Aim and Scope

Advances in Machine Learning, IoT, and Data Security is a leading international journal dedicated to the advancement of knowledge in the interconnected fields of machine learning, the Internet of Things (IoT), and data security. The journal aims to serve as a platform for both researchers and industry professionals to explore cutting-edge innovations, theoretical advancements, and practical applications within these rapidly evolving domains.

The journal welcomes original research, comprehensive reviews, and case studies that contribute to the understanding and development of machine learning algorithms, IoT frameworks, and robust data security models. With a focus on interdisciplinary approaches, the journal emphasizes how these technologies converge to solve complex real-world problems, enhance connectivity, and safeguard digital ecosystems.

Key Areas of Interest

Research Articles: Original contributions exploring innovative methodologies, algorithms, and applications in machine learning, IoT systems, and data security.

Review Articles: Comprehensive reviews that critically assess state-of-the-art technologies and propose future directions in machine learning, IoT, and data security.

Application and Case Studies: Real-world implementations of machine learning models, IoT architectures, or data security protocols that demonstrate their impact on industries such as healthcare, finance, smart cities, and more.

Technical Notes: Brief reports on emerging techniques, tools, and technologies that address challenges in the integration of machine learning, IoT, and security.

The journal seeks to foster collaboration between academia and industry, bridging the gap between theoretical research and practical deployment across various sectors.