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Articles 1 - 5 of 5
Full-Text Articles in Engineering
Statistics-Based Anomaly Detection And Correction Method For Amazon Customer Reviews, Ishani Chatterjee
Statistics-Based Anomaly Detection And Correction Method For Amazon Customer Reviews, Ishani Chatterjee
Dissertations
People nowadays use the Internet to project their assessments, impressions, ideas, and observations about various subjects or products on numerous social networking sites. These sites serve as a great source of gathering information for data analytics, sentiment analysis, natural language processing, etc. The most critical challenge is interpreting this data and capturing the sentiment behind these expressions. Sentiment analysis is analyzing, processing, concluding, and inferencing subjective texts with the views. Companies use sentiment analysis to understand public opinions, perform market research, analyze brand reputation, recognize customer experiences, and study social media influence. According to the different needs for aspect granularity, …
Unsupervised Learning For Anomaly Detection In Remote Sensing Imagery, Husam A. Alfergani
Unsupervised Learning For Anomaly Detection In Remote Sensing Imagery, Husam A. Alfergani
Theses and Dissertations
Landfill fire is a potential hazard of waste mismanagement, and could occur both on and below the surface of active and closed sites. Timely identification of temperature anomalies is critical in monitoring and detecting landfill fires, to issue warnings that can help extinguish fires at early stages. The overarching objective of this research is to demonstrate the applicability and advantages of remote sensing data, coupled with machine learning techniques, to identify landfill thermal states that can lead to fire, in the absence of onsite observations. This dissertation proposed unsupervised learning techniques, notably variational auto-encoders (VAEs), to identify temperature anomalies from …
Cybersecurity Risk Assessment Using Graph Theoretical Anomaly Detection And Machine Learning, Goksel Kucukkaya
Cybersecurity Risk Assessment Using Graph Theoretical Anomaly Detection And Machine Learning, Goksel Kucukkaya
Engineering Management & Systems Engineering Theses & Dissertations
The cyber domain is a great business enabler providing many types of enterprises new opportunities such as scaling up services, obtaining customer insights, identifying end-user profiles, sharing data, and expanding to new communities. However, the cyber domain also comes with its own set of risks. Cybersecurity risk assessment helps enterprises explore these new opportunities and, at the same time, proportionately manage the risks by establishing cyber situational awareness and identifying potential consequences. Anomaly detection is a mechanism to enable situational awareness in the cyber domain. However, anomaly detection also requires one of the most extensive sets of data and features …
Network Traffic Anomaly Detection Method For Imbalanced Data, Shuqin Dong, Bin Zhang
Network Traffic Anomaly Detection Method For Imbalanced Data, Shuqin Dong, Bin Zhang
Journal of System Simulation
Abstract: Aiming at the poor detection performances caused by the low feature extraction accuracy of rare traffic attacks from scarce samples, a network traffic anomaly detection method for imbalanced data is proposed. A traffic anomaly detection model is designed, in which the traffic features in different feature spaces are learned by alternating activation functions, architectures, corrupted rates and dropout rates of stacked denoising autoencoder (SDA), and the low accuracy in extracting features of rare traffic attacks in a single space is solved. A batch normalization algorithm is designed, and the Adam algorithm is adopted to train parameters of …
A Systematic Review Of Convolutional Neural Network-Based Structural Condition Assessment Techniques, Sandeep Sony, Kyle Dunphy, Ayan Sadhu, Miriam A M Capretz
A Systematic Review Of Convolutional Neural Network-Based Structural Condition Assessment Techniques, Sandeep Sony, Kyle Dunphy, Ayan Sadhu, Miriam A M Capretz
Electrical and Computer Engineering Publications
With recent advances in non-contact sensing technology such as cameras, unmanned aerial and ground vehicles, the structural health monitoring (SHM) community has witnessed a prominent growth in deep learning-based condition assessment techniques of structural systems. These deep learning methods rely primarily on convolutional neural networks (CNNs). The CNN networks are trained using a large number of datasets for various types of damage and anomaly detection and post-disaster reconnaissance. The trained networks are then utilized to analyze newer data to detect the type and severity of the damage, enhancing the capabilities of non-contact sensors in developing autonomous SHM systems. In recent …