

    Cognitive Robotics and Autonomous Decision-Making
    Human–Robot Collaboration in Industry 5.0
    Swarm Robotics for Environmental and Industrial Applications
    Soft Robotics for Medical and Assistive Systems
    Autonomous Navigation and Path Planning in Unknown Environments
    AI-Enhanced Vision and Perception in Robots
    Intelligent Drones and UAV Applications
    Natural Language Interfaces for Human–Machine Interaction
    Robotics for Disaster Response and Climate Applications
    Ethics, Trust, and Safety in Human–Robot Systems

Track 5: Signal and Image Processing using Computational Intelligence

    Deep Learning for Medical Image Analysis
    AI for Satellite and Remote Sensing Applications
    Generative Models for Image Enhancement and Synthesis
    Multimodal Fusion of Audio, Video, and Sensor Signals
    Hyperspectral and Multispectral Image Processing
    Real-Time Video Analytics for Smart Surveillance
    Adversarial Attacks and Defenses in Image Processing
    AI-Driven Speech Recognition and Language Models
    Edge-AI for Low-Latency Signal Processing
    Virtual, Augmented, and Mixed Reality Signal Processing

Track 6: Cybersecurity, Data Privacy, and Trustworthy Systems

    Privacy-Preserving Machine Learning (Federated, Differential Privacy)
    AI for Intrusion Detection and Anomaly Detection
    Blockchain for Security in IoT and Devices
    Quantum-Safe Cryptography for Future-Proof Security
    Zero-Trust Architectures in Intelligent Systems
    Cyber-Physical Security in Smart Grids and Robotics
    Secure Data Sharing and Compliance (GDPR, HIPAA)
    AI-Powered Threat Intelligence and Cyber Defense
    Explainable and Trustworthy AI for Security Applications
    Risk Management and Resilient Design for Smart Devices

Track 7: Computational Intelligence for Sustainable Systems and Devices

    AI for Climate Change Modeling and Prediction
    Green AI and Energy-Efficient Machine Learning Models
    Computational Intelligence for Renewable Energy Optimization
    Smart Grid Management with AI-Driven Decision Making
    Intelligent Transportation and Mobility Systems
    AI for Water Resource Management and Agriculture
    Waste Management and Circular Economy Systems
    Disaster Prediction and Climate-Resilient Infrastructure
    AI for Smart City Governance and Citizen Engagement
    Computational Intelligence for Net-Zero and Sustainability Goals