Digital Commons Network^™

Improving Inference Speed Of Perception Systems In Autonomous Unmanned Ground Vehicles, Bradley Selee

All Theses

Autonomous vehicle (AV) development has become one of the largest research challenges in businesses and research institutions. While much research has been done, autonomous driving still requires extensive amounts of research due to its immense, multi-factorial difficulty. Autonomous vehicles rely on many complex systems to function, make accurate decisions, and, above all, provide maximum safety. One of the most crucial components of autonomous driving is the perception system.

The perception system allows the vehicle to identify its surroundings and make accurate, but safe, decisions through the use of computer vision techniques like object detection, image segmentation, and path planning. Due …

Combining Physics-Based Models With Deep Learning Image Synthesis And Uncertainty In Intraoperative Cone-Beam Ct Of The Brain, Xiaoxuan Zhang, Alejandro Sisniega, Wojciech B Zbijewski, Junghoon Lee, Craig K Jones, Runze Han, Prasad Vagdargi, Patrick A Helm, Mark Luciano, William S Anderson, Jeffrey H Siewerdsen

Faculty, Staff and Student Publications

BACKGROUND: Image-guided neurosurgery requires high localization and registration accuracy to enable effective treatment and avoid complications. However, accurate neuronavigation based on preoperative magnetic resonance (MR) or computed tomography (CT) images is challenged by brain deformation occurring during the surgical intervention.

PURPOSE: To facilitate intraoperative visualization of brain tissues and deformable registration with preoperative images, a 3D deep learning (DL) reconstruction framework (termed DL-Recon) was proposed for improved intraoperative cone-beam CT (CBCT) image quality.

METHODS: The DL-Recon framework combines physics-based models with deep learning CT synthesis and leverages uncertainty information to promote robustness to unseen features. A 3D generative adversarial network …

Predicting Students Performance In Online Education Through Deep Learning Model, Mohammed H. R. Khalaf, Zeinab M. Abdel Azim

Information Sciences Letters

This epidemic has prompted the development of Education 4.0, virtual learning, and the demand to adapt educational practices to meet the needs of younger demographics. A rising epidemic has necessitated the shutdown of campuses where education programs are now being carried out online in educational institutions all over the globe. The report includes a study on the effectiveness and perceptions of students toward digital learning during the pandemic. A Convolutional Neural Network (CNN) and Particle swarm optimization model, which forecasts the student’s learning rates, are used to tackle this issue. This study will categorize student performance into low, medium, and …

Automated Classification Of Pectinodon Bakkeri Teeth Images Using Machine Learning, Jacob A. Bahn

MS in Computer Science Project Reports

Microfossil dinosaur teeth are studied by paleontologists in order to better under- stand dinosaurs. Currently, tooth classification is a long, manual, error-ridden process. Deep learning offers a solution that allows for an automated way of classifying images of these microfossil teeth. In this thesis, we aimed to use deep learning in order to develop an automated approach for classifying images of Pectinodon bakkeri teeth. The proposed model was trained using a custom topology and it classified the images based on clusters created via K-Means. The model had an accuracy of 71%, a precision of 71%, a recall of 70.5%, and …

To Study Heart Rate Dynamics Of Preterm Infants Using Lstm Based Deep Learning Model, Soniya Poudel Chhetri

Electrical Engineering Theses

Babies born prematurely are more likely to experience serious medical problems called Bradycardia, where their heart rate drops below 100 beats per minute(bpm). Bradycardia can decrease cerebral blood velocities by 10-50% from their normal level [6], potentially harming the infant's developing brain and other vital organs. Healthcare professionals working in Neonatal Intensive Care Units (NICUs) face challenges evaluating the likelihood of these events occurring in preterm babies based on their physiological signals. Forecasting and tracking bradycardia events before time will help save thousands of preterm infants from losing their lives. While time series analysis of heart rate data is one …

Cancer Risk Prediction And Interpretation From Electronic Health Records, Zhao Li

Dissertations and Theses (Open Access)

Cancer as a leading cause of death is a complex disease with many different forms. Cancer risk prediction is important for identifying high-risk individuals in early stages and enabling targeted screening and interventions to reduce mortality rates and improve clinical outcomes. Electronic health records (EHR) provide a vast amount of data that can be used for predictive model development, allowing for the detection of subtle patterns and associations that may not be apparent in smaller datasets. Various approaches, including statistical and artificial intelligence models, have been developed to predict an individual's risk of cancer based on these data resources. Methods …

An Untrained Deep Learning Method For Reconstructing Dynamic Mr Images From Accelerated Model-Based Data, Kalina P Slavkova, Julie C Dicarlo, Viraj Wadhwa, Sidharth Kumar, Chengyue Wu, Thomas E Yankeelov

Faculty, Staff and Student Publications

PURPOSE: To implement physics-based regularization as a stopping condition in tuning an untrained deep neural network for reconstructing MR images from accelerated data.

