Citation: Allawi, Z.T. A Pattern-Recognizer Artificial Neural Network for the Prediction of New Crescent Visibility in Iraq. Computation 2022, 10, 186. https:// doi.org/10.3390/computation10100186 Academic Editor: Jaroslaw Krzywanski Received: 21 September 2022 Accepted: 6 October 2022 Published: 13 October 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). computation Article A Pattern-Recognizer Artificial Neural Network for the Prediction of New Crescent Visibility in Iraq Ziyad T. Allawi Department of Computer Engineering, College of Engineering, University of Baghdad, Baghdad 10071, Iraq; ziyad.allawi@coeng.uobaghdad.edu.iq Abstract: Various theories have been proposed since in last century to predict the first sighting of a new crescent moon. None of them uses the concept of machine and deep learning to process, interpret and simulate patterns hidden in databases. Many of these theories use interpolation and extrapolation techniques to identify sighting regions through such data. In this study, a pattern recognizer artificial neural network was trained to distinguish between visibility regions. Essential parameters of crescent moon sighting were collected from moon sight datasets and used to build an intelligent system of pattern recognition to predict the crescent sight conditions. The proposed ANN learned the datasets with an accuracy of more than 72% in comparison to the actual observational results. ANN simulation gives a clear insight into three crescent moon visibility regions: invisible (I), probably visible (P), and certainly visible (V). The proposed ANN is suitable for building lunar calendars, so it was used to build a four-year calendar on the horizon of Baghdad. The built calendar was compared with the official Hijri calendar in Iraq. Keywords: pattern recognition; artificial neural network; deep learning; crescent moon early sighting 1. Introduction The early sighting of a new crescent moon is one of the earliest astronomical activities performed by human civilizations. This sighting marked the start of the lunar month in many such civilizations. The first evidence of these activities came to us from the Babylonians (6th Century B.C.), who lived in the fertile lands of Mesopotamia (modern-day Iraq) [1]. They relied on the crescent moon to indicate the start of their calendar months and years. They used a standard criterion to predict the moon’s sight, which was sufficient for that purpose. Other civilizations, such as Indians and Chinese, also used lunar sight and still do to this day, although use different criteria to determine the start of a lunar month [2]. Jewish people also still use the moon cycle to indicate the start of the months in their lunisolar calendar, although they use arithmetic calculation rather than moon sighting. Centuries after the Babylonians, in the 7th Century C.E., a new religion, Islam, had emerged from the deserts of the Arabian Peninsula. Three of the five main pillars of Islam depend on the start of the lunar months. They are Fasting-Breakfasting “Siam-Fitr”, Pilgrimage “Hajj”, and Alms “Zakat”. The first two pillars are performed in a specific lunar month in a year, while the third is performed once in the first month of each lunar year [2]. Reckoning of the first crescent moon after the conjunction is an essential indication for Muslims to begin their lunar months and perform their religious duties. Through the Islamic golden age (between the 9th and 11th centuries C.E.), many famous Muslim astronomers laid down specific criteria for moon sighting that depended on the age of the moon (the time passed since the conjunction) and the lag of the moon (the time between the sunset and the moonset) [3]. Some considered the arc length of celestial degrees between the sun and the moon (elongation) at sunset. However, they could not predict the sighting perfectly because they did not consider the local conditions of the horizon at the time of observation, and they omitted the effect of changing the apparent width of the crescent moon and its relation with the brightness of the sky [4]. Computation 2022, 10, 186. https://doi.org/10.3390/computation10100186 https://www.mdpi.com/journal/computation