# Anomaly Detection Techniques and Applications

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/anomaly-detection-techniques-and-applications/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on the detection of anomalies in high-dimensional data, particularly in the context of video analysis, surveillance, and time series data. It covers a wide range of techniques including unsupervised learning, outlier detection, deep learning, and novelty detection for identifying abnormal patterns and events. |
| Domain | Physical Sciences |
| Field | Computer Science |
| OpenAlex ID | t11512 |
| Works | 286 |

## Topic papers all

Showing 15 of 286.

- [Large-Scale Video Classification with Convolutional Neural Networks](https://scholariq.org/papers/large-scale-video-classification-with-convolutional-neural-networks/)
- [Deep learning for time series classification: a review](https://scholariq.org/papers/deep-learning-for-time-series-classification-a-review/)
- [Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis](https://scholariq.org/papers/learning-hierarchical-invariant-spatio-temporal-features-for-action-recognition/)
- [Anomaly Detection via Reverse Distillation from One-Class Embedding](https://scholariq.org/papers/anomaly-detection-via-reverse-distillation-from-one-class-embedding/)
- [Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks](https://scholariq.org/papers/classification-of-covid-19-patients-from-chest-ct-images-using-multi-objective/)
- [A survey on machine learning for data fusion](https://scholariq.org/papers/a-survey-on-machine-learning-for-data-fusion/)
- [End-to-End Learning of Action Detection from Frame Glimpses in Videos](https://scholariq.org/papers/end-to-end-learning-of-action-detection-from-frame-glimpses-in-videos/)
- [Understanding adversarial attacks on deep learning based medical image analysis systems](https://scholariq.org/papers/understanding-adversarial-attacks-on-deep-learning-based-medical-image-analysis/)
- [Data Mining for the Internet of Things: Literature Review and Challenges](https://scholariq.org/papers/data-mining-for-the-internet-of-things-literature-review-and-challenges/)
- [Multi-task Learning for Detecting and Segmenting Manipulated Facial Images and Videos](https://scholariq.org/papers/multi-task-learning-for-detecting-and-segmenting-manipulated-facial-images-and/)
- [Vehicle classification in distributed sensor networks](https://scholariq.org/papers/vehicle-classification-in-distributed-sensor-networks/)
- [A deep hybrid learning model to detect unsafe behavior: Integrating convolution neural networks and long short-term memory](https://scholariq.org/papers/a-deep-hybrid-learning-model-to-detect-unsafe-behavior-integrating-convolution/)
- [An effective feature engineering for DNN using hybrid PCA-GWO for intrusion detection in IoMT architecture](https://scholariq.org/papers/an-effective-feature-engineering-for-dnn-using-hybrid-pca-gwo-for-intrusion/)
- [Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks](https://scholariq.org/papers/reflection-backdoor-a-natural-backdoor-attack-on-deep-neural-networks/)
- [Recent advances in convolutional neural network acceleration](https://scholariq.org/papers/recent-advances-in-convolutional-neural-network-acceleration/)

## Topic primary papers

Showing 15 of 81.

- [Anomaly Detection via Reverse Distillation from One-Class Embedding](https://scholariq.org/papers/anomaly-detection-via-reverse-distillation-from-one-class-embedding/)
- [A survey on machine learning for data fusion](https://scholariq.org/papers/a-survey-on-machine-learning-for-data-fusion/)
- [Recent advances in convolutional neural network acceleration](https://scholariq.org/papers/recent-advances-in-convolutional-neural-network-acceleration/)
- [A Review on Machine Learning Styles in Computer Vision—Techniques and Future Directions](https://scholariq.org/papers/a-review-on-machine-learning-styles-in-computer-vision-techniques-and-future/)
- [An intelligent emergency response system: preliminary development and testing of automated fall detection](https://scholariq.org/papers/an-intelligent-emergency-response-system-preliminary-development-and-testing-of/)
- [Video anomaly detection with spatio-temporal dissociation](https://scholariq.org/papers/video-anomaly-detection-with-spatio-temporal-dissociation/)
- [Robust Unsupervised Video Anomaly Detection by Multipath Frame Prediction](https://scholariq.org/papers/robust-unsupervised-video-anomaly-detection-by-multipath-frame-prediction/)
- [Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detection](https://scholariq.org/papers/divide-and-assemble-learning-block-wise-memory-for-unsupervised-anomaly/)
- [Forecasting Interactive Dynamics of Pedestrians with Fictitious Play](https://scholariq.org/papers/forecasting-interactive-dynamics-of-pedestrians-with-fictitious-play/)
- [An Incremental Learning Framework for Human-Like Redundancy Optimization of Anthropomorphic Manipulators](https://scholariq.org/papers/an-incremental-learning-framework-for-human-like-redundancy-optimization-of/)
- [Tracing the evolution of AI in the past decade and forecasting the emerging trends](https://scholariq.org/papers/tracing-the-evolution-of-ai-in-the-past-decade-and-forecasting-the-emerging/)
- [Adversarial Attacks on Deep Neural Networks for Time Series Classification](https://scholariq.org/papers/adversarial-attacks-on-deep-neural-networks-for-time-series-classification/)
- [Temporal Logics for Learning and Detection of Anomalous Behavior](https://scholariq.org/papers/temporal-logics-for-learning-and-detection-of-anomalous-behavior/)
- [Anomaly detection in cyber-physical systems: A formal methods approach](https://scholariq.org/papers/anomaly-detection-in-cyber-physical-systems-a-formal-methods-approach/)
- [Recent trends in crowd analysis: A review](https://scholariq.org/papers/recent-trends-in-crowd-analysis-a-review/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.