METHODS: The ConvDecoder (CD) neural network was trained with a physics-based regularization term incorporating the spoiled gradient echo equation that describes variable-flip angle data. Fully-sampled variable-flip angle k-space data were retrospectively accelerated by factors of R = {8, 12, 18, 36} and reconstructed with CD, CD with the proposed regularization (CD + r), locally low-rank (LR) reconstruction, and compressed sensing with L1-wavelet regularization (L1). Final images from CD + r training were evaluated at the "argmin" of …

Deep Learning For Online Fashion: A Novel Solution For The Retail E-Commerce Industry, Zachary O. Harris, Gowtham G. Katta, Robert Slater, Joseph L. Woodall Iv

SMU Data Science Review

The online shopping experience for clothing can be further enhanced by implementing Deep Learning techniques, such as Computer Vision and personalized recommendation systems. Automation, as a principle, can be applied to solving problems surrounding efficacy, efficiency, and security. It also provides a layer of abstraction for the user during the online shopping experience. This research aims to apply Deep Learning methods and principles of automation to augment the e-commerce fashion market in a novel way. After using these methods, it was found that Convolutional Autoencoders and Item-to-Item Based Recommenders may be used to accurately and precisely recommend articles of clothing …

A Data Augmented Method For Plant Disease Leaf Image Recognition Based On Enhanced Gan Model Network, Mingyuan Xin, Ling Weay Ang, Sellappan Palaniappan

Journal of Informatics and Web Engineering

The identification of plant disease leaves based on deep learning is the key to control the development and spread of plant diseases. In this paper, the existing problems of traditional classification and recognition of plant disease leaves and the limitations of deep learning-based plant disease leaf training are analysed. An enhanced GAN model network based on the Wasserstein GAN loss function has been developed to address the limited training images of plant disease leaves. The self-attention layer is added into the self-encoding structure of the generating network. The effectiveness of data generated by the encoder is increased after the self-attention …

Chatgpt As Metamorphosis Designer For The Future Of Artificial Intelligence (Ai): A Conceptual Investigation, Amarjit Kumar Singh (Library Assistant), Dr. Pankaj Mathur (Deputy Librarian)

Library Philosophy and Practice (e-journal)

Abstract

Purpose: The purpose of this research paper is to explore ChatGPT’s potential as an innovative designer tool for the future development of artificial intelligence. Specifically, this conceptual investigation aims to analyze ChatGPT’s capabilities as a tool for designing and developing near about human intelligent systems for futuristic used and developed in the field of Artificial Intelligence (AI). Also with the helps of this paper, researchers are analyzed the strengths and weaknesses of ChatGPT as a tool, and identify possible areas for improvement in its development and implementation. This investigation focused on the various features and functions of ChatGPT that …

Deep Learning Methods For Prediction Of And Escape From Protein Recognition, Bowen Dai

Dartmouth College Ph.D Dissertations

Protein interactions drive diverse processes essential to living organisms, and thus numerous biomedical applications center on understanding, predicting, and designing how proteins recognize their partners. While unfortunately the number of interactions of interest still vastly exceeds the capabilities of experimental determination methods, computational methods promise to fill the gap. My thesis pursues the development and application of computational methods for several protein interaction prediction and design tasks. First, to improve protein-glycan interaction specificity prediction, I developed GlyBERT, which learns biologically relevant glycan representations encapsulating the components most important for glycan recognition within their structures. GlyBERT encodes glycans with a branched …

Graph Analysis On Social Networks, Shen Lu

USF Tampa Graduate Theses and Dissertations

With the development of transportation network, social network, and communication network, there are many applications in streaming data. For example, traffic congestion happens between the origin and destination of daily trips. Traffic analysis can help plan the trips so that traffic congestion can be avoided. Social network and communication network represent the behaviors of the entire population. People build connections based on their hobbies, daily activities, photos, videos, simple messages, and even anonymous web surfing. All of these can be turned into commercial use, such as product marketing, business network building, and technology trending. Data science is about how to …

