Digital Commons Network^™

Enabling The Clinical Application Of Artificial Intelligence In Genomics: A Perspective Of The Amia Genomics And Translational Bioinformatics Workgroup, Nephi A Walton, Radha Nagarajan, Chen Wang, Murat Sincan, Robert R Freimuth, David B Everman, Derek C Walton, Scott P Mcgrath, Dominick J Lemas, Panayiotis V Benos, Alexander V Alekseyenko, Qianqian Song, Ece Gamsiz Uzun, Casey Overby Taylor, Alper Uzun, Thomas Nate Person, Nadav Rappoport, Zhongming Zhao, Marc S Williams

Faculty, Staff and Student Publications

OBJECTIVE: Given the importance AI in genomics and its potential impact on human health, the American Medical Informatics Association-Genomics and Translational Biomedical Informatics (GenTBI) Workgroup developed this assessment of factors that can further enable the clinical application of AI in this space.

PROCESS: A list of relevant factors was developed through GenTBI workgroup discussions in multiple in-person and online meetings, along with review of pertinent publications. This list was then summarized and reviewed to achieve consensus among the group members.

CONCLUSIONS: Substantial informatics research and development are needed to fully realize the clinical potential of such technologies. The development of …

Machine-Learning-Assisted Leak Detection Using Distributed Temperature And Acoustic Sensors, Hayden Gemeinhardt, Jyotsna Sharma

Faculty Publications

Small leaks, many of which often go undetected using conventional gauges, remain an urgent problem for an aging pipeline infrastructure. This article proposes a method that allows for an automated and robust leak detection and localization system by fusing distributed acoustic sensor (DAS) and distributed temperature sensor (DTS) data for machine learning. Distributed fiber-optic sensing creates an advantage over conventional gauges by providing real-time continuous measurements along the entire length of the fiber-instrumented pipeline at a high spatiotemporal resolution and sensitivity that can detect small leaks, as well as the leak location. High sensitivity, however, can create noisy data. Thus, …

Reusing Deep Learning Models: Challenges And Directions In Software Engineering, James C. Davis, Purvish Jajal, Wenxin Jiang, Taylor R. Schorlemmer, Nicholas Synovic, George K. Thiruvathukal

Computer Science: Faculty Publications and Other Works

Deep neural networks (DNNs) achieve state-of-the-art performance in many areas, including computer vision, system configuration, and question-answering. However, DNNs are expensive to develop, both in intellectual effort (e.g., devising new architectures) and computational costs (e.g., training). Re-using DNNs is a promising direction to amortize costs within a company and across the computing industry. As with any new technology, however, there are many challenges in re-using DNNs. These challenges include both missing technical capabilities and missing engineering practices. This vision paper describes challenges in current approaches to DNN re-use. We summarize studies of re-use failures across the spectrum of re-use techniques, …

Detection And Classification Of Sporadic E Using Convolutional Neural Networks, J. A. Ellis, Daniel J. Emmons, M. B. Cohen

Faculty Publications

In this work, convolutional neural networks (CNN) are developed to detect and characterize sporadic E (E_s), demonstrating an improvement over current methods. This includes a binary classification model to determine if Es is present, followed by a regression model to estimate the Es ordinary mode critical frequency (foEs), a proxy for the intensity, along with the height at which the Es layer occurs (hEs). Signal-to-noise ratio (SNR) and excess phase profiles from six Global Navigation Satellite System (GNSS) radio occultation (RO) missions during the years 2008–2022 are used as the inputs of the model. Intensity (foEs) and the …

Impact Of Weather Factors On Airport Arrival Rates: Application Of Machine Learning In Air Transportation, Robert W. Maxson, Dothang Truong, Woojin Choi

Journal of Aviation Technology and Engineering

Weather is responsible for approximately 70% of air transportation delays in the National Airspace System, and delays resulting from convective weather alone cost airlines and passengers millions of dollars each year due to delays that could be avoided. This research sought to establish relationships between environmental variables and airport efficiency estimates by data mining archived weather and airport performance data at ten geographically and climatologically different airports. Several meaningful relationships were discovered from six out of ten airports using various machine learning methods within an overarching data mining protocol, and the developed models were tested using historical data.

