The automatic detection of surface-level irregularities—defects or anomalies—in 3D data is of significant interest for various real-world purposes, such as industrial quality inspection, ...

Step 1 I got a students dataset from Kaggle and imported it into Jupyter. Then I verified the data by checking .shape, .info(), and .head() to confirm rows, columns, and sample records. Step 2 I ...

Abstract: Outlier detection is an effective technique for identifying abnormal samples in complex data. Random walks effectively detect outliers by analyzing graph transition patterns. However, ...

Earnings announcements are one of the few scheduled events that consistently move markets. Prices react not just to the reported numbers, but to how those numbers compare with expectations. A small ...

Abstract: Most existing outlier detection methods rely on a single and fine-grained data representation, making them vulnerable to noise and inefficient in capturing local anomalies. Granular-ball ...

IC manufacturers are increasingly relying on intelligent data processing to prevent downtime, improve yields, and reduce scrap. They are integrating that with fault detection and classification (FDC) ...

They look, move and even smell like the kind of furry Everglades marsh rabbit a Burmese python would love to eat. But these bunnies are robots meant to lure the giant invasive snakes out of their ...

ABSTRACT: In recent decades, the impact of climate change on natural resources has increased. However, the main challenges associated with the collection of meteorological data include the presence of ...

ABSTRACT: In recent decades, the impact of climate change on natural resources has increased. However, the main challenges associated with the collection of meteorological data include the presence of ...

This repository contains a Python-based automated outlier detection framework. It is designed to process datasets containing numerical time-series data and detect anomalies using various statistical ...