Master of Computing and Information Systems, Youngstown State University, 2024, Department of Computer Science and Information Systems

The field of coding education is rapidly evolving, with emerging technologies playing a pivotal role in transforming traditional learning methodologies. This thesis introduces Dr. Lego, an innovative framework designed to revolutionize the assessment and understanding of block-based coding through the integration of sophisticated deep learning models. Dr. Lego combines cutting-edge technologies such as MobileNetV3 (Howard, 2019), for visual recognition and BERT (Devlin et al., 2018), and XLNet (Yang et al., 2019) for natural language processing to offer a comprehensive approach to evaluating coding proficiency. The research methodology involves the meticulous curation of a diverse dataset comprising projects from the LEGO SPIKE app (LEGO Education, 2022), ensuring that the models are subjected to a broad range of coding scenarios. Leveraging the dynamic educational environment provided by the LEGO SPIKE app (LEGO Education, 2022), Dr. Lego empowers users to design and implement various coding projects, fostering hands-on learning experiences. This thesis delves into methodologies aimed at enhancing coding education by exploring model integration, data generation, and fine-tuning of pre-trained models. Dr. Lego not only evaluates coding proficiency but also provides cohesive and insightful feedback, enhancing the learning experience for users. The adaptability of the framework highlights its potential to shape the future of coding education, paving the way for a new era of interactive and engaging learning experiences.

Committee: Abdu Arslanyilmaz PhD (Advisor); Feng Yu PhD (Committee Member); Carrie Jackson EdD, BCBA (Committee Member) Subjects: Computer Science; Engineering; Information Systems; Robotics; Teaching

Master of Science in Electrical Engineering, University of Dayton, 2024, Electrical Engineering

Remote sensing techniques are capable of capturing large scenes of data over several sensing domains. Hyperspectral imagery (HSI), often accompanied with lIDAR and orthoimagery sensors during collection, can provide deeper contextual information for a wide range of applications in many different fields. Complex characteristics across spectral bands in addition to high-dimensionality of HSI data present challenges to accurate classification. Generally, dimensionality reduction of the input hyperspectral data cube is performed through multi-phase analytical algorithms as a pre-processing step before further analysis to include machine learning networks. These networks commonly rely on labeled training data for segmentation. Annotating ground truth aerial data can prove to be a cumbersome endeavor that may require specific expertise for accurate assessment. This inspires exploratory research for useful unsupervised feature-learning approaches that can withdraw essential information from HSI data to map scenes without labeled data thereby providing a start-to-finish scene segmentation process.

Committee: Vijayan Asari (Committee Chair); Theus Aspiras (Advisor); Brad Ratliff (Advisor) Subjects: Electrical Engineering; Engineering; Environmental Geology; Environmental Science; Environmental Studies; Geology; Geophysics; Remote Sensing; Urban Planning

Master of Computer Science (M.C.S.), University of Dayton, 2021, Computer Science

The human eye perceives up to 80% of all the impressions and acts as the best shield from threat. While it is believed and accepted that vision is a predominant sense in people, as per the World Health Organization, around 40 million individuals on the planet are blind, and 250 million have some type of visual disability. As a result, a lot of research and papers are being suggested to create accurate and efficient navigation models utilizing computer vision and deep learning approaches. These models should be fast and efficient, and they should be able to run on low-power mobile devices to provide real-time outdoor assistance. Our objective is to extract and categorize the information from the live stream and provide audio feedback to the user within the University campus. The classification of the objects in the stream is done by a CNN model and sent as an input for the voice feedback, which is divided into several frames using the OpenCV library and converted to audio information for the user in the real-time environment using the Google text to speech module. The results generated by the CNN model for image classification have an accuracy of over 95 percent, and real-time audio conversion is a rapid transition technique, resulting in an algorithm that performs competing with other prior state-of-art methods. We also want to integrate the application in smartphones, into our mobile app to provide a more user-friendly experience for the end-users.

Committee: Dr. Mehdi R. Zargham (Advisor) Subjects: Computer Science

Master of Science (M.S.), University of Dayton, 2019, Electrical Engineering

We propose a method that is designed to detect manipulations in images based on the phenomenology of color. Segmented regions of the image are converted to chromaticity coordinates and compared to the white point, D65. If an image had been manipulated, the chromaticity coordinates will have a shifted white point relative to D65, the accepted average white point. We classify the image forgery using a convolutional neural network using a histogram of relevant statistics that indicate the white point shift. We verify this using a real world data set to demonstrate its effectiveness.

Committee: Keigo Hirakawa (Advisor); Vijayan Asari (Committee Member); Temesgen Kebede (Committee Member) Subjects: Electrical Engineering; Engineering

Master of Science (M.S.), University of Dayton, 2017, Electrical Engineering

Convolutional neural networks (CNNs) lack ample methods to improve performance without either adding more input data, modifying existing data, or changing network design. This work seeks to add to the methods available that do not require more data or a trial and error approach to network design. This thesis seeks to demonstrate that a polynomial layer inserted into a CNN, compared to all other factors being equal has great potential to improve classification rates. There are some methods that seek to help fill the gap that this research also investigates an alternative solution. Most other methods in the similar problem space look at ways to improve performance of existing layers, such as modifying the type of pooling or activation functions. Also, methods discussed later, Dropout and DropConnect zero out nodes or connections, respectively, seeking to improve performance. This research focused on adding a new type of layer to typical CNNs, the polynomial layer. This layer adds a local connectivity to each of the perceptrons creating N connections up to the Nth power of the initial value of the perceptron. This is done in either the convolutional portion or the fully connected portion, with the idea that the higher dimensionality allows for better description of the input space. This idea was tested on two datasets, MNIST and CIFAR10, both classification databases with 10 classes. These datasets contain 28×28 grayscale and 32×32 RGB images, respectively. It was determined that the polynomial layer universally enabled the tested CNN to perform better on the MNIST data and the convolutional layer polynomials aid CNNs that are trained at a lower learning rate on the CIFAR10 dataset. Looking forward, more CNN designs should be analyzed, along with more learning rates, including ones with a variable rate. Additionally, performing tests on a wider range of datasets would also enable a broader understanding.

Committee: Vijayan Asari Ph.D. (Advisor); Theus Aspiras PH.D. (Committee Member); Eric Balster Ph.D. (Committee Member) Subjects: Electrical Engineering

Master of Science in Computer Engineering, University of Dayton, 2017, Electrical and Computer Engineering

This thesis proposes the use of a genetic algorithm (GA) to optimize the accuracy of a convolutional neural network (CNN). The GA modifies the structure of the CNN such as the number of convolutional filters, strides, kernel size, nodes, learning parameters, etc. Each modification of the network is trained and evaluated. Mutation of evolved networks create more successful networks over multiple generations. The final evolved network is 4.77% more accurate than a network pro- posed in the previous literature. Additionally, the evolved network is 13.4% less computationally complex.

Committee: Eric Balster (Advisor); Tarek Taha (Committee Member); Frank Scarpino (Committee Member) Subjects: Artificial Intelligence; Computer Engineering; Computer Science