Open Access. Powered by Scholars. Published by Universities.®
Digital Communications and Networking Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- American Popular Culture (1)
- American Studies (1)
- Art Practice (1)
- Artificial Intelligence and Robotics (1)
- Arts and Humanities (1)
-
- Aviation (1)
- Aviation Safety and Security (1)
- Computational Engineering (1)
- Computer Sciences (1)
- Computer and Systems Architecture (1)
- Digital Circuits (1)
- Digital Humanities (1)
- Electrical and Computer Engineering (1)
- Film and Media Studies (1)
- Fine Arts (1)
- Food Studies (1)
- Other Computer Engineering (1)
- Other Languages, Societies, and Cultures (1)
- Photography (1)
- Physical Sciences and Mathematics (1)
- Social and Behavioral Sciences (1)
- Sociology (1)
- Sociology of Culture (1)
- Visual Studies (1)
- Institution
Articles 1 - 4 of 4
Full-Text Articles in Digital Communications and Networking
Machine Learning And Artificial Intelligence Methods For Cybersecurity Data Within The Aviation Ecosystem, Anna Baron Garcia
Machine Learning And Artificial Intelligence Methods For Cybersecurity Data Within The Aviation Ecosystem, Anna Baron Garcia
Doctoral Dissertations and Master's Theses
Aviation cybersecurity research has proven to be a complex topic due to the intricate nature of the aviation ecosystem. Over the last two decades, research has been centered on isolated modules of the entire aviation systems, and it has lacked the state-of-the-art tools (e.g. ML/AI methods) that other cybersecurity disciplines have leveraged in their fields. Security research in aviation in the last two decades has mainly focused on: (i) reverse engineering avionics and software certification; (ii) communications due to the rising new technologies of Software Defined Radios (SDRs); (iii) networking cybersecurity concerns such as the inter and intra connections of …
Deep Learning Methods For Fingerprint-Based Indoor And Outdoor Positioning, Fahad Alhomayani
Deep Learning Methods For Fingerprint-Based Indoor And Outdoor Positioning, Fahad Alhomayani
Electronic Theses and Dissertations
Outdoor positioning systems based on the Global Navigation Satellite System have several shortcomings that have deemed their use for indoor positioning impractical. Location fingerprinting, which utilizes machine learning, has emerged as a viable method and solution for indoor positioning due to its simple concept and accurate performance. In the past, shallow learning algorithms were traditionally used in location fingerprinting. Recently, the research community started utilizing deep learning methods for fingerprinting after witnessing the great success and superiority these methods have over traditional/shallow machine learning algorithms. The contribution of this dissertation is fourfold:
First, a Convolutional Neural Network (CNN)-based method for …
Recipe For Disaster, Zac Travis
Recipe For Disaster, Zac Travis
MFA Thesis Exhibit Catalogs
Today’s rapid advances in algorithmic processes are creating and generating predictions through common applications, including speech recognition, natural language (text) generation, search engine prediction, social media personalization, and product recommendations. These algorithmic processes rapidly sort through streams of computational calculations and personal digital footprints to predict, make decisions, translate, and attempt to mimic human cognitive function as closely as possible. This is known as machine learning.
The project Recipe for Disaster was developed by exploring automation in technology, specifically through the use of machine learning and recurrent neural networks. These algorithmic models feed on large amounts of data as a …
Development Of Self-Adaptive Back Propagation And Derivative Free Training Algorithms In Artificial Neural Networks, Shamsuddin Ahmed
Development Of Self-Adaptive Back Propagation And Derivative Free Training Algorithms In Artificial Neural Networks, Shamsuddin Ahmed
Theses: Doctorates and Masters
Three new iterative, dynamically self-adaptive, derivative-free and training parameter free artificial neural network (ANN) training algorithms are developed. They are defined as self-adaptive back propagation, multi-directional and restart ANN training algorithms. The descent direction in self-adaptive back propagation training is determined implicitly by a central difference approximation scheme, which chooses its step size according to the convergence behavior of the error function. This approach trains an ANN when the gradient information of the corresponding error function is not readily available. The self- adaptive variable learning rates per epoch are determined dynamically using a constrained interpolation search. As a result, appropriate …