Digital Commons Network^™

Ai-Driven Deep Learning Techniques In Protein Structure Prediction, Lingtao Chen, Qiaomu Li, Kazi Fahim Ahmad Nasif, Ying Xie, Bobin Deng, Shuteng Niu, Seyedamin Pouriyeh, Zhiyu Dai, Jiawei Chen, Chloe Yixin Xie

2020-Current year OA Pubs

Protein structure prediction is important for understanding their function and behavior. This review study presents a comprehensive review of the computational models used in predicting protein structure. It covers the progression from established protein modeling to state-of-the-art artificial intelligence (AI) frameworks. The paper will start with a brief introduction to protein structures, protein modeling, and AI. The section on established protein modeling will discuss homology modeling, ab initio modeling, and threading. The next section is deep learning-based models. It introduces some state-of-the-art AI models, such as AlphaFold (AlphaFold, AlphaFold2, AlphaFold3), RoseTTAFold, ProteinBERT, etc. This section also discusses how AI techniques …

Transformer-Based Deep Learning Prediction Of 10-Degree Humphrey Visual Field Tests From 24-Degree Data, Min Shi, Anagha Lokhande, Yu Tian, Yan Luo, Mohammad Eslami, Saber Kazeminasab, Tobias Elze, Lucy Shen, Louis Pasquale, Sarah Wellik, Carlos Gustavo De Moraes, Jonathan Myers, Nazlee Zebardast, David Friedman, Michael Boland, Mengyu Wang

Wills Eye Hospital Papers

PURPOSE: To predict 10-2 Humphrey visual fields (VFs) from 24-2 VFs and associated non-total deviation features using deep learning.

METHODS: We included 5189 reliable 24-2 and 10-2 VF pairs from 2236 patients, and 28,409 reliable pairs of macular OCT scans and 24-2 VF from 19,527 eyes of 11,560 patients. We developed a transformer-based deep learning model using 52 total deviation values and nine VF test features to predict 68 10-2 total deviation values. The mean absolute error, root mean square error, and the R2 were evaluation metrics. We further evaluated whether the predicted 10-2 VFs can improve the structure-function relationship …

High Prevalence Of Artifacts In Optical Coherence Tomography With Adequate Signal Strength, Wei-Chun Lin, Aaron Coyner, Charles Amankwa, Abigail Lucero, Gadi Wollstein, Joel Schuman, Hiroshi Ishikawa

Wills Eye Hospital Papers

PURPOSE: This study aims to investigate the prevalence of artifacts in optical coherence tomography (OCT) images with acceptable signal strength and evaluate the performance of supervised deep learning models in improving OCT image quality assessment.

METHODS: We conducted a retrospective study on 4555 OCT images from 546 patients, with each image having an acceptable signal strength (≥6). A comprehensive analysis of prevalent OCT artifacts was performed, and five pretrained convolutional neural network models were trained and tested to infer images based on quality.

RESULTS: Our results showed a high prevalence of artifacts in OCT images with acceptable signal strength. Approximately …

Checklist For Reproducibility Of Deep Learning In Medical Imaging, Mana Moassefi, Yashbir Singh, Gian Marco Conte, Bardia Khosravi, Pouria Rouzrokh, Sanaz Vahdati, Nabile Safdar, Linda Moy, Felipe Kitamura, Amilcare Gentili, Paras Lakhani, Nina Kottler, Safwan Halabi, Joseph Yacoub, Yuankai Hou, Khaled Younis, Bradley Erickson, Elizabeth Krupinski, Shahriar Faghani

Department of Radiology Faculty Papers

The application of deep learning (DL) in medicine introduces transformative tools with the potential to enhance prognosis, diagnosis, and treatment planning. However, ensuring transparent documentation is essential for researchers to enhance reproducibility and refine techniques. Our study addresses the unique challenges presented by DL in medical imaging by developing a comprehensive checklist using the Delphi method to enhance reproducibility and reliability in this dynamic field. We compiled a preliminary checklist based on a comprehensive review of existing checklists and relevant literature. A panel of 11 experts in medical imaging and DL assessed these items using Likert scales, with two survey …