Multimodal Image Retrieval Combining Image And Text, Md Imran Sarker

Theses and Dissertations

Retrieving visual or textual similarities from an image query and vice versa has drawn much interest in computer vision. With the growth of the E-commerce marketplace, image retrieval provides excellent competitive opportunities for vendors and customers through a robust recommendation system. Feature integration has always been an essential task for multimodal-based image retrieval approaches. However, different existing matching techniques have been used separately for visual and text similarity. Still, researchers are looking for new methods when it comes to multimodal Image Retrieval. In this paper, I study the image retrieval task, where the input query is an image plus text …

An Nlp-Enhanced Approach To Test Comorbidities Risk Scoring Based On Unstructured Health Data For Hospital Readmissions Prediction, Tahir Hameed, Haris Jamal Khan, Saad Khan, Mutahira Khalid, Asim Abbas, Syed Ahmad Chan Bukhari

Management Faculty Publications

Hospital readmissions have emerged as a key healthcare quality indicator since the passing of the Affordable Care Act in 2010. It is easier to predict the readmission risk of patients without complications, but comorbidities, such as diabetes and cardiovascular diseases, make it difficult to accurately assess the readmission risk. 30-days hospital readmissions (30DRA) risk models typically rely on demographic, socioeconomic, and medical variables from structured data, such as diagnosis, vitals, lab reports, and comorbidities, etc. Comorbidity indices help in assessing overall disease burden by accounting for the disease codes in electronic health records (EHRs). With the advent of natural language …

Improving Automatic Melanoma Diagnosis Using Deep Learning-Based Segmentation Of Irregular Networks, Anand K. Nambisan, Akanksha Maurya, Norsang Lama, Thanh Phan, Gehana Patel, Keith Miller, Binita Lama, Jason Hagerty, Ronald Stanley, William V. Stoecker

Chemistry Faculty Research & Creative Works

Deep Learning Has Achieved Significant Success in Malignant Melanoma Diagnosis. These Diagnostic Models Are Undergoing a Transition into Clinical Use. However, with Melanoma Diagnostic Accuracy in the Range of Ninety Percent, a Significant Minority of Melanomas Are Missed by Deep Learning. Many of the Melanomas Missed Have Irregular Pigment Networks Visible using Dermoscopy. This Research Presents an Annotated Irregular Network Database and Develops a Classification Pipeline that Fuses Deep Learning Image-Level Results with Conventional Hand-Crafted Features from Irregular Pigment Networks. We Identified and Annotated 487 Unique Dermoscopic Melanoma Lesions from Images in the ISIC 2019 Dermoscopic Dataset to Create a …

Prediction Of Pathologic Complete Response To Neoadjuvant Systemic Therapy In Triple Negative Breast Cancer Using Deep Learning On Multiparametric Mri, Zijian Zhou, Beatriz E Adrada, Rosalind P Candelaria, Nabil A Elshafeey, Medine Boge, Sanaz Pashapoor, Zhan Xu, Bikash Panthi, Jong Bum Son, Mary S Guirguis, Miral M Patel, Gary J Whitman, Tanya W Moseley, Marion E Scoggins, Jason B White, Jennifer K Litton, Vicente Valero, Kelly K Hunt, Debu Tripathy, Wei Yang, Peng Wei, Clinton Yam, Mark D Pagel, Gaiane M Rauch, Jingfei Ma

Faculty, Staff and Student Publications

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer. Neoadjuvant systemic therapy (NAST) followed by surgery are currently standard of care for TNBC with 50-60% of patients achieving pathologic complete response (pCR). We investigated ability of deep learning (DL) on dynamic contrast enhanced (DCE) MRI and diffusion weighted imaging acquired early during NAST to predict TNBC patients' pCR status in the breast. During the development phase using the images of 130 TNBC patients, the DL model achieved areas under the receiver operating characteristic curves (AUCs) of 0.97 ± 0.04 and 0.82 ± 0.10 for the training and the …

Integrated Mrna Sequence Optimization Using Deep Learning, Jingjing Guo, Hongguang Fu, Xiaobo Zhou

Faculty, Staff and Student Publications

The coronavirus disease of 2019 pandemic has catalyzed the rapid development of mRNA vaccines, whereas, how to optimize the mRNA sequence of exogenous gene such as severe acute respiratory syndrome coronavirus 2 spike to fit human cells remains a critical challenge. A new algorithm, iDRO (integrated deep-learning-based mRNA optimization), is developed to optimize multiple components of mRNA sequences based on given amino acid sequences of target protein. Considering the biological constraints, we divided iDRO into two steps: open reading frame (ORF) optimization and 5' untranslated region (UTR) and 3'UTR generation. In ORF optimization, BiLSTM-CRF (bidirectional long-short-term memory with conditional random …

