About the Journal
Journal of IoT and Machine Learning (JoITML) is a double blind peer-reviewed, bi-annual journal, which aims to provide readers with the most recent findings in the field of internet of things and machine learning. Each paper accepted for publication in the journal have a significant empirical or theoretical contribution. The journal welcomes research articles in all areas of computer science-related submissions.
JoITML aims to foster interdisciplinary research and collaboration by providing a platform for researchers, academics, and industry professionals to share their original contributions and innovative ideas. The scope of the journal encompasses a wide range of topics, including but not limited to IoT architectures and protocols, sensor networks, data analytics, predictive modeling, deep learning, reinforcement learning, edge computing, and IoT-based applications. By disseminating cutting-edge research and practical insights, the Journal of IoT and Machine Learning strives to accelerate the development and adoption of IoT and ML technologies, ultimately shaping the future of interconnected systems and intelligent decision-making.
Frequency: Bi-annual (Jan-Jun, Jul-Dec)| Double-blind peer reviewed|
DOI: https://doi.org/10.48001/JoITML
Start Year: 2024 | Format: Online |Language: English |Subject: Computer Science
Publisher: QTanalytics India URL: https://qtanalytics.in
e-ISSN: 3048-5312
Focus and Scope:
- Computer Vision and Image Processing
- Data Analysis and Pattern Recognition
- Data Analytics and Decision-Making
- Edge Computing and Fog Computing
- Feature Extraction and Representation Learning
- Interconnected Devices
- Internet of Things (IoT) Technologies
- IoT Applications and Use Cases
- Learning Algorithms
- Machine Learning (ML) Algorithms and Techniques
- Model Interpretability and Explainability
- Natural Language Processing (NLP) and Text Analysis
- Scalability and Performance Optimization
- Security and Privacy in IoT
- Standards and Protocols
