7th International Conference on AI, Machine Learning and Deep Learning (AIMLDL 2026)
July 16 ~ 17, 2026, London, United Kingdom Scope & Topics 7th International Conference on AI, Machine Learning and Deep Learning (AIMLDL 2026) invites high quality research contributions from academia, industry, and government organizations. AIMLDL 2026 serves as a premier international forum for presenting cutting edge advances in Artificial Intelligence, Machine Learning, Deep Learning, Generative AI, Autonomous Agents, Foundation Models, and emerging intelligent systems.
.As AI continues to transform science, industry, and society, AIMLDL 2026 aims to bring together leading researchers, practitioners, and innovators to exchange ideas, discuss breakthroughs, and explore the future of intelligent technologies. The conference welcomes original research papers, survey articles, case studies, and industrial applications that demonstrate significant advances in theory, methodology, algorithms, systems, and real world deployments
Authors are invited to submit papers in the following areas, but not limited to:
Topics of interest include, but are not limited to, the following Foundations of Artificial Intelligence
- AI Algorithms, Models, and Theory
- Optimization Methods for AI and ML
- Probabilistic Reasoning and Graphical Models
- Fuzzy Logic, Rough Sets, and Uncertainty Modeling
- Evolutionary Computation and Swarm Intelligence
- Neuro Symbolic AI and Logic Guided Learning
- AI for Law, Policy, and Regulatory Reasoning
Machine Learning and Data Centric AI
- Supervised, Unsupervised, and Semi Supervised Learning
- Data Centric AI, Data Quality Engineering, and Data Governance
- Automated Machine Learning (AutoML) and Meta Learning
- Ensemble Learning and Hybrid Learning Methods
- Learning with Imbalanced, Noisy, or Limited Data
- Continual Learning and Lifelong Learning
- Causal AI, Causal Discovery, and Counterfactual Reasoning
Deep Learning and Neural Architectures
- Deep Neural Networks, Transformers, and Modern Architectures
- Foundation Models and Large Scale Pretrained Models
- Efficient Deep Learning (Pruning, Quantization, Distillation)
- Neural Architecture Search (NAS)
- Multimodal Deep Learning (Vision Language Audio)
- Graph Neural Networks (GNNs)
- Neuroscience Inspired AI and Brain Inspired Architectures
Generative AI and Autonomous AI Agents
- Generative Models (GANs, VAEs, Diffusion Models)
- Large Language Models (LLMs) and Multimodal Foundation Models
- Retrieval Augmented Generation (RAG) and Vector Search
- Autonomous AI Agents and Agentic Workflows
- Multi Agent Systems and Collaborative AI
- Prompt Engineering and AI Driven Reasoning
- Hallucination Mitigation, Guardrails, and Safety Layers
- AI Generated Content Detection, Watermarking, and Provenance
Reinforcement Learning and Decision Making
- Deep Reinforcement Learning
- Multi Agent Reinforcement Learning
- Offline and Batch RL
- Safe, Robust, and Explainable RL
- Planning, Reasoning, and Sequential Decision Making
Natural Language Processing and Speech Technologies
- Transformer Based NLP and Sequence Modeling
- Information Extraction, Text Mining, and Semantic Understanding
- Conversational AI, Dialogue Systems, and Chatbots
- Speech Recognition, Speech Synthesis, and Audio Processing
- Multilingual, Low Resource, and Cross Lingual NLP
Computer Vision and Visual Intelligence
- Image Recognition, Object Detection, and Scene Understanding
- Video Analytics, Action Recognition, and Video Generation
- 3D Vision, AR/VR, and Spatial Computing
- Vision Language Models and Multimodal Perception
- Visual Information Processing and Multimedia AI
AI for Cybersecurity and Trustworthy AI
- AI Driven Threat Detection and Cyber Defense
- Adversarial Machine Learning and Robustness
- Privacy Preserving AI (Federated Learning, Differential Privacy)
- Explainable AI (XAI), Fairness, and Responsible AI
- AI Safety, Alignment, and Governance
- AI Risk Management and Compliance Engineering
AI Systems, Platforms and Deployment
AI on Cloud, Edge, and IoT Platforms
- Distributed AI Systems and Scalable ML Infrastructure
- MLOps, Model Deployment, Monitoring, and Lifecycle Management
- ML System Observability, Drift Detection, and AI Incident Response
- Real Time AI, Embedded AI, and Resource Constrained Inference
- AI Hardware Acceleration (GPUs, TPUs, Neuromorphic Computing)
Robotics, Embodied AI and Intelligent Control
- Robot Learning and Embodied AI
- Sim to Real Transfer and Autonomous Navigation
- Intelligent Control Systems and Decision Making
- Human Robot Interaction and Collaborative Robotics
- Sensor Fusion and Perception for Robotics
Applied AI and Domain Specific Intelligence
- Bioinformatics, Biometrics, and Computational Biology
- AI for Healthcare, Medicine, and Drug Discovery
- Financial AI, Risk Modeling, and Algorithmic Trading
- Business Analytics, Decision Intelligence, and Predictive Modeling
- Geo Informatics, Environmental AI, and Climate Modeling
- Logistics, Supply Chain Optimization, and Smart Manufacturing
- Recommendation Systems and Personalization
- AI for Education, Smart Cities, and Social Good
- AI for Sustainability, Energy Systems, and Climate Action
- AI for Science (Physics, Chemistry, and Materials Science)
Paper Submission Authors are invited to submit papers through the conference Submission System by April 25, 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 The proceedings of the conference will be published by Computer Science Conference Proceedings in Computer Science & Information Technology (CS & IT) series (Confirmed). Selected papers from AIMLDL 2026, after further revisions, will be published in the special issue of the following journals. Important Dates | Submission Deadline | : | April 25, 2026 | | Authors Notification | : | June 20, 2026 | | Final Manuscript Due | : | June 27, 2026 |
Co - Located Event ***** The invited talk proposals can be submitted to arit@nlpd2026.org
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