Training Data Augmentation With Data Distilled By Principal Component Analysis, Nikolay Metodiev Sirakov, Tahsin Shahnewaz, Arie Nakhmmani

Faculty Publications

This work develops a new method for vector data augmentation. The proposed method applies principal component analysis (PCA), determines the eigenvectors of a set of training vectors for a machine learning (ML) method and uses them to generate the distilled vectors. The training and PCA-distilled vectors have the same dimension. The user chooses the number of vectors to be distilled and augmented to the set of training vectors. A statistical approach determines the lowest number of vectors to be distilled such that when augmented to the original vectors, the extended set trains an ML classifier to achieve a required accuracy. …

Emotion Recognition Using Various Measures And Computational Methods: Review, Abir Hamrouni, Fatima Bendella

Iraqi Journal for Computer Science and Mathematics

Emotion recognition has garnered significant attention as a burgeoning research domain, owing to its potential applications across diverse fields such ashuman-computer interaction, affective gaming, marketing, and human-robotinteraction.Accurately interpreting and appropriately responding to human emotions remains a critical challenge in the development of systems. This obstacle necessitates a thoroughunderstanding of emotions to enhance user experiences within suchsystems.This paper conducts a comprehensive review focusing on advancements in emotion recognition techniques, with an emphasis on leveraging a variety of sensors and computational methods. Our study findingshighlight the significantimprovement to emotion recognition accuracy when multiple measures and computational methods, rather than a single modality, …

Early Detection Of Cardiovascular Disease Utilizing Machine Learning Techniques: Evaluating The Predictive Capabilities Of Seven Algorithms, Maad M. Mijwil, Alaa K. Faieq, Mohammad Aljanabi

Iraqi Journal for Computer Science and Mathematics

Heart disease is the leading cause of death indeveloped countries, as it causes many deaths annually. Despite the availability of effective treatments, heartdisease remains a significant challenge to public health, so early detection is essential in enhancing patient outcomesand reducing mortality.Artificial intelligence seeks to help physicians make the right decisions about a patient's health condition. In this regard, the authors decided to utilize machine learning techniques (k-nearest neighbor, decision tree, linear regression, support vector machine, naïve bayes, multilayer perceptron, random forest) to contribute to the classification of the heart disease dataset, where it is determined whether a person is suffering …

Intelligent Traffic-Service Mapping Of Network For Advanced Industrial Iot Edge Computing, Bowen Liu, Tao Zheng, Kyi Thar, Mikael Gidlund, Xiaoting Ma, Bo Lei, Hongke Zhang, Mohsen Guizani

Machine Learning Faculty Publications

The increasing number of IoT devices in the network brings new challenges to the network carrying capacity of intelligent edge computing, and the complicated network services make the demand for network resources in industrial production scenarios or ordinary network users often exceed the carrying capacity of the edge computing network. To alleviate this problem, this paper proposes an intelligent edge computing architecture that introduces network service identification, extracts and analyses the data characteristics of network traffic, and designs appropriate algorithms to classify network traffic into six different service types. This enables real-time and computing-requiring tasks to be prioritised in the …

Insertion Detection System Employing Neural Network Mlp And Detection Trees Using Different Techniques, M. H. Al-Mashagbeh, W. Salameh, A. B. Alamareen, S. Abu Asal

Journal of Statistics Applications & Probability

by addressing intruder attacks, network security experts work to maintain services available at all times. The Intrusion Detection System (IDS) is one of the available mechanisms for detecting and classifying any abnormal behavior. As a result, the IDS must always be up to date with the most recent intruder attack signatures to maintain the confidentiality, integrity, and availability of the services. This paper shows how the NSL-KDD dataset may be used to test and evaluate various Machine Learning techniques. It focuses mostly on the NLS-KDD pre-processing step to create an acceptable and balanced experimental data set to improve accuracy and …

Integrating Uav Hyperspectral Data And Radiative Transfer Model Simulation To Estimate Maize Leaf And Canopy Nitrogen Content, J. Li, Y Ge, L. A. Puntel, D. Heeren, G. Bai, G. R. Balboa, J. A. Gamon, T. J. Arkebauer, Y. Shi

School of Natural Resources: Faculty Publications

No abstract provided.

Piloting Of Specific Common Tools In Support Of Digital And Online Learning, Orlin Marinov, Karlis Valtins, Janina Fengel, K.C. O'Rourke, Lubomir Dimitrov, Rossen Radonov

Conference Proceedings

This publication is an insightful exploration into the evolving landscape of digital and online learning, with a specific focus on the pilot testing and implementation of innovative educational tools. It aims to critically assess the effectiveness of these tools in enhancing the educational experience and the overall learning outcomes in digital environments.

