Digital Commons Network^™

Cardiogpt: An Ecg Interpretation Generation Model, Guohua Fu, Jianwei Zheng, Islam Abudayyeh, Chizobam Ani, Cyril Rakovski, Louis Ehwerhemuepha, Hongxia Lu, Yongjuan Guo, Shenglin Liu, Huimin Chu, Bing Yang

Mathematics, Physics, and Computer Science Faculty Articles and Research

Numerous supervised learning models aimed at classifying 12-lead electrocardiograms into different groups have shown impressive performance by utilizing deep learning algorithms. However, few studies are dedicated to applying the Generative Pre-trained Transformer (GPT) model in interpreting electrocardiogram (ECG) using natural language. Thus, we are pioneering the exploration of this uncharted territory by employing the CardioGPT model to tackle this challenge. We used a dataset of ECGs (standard 10s, 12-channel format) from adult patients, with 60 distinct rhythms or conduction abnormalities annotated by board-certified, actively practicing cardiologists. The ECGs were collected from The First Affiliated Hospital of Ningbo University and Shanghai …

Dilf: Differentiable Rendering-Based Multi-View Image-Language Fusion For Zero-Shot 3d Shape Understanding, Xin Ning, Zaiyang Yu, Lusi Li, Weijun Li, Prayag Tiwari

Computer Science Faculty Publications

Zero-shot 3D shape understanding aims to recognize “unseen” 3D categories that are not present in training data. Recently, Contrastive Language–Image Pre-training (CLIP) has shown promising open-world performance in zero-shot 3D shape understanding tasks by information fusion among language and 3D modality. It first renders 3D objects into multiple 2D image views and then learns to understand the semantic relationships between the textual descriptions and images, enabling the model to generalize to new and unseen categories. However, existing studies in zero-shot 3D shape understanding rely on predefined rendering parameters, resulting in repetitive, redundant, and low-quality views. This limitation hinders the model’s …

Machine-Learning-Based Vulnerability Detection And Classification In Internet Of Things Device Security, Sarah Bin Hulayyil, Shancang Li, Li Da Xu

Information Technology & Decision Sciences Faculty Publications

Detecting cyber security vulnerabilities in the Internet of Things (IoT) devices before they are exploited is increasingly challenging and is one of the key technologies to protect IoT devices from cyber attacks. This work conducts a comprehensive survey to investigate the methods and tools used in vulnerability detection in IoT environments utilizing machine learning techniques on various datasets, i.e., IoT23. During this study, the common potential vulnerabilities of IoT architectures are analyzed on each layer and the machine learning workflow is described for detecting IoT vulnerabilities. A vulnerability detection and mitigation framework was proposed for machine learning-based vulnerability detection in …

Deep-Learning-Based Classification Of Digitally Modulated Signals Using Capsule Networks And Cyclic Cumulants, John A. Snoap, Dimitrie C. Popescu, James A. Latshaw, Chad M. Spooner

Electrical & Computer Engineering Faculty Publications

This paper presents a novel deep-learning (DL)-based approach for classifying digitally modulated signals, which involves the use of capsule networks (CAPs) together with the cyclic cumulant (CC) features of the signals. These were blindly estimated using cyclostationary signal processing (CSP) and were then input into the CAP for training and classification. The classification performance and the generalization abilities of the proposed approach were tested using two distinct datasets that contained the same types of digitally modulated signals, but had distinct generation parameters. The results showed that the classification of digitally modulated signals using CAPs and CCs proposed in the paper …

Tutorial: Neuro-Symbolic Ai For Mental Healthcare, Kaushik Roy, Usha Lokala, Manas Gaur, Amit Sheth

Publications

Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to platform for supporting patients seeking MHCare. The creation of peer-support groups without social stigma has resulted in patients transitioning from clinical settings to SocMedia supported interactions for quick help. Researchers started exploring SocMedia content in search of cues that showcase correlation or causation between different MH conditions to design better interventional strategies. User-level Classification-based AI systems were designed to leverage diverse SocMedia data from various MH conditions, …

A High-Precision Machine Learning Algorithm To Classify Left And Right Outflow Tract Ventricular Tachycardia, Jianwei Zhang, Guohua Fu, Islam Abudayyeh, Magdi Yacoub, Anthony Chang, William Feaster, Louis Ehwerhemuepha, Hesham El-Askary, Xianfeng Du, Bin He, Mingjun Feng, Yibo Yu, Binhao Wang, Jing Liu, Hai Yao, Hulmin Chu, Cyril Rakovski

Mathematics, Physics, and Computer Science Faculty Articles and Research

Introduction: Multiple algorithms based on 12-lead ECG measurements have been proposed to identify the right ventricular outflow tract (RVOT) and left ventricular outflow tract (LVOT) locations from which ventricular tachycardia (VT) and frequent premature ventricular complex (PVC) originate. However, a clinical-grade machine learning algorithm that automatically analyzes characteristics of 12-lead ECGs and predicts RVOT or LVOT origins of VT and PVC is not currently available. The effective ablation sites of RVOT and LVOT, confirmed by a successful ablation procedure, provide evidence to create RVOT and LVOT labels for the machine learning model.