Interpretable And Evidential Deep Learning For Medical Image Analysis, Sai Chandra Kosaraju

UNLV Theses, Dissertations, Professional Papers, and Capstones

Automatic histopathological Whole Slide Image (WSI) analysis has been highlighted along with the advancements in microscopic imaging techniques, but manual examination and diagnosis of WSIs are time-consuming and tiresome. Recently, deep convolutional neural networks have succeeded in histopathological image analysis. Especially, Convolutional Neural Networks CNN models such as Inception and DenseNet have achieved effective performance. However, automatic histopathological WSI analysis still has significant drawbacks such as considering deep learning as black-box models, predicting disease independently on a small part of images (patch images) extracted from WSIs, limitations in predicting a single slide-based score for a patient, and capturing disease-specific morphology …

Artificial Intelligence In Fusion Protein Three-Dimensional Structure Prediction: Review And Perspective, Himansu Kumar, Pora Kim

Faculty, Staff and Student Publications

Recent advancements in artificial intelligence (AI) have accelerated the prediction of unknown protein structures. However, accurately predicting the three-dimensional (3D) structures of fusion proteins remains a difficult task because the current AI-based protein structure predictions are focused on the WT proteins rather than on the newly fused proteins in nature. Following the central dogma of biology, fusion proteins are translated from fusion transcripts, which are made by transcribing the fusion genes between two different loci through the chromosomal rearrangements in cancer. Accurately predicting the 3D structures of fusion proteins is important for understanding the functional roles and mechanisms of action …

Ensemble Learning For Accurate Prediction Of Heart Sounds Using Gammatonegram Images, Sinam Ashinikumar Singh, Sinam Ajitkumar Singh, Aheibam Dinamani Singh

Turkish Journal of Electrical Engineering and Computer Sciences

The analysis of heart sound signals constitutes a pivotal domain in healthcare, with the prediction of imbalanced heart sounds offering critical diagnostic insights. However, the inherent diversity in cardiac sound patterns presents a substantial challenge in predicting imbalanced signals. Many scientific disciplines have focused a great deal of emphasis on the problem of class inequality. We introduce an ensemble learning approach employing a convolutional neural network model-based deep learning algorithm to effectively tackle the challenges associated with predicting imbalanced heart sound signals. We use a Gammatone filter bank to extract relevant features from the heard sound signal. Our approach leverages …

Deepface: Deep-Learning-Based Framework To Contextualize Orofacial-Cleft-Related Variants During Human Embryonic Craniofacial Development, Yulin Dai, Toshiyuki Itai, Guangsheng Pei, Fangfang Yan, Yan Chu, Xiaoqian Jiang, Seth M Weinberg, Nandita Mukhopadhyay, Mary L Marazita, Lukas M Simon, Peilin Jia, Zhongming Zhao

Faculty, Staff and Student Publications

Orofacial clefts (OFCs) are among the most common human congenital birth defects. Previous multiethnic studies have identified dozens of associated loci for both cleft lip with or without cleft palate (CL/P) and cleft palate alone (CP). Although several nearby genes have been highlighted, the "casual" variants are largely unknown. Here, we developed DeepFace, a convolutional neural network model, to assess the functional impact of variants by SNP activity difference (SAD) scores. The DeepFace model is trained with 204 epigenomic assays from crucial human embryonic craniofacial developmental stages of post-conception week (pcw) 4 to pcw 10. The Pearson correlation coefficient between …

Artificial Intelligence Modeling Of Alzheimer's Disease And Environmental Science, Mohamed Abu Sheha

USF Tampa Graduate Theses and Dissertations

A data-driven statistical model operates as a mathematical illustration of a tangible issuefaced in reality, enabling the creation of predictions or decisions based on data. The process of utilizing probability and statistical principles in statistical models is essential for deriving meaningful conclusions from the data. The research conducted in this dissertation utilizes artificial intelligence (AI) models to integrate findings across health and environmental sci- ence.

