About the Journal

Journal Title: International Transactions in Machine Learning

Scope:

International Transactions in Machine Learning is a peer-reviewed, interdisciplinary journal dedicated to advancing the field of machine learning through the dissemination of high-quality research and innovation. The journal welcomes contributions from researchers, scientists, engineers, and practitioners in academia, industry, and beyond.

Aims and Objectives: The primary objectives of International Transactions in Machine Learning are as follows:

1. Advancement of Machine Learning: The journal aims to foster the development and dissemination of cutting-edge research and developments in the field of machine learning. It provides a platform for scholars and practitioners to share their knowledge, insights, and innovations, contributing to the growth of machine learning as a scientific discipline.

2. Interdisciplinary Collaboration: International Transactions in Machine Learning encourages interdisciplinary collaboration by bringing together researchers from various domains, including computer science, statistics, data science, and artificial intelligence. This interdisciplinary approach enables the exploration of diverse applications and methodologies within the machine learning domain.

3. Dissemination of Knowledge: The journal is committed to the dissemination of knowledge in the form of research papers, reviews, case studies, and technical notes. It serves as a central repository for high-quality research output, ensuring that the latest findings and trends in machine learning are readily accessible to the global research community.

4. Innovation and Practical Applications: International Transactions in Machine Learning seeks to promote innovation in machine learning techniques and their practical applications. It encourages researchers to address real-world challenges, explore novel algorithms, and apply machine learning to domains such as healthcare, finance, robotics, and more.

Scope of Topics: The journal covers a broad spectrum of topics within the field of machine learning, including but not limited to:

• Supervised, unsupervised, and reinforcement learning algorithms
• Deep learning, neural networks, and convolutional neural networks
• Natural language processing and text analysis
• Computer vision and image recognition
• Data mining, feature engineering, and dimensionality reduction
• Machine learning for healthcare, finance, and cybersecurity
• Explainable AI and interpretability in machine learning
• Ethical considerations and bias in machine learning
• Machine learning tools, frameworks, and libraries
• Optimization techniques and hyperparameter tuning
• Transfer learning and domain adaptation
• Machine learning in edge computing and IoT applications

Types of Contributions: The journal welcomes the following types of contributions:

1. Research Papers: Original research articles that present novel findings and contributions to the field of machine learning.

2. Reviews: Comprehensive reviews of current research trends, methodologies, and applications within the machine learning domain.

3. Case Studies: In-depth analyses of practical applications of machine learning in real-world scenarios.

4. Technical Notes: Short articles that provide insights into specific machine learning techniques, challenges, or tools.

Audience: International Transactions in Machine Learning is intended for researchers, academics, professionals, and practitioners who are engaged in or interested in the field of machine learning. It is an invaluable resource for those seeking to stay informed about the latest developments and best practices in this dynamic and rapidly evolving field.

Publication Frequency: The journal is published quarterly to ensure the timely dissemination of research findings and maintain a steady flow of valuable contributions to the field of machine learning.

Peer Review: All contributions to International Transactions in Machine Learning undergo a rigorous peer review process to ensure the publication of high-quality and scientifically sound research.

International Transactions in Machine Learning is dedicated to promoting the exchange of knowledge, facilitating collaboration, and contributing to the growth of the machine learning community worldwide. It invites researchers and practitioners to contribute to its mission by submitting their original work and actively engaging with the vibrant discourse on machine learning and its applications.

Impact Factor: 7.10