About the Journal

BIMA (Bulletin of Intelligent Machines and Algorithms) is an international peer-reviewed journal dedicated to promoting research in the fields of artificial intelligence, machine learning, and algorithms. BIMA serves as a platform for publishing the latest research findings and innovative applications in these rapidly evolving fields. The journal aims to contribute to the academic and professional development of researchers, practitioners, and educators by publishing high-quality articles that provide in-depth insights into the theoretical, practical, and computational aspects of intelligent systems and algorithms.

Focus and Scope

BIMA publishes original research articles, reviews, and technical reviews on various topics related to intelligent machines and algorithms. The scope of this journal includes, but is not limited to:

  • Artificial Intelligence: Methodologies, algorithms, and architectures for building intelligent systems, including knowledge representation, reasoning, learning, and perception.
  • Machine Learning: Supervised, unsupervised, semi-supervised, and reinforcement learning algorithms; applications in real-world problems.
  • Deep Learning: Advanced neural network architectures such as CNNs, RNNs, Transformers, and their applications in various domains including image, video, text, and signal processing.
  • Computer Vision: Image processing, object detection and recognition, image segmentation, motion analysis, and visual scene understanding in intelligent systems.
  • Data Mining: Techniques for extracting patterns and knowledge from large datasets.
  • Optimisation Algorithms: Theory and applications of optimisation techniques in continuous and discrete domains.
  • Computational Intelligence: Evolutionary algorithms, fuzzy logic, and swarm intelligence systems.
  • Natural Language Processing (NLP): Advances in language understanding, translation, and text analysis.
  • Applications: Applications of artificial intelligence and algorithms in healthcare, finance, industry, education, and other fields.
  • Robotics and Autonomous Systems: Intelligent robots, human-robot interaction, and autonomous vehicles.

Publication Frequency

BIMA is published bimonthly, with issues scheduled for January, March, May, July, September, and November. Each issue contains peer-reviewed articles that reflect the latest developments in the field of intelligent machines and algorithms.

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Current Issue

Vol. 1 No. 3 (2026): BIMA March 2026 Issue
View Vol. 1 No. 3 (2026): BIMA March 2026 Issue

This issue presents various research studies discussing the application of machine learning in the fields of education, healthcare, agriculture, and public policy. In general, the articles published in this issue emphasize the importance of data-driven predictive models in improving decision-making accuracy and system efficiency.

Research in the field of education indicates that ensemble learning approaches can improve the early detection of at-risk students, particularly on imbalanced datasets. In the healthcare sector, machine learning models have proven effective for the early prediction of diseases such as diabetes and cervical cancer, with the addition of explainable AI approaches to enhance the interpretability of results. In the agricultural sector, comparative studies show that simple models can still deliver optimal performance in predicting crop yields. Meanwhile, social media-based sentiment analysis provides insights into public perceptions of government policies in a more objective manner.

Overall, this issue highlights the critical role of machine learning in addressing complex problems adaptively, accurately, and practically across various sectors.

DOI: https://doi.org/10.65780/bima.v1i3
Published: 2026-03-31

Articles

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