The first research study in this dissertation, Alzheimer’s disease is a mental health issue and a brain aging dilemma that makes it difficult for older people to complete daily tasks without assistance. The physician uses …

Evaluating The Relationship Between Magnetic Resonance Image Quality Metrics And Deep Learning-Based Segmentation Accuracy Of Brain Tumors, Rajarajeswari Muthusivarajan, Adrian Celaya, Joshua P Yung, James P Long, Satish E Viswanath, Daniel S Marcus, Caroline Chung, David Fuentes

Faculty, Staff and Student Publications

Background: Magnetic resonance imaging (MRI) scans are known to suffer from a variety of acquisition artifacts as well as equipment-based variations that impact image appearance and segmentation performance. It is still unclear whether a direct relationship exists between magnetic resonance (MR) image quality metrics (IQMs) (e.g., signal-to-noise, contrast-to-noise) and segmentation accuracy.

Purpose: Deep learning (DL) approaches have shown significant promise for automated segmentation of brain tumors on MRI but depend on the quality of input training images. We sought to evaluate the relationship between IQMs of input training images and DL-based brain tumor segmentation accuracy toward developing more generalizable models …

Evaluating Automatically Generated Normal Tissue Contours For Safe Use In Head And Neck And Cervical Cancer Treatment Planning, Raphael Douglas, Adenike Olanrewaju, Raymond Mumme, Lifei Zhang, Beth M Beadle, Laurence Edward Court

Faculty, Staff and Student Publications

Purpose: Volumetric-modulated arc therapy (VMAT) is a widely accepted treatment method for head and neck (HN) and cervical cancers; however, creating contours and plan optimization for VMAT plans is a time-consuming process. Our group has created an automated treatment planning tool, the Radiation Planning Assistant (RPA), that uses deep learning models to generate organs at risk (OARs), planning structures and automates plan optimization. This study quantitatively evaluates the quality of contours generated by the RPA tool.

Methods: For patients with HN (54) and cervical (39) cancers, we retrospectively generated autoplans using the RPA. Autoplans were generated using deep-learning and RapidPlan …

From Graph Theory For Robust Deep Networks To Graph Learning For Multimodal Cancer Analysis, Asim Waqas

USF Tampa Graduate Theses and Dissertations

This dissertation explores the intersection of graph theory and deep learning, focusing on enhancing the robustness of deep neural networks (DNNs) and applying these advancements to complex problems like cancer diagnosis and treatment. We investigate the structural properties of graphs and their influence on neural network performance, particularly in multimodal learning. The work delves into the design space of DNN architectures using graph-theoretic measures, transforming graphs into DNN architectures for various tasks, and examining their robustness against noise and adversarial attacks. The study extends to medical imaging, highlighting advanced DNN architectures like U-Net for brain tumor segmentation. It addresses the …

An Improved Method For Diagnosis Of Parkinson’S Disease Using Deep Learning Models Enhanced With Metaheuristic Algorithm, Babita Majhi, Aarti Kashyap, Siddhartha Suprasad Mohanty, Sujata Dash, Saurav Mallik, Aimin Li, Zhongming Zhao

Faculty, Staff and Student Publications

Parkinson's disease (PD) is challenging for clinicians to accurately diagnose in the early stages. Quantitative measures of brain health can be obtained safely and non-invasively using medical imaging techniques like magnetic resonance imaging (MRI) and single photon emission computed tomography (SPECT). For accurate diagnosis of PD, powerful machine learning and deep learning models as well as the effectiveness of medical imaging tools for assessing neurological health are required. This study proposes four deep learning models with a hybrid model for the early detection of PD. For the simulation study, two standard datasets are chosen. Further to improve the performance of …

