4th International Conference on Soft Computing, Data mining and Data Science (SCDD 2026)

• 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

• International Journal of Database Management Systems (IJDMS)
• International Journal on Soft Computing (IJSC)
• International Journal of Data Mining & Knowledge Management Process (IJDKP)
• International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI)
• International Journal of UbiComp (IJU)

• Submission Deadline; May 02, 2026
• Authors Notification; May21, 2026
• Registration & camera – Ready Paper Due; May25, 2026