4th International Conference on Soft Computing, Data mining and Data Science (SCDD 2026) May 30 ~ 31, 2026, Virtual Conference Scope & Topics The 4th International Conference on Soft Computing, Data mining and Data Science (SCDD 2026) serves as a premier international forum for presenting and exchanging the latest innovations, research findings, and developments across the rapidly evolving fields of Soft Computing, Data Mining and Data Science. As intelligent systems, large scale data analytics, and advanced machine learning continue to transform science, engineering and industry, SCDD 2026 aims to bring together researchers, practitioners, and industry experts to explore both foundational advances and emerging applications.
The conference welcomes significant contributions that address theoretical frameworks, methodological progress, system architectures, and real world implementations. By fostering collaboration between academia and industry, SCDD 2026 provides a dynamic platform for discussing cutting edge techniques, identifying new research directions, and sharing practical insights that shape the future of intelligent and data driven technologies.
Authors are invited to submit high quality research papers, case studies, survey articles, and industrial experiences that demonstrate substantial advances in the field. Submissions may address, but are not limited to, the topics listed below. Topics of interest include, but are not limited to, the following Soft Computing, Bio Inspired Intelligence and Hybrid Systems
- Foundations of Soft Computing
- Soft Computing Theory and Hybrid Soft Computing Models
- Fuzzy Logic, Fuzzy Systems and Neuro Fuzzy Models
- Rough Sets, Soft Sets and Granular Computing
- Probabilistic Reasoning and Uncertainty Modeling
- Evolutionary Computing and Genetic Algorithms
- Swarm Intelligence and Collective Intelligence
- Nature Inspired and Bio Inspired Computing
- Artificial Immune Systems
- Multi Agent Systems and Distributed Intelligent Systems
- Reactive Distributed AI and Autonomous Agents
- Hybrid Intelligent Systems and Cognitive Computing
Emerging Soft Computing Themes
- Explainable Soft Computing and Interpretable Intelligent Systems
- Soft Computing for Optimization and Decision Support
- Soft Computing for Robotics, CPS and Autonomous Systems
- Soft Computing for Edge/Embedded Intelligence
- Soft Computing for Sustainable and Green AI
Machine Learning, Deep Learning and Advanced AI
- Core Machine Learning
- Supervised, Unsupervised and Semi Supervised Learning
- Reinforcement Learning and Multi Agent RL
- Transfer Learning, Meta Learning and Continual Learning
- Learning from Noisy, Imbalanced and Low Quality Data
- Online Learning, Streaming ML and Adaptive Models
- Feature Engineering, Feature Selection and Dimensionality Reduction
Deep Learning and Foundation Models
- Deep Neural Networks, CNNs, RNNs, Transformers
- Foundation Models and Large Language Models (LLMs)
- Retrieval Augmented Generation (RAG)
- Multimodal AI (Vision + Text + Audio + Sensors)
- Vision Language Models (VLMs) and Vision Language Action (VLA) Models
- Generative AI: GANs, Diffusion Models and Synthetic Data
- Graph Neural Networks and Graph Foundation Models
- Self Supervised and Contrastive Learning
- Efficient ML: Pruning, Quantization, Distillation, TinyML
Advanced AI Paradigms
- Agentic AI and Autonomous AI Systems
- Program Synthesis, Code LLMs and AI for Software Engineering
- Causal Machine Learning and Causal Discovery
- Neuro Symbolic AI and Hybrid Reasoning
- Automated Machine Learning (AutoML) and Neural Architecture Search
- AI for Scientific Discovery and Computational Science
- Embodied AI and Robot Learning
Responsible and Trustworthy AI
- Explainable AI (XAI) and Model Interpretability
- Fairness, Accountability, Transparency and Ethics in AI
- Adversarial ML, Robustness and Safety
- Privacy Preserving ML (DP, FL, MPC)
- AI Governance, Regulation and Alignment
- LLM Safety, Hallucination Mitigation and Evaluation
Data Mining, Knowledge Discovery and Big Data Analytics
- Foundations of Data Mining
- Classification, Regression, Clustering and Association Rules
- Outlier Detection, Anomaly Detection and Novelty Discovery
- Pattern Recognition and Statistical Learning
- Data Pre Processing, Cleaning and Transformation
- Knowledge Representation, Reasoning and Inference
Advanced Data Mining Techniques
- Graph Mining, Network Mining and Temporal Graph Learning
- Spatial, Temporal and Spatio Temporal Data Mining
- Data Stream Mining and Real Time Analytics
- High Dimensional Data Mining and Feature Selection
- Mining from Noisy, Incomplete and Low Quality Data
- Concept Drift, Evolving Data and Dynamic Models
- Causal Discovery and Causal Representation Learning
Big Data and Scalable Analytics
- Big Data Frameworks, Architectures and Pipelines
- Distributed and Parallel Data Mining Algorithms
- Cloud Native Data Processing and Serverless Analytics
- Big Data Search, Indexing and Retrieval
- Big Data Security, Privacy and Governance
- Data Lakes, Lakehouse Architectures and Data Warehousing
Knowledge Discovery and KDD Process
- Knowledge Discovery Frameworks and Pipelines
- Pre and Post Processing in KDD
- Interactive Data Exploration and Visual Analytics
- Integrating Constraints and Domain Knowledge in KDD
- Causal Inference, Predictive Modeling and Knowledge Consolidation
Data Science, Applications and Modern Data Ecosystems
- Data Science Foundations
- Data Science Methodologies and Best Practices
- Statistical Modeling, Forecasting and Time Series Analysis
- Time Series Foundation Models and Spatio Temporal Forecasting
- Information Retrieval, Neural IR and Search Systems
- Data Visualization and Visual Data Mining
Domain Specific Data Science
- Healthcare and Biomedical Data Science
- Educational Data Mining and Learning Analytics
- Disaster Prediction, Climate Analytics and Environmental Data Science
- Financial Data Science, Risk Analytics and Fraud Detection
- Industrial Data Science and Smart Manufacturing
Modern Data Ecosystems
- Data Engineering, Pipelines and MLOps
- Data Versioning, Lineage and Data Observability
- Data Quality Engineering and Data Reliability
- Graph Databases, Knowledge Graphs and Semantic Data Systems
- IoT Data Analytics and Sensor Data Processing
- Text, Video, Multimedia and Web Mining
- Social Media Analytics and Behavioral Data Science
Emerging Data Science Trends
- Data Centric AI and Data Quality Optimization
- Synthetic Data Ecosystems and Evaluation
- Human in the Loop Data Science
- Responsible Data Science and Ethical Data Practices
- Vector Databases, Embedding Stores and Retrieval Systems
- Privacy Preserving Data Systems and Secure Data Collaboration
Paper Submission Authors are invited to submit papers through the conference Submission System by May 02, 2026. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this conference. The proceedings of the conference will be published by International Journal on Cybernetics & Informatics (IJCI) (Confirmed). Selected papers from SCDD 2026, after further revisions, will be published in the special issues of the following journals. Important Dates | Submission Deadline | : | May 02, 2026 | | Authors Notification | : | May 21, 2026 | | Final Manuscript Due | : | May 25, 2026 |
Co - Located Event
***** The invited talk proposals can be submitted to scdd@scdd2026.org
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