Facial Skin Analysis In Malaysians Using Yolov5: A Deep Learning Perspective, Ying Huey Gan, Shih Yin Ooi, Ying Han Pang, Yi Hong Tay, Quan Fong Yeo

Journal of Informatics and Web Engineering

Nowadays, people are more concerned about their skin conditions and are more willing to spend money and time on facial care routines. The beauty sector market is increasing, and more skin type readers are being created to help people determine their skin type. While various skin type readers are in the market, each is invented and tested abroad. Those skin type readers in the beauty market are not applied well on Malaysian skin. Therefore, this paper proposes a facial skin analysis system tailored primarily for Malaysian skin. This paper integrated object detection and deep learning algorithms in developing skin-type readers. …

Performance Evaluation Of Yolo Models In Plant Disease Detection, Usman Ali, Maizatul Akmar Ismail, Riyaz Ahamed Ariyaluran Habeeb, Syed Roshaan Ali Shah

Journal of Informatics and Web Engineering

Plant diseases significantly impact global agriculture, leading to substantial production losses and economic consequences. Timely disease detection can enhance crop yield, optimize resource utilization, reduce costs, and mitigate environmental effects, ultimately ensuring high-quality food production. Deep learning, specifically computer vision-based techniques, have proven invaluable in tasks like image classification, segmentation, and object detection. Deep Learning techniques such as You Only Look Once (YOLO) models are state of the art neural network algorithms used for accurate object detection. In this study, YOLOv5, YOLOv7 and YOLOv8 models were trained on CCL’20 dataset for citrus disease detection. Data augmentation techniques such as image …

Unveiling The Efficacy Of Ai-Based Algorithms In Phishing Attack Detection, Tajamul Shahzad, Kashif Aman

Journal of Informatics and Web Engineering

Phishing poses a significant challenge in an ever-evolving world. The increased usage of the Internet has resulted in the emergence of a different kind of theft referred to as cybercrime. The term cybercrime describes the act of invading privacy and illegitimately obtaining personal information using digital platform. Primarily an approach named phishing is employed, which involves the use of spoof emails or bogus websites by the attackers to get the victim's personal information like their account credentials, debit, or credit card’s number, etc. To give the brief knowledge of phishing attacks and their types of the objective of this work …

Machine Learning Multimodal Framework For Fake News Detection And Mitigation, Nada A. Gaballah

Theses and Dissertations

Social media has become our new reality, people wake up every morning and the first thing they do before getting out of bed, is check their social media. Nowadays, people rarely read newspapers, they even rarely watch TV news or listen to radio broadcasts. In recent years, we have witnessed lots of fake news roaming social media every second, with people simply believing it and spreading it even more without checking the credibility of this news. This fake news affected several domains like what happened in the US election in 2016 and again in 2020, the false information about Covid-19 …

Autonomous Microgrid System, Xavier Kuehn, Brian Xiong

Computer Science and Engineering Senior Theses

Microgrids have made a revolutionary change in the realm of energy distribution due to the features that they offer, including localized, resilient, and sustainable energy solutions. Operating renewable resources in a microgrid while maintaining generation-load balance and acceptable voltage-frequency limits has been an open research problem. This thesis presents smart python agents for microgrid systems to automate the operations and control of microgrid renewable resources in an effort to provide resilient solutions to the intermittence issues that could potentially arise within the microgrid energy system. The smart agents operate the microgrids by not only integrating the use of renewable energy …

Instant Hdr-Nerf: Fast Learning Of High Dynamic Range View Synthesis With Unknown Exposure Settings, Nam Nguyen

Master's Theses

We propose Instant High Dynamic Range Neural Radiance Fields (Instant HDR-NeRF), a method of learning high dynamic range (HDR) view synthesis from a set of low dynamic range (LDR) views with unknown and varying exposure and white balance in as little as minutes. Our method can render novel HDR views without ground-truth supervision, and novel LDR views in different exposure settings, including those that match the ground-truth LDR views. The key to our method is to model the physical process of the camera with two implicit MLPs: a radiance field and a monotonically increasing tone-mapper. Built upon Instant Neural Graphics …

