Digital Commons Network^™

Automated Segmentation Of Temporal Bone Structures, Daniel Allen

Electronic Thesis and Dissertation Repository

Mastoidectomy is a challenging surgical procedure that is difficult to perform and practice. As supplementation to current training techniques, surgical simulators have been developed with the ability to visualize and operate on temporal bone anatomy. Medical image segmentation is done to create three-dimensional models of anatomical structures for simulation. Manual segmentation is an accurate but time-consuming process that requires an expert to label each structure on images. An automatic method for segmentation would allow for more practical model creation. The objective of this work was to create an automated segmentation algorithm for structures of the temporal bone relevant to mastoidectomy. …

Similarity-Based Chained Transfer Learning For Energy Forecasting With Big Data, Yifang Tian, Ljubisa Sehovac, Katarina Grolinger

Electrical and Computer Engineering Publications

Smart meter popularity has resulted in the ability to collect big energy data and has created opportunities for large-scale energy forecasting. Machine Learning (ML) techniques commonly used for forecasting, such as neural networks, involve computationally intensive training typically with data from a single building or a single aggregated load to predict future consumption for that same building or aggregated load. With hundreds of thousands of meters, it becomes impractical or even infeasible to individually train a model for each meter. Consequently, this paper proposes Similarity-Based Chained Transfer Learning (SBCTL), an approach for building neural network-based models for many meters by …

Forecasting Building Energy Consumption With Deep Learning: A Sequence To Sequence Approach, Ljubisa Sehovac, Cornelius Nesen, Katarina Grolinger

Electrical and Computer Engineering Publications

Energy Consumption has been continuously increasing due to the rapid expansion of high-density cities, and growth in the industrial and commercial sectors. To reduce the negative impact on the environment and improve sustainability, it is crucial to efficiently manage energy consumption. Internet of Things (IoT) devices, including widely used smart meters, have created possibilities for energy monitoring as well as for sensor based energy forecasting. Machine learning algorithms commonly used for energy forecasting such as feedforward neural networks are not well-suited for interpreting the time dimensionality of a signal. Consequently, this paper uses Recurrent Neural Networks (RNN) to capture time …

Security Framework For The Internet Of Things Leveraging Network Telescopes And Machine Learning, Farooq Israr Ahmed Shaikh

USF Tampa Graduate Theses and Dissertations

The recent advancements in computing and sensor technologies, coupled with improvements in embedded system design methodologies, have resulted in the novel paradigm called the Internet of Things (IoT). IoT is essentially a network of small embedded devices enabled with sensing capabilities that can interact with multiple entities to relay information about their environments. This sensing information can also be stored in the cloud for further analysis, thereby reducing storage requirements on the devices themselves. The above factors, coupled with the ever increasing needs of modern society to stay connected at all times, has resulted in IoT technology penetrating all facets …

Strawberry Detection Under Various Harvestation Stages, Yavisht Fitter

Master's Theses

This paper analyzes three techniques attempting to detect strawberries at various stages in its growth cycle. Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP) and Convolutional Neural Networks (CNN) were implemented on a limited custom-built dataset. The methodologies were compared in terms of accuracy and computational efficiency. Computational efficiency is defined in terms of image resolution as testing on a smaller dimensional image is much quicker than larger dimensions. The CNN based implementation obtained the best results with an 88% accuracy at the highest level of efficiency as well (600x800). LBP generated moderate results with a 74% detection accuracy …

Learning Models For Corrupted Multi-Dimensional Data: Fundamental Limits And Algorithms, Ishan Jindal

Wayne State University Dissertations

Developing machine learning models for unstructured multi-dimensional datasets such as datasets with unreliable labels and noisy multi-dimensional signals with or without missing information have becoming a central necessity. We are not always fortunate enough to get noise-free datasets for developing classification and representation models. Though there is a number of techniques available to deal with noisy datasets, these methods do not exploit the multi-dimensional structures of the signals, which could be used to improve the overall classification and representation performance of the model.

In this thesis, we develop a Kronecker-structure (K-S) subspace model that exploits the multi-dimensional structure of the …

Deep Learning: Edge-Cloud Data Analytics For Iot, Katarina Grolinger, Ananda M. Ghosh

Electrical and Computer Engineering Publications

Sensors, wearables, mobile and other Internet of Thing (IoT) devices are becoming increasingly integrated in all aspects of our lives. They are capable of collecting massive quantities of data that are typically transmitted to the cloud for processing. However, this results in increased network traffic and latencies. Edge computing has a potential to remedy these challenges by moving computation physically closer to the network edge where data are generated. However, edge computing does not have sufficient resources for complex data analytics tasks. Consequently, this paper investigates merging cloud and edge computing for IoT data analytics and presents a deep learning-based …

Artificial Intelligence In The Assessment Of Transmission And Distribution Systems Under Natural Disasters Using Machine Learning And Deep Learning Techniques In A Knowledge Discovery Framework, Rossana Villegas

Open Access Theses & Dissertations

Warming trends and increasing temperatures have been observed and reported by federal agencies, such as the National Oceanic and Atmospheric Administration (NOAA). Extreme-weather events, especially hurricanes, tornadoes and winter storms, are among the highly devastating natural disasters responsible for massive and prolonged power outages in Electrical Transmission and Distribution Systems (ETDS). Moreover, the failure rate probability of any system component under extreme-weather tends to increase in the impacted geographic area. This Dissertation proposes an Artificial Intelligence (AI) Decision Support System that can predict damage in the ETDS and allow operators to mitigate disastrous extreme weather events. The document reports the …

The Application Of Index Based, Region Segmentation, And Deep Learning Approaches To Sensor Fusion For Vegetation Detection, David L. Stone

Theses and Dissertations

This thesis investigates the application of index based, region segmentation, and deep learning methods to the sensor fusion of omnidirectional (O-D) Infrared (IR) sensors, Kinnect sensors, and O-D vision sensors to increase the level of intelligent perception for unmanned robotic platforms. The goals of this work is first to provide a more robust calibration approach and improve the calibration of low resolution and noisy IR O-D cameras. Then our goal was to explore the best approach to sensor fusion for vegetation detection. We looked at index based, region segmentation, and deep learning methods and compared them with a goal of …

Deep Neural Network Learning-Based Classifier Design For Big-Data Analytics, Krishnan Raghavan

Doctoral Dissertations

"In this digital age, big-data sets are commonly found in the field of healthcare, manufacturing and others where sustainable analysis is necessary to create useful information. Big-data sets are often characterized by high-dimensionality and massive sample size. High dimensionality refers to the presence of unwanted dimensions in the data where challenges such as noise, spurious correlation and incidental endogeneity are observed. Massive sample size, on the other hand, introduces the problem of heterogeneity because complex and unstructured data types must analyzed. To mitigate the impact of these challenges while considering the application of classification, a two step analysis approach is …

Reinforcement Learning And Game Theory For Smart Grid Security, Shuva Paul

Electronic Theses and Dissertations

This dissertation focuses on one of the most critical and complicated challenges facing electric power transmission and distribution systems which is their vulnerability against failure and attacks. Large scale power outages in Australia (2016), Ukraine (2015), India (2013), Nigeria (2018), and the United States (2011, 2003) have demonstrated the vulnerability of power grids to cyber and physical attacks and failures. These incidents clearly indicate the necessity of extensive research efforts to protect the power system from external intrusion and to reduce the damages from post-attack effects. We analyze the vulnerability of smart power grids to cyber and physical attacks and …