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Physical Sciences and Mathematics Commons™
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- Big data (1)
- Black Widow Optimization algorithm (1)
- Change detection (1)
- Computer vision (1)
- Convolution neural networks (1)
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- Credit Default Swap (1)
- Data mining (1)
- Feature selection (1)
- Hawkes processes (1)
- Hopfield Neural Network (1)
- Landscape similarity (1)
- Machine learning (1)
- Metaheuristic algorithms (1)
- Pattern comparison (1)
- Point process (1)
- Remote sensing imagery (1)
- Self-exciting process (1)
- Simulation and prediction (1)
- Terror attacks (1)
- Transformer Neural Network (1)
Articles 1 - 4 of 4
Full-Text Articles in Physical Sciences and Mathematics
Change Detection And Landscape Similarity Comparison Using Computer Vision Methods, Karim Malik Wilfrid Laurier University
Change Detection And Landscape Similarity Comparison Using Computer Vision Methods, Karim Malik Wilfrid Laurier University
Theses and Dissertations (Comprehensive)
Human-induced disturbances of terrestrial and aquatic ecosystems continue at alarming rates. With the advent of both raw sensor and analysis-ready datasets, the need to monitor ecosystem disturbances is now more imperative than ever; yet the task is becoming increasingly complex with increasing sources and varieties of earth observation data. In this research, computer vision methods and tools are interrogated to understand their capability for comparing spatial patterns. A critical survey of literature provides evidence that computer vision methods are relatively robust to scale and highlights issues involved in parameterization of computer vision models for characterizing significant pattern information in a …
Self-Exciting Point Process For Modelling Terror Attack Data, Siyi Wang
Self-Exciting Point Process For Modelling Terror Attack Data, Siyi Wang
Theses and Dissertations (Comprehensive)
Terrorism becomes more rampant in recent years because of separatism and extreme nationalism, which brings a serious threat to the national security of many countries in the world. The analysis of spatial and temporal patterns of terror data is significant in containing terrorism. This thesis focuses on building and applying a temporal point process called self-exciting point process to fit the terror data from 1970 to 2018 of 10 countries. The data come from the Global Terrorism database. Further, an application in predicting the number of terror events based on the self-exciting model is another main innovative idea, in which …
Binary Black Widow Optimization Algorithm For Feature Selection Problems, Ahmed Al-Saedi
Binary Black Widow Optimization Algorithm For Feature Selection Problems, Ahmed Al-Saedi
Theses and Dissertations (Comprehensive)
This thesis addresses feature selection (FS) problems, which is a primary stage in data mining. FS is a significant pre-processing stage to enhance the performance of the process with regards to computation cost and accuracy to offer a better comprehension of stored data by removing the unnecessary and irrelevant features from the basic dataset. However, because of the size of the problem, FS is known to be very challenging and has been classified as an NP-hard problem. Traditional methods can only be used to solve small problems. Therefore, metaheuristic algorithms (MAs) are becoming powerful methods for addressing the FS problems. …
Modeling Multivariate Hopfield-Transformer Hawkes Process: Application To Sovereign Credit Default Swaps, Mohsen Bahremani
Modeling Multivariate Hopfield-Transformer Hawkes Process: Application To Sovereign Credit Default Swaps, Mohsen Bahremani
Theses and Dissertations (Comprehensive)
Hawkes process was evolved so that the past events contribute to the occurrence time of future events by self-exciting or mutually exciting. However, many real-world data do not follow the Hawkes process's assumptions (i.e., positivity, additivity, and exponential decay) and become more complex to be modeled by the traditional Hawkes processes, so the neural Hawkes process was developed to tackle the challenges. However, Recurrent Neural Networks (RNN) fail to capture long-term dependencies among multiple point processes, and Transformer Hawkes processes only address temporal characteristics of Hawkes processes. In this thesis, we proposed a combination of neural networks and Hawkes processes …