This manuscript is particularly relevant for educators, education technologists, policymakers, and scholars interested in the advancement of digital education. It serves as a pivotal resource, providing valuable insights into the utilization and effectiveness of digital tools in education. The publication's contribution to academic scholarship is significant, offering a …

An Integrated Framework For Video-Based Deepfake Forensic Analysis, Bashaer Mohamed Alsalami

Theses

Deepfakes are AI-generated synthetic media that use AI algorithms to mimic human faces, voices, and actions with high levels of realism. They utilize advanced machine learning and deep learning techniques to generate realistic synthetic media, making the distinction between real and fake content increasingly difficult. Deepfakes were initially developed for entertainment and creative applications. However, they are currently being misused in various domains, posing significant security and ethical challenges. These risks necessitate effective deepfake detection systems to mitigate potential harm and preserve the integrity of digital communication. Nonetheless, the existing detection methods are inadequate and lack a holistic detection mechanism, …

Orthogonal Single-View And Multi-View Feature Selection Models Via Spectral Theory Based Methods, Zachary Chairez

Mathematics Dissertations - Archive

As the digital world continues to grow in the age of big data, there beckons a need for efficient and robust methods for data exploration. Under the umbrella of machine learning, feature selection positions itself as a fruitful approach that uncovers buried truths and illuminates important features of data, all while minimizing long-term storage requirements. In this work, we introduce a set of novel single-view and multi-view supervised feature selection models which are embedded with orthogonality constraints to maintain data's structural integrity while confining the optimal solution's search space.

Taking advantage of the underlying framework of these types of models, …

Engravings, Secrets, And Interpretability Of Neural Networks, Nathaniel Hobbs, Periklis A Papakonstantinou, Jaideep Vaidya

Faculty, Staff and Student Publications

This work proposes a definition and examines the problem of undetectably engraving special input/output information into a Neural Network (NN). Investigation of this problem is significant given the ubiquity of neural networks and society's reliance on their proper training and use. We systematically study this question and provide (1) definitions of security for secret engravings, (2) machine learning methods for the construction of an engraved network, (3) a threat model that is instantiated with state-of-the-art interpretability methods to devise distinguishers/attackers. In this work, there are two kinds of algorithms. First, the constructions of engravings through machine learning training methods. Second, …

Machine Learning-Based Disease Classification In Tomato (Solanum Lycopersicum) Plants = Klasifikasi Penyakit Berbasis Pembelajaran Mesin Pada Tanaman Tomat (Solanum Lycopersicum), Md Towfiqur Rahman, Sudipto Dhar Dipto, Israt Jahan June, Abdul Momin, Muhammad Rashed Al Mamun

Department of Agricultural and Biological Systems Engineering: Faculty Publications

English abstract

In Bangladesh, tomato cultivation faces significant challenges due to its susceptibility to various microorganisms, parasites, and bacterial infections. Typically, the early symptoms of these diseases first appear in roots and leaves, complicating timely detection. This study addresses the challenge of timely and accurate detection of diseases in tomato plants, crucial for effective plant protection management. Conventional manual inspection methods are time-consuming and subjective, resulting in delays in implementing necessary protection measures. Therefore, an image processing technique and machine learning algorithms were used for rapid and robust detection of diseases in tomato plant leaves, aiming to streamline the detection …

Automatic Hemorrhage Segmentation In Brain Ct Scans Using Curriculum-Based Semi-Supervised Learning, Solayman H. Emon, Tzu-Liang (Bill) Tseng, Michael Pokojovy, Peter Mccaffrey, Scott Moen, Md Fashiar Rahman

Mathematics & Statistics Faculty Publications

One of the major neuropathological consequences of traumatic brain injury (TBI) is intracranial hemorrhage (ICH), which requires swift diagnosis to avert perilous outcomes. We present a new automatic hemorrhage segmentation technique via curriculum-based semi-supervised learning. It employs a pre-trained lightweight encoder-decoder framework (MobileNetV2) on labeled and unlabeled data. The model integrates consistency regularization for improved generalization, offering steady predictions from original and augmented versions of unlabeled data. The training procedure employs curriculum learning to progressively train the model at diverse complexity levels. We utilize the PhysioNet dataset to train and evaluate the proposed approach. The performance results surpass those of …

Research On Multiscale Openstreetmap In China: Data Quality Assessment With Ewm-Topsis And Gdp Modeling, Chuqiao Han, Binbin Lu, Jianghua Zheng, Danlin Yu, Shudan Zheng

Department of Earth and Environmental Studies Faculty Scholarship and Creative Works

