Matching Items (20)
Filtering by
- All Subjects: deep learning
- Creators: Computer Science and Engineering Program
Description
This paper presents work that was done to create a system capable of facial expression recognition (FER) using deep convolutional neural networks (CNNs) and test multiple configurations and methods. CNNs are able to extract powerful information about an image using multiple layers of generic feature detectors. The extracted information can be used to understand the image better through recognizing different features present within the image. Deep CNNs, however, require training sets that can be larger than a million pictures in order to fine tune their feature detectors. For the case of facial expression datasets, none of these large datasets are available. Due to this limited availability of data required to train a new CNN, the idea of using naïve domain adaptation is explored. Instead of creating and using a new CNN trained specifically to extract features related to FER, a previously trained CNN originally trained for another computer vision task is used. Work for this research involved creating a system that can run a CNN, can extract feature vectors from the CNN, and can classify these extracted features. Once this system was built, different aspects of the system were tested and tuned. These aspects include the pre-trained CNN that was used, the layer from which features were extracted, normalization used on input images, and training data for the classifier. Once properly tuned, the created system returned results more accurate than previous attempts on facial expression recognition. Based on these positive results, naïve domain adaptation is shown to successfully leverage advantages of deep CNNs for facial expression recognition.
ContributorsEusebio, Jose Miguel Ang (Author) / Panchanathan, Sethuraman (Thesis director) / McDaniel, Troy (Committee member) / Venkateswara, Hemanth (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Despite the advancement of online tools for activities related to the core experience of taking classes on a college campus, there has been a relatively small amount of research into implementing online tools for ancillary academic resources (e.g. tutoring centers, review sessions, etc.). Previous work and a study conducted for this paper indicates that there is value in creating these online tools but that there is value in maintaining an in-person component to these services. Based on this, a system which provides personalized, easily-accessible, simple access to these services is proposed. Designs for user-centered online-tools that provides access to and interaction with tutoring centers and review sessions are described and prototypes are developed to demonstrate the application of design principles for online tools for academic services.
ContributorsBerk, Nicholas Robert (Author) / Balasooriya, Janaka (Thesis director) / Eaton, John (Committee member) / Walker, Erin (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2013-12
Description
As one of the first attempts to research multimedia platforms for older adults when learning an online photo-editing software, this study examined whether an audio only, a text only, or a combination of an audio and text tutorial would be the most effective teaching method. Elderly adults aged 65 and older (N-45) were randomly assigned to one of the three conditions. They first went through a training phase that utilized their assigned condition to teach five tasks within the photo-editing program, and they were then tested on how well they learned these tasks as well as a transfer task. It was predicted that the multimedia condition would increase learning efficiency, produce more successes in the transfer task, and decrease cognitive load compared to the two unimodal conditions. The multimedia condition (text and audio) had no significant effect on transfer task successes or decreases in cognitive load compared to the unimodal conditions (text only and audio only). The multimedia condition, however, did produce significantly less errors on Tasks 2, 4, and 5 than the unimodal conditions. This suggests that redundancy principles may play an important role when designing learning platforms for elderly users, and that age needs to be considered as an additional factor during the technological design process.
ContributorsSwieczkowski, Hannah Elizabeth (Author) / Atkinson, Robert (Thesis director) / Chavez, Helen (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Classical planning is a field of Artificial Intelligence concerned with allowing autonomous agents to make reasonable decisions in complex environments. This work investigates
the application of deep learning and planning techniques, with the aim of constructing generalized plans capable of solving multiple problem instances. We construct a Deep Neural Network that, given an abstract problem state, predicts both (i) the best action to be taken from that state and (ii) the generalized “role” of the object being manipulated. The neural network was tested on two classical planning domains: the blocks world domain and the logistic domain. Results indicate that neural networks are capable of making such
predictions with high accuracy, indicating a promising new framework for approaching generalized planning problems.
the application of deep learning and planning techniques, with the aim of constructing generalized plans capable of solving multiple problem instances. We construct a Deep Neural Network that, given an abstract problem state, predicts both (i) the best action to be taken from that state and (ii) the generalized “role” of the object being manipulated. The neural network was tested on two classical planning domains: the blocks world domain and the logistic domain. Results indicate that neural networks are capable of making such
predictions with high accuracy, indicating a promising new framework for approaching generalized planning problems.
ContributorsNakhleh, Julia Blair (Author) / Srivastava, Siddharth (Thesis director) / Fainekos, Georgios (Committee member) / Computer Science and Engineering Program (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Medical records are increasingly being recorded in the form of electronic health records (EHRs), with a significant amount of patient data recorded as unstructured natural language text. Consequently, being able to extract and utilize clinical data present within these records is an important step in furthering clinical care. One important aspect within these records is the presence of prescription information. Existing techniques for extracting prescription information — which includes medication names, dosages, frequencies, reasons for taking, and mode of administration — from unstructured text have focused on the application of rule- and classifier-based methods. While state-of-the-art systems can be effective in extracting many types of information, they require significant effort to develop hand-crafted rules and conduct effective feature engineering. This paper presents the use of a bidirectional LSTM with CRF tagging model initialized with precomputed word embeddings for extracting prescription information from sentences without requiring significant feature engineering. The experimental results, run on the i2b2 2009 dataset, achieve an F1 macro measure of 0.8562, and scores above 0.9449 on four of the six categories, indicating significant potential for this model.
