29th AMSTERDAM International Congress on Advances in Artificial Intelligence & Its Applications (ARAIA-26) scheduled on July 6-8, 2026 Amsterdam (Netherlands) is for the engineers, practitioners, scientists, researchers, scholars, and students from all around the world and it also includes the industry people to present ongoing research activities, and hence to foster research relations between Academia and industry. The conference is being organized by Emirates Research Publication (ERPUB) operting under Pilares D Elegancia LDA (Portugal). This conference provides opportunities for the delegates to share new ideas and application experiences face to face, to establish business or research relations and to find global partners for future collaboration. All the submitted conference papers will be peer reviewed by the program/technical committees of the Conference.

Call for papers/Topics

Topics of Interest for Submission include, but are Not Limited to:

1. Core Branches of Artificial Intelligence

These represent the foundational pillars of the field.

• Machine Learning (ML)

  • Supervised Learning: Classification, Regression, Ensemble Methods (Random Forest, XGBoost).

  • Unsupervised Learning: Clustering (K-Means, Hierarchical), Dimensionality Reduction (PCA, t-SNE), Association Rules.

  • Reinforcement Learning (RL): Q-Learning, Policy Gradients, Multi-agent RL, Inverse Reinforcement Learning.

  • Learning Paradigms: Transfer Learning, Active Learning, Federated Learning (Privacy-preserving), Few-shot/Zero-shot Learning.

• Deep Learning (DL)

  • Neural Architectures: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs & LSTMs), Transformers.

  • Generative Models: Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Diffusion Models.

  • Optimization: Backpropagation, Gradient Descent variants, Weight Initialization, Regularization (Dropout, Batch Norm).

• Natural Language Processing (NLP)

  • Linguistics: Tokenization, Stemming/Lemmatization, POS Tagging, Named Entity Recognition (NER).

  • Semantics & Synthesis: Sentiment Analysis, Text Summarization, Machine Translation, Question Answering.

  • Large Language Models (LLMs): Prompt Engineering, Retrieval-Augmented Generation (RAG), Fine-tuning, Agentic Workflows.

• Computer Vision (CV)

  • Image Analysis: Object Detection, Image Segmentation, Facial Recognition, Scene Reconstruction.

  • Video Processing: Motion Tracking, Action Recognition, Temporal Pattern Analysis.

  • 3D Vision: Point Cloud Processing, Spatial Intelligence, Neural Radiance Fields (NeRFs).

2. Advanced & Specialized AI Fields

Beyond the basics, these fields deal with specialized reasoning and biological mimicry.

• Robotics & Autonomous Systems

  • Control & Navigation: SLAM (Simultaneous Localization and Mapping), Path Planning, Human-Robot Interaction (HRI).

  • Bio-inspired Robotics: Swarm Intelligence, Soft Robotics, Morphological Computing.

• Knowledge Representation & Reasoning

  • Expert Systems: Rule-based engines, Inference Engines, Knowledge Graphs.

  • Fuzzy Logic: Handling "degrees of truth" rather than binary (true/false) logic.

• Cognitive Computing

  • Simulating human thought processes, Emotional AI (Affective Computing), and Theory of Mind.

3. Real-World Applications (Applied AI)

How AI is integrated into specific industries as of 2026.

IndustrySubtopics & Use CasesHealthcareAI-assisted Radiology, Drug Discovery, Personalized Medicine, Robotic Surgery, Predictive Diagnostics.FinanceAlgorithmic Trading, Fraud Detection, Credit Scoring, Automated Auditing, Robo-advisors.ManufacturingPredictive Maintenance, Quality Inspection (CV), Digital Twins, Supply Chain Optimization.TransportationAutonomous Vehicles, Traffic Flow Optimization, Drone Delivery, Fleet Management.CybersecurityThreat Hunting, Automated Incident Response, Deepfake Detection, Anomaly Detection.EnvironmentPrecision Farming, Climate Modeling, Wildlife Conservation, Smart Grid Management.4. AI Ethics, Governance & Safety

Critical topics ensuring the responsible development of the technology.

• Trustworthy AI: Explainable AI (XAI), Interpretability, Bias Detection, and Mitigation.

• AI Safety: Alignment Problem (ensuring AI goals match human values), Robustness against Adversarial Attacks.

• Governance & Law: AI Regulations (e.g., EU AI Act), Intellectual Property in Generative AI, AI in Jurisprudence.

• Societal Impact: AI & Labor (Automation/Job Displacement), Digital Divide, Algorithmic Transparency.

5. Implementation & Engineering

The "how-to" of building AI in production.

• MLOps: Model Versioning, Continuous Integration/Deployment for ML, Model Monitoring, and Data Drift.

• Hardware for AI: Neuromorphic Computing, TPUs/GPUs, Quantum Machine Learning, Edge AI (Running models on local devices).

• Data Engineering: Feature Engineering, Data Labeling/Annotation, Vector Databases, Data Governance.