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 (MS), Wright State University, 2023, Computer Science

The Industrial Internet of Things (IIoT) refers to a set of smart devices, i.e., actuators, detectors, smart sensors, and autonomous systems connected throughout the Internet to help achieve the purpose of various industrial applications. Unfortunately, IIoT applications are increasingly integrated into insecure physical environments leading to greater exposure to new cyber and physical system attacks. In the current IIoT security realm, effective anomaly detection is crucial for ensuring the integrity and reliability of critical infrastructure. Traditional security solutions may not apply to IIoT due to new dimensions, including extreme energy constraints in IIoT devices. Deep learning (DL) techniques like Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM) have been the focus of recent research to increase the precision and effectiveness of anomaly identification. This Thesis initially investigates a unique hybrid DL-enabled approach that provide the needed security analysis before successful attacks are launched against IIoT infrastructure. For that, different hybrid models are developed, trained, tested, and validated using Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), Short-Term Memory (LSTM), Autoencoder, and XGBoost algorithms. Experimental results show that the proposed XGBoost ML model exhibits the highest performance, as compared to other models, and excels across multiple metrics, including recall, precision, F1-score, and false alarm rate (FAR). The results also show that hybrid CNN+GRU model is closely behind, which exhibited strong performance in accurately detecting anomalies in encrypted IoT traffic. Specifically, Our experimental results show that the developed hybrid CNN+GRU model outperforms the others, achieving an accuracy of 94.94%, a recall of 92.29%, a precision of 98.49%, an F1 score of 95.24%, and a low false alarm rate of 0.001. However, it is (open full item for complete abstract)

Committee: Fathi Amsaad Ph.D. (Advisor); Lingwei Chen Ph.D. (Committee Member); Michael L. Raymer Ph.D. (Committee Member); Anton Netchaev Ph.D. (Committee Member) Subjects: Computer Science

Doctor of Philosophy (PhD), Ohio University, 1994, Electrical Engineering & Computer Science (Engineering and Technology)

This dissertation is devoted to the development of small area, low power, mixed-mode circuits for hybrid implementation of neural networks. Current-mode and voltage-mode techniques were investigated to design these building blocks: mixed-mode multipliers, D/A converters, multiplying D/A converters, winner-take-all circuits. Small area and low power design issues were addressed. Designed circuit structure were modified to cut down area and power dissipation. New switchable current source, voltage buffer, and analog switch were proposed to improve the circuits' performance. An image processor was designed to show the application potential of the developed building blocks. Extensive simulations were made to verify the design. It has been shown that low power designs can be obtained by making transistors operate in the weak inversion region for current-mode circuits, and using the capacitor arrays for voltage-mode circuits. With the designed circuits, it is easy to integrate a large number of neurons in a single chip. The proposed methods and circuits make a significant contribution to fully exploiting the great computation power of artificial neural networks.

Committee: Janusz Starzyk (Advisor) Subjects: