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All Journal Budapest International Research and Critics in Linguistics and Education Journal (Birle Journal) Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Budapest International Research in Exact Sciences (BirEx Journal) Britain International of Exact Sciences Journal (BIoEx Journal) LingLit Journal
Goshu, Belay Sitotaw
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Physics Social Sciences Transportation Other

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Predicting the Borana Lunar-Stellar Calendar: An Astronomical Feature Engineering and Machine Learning Approach Goshu, Belay Sitotaw
Britain International of Exact Sciences (BIoEx) Journal Vol 8 No 2 (2026): Britain International of Exact Sciences Journal, May
Publisher : Britain International for Academic Research (BIAR) Publisher

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Abstract

The Borana calendar of southern Ethiopia and northern Kenya is a unique lunar stellar system where months are defined by new moon conjunctions with specific anchor stars (Triangulum, Pleiades, Aldebaran, Bellatrix, Orion Saiph, Sirius). Unlike arithmetic calendars, it relies on empirical observation by Borana ayyantu (calendar keepers), making prediction challenging. This study aimed to formalize the Borana calendar's astronomical logic using machine learning, predicting new moon conjunction dates, month names (1–12 or intercalary), and day name indices (0–26) from celestial features. Synthetic astronomical data were generated based on synodic month variations, stellar longitudes, and intercalation rules. Features included Moon longitude, angular distance to anchor stars, and cumulative month counts. An LSTM network predicted conjunction dates, while Random Forest classifiers predicted month and day names. Performance was evaluated against baseline arithmetic models. The LSTM achieved Mean Absolute Error of 0.230 days for conjunction dates, improving 7.3% over the mean synodic month baseline. Month classification accuracy reached 94.1%, and day classification 87.5%. Feature importance confirmed angular distance to anchor stars as the strongest predictor. Borana New Year (2027–2070) was predicted between August 18 and October 22. Machine learning successfully captures the Borana calendar's empirical logic, though accurate long term forecasting requires high precision ephemerides and field validation. The framework provides a reproducible methodology for formalizing indigenous timekeeping systems. Future work should integrate JPL ephemerides, ethnographic field data, and open source software tools to support Borana calendar preservation and prediction.
Machine Learning-Enhanced Prediction of Lunar Crescent Visibility for Unified Hijri Calendar Determination: A Global and Regional Framework Goshu, Belay Sitotaw; Ridwan, Muhammad
Budapest International Research and Critics Institute-Journal (BIRCI-Journal) Vol 9, No 2 (2026): Budapest International Research and Critics Institute May
Publisher : Budapest International Research and Critics University

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The lunar Hijri calendar governs religious observances for approximately 1.9 billion Muslims worldwide, yet disunity in crescent sighting criteria leads to inconsistent Ramadan and Eid dates across regions. Traditional visibility criteria (Yallop, Odeh) rely on simplified parametric approximations that inadequately capture complex atmospheric and geographical interactions. This study develops and validates a machine learning-enhanced framework for predicting lunar crescent visibility to support unified Hijri calendar determination through global and regionally-adapted models. A comprehensive dataset of 7,488 observations spanning 13 years (2013–2025) across 24 countries and five geographical regions was compiled. Feature engineering created 15 predictive parameters including interaction terms and composite indices. Eight supervised learning algorithms were evaluated with hyperparameter optimization using randomized search, genetic algorithms, and particle swarm optimization. Ensemble methods including voting, stacking, and hybrid configurations were developed and validated using 5-fold cross-validation. Findings: The hybrid ensemble model achieved superior performance (AUC 0.906, F1-score 0.888), outperforming traditional criteria by 17–19%. Engineered interaction features (elongation × altitude, lag time × altitude) demonstrated highest predictive importance. Regional analysis revealed visibility rate variations from 97.7% (Oceania) to 98.7% (Asia), supporting geographically-calibrated models. Long-term Ramadan predictions (2027–2075) confirmed the 33-year lunar cycle with mean interval of 354.37 days. Conclusion: Machine learning provides robust, evidence-based crescent visibility prediction that exceeds traditional criteria accuracy while capturing complex parameter interactions. The framework supports both global unification and region-specific applications. Recommendation: Religious authorities should adopt probabilistic, multi-model ensemble predictions with confidence scoring for calendar determination, supported by continuous validation against global observational networks.
Determinants of Health in Abrahamic Scriptures: A Comparative Thematic Analysis of the Quran, Bible, and Torah Goshu, Belay Sitotaw; Muhammad Ridwan
LingLit Journal Scientific Journal for Linguistics and Literature Vol 6 No 4 (2025): Linglit Journal: Scientific Journal of Linguistics and Literature, December
Publisher : Britain International for Academic Research (BIAR-Publisher)

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Mainstream models of health determinants, including the Dahlgren Whitehead rainbow model and WHO frameworks, overlook spiritual and religious factors despite growing evidence of their influence on health outcomes. Abrahamic scriptures the Quran, Bible, and Torah contain extensive guidance on health, yet no systematic comparative analysis has mapped their determinants. This study aimed to identify, categorise, and compare health determinants articulated in the Quran, Bible, and Torah, and to integrate findings into contemporary public health discourse. A comparative qualitative thematic analysis was conducted. Deductive codes were derived from existing determinant models; inductive codes emerged from scriptural analysis. Texts included the Quran (Arabic with Saheeh International translation), the Bible (NIV/NRSV), and the Torah (Jewish Publication Society translation). Rigour was ensured through audit trails, peer debriefing, and negative case analysis. Four determinant categories were identified: metaphysical (divine will, sin, spiritual forces, prayer), behavioural (diet, hygiene, rest, sexual ethics, intoxicants), social (charity, community responsibility, justice, governance), and psychological (faith, gratitude, repentance). All three scriptures affirm metaphysical determinants. Behavioural determinants are strongest in the Quran and Torah; social determinants are strongest in the Bible; psychological determinants are strong in the Quran and Bible, moderate in the Torah. Conclusion: Abrahamic scriptures present a holistic model in which the divine human relationship is the primary health determinant, extending beyond secular frameworks. Public health practice should integrate spiritual determinants through culturally competent promotion, faith based interventions, and clinical spiritual assessment. Future research should quantify scriptural determinants and extend analysis to other religious traditions.
Co-Authors Abas, Ermias Alemu, Yonas Tadesse Ayalew, Anteneh Berhanu, Hana Cheiklu, Robel Cheru, Nega Fitria Fitria Lakew, Eyoel Merga, Bikila Misganu, Tewodros Muhammad Ridwan Muhammad Ridwan Ramlan, H. Silalahi, Nurvita Sari Musdiani Simegn, Tigist Weldeamueal, Melaku Masresha Woldeamanue, Melaku Masresha Woldeamanueal, Melaku Masresha Woldeamaueal, Melaku Masresha