Anomaly Detection Procedures in a Real World Dataset by Using Deep-Learning Approaches

2019 ◽  
pp. 303-314
Author(s):  
Alabbas Alhaj Ali ◽  
Abdul Rasheeq ◽  
Doina Logofătu ◽  
Costin Bădică
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Real World ◽  
Learning Approaches

scholarly journals Experiments on city train vibration anomaly detection Using deep learning approaches

2020 ◽  
Vol 20 (1) ◽  
pp. 329
Author(s):  
Taehee Kim ◽  
Cheolwoo Ro ◽  
Kiho Suh
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Real World ◽  
Data Science ◽  
Automated Detection ◽  
Learning Approaches ◽  
Real World Data ◽  
Vibration Data ◽  
Preliminary Research ◽  
Train Vibration

Anomaly detection is widely in demand in the field where automated detection of anomalous conditions in many observation tasks. While conventional data science approaches have shown interesting results, deep learning approaches to anomaly detection problems reveal new perspectives of possibilities especially where massive amount of data need to be handled. We develop anomaly detection applications on city train vibration data using deep learning approaches. We carried out preliminary research on anomaly detection in general and applied our real world data to existing solutions. In this paper, we provide a survey on anomaly detection and analyse our results of experiments using deep learning approaches.

Real-World Anomaly Detection Using Deep Learning

2020 ◽  
pp. 333-342
Author(s):  
Unnati Koppikar ◽  
C. Sujatha ◽  
Prakashgoud Patil ◽  
Uma Mudenagudi
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Real World

scholarly journals Road Anomaly Detection Through Deep Learning Approaches

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 117390-117404
Author(s):  
Dawei Luo ◽  
Jianbo Lu ◽  
Gang Guo
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Learning Approaches

scholarly journals Privacy-Preserving Deep Learning for the Detection of Protected Health Information in Real-World Data: Comparative Evaluation (Preprint)

2019 ◽  
Author(s):  
Sven Festag ◽  
Cord Spreckelsen
Keyword(s):  
Deep Learning ◽  
Health Information ◽  
Clinical Data ◽  
Real World ◽  
Privacy Preserving ◽  
Stochastic Gradient Descent ◽  
Data Sets ◽  
Learning Approaches ◽  
Protected Health Information ◽  
Private Data

BACKGROUND Collaborative privacy-preserving training methods allow for the integration of locally stored private data sets into machine learning approaches while ensuring confidentiality and nondisclosure. OBJECTIVE In this work we assess the performance of a state-of-the-art neural network approach for the detection of protected health information in texts trained in a collaborative privacy-preserving way. METHODS The training adopts distributed selective stochastic gradient descent (ie, it works by exchanging local learning results achieved on private data sets). Five networks were trained on separated real-world clinical data sets by using the privacy-protecting protocol. In total, the data sets contain 1304 real longitudinal patient records for 296 patients. RESULTS These networks reached a mean F1 value of 0.955. The gold standard centralized training that is based on the union of all sets and does not take data security into consideration reaches a final value of 0.962. CONCLUSIONS Using real-world clinical data, our study shows that detection of protected health information can be secured by collaborative privacy-preserving training. In general, the approach shows the feasibility of deep learning on distributed and confidential clinical data while ensuring data protection.

scholarly journals Deep Learning for COVID-19 Diagnosis from CT Images

2021 ◽  
Vol 11 (17) ◽  
pp. 8227 ◽  
Author(s):  
Andrea Loddo ◽  
Fabio Pili ◽  
Cecilia Di Ruberto
Keyword(s):  
Deep Learning ◽  
Real World ◽  
Heterogeneous Data ◽  
Learning Approaches ◽  
Reference Dataset ◽  
Heterogeneous Data Sources ◽  
Patient Status ◽  
Learning Techniques ◽  
The Cross ◽  
Public Datasets

COVID-19, an infectious coronavirus disease, caused a pandemic with countless deaths. From the outset, clinical institutes have explored computed tomography as an effective and complementary screening tool alongside the reverse transcriptase-polymerase chain reaction. Deep learning techniques have shown promising results in similar medical tasks and, hence, may provide solutions to COVID-19 based on medical images of patients. We aim to contribute to the research in this field by: (i) Comparing different architectures on a public and extended reference dataset to find the most suitable; (ii) Proposing a patient-oriented investigation of the best performing networks; and (iii) Evaluating their robustness in a real-world scenario, represented by cross-dataset experiments. We exploited ten well-known convolutional neural networks on two public datasets. The results show that, on the reference dataset, the most suitable architecture is VGG19, which (i) Achieved 98.87% accuracy in the network comparison; (ii) Obtained 95.91% accuracy on the patient status classification, even though it misclassifies some patients that other networks classify correctly; and (iii) The cross-dataset experiments exhibit the limitations of deep learning approaches in a real-world scenario with 70.15% accuracy, which need further investigation to improve the robustness. Thus, VGG19 architecture showed promising performance in the classification of COVID-19 cases. Nonetheless, this architecture enables extensive improvements based on its modification, or even with preprocessing step in addition to it. Finally, the cross-dataset experiments exposed the critical weakness of classifying images from heterogeneous data sources, compatible with a real-world scenario.

