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Physical Sciences and Mathematics Commons™
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Full-Text Articles in Physical Sciences and Mathematics
Machine Learning For Stock Prediction Based On Fundamental Analysis, Yuxuan Huang, Luiz Fernando Capretz, Danny Ho
Machine Learning For Stock Prediction Based On Fundamental Analysis, Yuxuan Huang, Luiz Fernando Capretz, Danny Ho
Electrical and Computer Engineering Publications
Application of machine learning for stock prediction is attracting a lot of attention in recent years. A large amount of research has been conducted in this area and multiple existing results have shown that machine learning methods could be successfully used toward stock predicting using stocks’ historical data. Most of these existing approaches have focused on short term prediction using stocks’ historical price and technical indicators. In this paper, we prepared 22 years’ worth of stock quarterly financial data and investigated three machine learning algorithms: Feed-forward Neural Network (FNN), Random Forest (RF) and Adaptive Neural Fuzzy Inference System (ANFIS) for …
Reinforcement Learning Algorithms: An Overview And Classification, Fadi Almahamid, Katarina Grolinger
Reinforcement Learning Algorithms: An Overview And Classification, Fadi Almahamid, Katarina Grolinger
Electrical and Computer Engineering Publications
The desire to make applications and machines more intelligent and the aspiration to enable their operation without human interaction have been driving innovations in neural networks, deep learning, and other machine learning techniques. Although reinforcement learning has been primarily used in video games, recent advancements and the development of diverse and powerful reinforcement algorithms have enabled the reinforcement learning community to move from playing video games to solving complex real-life problems in autonomous systems such as self-driving cars, delivery drones, and automated robotics. Understanding the environment of an application and the algorithms’ limitations plays a vital role in selecting the …
Emerging Challenges In Smart Grid Cybersecurity Enhancement: A Review, Fazel Mohammadi
Emerging Challenges In Smart Grid Cybersecurity Enhancement: A Review, Fazel Mohammadi
Electrical and Computer Engineering Publications
In this paper, a brief survey of measurable factors affecting the adoption of cybersecurity enhancement methods in the smart grid is provided. From a practical point of view, it is a key point to determine to what degree the cyber resilience of power systems can be improved using cost-effective resilience enhancement methods. Numerous attempts have been made to the vital resilience of the smart grid against cyber-attacks. The recently proposed cybersecurity methods are considered in this paper, and their accuracies, computational time, and robustness against external factors in detecting and identifying False Data Injection (FDI) attacks are evaluated. There is …
Deep Learning For High-Impedance Fault Detection: Convolutional Autoencoders, Khushwant Rai, Firouz Badrkhani Ajaei, Farnam Hojatpanah, Katarina Grolinger
Deep Learning For High-Impedance Fault Detection: Convolutional Autoencoders, Khushwant Rai, Firouz Badrkhani Ajaei, Farnam Hojatpanah, Katarina Grolinger
Electrical and Computer Engineering Publications
High-impedance faults (HIF) are difficult to detect because of their low current amplitude and highly diverse characteristics. In recent years, machine learning (ML) has been gaining popularity in HIF detection because ML techniques learn patterns from data and successfully detect HIFs. However, as these methods are based on supervised learning, they fail to reliably detect any scenario, fault or non-fault, not present in the training data. Consequently, this paper takes advantage of unsupervised learning and proposes a convolutional autoencoder framework for HIF detection (CAE-HIFD). Contrary to the conventional autoencoders that learn from normal behavior, the convolutional autoencoder (CAE) in CAE-HIFD …