About the Journal

The Journal of Intelligent Machine Learning and IoT Enabled Applications invites contributions on pioneering advancements in the fields of machine learning, artificial intelligence, and Internet of Things (IoT) applications. It is published by Scientific Intelli-press, periodically in May and December since 2025.

Authors are encouraged to submit original research that showcases innovations aimed at advancing the state-of-the-art in areas such as intelligent algorithms, machine learning models, IoT-driven data acquisition and processing, edge and cloud computing for IoT, smart sensor technologies, adaptive and autonomous systems, and real-time data fusion techniques. The journal also welcomes papers on the development of novel methodologies for performance evaluation of intelligent systems, optimization of machine learning models for IoT environments, and the integration of IoT and AI in connected ecosystems. Our mission is to disseminate cutting-edge knowledge that bridges the gap between machine learning theory and its practical applications in the IoT domain, fostering a deeper understanding of how these technologies can be leveraged to create intelligent, responsive, and sustainable systems.

The Journal of Intelligent Machine Learning and IoT Enabled Applications is a broad-based, peer reviewed journal that publishes original research in all the disciplines of computer science (Machine Learning and IoT) including various inter-disciplinary aspects.

The Journal welcomes submissions from a wide range of topics including, but not limited to:

  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
  • Deep Learning
  • Transfer Learning
  • Federated Learning
  • Meta-Learning
  • Self-Supervised Learning
  • Generative Adversarial Networks (GANs)
  • Neural Architecture Search (NAS)
  • Explainable AI (XAI)
  • Few-Shot Learning
  • Anomaly Detection
  • Dimensionality Reduction
  • Bayesian Networks
  • Support Vector Machines (SVM)
  • Ensemble Methods
  • Graph Neural Networks (GNNs)
  • Attention Mechanisms
  • Optimization Algorithms (e.g., Gradient Descent)
  • AI in IoT Applications
  • IoT Data Acquisition and Processing
  • Edge Computing for IoT
  • Cloud Computing for IoT
  • Smart Sensors in IoT Networks
  • Adaptive IoT Systems
  • Autonomous IoT Systems
  • Real-Time IoT Data Fusion
  • Big Data Analytics in IoT
  • Security and Privacy in IoT
  • Energy-Efficient IoT Systems
  • IoT System Optimization
  • Performance Evaluation of IoT Systems
  • IoT Communication Protocols
  • Integration of AI and IoT in Smart Cities
  • IoT for Industrial Applications
  • Healthcare IoT Systems
  • IoT-Based Predictive Maintenance
  • Blockchain for IoT Security

The Journal of Intelligent Machine Learning and IoT Enabled Applications publishes papers in the following categories: Original Research, as well as relevant hardware and software architectures, Survey and Review Articles. All papers are evaluated based on their scientific merit and contribution to the field. Submissions undergo an initial screening to ensure compliance with research and publication ethics before proceeding to peer review. The journal evaluates each submission with a focus on sound scientific principles and practical relevance.