Methods: We randomly sampled training, validation, and testing …

Understanding And Predicting Retractions Of Published Work, Sai Ajay Modukuri, Sarah Rajtmajer, Anna Cinzia Squicciarini, Jian Wu, C. Lee Giles

Computer Science Faculty Publications

Recent increases in the number of retractions of published papers reflect heightened attention and increased scrutiny in the scientific process motivated, in part, by the replication crisis. These trends motivate computational tools for understanding and assessment of the scholarly record. Here, we sketch the landscape of retracted papers in the Retraction Watch database, a collection of 19k records of published scholarly articles that have been retracted for various reasons (e.g., plagiarism, data error). Using metadata as well as features derived from full-text for a subset of retracted papers in the social and behavioral sciences, we develop a random forest classifier …

A Survey Of Feature Extraction And Fusion Of Deep Learning For Detection Of Abnormalities In Video Endoscopy Of Gastrointestinal-Tract, Hussam Ali, Muhammad Sharif, Mussarat Yasmin, Mubashir Husain Rehmani, Farhan Riaz

Publications

A standard screening procedure involves video endoscopy of the Gastrointestinal tract. It is a less invasive method which is practiced for early diagnosis of gastric diseases. Manual inspection of a large number of gastric frames is an exhaustive, time-consuming task, and requires expertise. Conversely, several computer-aided diagnosis systems have been proposed by researchers to cope with the dilemma of manual inspection of the massive volume of frames. This article gives an overview of different available alternatives for automated inspection, detection, and classification of various GI abnormalities. Also, this work elaborates techniques associated with content-based image retrieval and automated systems for …

A Description Of A Humans Knowledge Using Artificial Intelligence, Dj Price

Mahurin Honors College Capstone Experience/Thesis Projects

There currently does not exist a way to easily view the relationships between a collection of written items (e.g. sports articles, diary entries, research papers). In recent years, novel machine learning methods have been developed which are very good at extracting semantic relationships from large numbers of documents. One of them is the (unsupervised) machine learning model Doc2Vec which constructs vectors for documents. The research project detailed in this paper uses this and other already existing algorithms to analyze the relationship between pieces of text. We set forth a broader ambition for this project before discussing the use and need …

Virtual Wrap-Up Presentation: Digital Libraries, Intelligent Data Analytics, And Augmented Description, Elizabeth Lorang, Leen-Kiat Soh, Yi Liu, Chulwoo Pack

CSE Conference and Workshop Papers

Includes framing, overview, and discussion of the explorations pursued as part of the Digital Libraries, Intelligent Data Analytics, and Augmented Description demonstration project, pursued by members of the Aida digital libraries research team at the University of Nebraska-Lincoln through a research services contract with the Library of Congress. This presentation covered: Aida research team and background for the demonstration project; broad outlines of “Digital Libraries, Intelligent Data Analytics, and Augmented Description”; what changed for us as a research team over the collaboration and why; deliverables of our work; thoughts toward “What next”; and deep-dives into the explorations. The machine learning …

Classifying Fiction And Non-Fiction Works Using Machine Learning, Rachna Gupta '21

Student Publications & Research

The objective of this project was to create a program that can determine whether an unknown text is a work of fiction or non-fiction using machine learning. Various datasets of speeches, ebooks, poems, scientific papers, and texts from Project Gutenberg and the Wolfram Example Data were utilized to train and test a Markov Chain machine learning model. A microsite was deployed with the final product that returns a probability of fictionality based on input from the user with 95% accuracy.

Watersheds For Semi-Supervised Classification, Aditya Challa, Sravan Danda, B. S.Daya Sagar, Laurent Najman

Journal Articles

Watershed technique from mathematical morphology (MM) is one of the most widely used operators for image segmentation. Recently watersheds are adapted to edge weighted graphs, allowing for wider applicability. However, a few questions remain to be answered - How do the boundaries of the watershed operator behave? Which loss function does the watershed operator optimize? How does watershed operator relate with existing ideas from machine learning. In this letter, a framework is developed, which allows one to answer these questions. This is achieved by generalizing the maximum margin principle to maximum margin partition and proposing a generic solution, morphMedian, resulting …

Transfer Learning For Detecting Unknown Network Attacks, Juan Zhao, Sachin Shetty, Jan Wei Pan, Charles Kamhoua, Kevin Kwiat

