Archives

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  BIMA January 2026 Issue

  Vol. 1 No. 2 (2026)

  This issue of the Bulletin of Intelligent Machines and Algorithms (BIMA) brings together five research articles that explore practical applications of artificial intelligence and machine learning across multiple domains. The published works address current challenges in renewable energy forecasting, healthcare analytics, cybersecurity, epidemiological prediction, and health-related data classification.

  Several contributions highlight the growing role of explainable and interpretable models in supporting reliable decision-making, particularly in health and public policy contexts. Other studies focus on efficient learning architectures that achieve strong predictive performance while remaining suitable for real-world deployment.

  Collectively, the articles in this issue reflect an emphasis on methodological soundness, applicability, and transparency. Through these contributions, BIMA continues to support the dissemination of applied research that advances intelligent systems while maintaining relevance to real-world problems and decision-making needs.

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  BIMA November 2025 Issue

  Vol. 1 No. 1 (2025)

  The inaugural issue of the Bulletin of Intelligent Machines and Algorithms (BIMA) marks the journal’s first contribution to the dissemination of research in artificial intelligence, data science, and machine learning. This issue presents five selected articles that demonstrate the application of intelligent algorithms across diverse interdisciplinary domains, including digital marketing, multimedia analysis, public sentiment studies, agriculture, and financial security.

  The contributions highlight a strong emphasis on model interpretability, algorithmic robustness, and practical relevance. Several studies integrate explainable machine learning techniques to support transparent decision-making, while others focus on ensemble and deep learning approaches to improve predictive accuracy in real-world settings.

  Together, the articles reflect BIMA’s commitment to publishing methodologically sound and application-oriented research, establishing a foundation for the journal’s role in advancing reliable and impactful intelligent systems research.

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  BIMA March 2026 Issue

  Vol. 1 No. 3 (2026)

  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.