Morp: Monocular Orientation Regression Pipeline, Jacob Gunderson

Master's Theses

Orientation estimation of objects plays a pivotal role in robotics, self-driving cars, and augmented reality. Beyond mere position, accurately determining the orientation of objects is essential for constructing precise models of the physical world. While 2D object detection has made significant strides, the field of orientation estimation still faces several challenges. Our research addresses these hurdles by proposing an efficient pipeline which facilitates rapid creation of labeled training data and enables direct regression of object orientation from a single image. We start by creating a digital twin of a physical object using an iPhone, followed by generating synthetic images using …

Development Of A Deep Learning Algorithm Using Electromyography (Emg) And Acceleration To Monitor Upper Extremity Behavior With Application To Individuals Post-Stroke, Nathan Dodd

Master's Theses

Stroke is a chronic illness which often impairs survivors for extended periods of time,

leaving the individual limited in motor function. The ability to perform daily activities

(ADL) is closely linked to motor recovery following a stroke. The objective of

this work is to employ surface electromyography (sEMG) gathered through a novel,

wearable armband sensor to monitor and quantify ADL performance. The first contribution

of this work seeks to develop a relationship between sEMG and and grip

aperture, a metric tied to the success of post-stroke individuals’ functional independence.

The second contribution of this work aims to develop a deep …

Context In Computer Vision: A Taxonomy, Multi-Stage Integration, And A General Framework, Xuan Wang

Dissertations, Theses, and Capstone Projects

Contextual information has been widely used in many computer vision tasks, such as object detection, video action detection, image classification, etc. Recognizing a single object or action out of context could be sometimes very challenging, and context information may help improve the understanding of a scene or an event greatly. However, existing approaches design specific contextual information mechanisms for different detection tasks.

In this research, we first present a comprehensive survey of context understanding in computer vision, with a taxonomy to describe context in different types and levels. Then we proposed MultiCLU, a new multi-stage context learning and utilization framework, …

Singleadv: Single-Class Target-Specific Attack Against Interpretable Deep Learning Systems, Eldor Abdukhamidov, Mohammed Abuhamad, George K. Thiruvathukal, Hyoungshick Kim, Tamer Abuhmed

Computer Science: Faculty Publications and Other Works

In this paper, we present a novel Single-class target-specific Adversarial attack called SingleADV. The goal of SingleADV is to generate a universal perturbation that deceives the target model into confusing a specific category of objects with a target category while ensuring highly relevant and accurate interpretations. The universal perturbation is stochastically and iteratively optimized by minimizing the adversarial loss that is designed to consider both the classifier and interpreter costs in targeted and non-targeted categories. In this optimization framework, ruled by the first- and second-moment estimations, the desired loss surface promotes high confidence and interpretation score of adversarial samples. By …

Humans Verification By Adopting Deep Recurrent Fingerphotos Network, Islam Nahedh Alabdoo, Mehmet Ali Yalçınkaya

Baghdad Science Journal

Fingerphoto can be considered as one of recent and interesting biometrics. It basically means a fingerprint image that is acquired by a smartphone in contactless manner. This paper proposes a new Deep Recurrent Learning (DRL) approach for verifying humans based on their fingerphoto image. It is called the Deep Recurrent Fingerphotos Network (DRFN). It compromises of input layer, sequence of hidden layers, output layer and essential feedback. The proposed DRFN sequentially accepts fingerphoto images of all personal fingers. It has the capability to change between the weights of each individual fingerphoto and provide verification. A huge number of fingerphoto images …