OpenStreetMap (OSM) is a voluntary platform designed to provide free and up-to-date geographic data. Since OSM is based on multisource geographic data provided by the public, the quality of the data has become a concern of researchers. In this study, a unified measure for evaluating OSM data quality was constructed, and the entropy weight method (EWM) and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) model were used to evaluate the quality of multiscale OSM data in China from 2014 to 2020. In addition to evaluating the data quality, the use of OSM data quality index factors in …

Adaptive Multi-Label Classification On Drifting Data Streams, Martha Roseberry

Theses and Dissertations

Drifting data streams and multi-label data are both challenging problems. When multi-label data arrives as a stream, the challenges of both problems must be addressed along with additional challenges unique to the combined problem. Algorithms must be fast and flexible, able to match both the speed and evolving nature of the stream. We propose four methods for learning from multi-label drifting data streams. First, a multi-label k Nearest Neighbors with Self Adjusting Memory (ML-SAM-kNN) exploits short- and long-term memories to predict the current and evolving states of the data stream. Second, a punitive k nearest neighbors algorithm with a self-adjusting …

Revolutionizing Campus Communication: Nlp-Powered University Chatbots, Ritu Ramakrishnan, Priyanka Thangamuthu, Austin Nguyen, Jinzhu Gao

Pacific Faculty Work

Artificial intelligence (AI) based chatbots leverage programmed software instructions to simulate human speech and user interaction. These versatile tools can be employed in various domains, from managing smart home devices to providing personal virtual assistants. They can also be useful in responding to common queries and can make information easier to access. In response to this need, we developed a specialized chatbot tailored for the academic environment by training an NLP model to answer frequently asked questions (FAQs) the need of searching through the university website. The main goal is to optimize user engagement and streamline information retrieval within a …

Synthetic Health Data: Real Ethical Promise And Peril, W. Nicholson Price Ii, Daniel Susser

Other Publications

Modern health research and development faces a dilemma. On the one hand, there is more data than ever — in electronic health records, in lab research, in public datasets, and on the internet — from which to extract potentially transformative scientific insights and to use as the basis for developing breakthrough health care technologies. On the other hand, using this data entails various risks: threats to patient privacy, skewed samples and approaches to analysis that can perpetuate demographic and other biases, and uneven access to data about rare conditions and small patient subgroups. Generating synthetic data has emerged as one …

Selecting And Evaluating Key Mds-Updrs Activities Using Wearable Devices For Parkinson's Disease Self-Assessment, Yuting Zhao, Xulong Wang, Xiyang Peng, Ziheng Li, Fengtao Nan, Menghui Zhuo, Jun Qi, Yun Yang, Zhong Zhao, Lida Xu, Po Yang

Information Technology & Decision Sciences Faculty Publications

Parkinson's disease (PD) is a complex neurodegenerative disease in the elderly. This disease has no cure, but assessing these motor symptoms will help slow down that progression. Inertial sensing-based wearable devices (ISWDs) such as mobile phones and smartwatches have been widely employed to analyse the condition of PD patients. However, most studies purely focused on a single activity or symptom, which may ignore the correlation between activities and complementary characteristics. In this paper, a novel technical pipeline is proposed for fine-grained classification of PD severity grades, which identify the most representative activities. We also propose a multi-activities combination scheme based …

Trading Cloud Computing Stocks Using Sma, Xianrong Zheng, Lingyu Li

Information Technology & Decision Sciences Faculty Publications

As cloud computing adoption becomes mainstream, the cloud services market offers vast profits. Moreover, serverless computing, the next stage of cloud computing, comes with huge economic potential. To capitalize on this trend, investors are interested in trading cloud stocks. As high-growth technology stocks, investing in cloud stocks is both rewarding and challenging. The research question here is how a trading strategy will perform on cloud stocks. As a result, this paper employs an effective method—Simple Moving Average (SMA)—to trade cloud stocks. To evaluate its performance, we conducted extensive experiments with real market data that spans over 23 years. Results show …

Ambient Temperature Modelling With Ecostress And Private Weather Stations, Gaurav Khatri

Theses

This thesis explores the development and application of a novel data architecture for predicting ambient temperatures across US cities, focusing on integrating multi-source data i.e. ECOSTRESS land surface temperatures, urban surface properties, and crowdsourced weather data. The methodology is designed for scalability and adaptability across different urban regions, employing rigorous data quality control to enhance prediction accuracy. The validation of this model across diverse urban settings, demonstrated through rigorous RMSE comparisons and spatial mapping, validates its superiority over traditional models. Through experiments in diverse climatic conditions in Madison, Wisconsin, and Las Vegas, Nevada, the study assesses the model’s generalizability and …