Data Integration Based Human Activity Recognition Using Deep Learning Models, Basamma Umesh Patil, D V Ashoka, Ajay Prakash B. V

Karbala International Journal of Modern Science

Regular monitoring of physical activities such as walking, jogging, sitting, and standing will help reduce the risk of many diseases like cardiovascular complications, obesity, and diabetes. Recently, much research showed that the effective development of Human Activity Recognition (HAR) will help in monitoring the physical activities of people and aid in human healthcare. In this concern, deep learning models with a novel automated hyperparameter generator are proposed and implemented to predict human activities such as walking, jogging, walking upstairs, walking downstairs, sitting, and standing more precisely and robustly. Conventional HAR systems are unable to manage real-time changes in the surrounding …

Hybrid Model For Motor Imagery Biometric Identification, Z. T. Al-Qaysi, Rasha A. Aljanabi, M A. Ahmed, Mahmood M. Salih

Iraqi Journal for Computer Science and Mathematics

Biometric systems are a continuouslyevolving and promising technologicaldomain that can be used in automatic systems for the unique and efficient identificationandauthenticationof individualswithout necessitating usersto carry or remember any physical tokens or passwords, in contrast totraditional methods such aspassword IDs. Biometricsare biological measurements or physical characteristics that can be used to ascertain and validate the identityof individuals.Recently, considerable interest has emergedin exploiting brain activity as a biometric identifier in automatic recognition systems, particularly focusing on data acquired through electroencephalography (EEG). Multiple research endeavorshaveindeed confirmed the presence of discriminative characteristics within brain signals recorded while performing specific cognitivetasks.However,EEG signals are inherently complex …

Cnn-Ps: Electroencephalogram Classification Of Brain States Using Hybrid Machine -Deep Learning Approach, Osama Mohsin Abdulaziz, O A. Saltykova

Iraqi Journal for Computer Science and Mathematics

Electroencephalography (EEG) has been used for quite some time as a diagnostic technique in neurology. The goal of this publication is to serve as a resource for researchers interested in applying deep learning methods to EEG data. This paper proposes a unique Hybrid Machine-Deep Learning model that can learn and classify EEG signals on its own. This method allows the model to classify EEG signals of varied sampling frequencies and durations automatically. The proposed model used feature extraction methods from artificial design and performed extensive tests with EEG data collected at varying sample rates to determine how well our suggested …

An Overview Of Content-Basedimage Retrieval Methods And Techniques, M H. Hadid, Qasim Mohammed Hussein, Z T. Al-Qaysi, M A. Ahmed, Mahmood M. Salih

Iraqi Journal for Computer Science and Mathematics

With the development of Internet technology and the popularity of digital devices, Content-Based Image Retrieval (CBIR) has been quickly developed and applied in various fields related to computer vision and artificial intelligence. Currently, it is possible to retrieve related images effectively and efficiently from a large-scale database with an input image. In the past ten years, great efforts have been made for new theories and models of CBIR, and many effective CBIR algorithms have been established.Content-based image retrieval helps to discover identical images in a big dataset that match a query image. The query image's representative feature similarities to the …

Towards A Robust And Efficient Deep Neural Network For The Lidar Point Cloud Perception, Zixiang Zhou

Graduate Thesis and Dissertation 2023-2024

In recent years, LiDAR has emerged as a crucial perception tool for robotics and autonomous vehicles. However, most LiDAR perception methods are adapted from 2D image-based deep learning methods, which are not well-suited to the unique geometric structure of LiDAR point cloud data. This domain gap poses challenges for the fast-growing LiDAR perception tasks. This dissertation aims to investigate suitable deep network structures tailored for LiDAR point cloud data, and therefore design a more efficient and robust LiDAR perception framework. Our approach to address this challenge is twofold. First, we recognize that LiDAR point cloud data is characterized by an …

Utilizing Slow Reading Techniques To Promote Deep Learning, Brian Baldi, Cynthia Mejia

International Journal for the Scholarship of Teaching & Learning

Slow reading has long been viewed as a teaching technique that engages students more deeply with course readings. Little systematic research, however, has been done to understand how this pedagogical strategy works in college classrooms. This study investigated how slow reading techniques promoted deep learning among undergraduate college students across two disciplines. Utilizing two food essays as the basis for a reading assignment, students in two courses participated in an intentionally scaffolded and paced slow reading exercise designed to encourage deeper personal engagement with course concepts. Theoretical implications from the research demonstrate connections between slow reading techniques and the existing …

