All full paper submissions will be subjected to a rigorous peer-review process conducted by experts in the field.

Evaluation will be based on criteria including originality, technical soundness, depth of research, relevance to the conference scope, scientific contribution, and clarity of presentation. Acceptance decisions will be primarily guided by the overall quality, novelty, and significance of the work, as well as its potential impact on both theoretical advancements and practical applications.

Conference Tracks

Track I

Machine Learning & Data Science

  • Supervised, unsupervised, and reinforcement learning.
  • Feature engineering and dimensionality reduction.
  • Explainable AI (XAI) and interpretable models.
  • Data mining, knowledge discovery, and pattern recognition.
  • Statistical learning and probabilistic models.
Track II

Deep Learning & Neural Networks

  • Convolutional neural networks (CNN) and architectures.
  • Recurrent networks, LSTM, and transformer models.
  • Generative models: GANs, VAEs, diffusion models.
  • Neural architecture search and model optimization.
  • Transfer learning and few-shot learning.
Track III

Computer Vision & Image Processing

  • Object detection, recognition, and segmentation.
  • Medical image analysis and biometric systems.
  • Video analysis and action recognition.
  • 3D vision, depth estimation, and scene understanding.
  • Remote sensing and satellite image processing.
Track IV

Natural Language Processing

  • Text classification, sentiment analysis, and opinion mining.
  • Machine translation and multilingual NLP.
  • Question answering, dialogue systems, and chatbots.
  • Information extraction and knowledge graphs.
  • Arabic and Amazigh NLP (regional focus).
Track V

IoT & Smart Systems

  • Intelligent sensor networks and edge AI.
  • Smart cities, smart grids, and sustainable systems.
  • Autonomous systems and robotics intelligence.
  • Industrial IoT and predictive maintenance.
  • Real-time embedded AI systems.
Track VI

Cybersecurity & AI

  • AI-driven intrusion detection and anomaly detection.
  • Adversarial machine learning and robustness.
  • Privacy-preserving AI and federated learning.
  • Threat intelligence and malware analysis with ML.
  • Blockchain and AI for secure systems.

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