VMASC Publications

Network attacks are serious concerns in today’s increasingly interconnected society. Recent studies have applied conventional machine learning to network attack detection by learning the patterns of the network behaviors and training a classification model. These models usually require large labeled datasets; however, the rapid pace and unpredictability of cyber attacks make this labeling impossible in real time. To address these problems, we proposed utilizing transfer learning for detecting new and unseen attacks by transferring the knowledge of the known attacks. In our previous work, we have proposed a transfer learning-enabled framework and approach, called HeTL, which can find the common …

Work-In-Progress Reports Submitted To The Library Of Congress As Part Of Digital Libraries, Intelligent Data Analytics, And Augmented Description, Chulwoo Pack, Yi Liu, Leen-Kiat Soh, Elizabeth Lorang

CSE Technical Reports

This document includes work-in-progress reports submitted to the Library of Congress as part of the Aida digital libraries research team's work on Digital Libraries, Intelligent Data Analytics, and Augmented Description: A Demonstration Project. These work-in-progress reports provide a snapshot glimpse, as well as underlying rationale and decision-making, at various points in the development of the project and its machine learning explorations. Reports cover explorations on historic newspapers, minimally-processed manuscript collections, materials digitized from physical originals and those digitized from microform surrogates, and investigate challenges related to image segmentation and document zoning, classification, document image quality analysis, metadata generation, and more.

Effective Plant Discrimination Based On The Combination Of Local Binary Pattern Operators And Multiclass Support Vector Machine Methods, Vi N T Le, Beniamin Apopei, Kamal Alameh

Research outputs 2014 to 2021

Accurate crop and weed discrimination plays a critical role in addressing the challenges of weed management in agriculture. The use of herbicides is currently the most common approach to weed control. However, herbicide resistant plants have long been recognised as a major concern due to the excessive use of herbicides. Effective weed detection techniques can reduce the cost of weed management and improve crop quality and yield. A computationally efficient and robust plant classification algorithm is developed and applied to the classification of three crops: Brassica napus (canola), Zea mays (maize/corn), and radish. The developed algorithm is based on the …

The Algorithmic Composition Of Classical Music Through Data Mining, Tom Donald Richmond, Imad Rahal

All College Thesis Program, 2016-2019

The desire to teach a computer how to algorithmically compose music has been a topic in the world of computer science since the 1950’s, with roots of computer-less algorithmic composition dating back to Mozart himself. One limitation of algorithmically composing music has been the difficulty of eliminating the human intervention required to achieve a musically homogeneous composition. We attempt to remedy this issue by teaching a computer how the rules of composition differ between the six distinct eras of classical music by having it examine a dataset of musical scores, rather than explicitly telling the computer the formal rules of …

Time-Series Link Prediction Using Support Vector Machines, Proceso L. Fernandez Jr, Jan Miles Co

Department of Information Systems & Computer Science Faculty Publications

The prominence of social networks motivates developments in network analysis, such as link prediction, which deals with predicting the existence or emergence of links on a given network. The Vector Auto Regression (VAR) technique has been shown to be one of the best for time-series based link prediction. One VAR technique implementation uses an unweighted adjacency matrix and five additional matrices based on the similarity metrics of Common Neighbor, Adamic-Adar, Jaccard’s Coefficient, Preferential Attachment and Research Allocation Index. In our previous work, we proposed the use of the Support Vector Machines (SVM) for such prediction task, and, using the same …

An Improved Smote Algorithm Based On Genetic Algorithm For Imbalanced Data Collection, Qiong Gu, Xian-Ming Wang, Zhao Wu, Bing Ning, Chun-Sheng Xin

Electrical & Computer Engineering Faculty Publications

Classification of imbalanced data has been recognized as a crucial problem in machine learning and data mining. In an imbalanced dataset, minority class instances are likely to be misclassified. When the synthetic minority over-sampling technique (SMOTE) is applied in imbalanced dataset classification, the same sampling rate is set for all samples of the minority class in the process of synthesizing new samples, this scenario involves blindness. To overcome this problem, an improved SMOTE algorithm based on genetic algorithm (GA), namely, GASMOTE was proposed. First, GASMOTE set different sampling rates for different minority class samples. A combination of the sampling rates …

A Comparative Study Of Threshold-Based Feature Selection Techniques, Huanjing Wang, Taghi M. Khoshgoftaar, Jason Van Hulse

Computer Science Faculty Publications

Abstract Given high-dimensional software measurement data, researchers and practitioners often use feature (metric) selection techniques to improve the performance of software quality classification models. This paper presents our newly proposed threshold-based feature selection techniques, comparing the performance of these techniques by building classification models using five commonly used classifiers. In order to evaluate the effectiveness of different feature selection techniques, the models are evaluated using eight different performance metrics separately since a given performance metric usually captures only one aspect of the classification performance. All experiments are conducted on three Eclipse data sets with different levels of class imbalance. The …