Dynamic Predictions Of Thermal Heating And Cooling Of Silicon Wafer, Hitesh Kumar

Master's Projects

Neural Networks are now emerging in every industry. All the industries are trying their best to exploit the benefits of neural networks and deep learning to make predictions or simulate their ongoing process with the use of their generated data. The purpose of this report is to study the heating pattern of a silicon wafer and make predictions using various machine learning techniques. The heating of the silicon wafer involves various factors ranging from number of lamps, wafer properties and points taken in consideration to capture the heating temperature. This process involves dynamic inputs which facilitates the heating of the …

Federated Learning For Protecting Medical Data Privacy, Abhishek Reddy Punreddy

Master's Projects

Deep learning is one of the most advanced machine learning techniques, and its prominence has increased in recent years. Language processing, predictions in medical research and pattern recognition are few of the numerous fields in which it is widely utilized. Numerous modern medical applications benefit greatly from the implementation of machine learning (ML) models and the disruptive innovations in the entire modern health care system. It is extensively used for constructing accurate and robust statistical models from large volumes of medical data collected from a variety of sources in contemporary healthcare systems [1]. Due to privacy concerns that restrict access …

Classification Of Darknet Traffic By Application Type, Shruti Sharma

Master's Projects

The darknet is frequently exploited for illegal purposes and activities, which makes darknet traffic detection an important security topic. Previous research has focused on various classification techniques for darknet traffic using machine learning and deep learning. We extend previous work by considering the effectiveness of a wide range of machine learning and deep learning technique for the classification of darknet traffic by application type. We consider the CICDarknet2020 dataset, which has been used in many previous studies, thus enabling a direct comparison of our results to previous work. We find that XGBoost performs the best among the classifiers that we …

Enhancing Facial Emotion Recognition Using Image Processing With Cnn, Sourabh Deokar

Master's Projects

Facial expression recognition (FER) has been a challenging task in computer vision for decades. With recent advancements in deep learning, convolutional neural networks (CNNs) have shown promising results in this field. However, the accuracy of FER using CNNs heavily relies on the quality of the input images and the size of the dataset. Moreover, even in pictures of the same person with the same expression, brightness, backdrop, and stance might change. These variations are emphasized when comparing pictures of individuals with varying ethnic backgrounds and facial features, which makes it challenging for deep-learning models to classify. In this paper, we …

Practical Ai Value Alignment Using Stories, Md Sultan Al Nahian

Theses and Dissertations--Computer Science

As more machine learning agents interact with humans, it is increasingly a prospect that an agent trained to perform a task optimally - using only a measure of task performance as feedback--can violate societal norms for acceptable behavior or cause harm. Consequently, it becomes necessary to prioritize task performance and ensure that AI actions do not have detrimental effects. Value alignment is a property of intelligent agents, wherein they solely pursue goals and activities that are non-harmful and beneficial to humans. Current approaches to value alignment largely depend on imitation learning or learning from demonstration methods. However, the dynamic nature …

Novel Architectures And Optimization Algorithms For Training Neural Networks And Applications, Vasily I. Zadorozhnyy

Theses and Dissertations--Mathematics

The two main areas of Deep Learning are Unsupervised and Supervised Learning. Unsupervised Learning studies a class of data processing problems in which only descriptions of objects are known, without label information. Generative Adversarial Networks (GANs) have become among the most widely used unsupervised neural net models. GAN combines two neural nets, generative and discriminative, that work simultaneously. We introduce a new family of discriminator loss functions that adopts a weighted sum of real and fake parts, which we call adaptive weighted loss functions. Using the gradient information, we can adaptively choose weights to train a discriminator in the direction …

Generalizable And Adaptable Data-Driven Methods For Overcoming Barriers To Practical Industrial Condition Monitoring, Matthew B. Russell

Theses and Dissertations--Electrical and Computer Engineering

The future of smart manufacturing relies on predictive maintenance systems that intelligently minimize expensive downtime through timely assessment of machine condition. Deep Learning (DL) has achieved excellent performance in industrial condition monitoring experiments, but the constraints of the manufacturing environment prevent many algorithms from being practically deployed on the factory floor. Ubiquitous sensing from online machines generates high velocity data streams that require new techniques for efficient transmission and storage. Despite these ever-increasing data lakes, many applications still lack the data needed for training DL fault diagnosis and wear tracking models since most data is unlabeled and only from nominal …