About the Journal

International Numeric Journal of Machine Learning and Robots (INJMR)

Introduction: The International Numeric Journal of Machine Learning and Robots (INJMR) is a prestigious scholarly journal dedicated to the advancement of knowledge in the fields of machine learning and robotics. INJMR serves as a beacon for researchers, scientists, and experts worldwide, providing a platform for the dissemination of cutting-edge research, the exchange of innovative ideas, and the cultivation of collaboration in these dynamic and transformative disciplines.

Mission: INJMR is driven by a mission to facilitate the exchange of knowledge and ideas, fostering groundbreaking research and innovation in machine learning and robotics. Our journal strives to:

  1. Promote Excellence: INJMR is committed to upholding the highest standards of excellence in research and scholarship within the domains of machine learning and robotics.

  2. Disseminate Knowledge: We aim to share novel insights, methodologies, and discoveries with the global scientific community, contributing to the collective understanding of these fields.

  3. Encourage Collaboration: INJMR acts as a catalyst for collaboration among researchers, academics, and professionals, bridging the gap between theory and practical application.

Key Focus Areas: INJMR encompasses a diverse array of topics within machine learning and robotics, with a particular emphasis on numerical methods and their real-world implementations. Our journal covers a wide spectrum of focus areas, including:

  1. Machine Learning Algorithms:

    • Deep Learning
    • Reinforcement Learning
    • Supervised and Unsupervised Learning
    • Ensemble Methods
    • Transfer Learning
    • Neural Networks

  2. Robotics and Automation:

    • Robot Design and Control
    • Human-Robot Interaction
    • Autonomous Systems
    • Swarm Robotics
    • Robot Perception and Sensing
    • Ethical and Societal Aspects of Robotics

  3. Applications:

    • Healthcare Robotics
    • Autonomous Vehicles
    • Industrial Automation
    • Natural Language Processing
    • Computer Vision
    • Financial Forecasting

Publication Format: INJMR publishes a range of scholarly contributions, including research articles, review papers, and technical notes. All submissions undergo a rigorous peer-review process to maintain the journal's commitment to quality and integrity. Our review process ensures that the research presented meets the highest scientific standards.

Audience: INJMR is primarily aimed at academics, researchers, engineers, and professionals who are keen to explore the latest developments in machine learning and robotics. The journal serves as an invaluable resource for those seeking to remain updated on emerging trends, methodologies, and practical applications in these burgeoning fields.

Impact and Contribution: INJMR plays a pivotal role in advancing the frontiers of machine learning and robotics by providing a conduit for the dissemination of innovative research findings. It enriches the global scientific community by encouraging collaboration and the exchange of knowledge among experts in these domains. The journal's dedication to numerical methods underscores its commitment to rigorous scientific exploration.

 The International Numeric Journal of Machine Learning and Robots (INJMR) stands as a cornerstone for individuals and organizations dedicated to the exploration and advancement of machine learning and robotics. Our journal's devotion to numerical methodologies, stringent peer-review process, and wide-ranging scope make it an indispensable resource for researchers and professionals striving to shape the future of these exciting and rapidly evolving disciplines. We invite you to join us on this remarkable journey of discovery and innovation.

Abstracting and Indexing

  • Emerging Sources Citation Index (in the process)
  • Scopus
  • Indian Citation Index
  • ROAD: the Directory of Open Access scholarly Resources
  • Research Gate
  • Google Scholar
  • Academia Database
  • DPI Digital Library

Note: If your article is selected, there is an open access fee of $1500 USD, which may be waived based on the paper's quality.