Results 51 to 60 of about 190,177 (262)
A Comparison of Outlier Detection Techniques for High-Dimensional Data
Outlier detection is a hot topic in machine learning. With the newly emerging technologies and diverse applications, the interest of outlier detection is increasing greatly.
Xiaodan Xu +3 more
doaj +1 more source
Multi-Level Clustering-Based Outlier’s Detection (MCOD) Using Self-Organizing Maps
Outlier detection is critical in many business applications, as it recognizes unusual behaviours to prevent losses and optimize revenue. For example, illegitimate online transactions can be detected based on its pattern with outlier detection.
Menglu Li, Rasha Kashef, Ahmed Ibrahim
doaj +1 more source
CHAOS: Chart Analysis with Outlier Samples
Charts play a critical role in data analysis and visualization, yet real-world applications often present charts with challenging or noisy features. However, "outlier charts" pose a substantial challenge even for Multimodal Large Language Models (MLLMs), which can struggle to interpret perturbed charts.
Moured, O +6 more
openaire +3 more sources
Accurate and noninvasive prostate cancer detection using plasma‐derived extracellular vesicle RNA
Plasma extracellular vesicles were captured with WGA‐conjugated magnetic beads and profiled for RNA biomarkers. A three‐RNA panel (NM_024955, NR_047469, and NR_002564) distinguished prostate cancer from healthy controls and benign prostatic hyperplasia, supporting a simple, noninvasive approach to improve prostate cancer detection.
Hanping Wei, Haoran Wu, Wei Feng
wiley +1 more source
Analysis and Detection in V-Formations with Outliers
RESUMEN: Una gran variedad de patrones de movimiento puede ser identificada cuando se estudia un conjunto de entidades móviles. Uno de estos patrones se denomina formación en V, ya que su forma se asemeja a dicha letra. Informalmente, un conjunto de entidades exhibe una formación en V si está ubicado en una de sus dos líneas características.
Moreno Arboleda, Francisco Javier +2 more
openaire +5 more sources
Outliers in data envelopment analysis [PDF]
Purpose The purpose of this paper is to improve the estimation of the production frontier in cases where outliers exist. We focus on the case when outliers appear above the true frontier due to measurement error. Design/methodology/approach The authors use stochastic data envelopment analysis (SDEA) to allow observed points above the frontier.
Taylor Boyd, Grace Docken, John Ruggiero
openaire +1 more source
Evolution‐guided yeast complementation reveals functional differences in human PSPH variants
Ancient genomes can help guide which human genetic variants are tested experimentally. This study applies that idea to PSPH, a gene involved in serine biosynthesis, and uses high‐throughput yeast complementation to compare variant function. The findings reveal measurable differences among selected alleles and illustrate the value of evolution‐guided ...
Mauricio Campa‐Álvarez +6 more
wiley +1 more source
Glioblastoma cells express calcitonin receptor variants (CT receptor isoforms) that may help them survive stress. Using qPCR, transcript‐specific long‐read nanopore sequencing, immunofluorescence co‐localisation and comparative sequence analysis, this study identifies a novel alternatively spliced CALCR transcript that encodes the CTb receptor isoform ...
Pragya Gupta +7 more
wiley +1 more source
Comparative assessment of crystallographic and cryo‐EM models in the Protein Data Bank
Raw data obtained by X‐ray crystallography or cryo‐EM result in experimental maps, ultimately fitted by atomic models. Although the physical principles are different, the final results can be viewed, compared, and evaluated in the same way. With cryogenic electron microscopy (cryo‐EM) on track to surpass X‐ray crystallography as the preferred method ...
Alexander Wlodawer +7 more
wiley +1 more source
An Outlier Detection Algorithm Based on Cross-Correlation Analysis for Time Series Dataset
Outlier detection is a very essential problem in a variety of application areas. Many detection methods are deficient for high-dimensional time series data sets containing both isolated and assembled outliers.
Hui Lu +3 more
doaj +1 more source

