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7 articles for “outlier detection methods”
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An Alternative Robust Measure of Outlier Detection in Univariate Data Sets
Abstract: A univariate outlier is a data point that consists of an extreme value on one variable. Here we applied two types of outlier detection methods: one is graphical and another is analytical. This paper presents graphical methods for different statistical outlier detection such as scatter diagram, box plot and normal probability plot. Furthermore, the quantitative methods for outlier detection in this paper are the IQR method, SD method, Z-score method, …
Published in Research & Reviews : Journal of Statistics · Vol. 8, Issue 1, 2019 · pp. 1–11 Read article
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Data Mining Methods for Terrorist Activity
Abstract: Identification of outliers is an important component of data mining since such observations can have a profound influence and distort the analysis. In the recent years, detection of outlier has been broadly applied in the field of data mining. It is an important method for many research applications such as credit card fraud, computer intrusion detection, and criminal activities in electronic commerce, medical diagnosis and anti-terrorism, image processing. Detection of …
Published in Research & Reviews : Journal of Statistics · Vol. 8, Issue 1, 2019 · pp. 38–43 Read article
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Data Mining Methods for Terrorist Activity
Abstract: ABSTRACTIdentification of outliers is an important component of data mining since such observations can have a profound influence and distort the analysis. In the recent years, detection of outlier has been broadly applied in the field of data mining. It is an important method for many research applications such as credit card fraud, computer intrusion detection, and criminal activities in electronic commerce, medical diagnosis and anti-terrorism, image processing, etc. Detection …
Published in Journal Of Network security · Vol. 5, Issue 3, 2017 · pp. 17–22 Read article
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Principal Component Analysis for Outlier Detection
Abstract: Principal Components Analysis (PCA) is one of the well-known and frequently used multivariate exploratory data analyses. PCA is concerned with analyzing and understanding data in high dimensions, that is to say, PCA method analyzes data sets that represent observations which are identified by several dependent variables that are inter-correlated. Multidimensional scaling (MDS) is a technique that makes a map displaying the comparative attitudes of a number of objects or cases, …
Published in Research & Reviews : Journal of Statistics · Vol. 7, Issue 1, 2018 · pp. 62s–68s Read article
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Optical Image Sensing and Analysis of Iron Ore Pellets: A Machine Learning Approach
Abstract: The present work is aimed to improve quality control in steel production using SEM imaging and machine learning. High-resolution SEM images of iron ore pellets, primarily composed of hematite and magnetite, are analyzed to understand their microstructural features, which significantly impact pellet performance during reduction processes. Traditional microstructure analysis is manual, time- consuming, and prone to inconsistencies. This study proposes an automated approach using K-Means Clustering, Canny Edge Detection, DBSCAN, …
Published in Journal of Instrumentation Technology & Innovations · Vol. 15, Issue 3, 2025 · pp. 7–18 Read article
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Classification of Mammogram Images by Machine Learning Algorithm Attacker Nodes
Abstract: Abstract— Breast cancer is basic in ladies' these days. In beginning when cells in the breast start to develop out of control. These cells generally structure a tumor that will frequently be seen on a x-beam or felt as a lump. Cells in almost any piece of the body can move toward becoming malignant growth and can spread to different regions of the body. There are very nearly 6 phases …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 6, Issue 2, 2019 · pp. 1–10 Read article
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Improving Dataset Integrity Through Automated Data Cleaning Techniques
Abstract: High-quality data is a fundamental requirement in data science for producing trustworthy analytical insights and effective machine learning models. Problems, including incomplete records, inconsistent entries, duplicate observations, and anomalous values, can severely reduce the accuracy and robustness of predictive systems. As modern datasets continue to expand in both volume and structural complexity, relying on manual data cleaning methods become time-consuming and error-prone, highlighting the growing importance of automated data preprocessing …
Published in International Journal of Data Structure Studies · Vol. 4, Issue 1, 2026 · pp. 40–45 Read article