Conference Information

IDAA 2027: International Conference on Intelligent Data Analysis and Applications

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Submission Date:
2026-12-31 Due in 69 days
Notification Date:
2027-03-31
Conference Date:
2027-06-11
Location:
Dhaka, Bangladesh
Years:
2
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Conference Partner Index (CP-I)

39.7 / 100
Ranked #5,221 of 5,759 conferences · Top 91%
Academic recognition (35%) No data - scored at the neutral baseline of 50 —
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%)
30
Community attention (10%)
10
Public record completeness (15%)
35

Inputs used: Editions on record: 2 · Researchers who opened this page in the past 24 months: 3

Missing from the public record: Historical acceptance rates (+4.5) · Past editions (+3.0) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 45% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.2 · Computed 2026-10-23

Call For Papers

IDAA 2027 (International Conference on Intelligent Data Analysis and Applications) is an academic conference held in Dhaka, Bangladesh on 2027-06-11. The paper submission deadline is 2026-12-31. Acceptance notifications are sent on 2027-03-31.

Topics & Areas Conference Tracks Explore the diverse domains of intelligent data analysis and its real-world applications at IDAA 2027. Track 1: Machine Learning & AI for Data Analysis Focus Advanced AI/ML techniques for intelligent data-driven solutions Supervised, Unsupervised, and Reinforcement Learning Deep Learning and Neural Network Architectures Explainable AI (XAI) and Interpretable Machine Learning Transfer Learning and Few-shot Learning Swarm Optimization and Evolutionary Algorithms Green AI for Sustainable Model Training Bias Mitigation and Fairness in AI Models Track 2: Data Analytics, Cloud, Edge and Fog Computing Focus Infrastructure, techniques, and ethics in large-scale data analysis Data Preprocessing and Feature Engineering Big Data Infrastructure (Hadoop, Spark, etc.) Data Mining and Pattern Recognition Cloud, Edge, and Fog Computing for Scalable Analytics Blockchain for Distributed Data Processing Ethical and Fair AI in Data Science Privacy-Preserving Data Analytics Track 3: Computer Vision and Natural Language Processing (NLP) Focus Visual and linguistic intelligence in modern AI systems Image and Video Analytics Scene Understanding and Object Detection Sentiment Analysis and NLP Applications Speech and Multimodal Processing Vision-Language Models (e.g., CLIP, BLIP) Transformers in Vision and Language Tasks Low-Resource and Cross-Lingual NLP Track 4: Healthcare and Biomedical Data Analysis Focus Intelligent systems for medical research and clinical applications AI-driven Disease Diagnosis and Risk Prediction Medical Imaging, Radiomics, and Computational Pathology Wearable Devices and Remote Health Monitoring AI in Drug Discovery and Precision Medicine Biomedical Signal and Genomic Data Analysis Internet of Medical Things (IoMT) Data Privacy and Ethics in Healthcare AI Track 5: Industry 4.0 and beyond, IoT, and Smart Systems Focus AI-integrated intelligent environments for automation and connectivity AI for Smart Manufacturing and Automation Intelligent Transportation and Smart Mobility IoT-based Sensing and Real-Time Data Analytics Edge AI and Fog Computing for Industrial Applications Cyber-Physical and Autonomous Systems Blockchain in Supply Chain and Industrial Security Swarm Robotics and Collaborative AI Agents Wireless Sensor Networks and 6G/5G Track 6: Security, Privacy, and Trustworthy AI Focus Building robust, secure, and ethically aligned AI systems AI for Cybersecurity and Threat Intelligence Blockchain for Secure and Transparent Data Handling Federated Learning and Differential Privacy Trustworthy and Human-Centered AI Design Fairness, Accountability, and Transparency in AI Bias Detection and Mitigation Strategies Robustness and Adversarial Attacks in AI Models
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