A New Crescent Moon Visibility Criteria using Circular Regression Model: A Case Study of Teluk Kemang, Malaysia

Ahmad, Nazhatulshima; Nawawi, Mohd; Zainuddin, Mohd Zambri; Nasir, Zuhaili Mohd; Yunus, Rossita M.; Mohamed, Ibrahim

doi:10.17576/jsm-2020-4904-15

Cited by 15 publications

(6 citation statements)

References 10 publications

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“…Quereshi [14] removed some irrelevant observations from this list and he worked on 463 observations. A recent work has used only 254 observations, of which only 81 are of positive sighting [15]. These examples are not enough to train a machine learning algorithm, hence we searched for more observations.…”

Section: A Dataset Collectionmentioning

confidence: 99%

Predicting the Visibility of the First Crescent

Ahmed¹

2018

KJCIS

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This study presents an application of machine learning to predict whether the first crescent of the lunar month will be visible to naked eye on a given date. The study presents a dataset of successful and unsuccessful attempts to find the first crescent at the start of the lunar month. Previously, this problem was solved by analytically deriving the equations for visibility parameter(s) and manually fixing threshold values. However, we applied supervised machine learning on the independent variables of the problem, and the system learnt about the criteria of classification. The system gives precision of 0.88 and recall of 0.87 and hence it treats both false positives and false negatives equally well.

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Section: A Dataset Collectionmentioning

confidence: 99%

Predicting the Visibility of the First Crescent

Ahmed¹

2018

KJCIS

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“…He found that the minimum 𝐴𝑅𝐶𝑉 was 8.4° and the minimum 𝐴𝑅𝐶𝐿 was 7.1°. Naz Ahmed et al [22] used circular regression models to establish a criterion for moon sighting and found that the minimum parameters were 7.28° for 𝐴𝑅𝐶𝐿, 3.74° for 𝐴𝑅𝐶𝑉, and 3.39° for moon latitude from the horizon.…”

Section: Related Workmentioning

confidence: 99%

Crescent Moon Visibility: A New Criterion using Deep learned Artificial Neural-Network

Allawi

2024

Iraqi Journal of Science

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Many authors investigated the problem of the early visibility of the new crescent moon after the conjunction and proposed many criteria addressing this issue in the literature. This article presented a proposed criterion for early crescent moon sighting based on a deep-learned pattern recognizer artificial neural network (ANN) performance. Moon sight datasets were collected from various sources and used to learn the ANN. The new criterion relied on the crescent width and the arc of vision from the edge of the crescent bright limb. The result of that criterion was a control value indicating the moon's visibility condition, which separated the datasets into four regions: invisible, telescope only, probably visible, and certainly visible. This criterion was used on the dataset for ANN learning to compare its efficiency with the actual moon visibility events.

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“…Next, in earth sciences is to measure the ocean waves (Caires and Wyatt, 2003). Ahmad et al (2020) recently applied circular data to study a new crescent moon visibility criterion. The circle's most common continuous probability distribution is a von Mises distribution (Mardia & Jupp 2000).…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation for Circular Simultaneous Functional Relationship Model (CSFRM) for Unequal Variances

BADARISAM,

Mohamad Anuar

2023

JOSMA

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In this study, we propose an extended model of the Circular Simultaneous Functional Relationship Model from the Circular Functional Relationship Model. In this case, the circular model and the circular variable will be applied where assuming the error variances are not equal. All estimations of the parameter followed von Mises distribution. The angular and slope parameters are obtained using the ms function, while concentration parameter estimation is obtained from the polyroot function provided in the SPLUS statistical package. The simulation study has been conducted to assess the efficiency of the proposed model. The simulation results showed that as sample size and concentration parameters increase, all parameters’ estimates are close to the true value and have a smaller bias. The illustration of real wind and wave direction data from two different bases of data is given to show its practical applicability. We note that the proposed method for parameter estimation works well with the proposed model.

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A New Crescent Moon Visibility Criteria using Circular Regression Model: A Case Study of Teluk Kemang, Malaysia

Cited by 15 publications

References 10 publications

Predicting the Visibility of the First Crescent

Predicting the Visibility of the First Crescent

Crescent Moon Visibility: A New Criterion using Deep learned Artificial Neural-Network

Parameter Estimation for Circular Simultaneous Functional Relationship Model (CSFRM) for Unequal Variances

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