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Your search keyword '"Zulkipli, Nur Syahirah"' showing total 5 results
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5 results on '"Zulkipli, Nur Syahirah"'

1. The multiple outliers detection for circular univariate data using different agglomerative clustering algorithms.

Author

Zulkipli, Nur Syahirah, Satari, Siti Zanariah, and Yusoff, Wan Nur Syahidah Wan

Subjects
*OUTLIER detection, *ALGORITHMS, *RESEARCH personnel, *SAMPLE size (Statistics)
Abstract

Circular data or also called angular data has been piqued the interest of researchers to explore and extend the procedure of outlier detection since many decades ago. Outliers are the set of observations that are significantly deviated or dissimilar from the rest of the dataset. In univariate circular data, the presence of outliers is acclaimed will affect the parameter estimates and inferences. This study proposes the procedure of detecting multiple outliers, particularly for univariate circular data based on agglomerative clustering algorithms. Three circular similarity measures are used to obtain the dendrogram from three agglomerative clustering algorithms. The outliers are detected by cutting the dendrogram at a specific height using the stopping rule and classifying the observations that exceed the stopping rule as potential outliers. The simulation studies that consider various data conditions with a certain level of contamination are conducted. Then, the results performance of the agglomerative clustering algorithms were compared and the best method for certain data conditions is chosen. It has been found that SL-Chang, CL-Chang and AL-Chang algorithms work the best in detecting outliers with low masking and swamping effect when sample size is small for any percentage of outliers and concentration parameter. Meanwhile, SL-Satari/Di, CL-Satari/Di, and AL-Satari/Di algorithms are recommended to be used for large sample sizes since these algorithms perform very well in detecting the outliers and have low masking and swamping effect at any percentage of outliers and concentration parameter. The proposed procedures are successfully applied in real data using a historical dataset in this study. [ABSTRACT FROM AUTHOR]

Published
2024
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2. Cranial Morphology Associated With Syndromic Craniosynostosis: A Potential Detection of Abnormality in Patient's Cranial Growth Using Angular Statistics.

Author

Zulkipli, Nur Syahirah, Satari, Siti Zanariah, Hariri, Firdaus, Abdullah, Norli Anida, Wan Yusoff, Wan Nur Syahidah, and Hussin, Abdul Ghapor

Subjects
SKULL radiography, CHILD development deviations -- Risk factors, SKULL, APERT syndrome, ACADEMIC medical centers, CONFIDENCE intervals, CRANIOFACIAL dysostosis, RISK assessment, RESEARCH funding, DESCRIPTIVE statistics, COMPUTED tomography, DATA analysis software, CHILD development deviations, DISEASE complications
Abstract

Introduction: Apert, Crouzon, and Pfeiffer syndromes are common genetic syndromes related to syndromic craniosynostosis (SC), whereby it is a congenital defect that occurs when the cranial growth is distorted. Identifying cranial angles associated with these 3 syndromes may assist the surgical team to focus on a specific cranial part during the intervention planning, thus optimizing surgical outcomes and reducing potential morbidity. Objective: The aim of this study is to identify the cranial angles, which are associated with Apert, Crouzon, and Pfeiffer syndromes. Methods: The cranial computed tomography scan images of 17 patients with SC and 22 control groups aged 0 to 12 years who were treated in the University Malaya Medical Centre were obtained, while 12 angular measurements were attained using the Mimics software. The angular data were then divided into 2 groups (patients aged 0 to 24 months and >24 months). This work proposes a 95% confidence interval (CI) for angular mean to detect the abnormality in patient's cranial growth for the SC syndromes. Results: The 95% CI of angular mean for the control group was calculated and used as an indicator to confirm the abnormality in patient's cranial growth that is associated with the 3 syndromes. The results showed that there are different cranial angles associated with these 3 syndromes. Conclusions: All cranial angles of the patients with these syndromes lie outside the 95% CI of angular mean of control group, indicating the reliability of the proposed CI in the identification of abnormality in the patient's cranial growth. [ABSTRACT FROM AUTHOR]

Published
2023
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5. Cranial Morphology Associated With Syndromic Craniosynostosis: A Potential Detection of Abnormality in Patient's Cranial Growth Using Angular Statistics.

Author

Zulkipli NS, Satari SZ, Hariri F, Abdullah NA, Wan Yusoff WNS, and Hussin AG

Subjects
Humans, Reproducibility of Results, Skull diagnostic imaging, Syndrome, Craniosynostoses surgery, Acrocephalosyndactylia diagnostic imaging, Acrocephalosyndactylia genetics, Craniofacial Dysostosis diagnostic imaging
Abstract

Introduction: Apert, Crouzon, and Pfeiffer syndromes are common genetic syndromes related to syndromic craniosynostosis (SC), whereby it is a congenital defect that occurs when the cranial growth is distorted. Identifying cranial angles associated with these 3 syndromes may assist the surgical team to focus on a specific cranial part during the intervention planning, thus optimizing surgical outcomes and reducing potential morbidity., Objective: The aim of this study is to identify the cranial angles, which are associated with Apert, Crouzon, and Pfeiffer syndromes., Methods: The cranial computed tomography scan images of 17 patients with SC and 22 control groups aged 0 to 12 years who were treated in the University Malaya Medical Centre were obtained, while 12 angular measurements were attained using the Mimics software. The angular data were then divided into 2 groups (patients aged 0 to 24 months and >24 months). This work proposes a 95% confidence interval (CI) for angular mean to detect the abnormality in patient's cranial growth for the SC syndromes., Results: The 95% CI of angular mean for the control group was calculated and used as an indicator to confirm the abnormality in patient's cranial growth that is associated with the 3 syndromes. The results showed that there are different cranial angles associated with these 3 syndromes., Conclusions: All cranial angles of the patients with these syndromes lie outside the 95% CI of angular mean of control group, indicating the reliability of the proposed CI in the identification of abnormality in the patient's cranial growth., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Published
2023
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