Sunfa Ata Zuyan machine learning models for moon phase  detection: algorithm, prototype and performance comparison

Moshayedi, Ata Jahangir; Chen, Zu-yan; Liao, Liefa; Li, Shuai

doi:10.12928/telkomnika.v20i1.22338

Cited by 13 publications

(11 citation statements)

References 35 publications

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“…The mathematical model that demonstrates the kinematic equations pertaining to differential drive robots was reviewed by A. J. Moshayedi et al (2022). The robot must constantly know the translation and rotation matrix for autonomous navigation.…”

Section: Section V: Face Detection Methods and Robotic Applicationmentioning

confidence: 99%

“…The robotic platform has a novel method for examining various lunar images. The form identification method is assisted in finding the object and revealing the moon phases by the SAZ algorithm [47]. Using Nvidia Jetson NX and the GuartBot, S. 2019) proposed a six-degree of freedom tracking method (Figure 6 b) that attaches a face detector with a camera to the robot's wrist to get real-time information on face depth and attitude.…”

Section: P B Nithin Et Al (mentioning

confidence: 99%

See 1 more Smart Citation

The Face Detection / Recognition , Perspective and Obstacles In Robotic: A Review

Uddin

Moshayedi

et al. 2022

EAI Endorsed Trans AI Robotics

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Facial recognition research is one of the different types of research in this world today. In recent years, facial recognition in robots has attracted increased study interest. Robotic platforms now utilize a variety of object detection methods, with face detection being a viable use. Face detection in robotics is a computer technique that recognizes human faces in digital pictures and is used in a range of applications. Different authors have performed their research in different ways on the use of detection systems. This paper aims to give future researchers a better idea of using facial recognition systems in robotics. In this study, we reviewed research by various authors over recent years to facilitate future facial recognition research. In addition, scholars have addressed the topics, how they have done so, and the specifics of their approaches are described. This paper reviewed an overview of hardware implementation and software implementation by various authors. It can automatically focus cameras or count the number of people who have entered a location. Commercial applications of the method include displaying tailored advertisements in response to a recognized face along with the algorithms, functions and architectures used in facial recognition and giving the opinions of various authors mentioned. The comparative analysis of facial recognition and its architecture system is highlighted.

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Section: Section V: Face Detection Methods and Robotic Applicationmentioning

confidence: 99%

Section: P B Nithin Et Al (mentioning

confidence: 99%

The Face Detection / Recognition , Perspective and Obstacles In Robotic: A Review

Uddin

Moshayedi

et al. 2022

EAI Endorsed Trans AI Robotics

View full text Add to dashboard Cite

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“…In the identification of timber defects using artificial intelligence, machine learning is tasked with developing algorithms that learn from datasets, and improving their accuracy over time without being explicitly programmed to do so. As opposed to other algorithms, machine learning is trained to forecast types of defects based on the explored dataset by leveraging its capability to recognize patterns and features [65]- [67]. Besides, the algorithms are capable of evolving over time as more data is processed, resulting in improved decision-making and prediction accuracy.…”

Section: Machine Learning In the Identification Of Timber Defectsmentioning

confidence: 99%

A review of the automated timber defect identification approach

Chun

Hashim²,

Ahmad³

et al. 2023

IJECE

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Timber quality control is undoubtedly a very laborious process in the secondary wood industry. Manual inspections by operators are prone to human error, thereby resulting in poor timber quality inspections and low production volumes. The automation of this process using an automated vision inspection (AVI) system integrated with artificial intelligence appears to be the most plausible approach due to its ease of use and minimal operating costs. This paper provides an overview of previous works on the automated inspection of timber surface defects as well as various machine learning and deep learning approaches that have been implemented for the identification of timber defects. Contemporary algorithms and techniques used in both machine learning and deep learning are discussed and outlined in this review paper. Furthermore, the paper also highlighted the possible limitation of employing both approaches in the identification of the timber defect along with several future directions that may be further explored.

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“…These satellites and telescopes process data using various features and imaging techniques, not only to capture images but also to identify these exoplanets 33 , 34 . In their paper, Moshayedi et al 35 have presented a prototype system that utilizes machine learning models to detect the various phases of the moon. The primary focus of their research is centered on analyzing a collection of moon images to determine the lunar phase that corresponds to each image.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting new crescent moon visibility applying machine learning algorithms

Al-Rajab

Loucif

Risheh

2023

Sci Rep

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The world's population is projected to grow 32% in the coming years, and the number of Muslims is expected to grow by 70%—from 1.8 billion in 2015 to about 3 billion in 2060. Hijri is the Islamic calendar, also known as the lunar Hijri calendar, which consists of 12 lunar months, and it is tied to the Moon phases where a new crescent Moon marks the beginning of each month. Muslims use the Hijri calendar to determine important dates and religious events such as Ramadan, Haj, Muharram, etc. Till today, there is no consensus on deciding on the beginning of Ramadan month within the Muslim community. This is mainly due to the imprecise observations of the new crescent Moon in different locations. Artificial intelligence and its sub-field machine learning have shown great success in their application in several fields. In this paper, we propose the use of machine learning algorithms to help in determining the start of Ramadan month by predicting the visibility of the new crescent Moon. The results obtained from our experiments have shown very good accurate prediction and evaluation performance. The Random Forest and Support Vector Machine classifiers have provided promising results compared to other classifiers considered in this study in predicting the visibility of the new Moon.

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Sunfa Ata Zuyan machine learning models for moon phase detection: algorithm, prototype and performance comparison

Cited by 13 publications

References 35 publications

The Face Detection / Recognition , Perspective and Obstacles In Robotic: A Review

The Face Detection / Recognition , Perspective and Obstacles In Robotic: A Review

A review of the automated timber defect identification approach

Predicting new crescent moon visibility applying machine learning algorithms

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