Quality Related Anomaly Detection

Quality-related anomaly detection focuses on identifying and classifying items based on their quality attributes, using computational methods to automate a process often performed manually. Current research emphasizes the application of machine learning, particularly deep learning architectures like artificial neural networks and ensemble methods, along with feature selection techniques to improve model accuracy and efficiency across diverse domains, including plant seedling classification, literary text analysis, and wood quality assessment. These advancements offer significant potential for improving efficiency and objectivity in various industries, from agriculture and manufacturing to digital media authentication and recommender systems.

Papers

September 12, 2024

Establish seedling quality classification standard for Chrysanthemum efficiently with help of deep clustering algorithm
Yanzhi Jing, Hongguang Zhao, Shujun Yu
Deep Clustering HELP Request Plant Monitoring Quality Related Anomaly Detection

April 5, 2024

Good Books are Complex Matters: Gauging Complexity Profiles Across Diverse Categories of Perceived Literary Quality
Yuri Bizzoni, Pascale Feldkamp, Ida Marie Lassen, Mia Jacobsen, Mads Rosendahl Thomsen, Kristoffer Nielbo
Textual Feature Category Shift Book Review Linguistic Landscape Complexity Measure Quality Related Anomaly Detection

October 20, 2023

Feature Selection and Hyperparameter Fine-tuning in Artificial Neural Networks for Wood Quality Classification
Mateus Roder, Leandro Aparecido Passos, João Paulo Papa, André Luis Debiaso Rossi
Neural Network Feature Selection Hyperparameter Tuning Wood Specie Quality Related Anomaly Detection

September 4, 2023

OutRank: Speeding up AutoML-based Model Search for Large Sparse Data sets with Cardinality-aware Feature Ranking
Blaž Škrlj, Blaž Mramor
Recommendation Task Modern Recommender System End to End AutoML Feature Similarity Feature Ranking Quality Related Anomaly Detection

September 5, 2022

Ensemble of Pre-Trained Neural Networks for Segmentation and Quality Detection of Transmission Electron Microscopy Images
Arun Baskaran, Yulin Lin, Jianguo Wen, Maria K. Y. Chan
Uncertainty Quantification Segmentation Based Approach Diverse Ensemble Microscopy Image ImageNet Dataset Bayesian Model Electron Microscopy Pre Trained Neural Network Quality Related Anomaly Detection

March 3, 2022

Detecting High-Quality GAN-Generated Face Images using Neural Networks
Ehsan Nowroozi, Mauro Conti, Yassine Mekdad
Neural Network Generative Adversarial Network Face Image Synthetic Face GAN Based Quality Related Anomaly Detection

Quality Related Anomaly Detection

Papers

Establish seedling quality classification standard for Chrysanthemum efficiently with help of deep clustering algorithm

Good Books are Complex Matters: Gauging Complexity Profiles Across Diverse Categories of Perceived Literary Quality

Feature Selection and Hyperparameter Fine-tuning in Artificial Neural Networks for Wood Quality Classification

OutRank: Speeding up AutoML-based Model Search for Large Sparse Data sets with Cardinality-aware Feature Ranking

Ensemble of Pre-Trained Neural Networks for Segmentation and Quality Detection of Transmission Electron Microscopy Images

Detecting High-Quality GAN-Generated Face Images using Neural Networks