会议信息

ICCISD 2026: IEEE International Conference on Computational Intelligence Systems and Devices

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截稿日期:
2026-01-12
通知日期:
2026-03-08
会议日期:
2026-07-23
会议地点:
Greater Noida, India
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会伴指数 (CP-I)

43.6 / 100
全站第 3,482 名 / 共 5,695 个会议 · 前 62%

人工智能与机器学习 第 431 / 741

证据有限：这个会议不在 CCF / ICORE / QUALIS 任何一份榜单里，也没有录用率数据，因此分数的大部分回落到了中性基准。
学术认可 (35%) 无数据 —— 按中性基准 50 分计入 —
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入 —
会议传承 (20%) 无数据 —— 按中性基准 50 分计入 —
社区关注 (10%)
8
资料公开度 (15%)
35

用到的输入: 过去 24 个月打开过本页的研究者：2 人

公开资料里还缺: 历年录用率 (+4.5) · 历届信息 (+3.0) · 最佳论文记录 (+2.3)
主办方认领本会议后，可直接在这一页补上；分数每晚重算。如何提升这个分数

置信度 25% —— 分数中有多大比例来自实际观测到的数据，而不是中性基准。 这个分数是怎么算出来的 · 查看完整榜单 · 算法版本 1.1 · 算于 2026-10-10

征稿

ICCISD 2026 (IEEE International Conference on Computational Intelligence Systems and Devices) is an academic conference held in Greater Noida, India on 2026-07-23. The paper submission deadline is 2026-01-12. Acceptance notifications are sent on 2026-03-08.

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
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