ContributorsRawal, Samarth Chetan (Author) / Baral, Chitta (Thesis director) / Anwar, Saadat (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Hackathons are 24-36 hour events where participants are encouraged to learn, collaborate, and build technological inventions with leaders, companies, and peers in the tech community. Hackathons have been sweeping the nation in the recent years especially at the collegiate level; however, there is no substantial research or documentation of the actual effects of hackathons especially at the collegiate level. This makes justifying the usage of valuable time and resources to host hackathons difficult for tech companies and academic institutions. This thesis specifically examines the effects of collegiate hackathons through running a collegiate hackathon known as Desert Hacks at Arizona State University (ASU). The participants of Desert Hacks were surveyed at the start and at the end of the event to analyze the effects. The results of the survey implicate that participants have grown in base computer programming skills, inclusion in the tech community, overall confidence, and motivation for the technological field. Through these results, this study can be used to help justify the necessity of collegiate hackathons and events similar.
ContributorsLe, Peter Thuan (Author) / Atkinson, Robert (Thesis director) / Chavez-Echeagaray, Maria Elena (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
Can a skill taught in a virtual environment be utilized in the physical world? This idea is explored by creating a Virtual Reality game for the HTC Vive to teach users how to play the drums. The game focuses on developing the user's muscle memory, improving the user's ability to play music as they hear it in their head, and refining the user's sense of rhythm. Several different features were included to achieve this such as a score, different levels, a demo feature, and a metronome. The game was tested for its ability to teach and for its overall enjoyability by using a small sample group. Most participants of the sample group noted that they felt as if their sense of rhythm and drumming skill level would improve by playing the game. Through the findings of this project, it can be concluded that while it should not be considered as a complete replacement for traditional instruction, a virtual environment can be successfully used as a learning aid and practicing tool.
ContributorsDinapoli, Allison (Co-author) / Tuznik, Richard (Co-author) / Kobayashi, Yoshihiro (Thesis director) / Nelson, Brian (Committee member) / Computer Science and Engineering Program (Contributor) / School of International Letters and Cultures (Contributor) / Computing and Informatics Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
This thesis project focused on comparing different aspects of traditional in person<br/>learning and remote online learning and how these two types of learning environments impact<br/>students in the elementary grade levels, specifically Kindergarten through sixth grade. For this<br/>thesis project, I conducted podcast interviews in which I interviewed many different teachers at<br/>different elementary grade levels. These teachers all had experience at some point with both<br/>traditional in person learning and remote online learning. All of these teachers have many<br/>different levels of experience and teach in various districts across the state of Arizona. The<br/>purpose of this thesis project was to learn and understand how these two different types of<br/>learning environments impact both students and teachers. Throughout this thesis project, I have<br/>become increasingly passionate in my future teaching career. I have learned so much about<br/>myself through this process and was able to improve my communication skills through<br/>conducting these interviews as well as significantly increase my knowledge on both in person<br/>learning and remote online learning.
ContributorsMullenmeister, Megan Marie (Author) / Pfister, Mark (Thesis director) / McKee, Dianne (Committee member) / Division of Teacher Preparation (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
This project is a series of two YouTube videos that follow me learning new skills. The first is soldering, and the second is jumping a bicycle. The goal of this project is to use it to hone my cinematography skills and to inspire other beginners to try new things by highlighting my own trials and tribulations and being vulnerable.
ContributorsNicholls, Joseph Kenji (Author) / Nascimento, Eliciana (Thesis director) / Meirelles, Rodrigo (Committee member) / The Sidney Poitier New American Film School (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Breast cancer is one of the most common types of cancer worldwide. Early detection and diagnosis are crucial for improving the chances of successful treatment and survival. In this thesis, many different machine learning algorithms were evaluated and compared to predict breast cancer malignancy from diagnostic features extracted from digitized images of breast tissue samples, called fine-needle aspirates. Breast cancer diagnosis typically involves a combination of mammography, ultrasound, and biopsy. However, machine learning algorithms can assist in the detection and diagnosis of breast cancer by analyzing large amounts of data and identifying patterns that may not be discernible to the human eye. By using these algorithms, healthcare professionals can potentially detect breast cancer at an earlier stage, leading to more effective treatment and better patient outcomes. The results showed that the gradient boosting classifier performed the best, achieving an accuracy of 96% on the test set. This indicates that this algorithm can be a useful tool for healthcare professionals in the early detection and diagnosis of breast cancer, potentially leading to improved patient outcomes.
ContributorsMallya, Aatmik (Author) / De Luca, Gennaro (Thesis director) / Chen, Yinong (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor)
Created2023-05