Novel Approach To Music Analysis Using Apache Spark, Nidhi Zare

Master's Projects

Music is one of the most common source of entertainment. Every user has their own taste of music and prefer to listen music that adheres to their taste and mood. There are various categories, called as music genres in which music can be classified. This research project addresses the challenge in music genre classification by using various deep learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Very Deep Convolutional Networks (VGGNet), ResNet and others. The primary objective of this research is to enhance the accuracy of music genre classification using a distributed computing framework Apache Spark. …

Robustness Of Learning Models To Label Flipping Attacks, Sarvagya Bhargava

Master's Projects

In this paper we compare traditional machine learning and deep learning models

trained on a malware dataset when subjected to adversarial attack based on label- flipping. Specifically, we investigate the robustness of different models when faced

with varying percentages of misleading labels, assessing their ability to maintain their accuracy in the face of such adversarial manipulations of the training data. This research aims to provide insights into which models are more robust, in the sense of being better able to resist intentional disruptions to the training data. We find that traditional machine learning models and boosting techniques are more robust …

Wall-E: An Autonomous Ai Rover For Precision Agriculture, Simar Ghumman

Master's Projects

Unmanned Ground Vehicles (UGVs) are emerging as a crucial tool in the world of precision agriculture. By working with UGVs equipped with machine learning, we can find solutions to a range of complex agricultural problems. My project, titled “Wall-E: Artificial Intelligence Robot for Precision Agriculture,” focuses on developing a UGV capable of navigating through agriculture fields autonomously while capturing data. Using machine learning, computer vision, and other sensor technologies, Wall-E is capable of estimating the total yield of crops, self-localization, mapping its environment in real time, and avoiding obstacles along its route. The purpose of this project is to automate …

The Impact Of Domain Name Server (Dns) Over Hypertext Transfer Protocol Secure (Https) On Cyber Security: Limitations, Challenges, And Detection Techniques, Muhammad Dawood, Shanshan Tu, Chuangbai Xiao, Muhammad Haris, Hisham Alasmary, Muhammad Waqas, Sadaqat Ur Rehman

Research outputs 2022 to 2026

The DNS over HTTPS (Hypertext Transfer Protocol Secure) (DoH) is a new technology that encrypts DNS traffic, enhancing the privacy and security of end-users. However, the adoption of DoH is still facing several research challenges, such as ensuring security, compatibility, standardization, performance, privacy, and increasing user awareness. DoH significantly impacts network security, including better end-user privacy and security, challenges for network security professionals, increasing usage of encrypted malware communication, and difficulty adapting DNS-based security measures. Therefore, it is important to understand the impact of DoH on network security and develop new privacy-preserving techniques to allow the analysis of DoH traffic …

Predictive Analytics In Bronchopulmonary Dysplasia: Past, Present, And Future, Bryan G Mcomber, Alvaro G Moreira, Kelsey Kirkman, Sebastian Acosta, Craig Rusin, Binoy Shivanna

Faculty, Staff and Students Publications

Bronchopulmonary dysplasia (BPD) remains a significant complication of prematurity, impacting approximately 18,000 infants annually in the United States. Advances in neonatal care have not reduced BPD, and its management is challenged by the rising survival of extremely premature infants and the variability in clinical practices. Leveraging statistical and machine learning techniques, predictive analytics can enhance BPD management by utilizing large clinical datasets to predict individual patient outcomes. This review explores the foundations and applications of predictive analytics in the context of BPD, examining commonly used data sources, modeling techniques, and metrics for model evaluation. We also highlight bioinformatics' potential role …

Benchmarking And Enhancing Generalization In Multilingual Speech Emotion Recognition, Mohamed Osman Ismael

Theses and Dissertations

Speech Emotion Recognition (SER) is pivotal in advancing human-computer interaction by enabling machines to understand and respond to human emotions. Despite significant progress with self-supervised learning models, SER systems often struggle with generalization across diverse languages and unseen data distributions, limiting their real-world applicability. This thesis addresses these challenges by first introducing a large-scale benchmark to evaluate the robustness and adaptability of state-of-the-art SER models in both in-domain and out-of-domain settings. The benchmark includes a diverse set of multilingual datasets, emphasizing cross-lingual and out-of-domain evaluations to assess model generalization. Surprisingly, we find that the Whisper model, originally designed for automatic …