scholarly journals Health Monitoring of Air Compressors Using Reconstruction-Based Deep Learning for Anomaly Detection with Increased Transparency

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 83
Author(s):  
Magnus Gribbestad ◽  
Muhammad Umair Hassan ◽  
Ibrahim A. Hameed ◽  
Kelvin Sundli
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Health Management ◽  
Main Task ◽  
Learning Approaches ◽  
Prognostics And Health Management ◽  
Normal Behaviour ◽  
Fault Detection And Identification ◽  
Industrial Level ◽  
Air Compressors

Anomaly detection refers to detecting data points, events, or behaviour that do not comply with expected or normal behaviour. For example, a typical problem related to anomaly detection on an industrial level is having little labelled data and a few run-to-failure examples, making it challenging to develop reliable and accurate prognostics and health management systems for fault detection and identification. Certain machine learning approaches for anomaly detection require normal data to train, which reduces the need for historical data with fault labels, where the main task is to differentiate between normal and anomalous behaviour. Several reconstruction-based deep learning approaches are explored in this work and compared towards detecting anomalies in air compressors. Anomalies in such systems are not point-anomalies, but instead, an increasing deviation from the normal condition as the system components start to degrade. In this paper, a descriptive range of the deviation based on the reconstruction-based techniques is proposed. Most anomaly detection approaches are considered black box models, predicting whether an event should be considered an anomaly or not. This paper proposes a method for increasing the transparency and explainability of reconstruction-based anomaly detection to indicate which parts of a system contribute to the deviation from expected behaviour. The results show that the proposed methods detect abnormal behaviour in air compressors accurately and reliably and indicate why it deviates. The proposed approach is capable of detecting faults without the need for historical examples of similar faults. The proposed method for explainable anomaly detection is crucial to any prognostics and health management (PHM) system due to its purpose of detecting deviations and identifying causes.

scholarly journals LSGCN: Long Short-Term Traffic Prediction with Graph Convolutional Networks

2020 ◽  
Author(s):  
Rongzhou Huang ◽  
Chuyin Huang ◽  
Yubao Liu ◽  
Genan Dai ◽  
Weiyang Kong
Keyword(s):  
Deep Learning ◽  
Real World ◽  
Traffic Management ◽  
Route Planning ◽  
Traffic Prediction ◽  
Learning Approaches ◽  
Short Term ◽  
Temporal Prediction ◽  
Convolutional Networks ◽  
Short Term Prediction

Traffic prediction is a classical spatial-temporal prediction problem with many real-world applications such as intelligent route planning, dynamic traffic management, and smart location-based applications. Due to the high nonlinearity and complexity of traffic data, deep learning approaches have attracted much interest in recent years. However, few methods are satisfied with both long and short-term prediction tasks. Target at the shortcomings of existing studies, in this paper, we propose a novel deep learning framework called Long Short-term Graph Convolutional Networks (LSGCN) to tackle both traffic prediction tasks. In our framework, we propose a new graph attention network called cosAtt, and integrate both cosAtt and graph convolution networks (GCN) into a spatial gated block. By the spatial gated block and gated linear units convolution (GLU), LSGCN can efficiently capture complex spatial-temporal features and obtain stable prediction results. Experiments with three real-world traffic datasets verify the effectiveness of LSGCN.

scholarly journals Learn, detect, and grasp objects in real-world settings

2020 ◽  
Vol 137 (6) ◽  
pp. 324-330
Author(s):  
Markus Vincze ◽  
Timothy Patten ◽  
Kiru Park ◽  
Dominik Bauer
Keyword(s):  
Deep Learning ◽  
Pose Estimation ◽  
Real World ◽  
Robot Vision ◽  
Mobile Manipulator ◽  
Learning Approaches ◽  
Learning Methods ◽  
Digital Twin ◽  
The World ◽  
Physics Simulation

Abstract Experts predict that future robot applications will require safe and predictable operation: robots will need to be able to explain what they are doing to be trusted. To reach this goal, they will need to perceive their environment and its object to better understand the world and the tasks they have to perform. This article gives an overview of present advances with the focus on options to learn, detect, and grasp objects. With the approach of colour and depth (RGB-D) cameras and the advances in AI and deep learning methods, robot vision has been pushed considerably over the last years. We summarise recent results for pose estimation of objects and work on verifying object poses using a digital twin and physics simulation. The idea is that any hypothesis from an object detector and pose estimator is verified leveraging on the continuous advances in deep learning approaches to create object hypotheses. We then show that the object poses are robust enough such that a mobile manipulator can approach the object and grasp it. We intend to indicate that it is now feasible to model, recognise and grasp many objects with good performance, though further work is needed for applications in industrial settings.

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