Context Aware Music Recommendation And Playlist Generation, Elias Mann

SMU Journal of Undergraduate Research

There are many reasons people listen to music, and the type of music is largely determined by what the listener may be doing while they listen. For example, one may listen to one type of music while commuting, another while exercising, and yet another while relaxing. Without access to the physiological state of the user, current music recommendation methods rely on collaborative filtering - recommending music based on what other similar users listen to - and content based filtering - recommending songs based on their similarities to songs the user already prefers. With the rise in popularity of smart devices …

Ver-Net: A Hybrid Transfer Learning Model For Lung Cancer Detection Using Ct Scan Images, Anindita Saha, Shahid Mohammad Ganie, Pijush Kanti Dutta Pramanik, Rakesh Kumar Yadav, Saurav Mallik, Zhongming Zhao

Faculty, Staff and Student Publications

BACKGROUND: Lung cancer is the second most common cancer worldwide, with over two million new cases per year. Early identification would allow healthcare practitioners to handle it more effectively. The advancement of computer-aided detection systems significantly impacted clinical analysis and decision-making on human disease. Towards this, machine learning and deep learning techniques are successfully being applied. Due to several advantages, transfer learning has become popular for disease detection based on image data.

METHODS: In this work, we build a novel transfer learning model (VER-Net) by stacking three different transfer learning models to detect lung cancer using lung CT scan images. …

Ai: Is The Future Robots?, Ruthanne Fischer

University Honors College

The goal of this thesis is to analyze how Artificial Intelligence is actively changing. The newly developed technology that is known as Artificial Intelligence can potentially have implications that are not yet known. Artificial Intelligence impacts the business world in many ways. Things such as ChatGPT and Deepfakes are some of the biggest forms of Artificial Intelligence today. It is important to know how Artificial Intelligence is changing every day because no one knows the true implications of a technology like this. It is crucial for companies to understand how this technology can potentially be harmful to their employees. Artificial …

Satellite Image Analysis And Sidewalk Classification Using Deep Learning Models, Subeksha Khanal

Master's Theses

Lack of sidewalk pavement can contribute to pedestrian fatalities and injuries in the USA. Although many researchers have conducted research on sidewalk detection, there are not many publicly available datasets that we would work on for sidewalk classification. In this study, I conducted classification tasks using pretrained CNN models including VGG16 and ResNet50. I extended these models by adding custom layers at the top of pretrained layers, employing various techniques to improve the classification accuracy.

The dataset comprises 4,731 images of sidewalk based on occlusion levels, including images where the sidewalk is visible from overhead and instances where sidewalk is …

Learning Scene Semantics For 3d Scene Retrieval, Natalie Gleason

Honors Theses

This project presents a comprehensive exploration into semantics-driven 3D scene retrieval, aiming to bridge the gap between 2D sketches/images and 3D models. Through four distinct research objectives, this project endeavors to construct a foundational infrastructure, develop methodologies for quantifying semantic similarity, and advance a semantics-based retrieval framework for 2D scene sketch-based and image-based 3D scene retrieval. Leveraging WordNet as a foundational semantic ontology library, the research proposes the construction of an extensive hierarchical scene semantic tree, enriching 2D/3D scenes with encoded semantic information. The methodologies for semantic similarity computation utilize this semantic tree to bridge the semantic disparity between 2D …

Deep Learning In Indus Valley Script Digitization, Deva Munikanta Reddy Atturu

Theses and Dissertations

This research introduces ASR-net(Ancient Script Recognition), a groundbreaking system that automatically digitizes ancient Indus seals by converting them into coded text, similar to Optical Character Recognition for modern languages. ASR-net, with an 95% success rate in identifying individual symbols, aims to address the crucial need for automated techniques in deciphering the enigmatic Indus script. Initially Yolov3 is utilized to create the bounding boxes around each graphemes present in the Indus Valley Seal. In addition to that we created M-net(Mahadevan) model to encode the graphemes. Beyond digitization, the paper proposes a new research challenge called the Motif Identification Problem (MIP) related …