Computer Sciences Commons^™

Efficient Machine Learning On Scientific Data Using Bayesian Optimization, Rui Xin

Theses and Dissertations

Deep Learning is pivotal in advancing data analysis across various scientific fields, from genomics to materials discovery. Despite its widespread use, efficiently learning from limited data and operating under resource constraints remains a significant challenge, often limiting its full potential in environments where data is scarce or resources are restricted. This dissertation explores Active Learning and Automated Machine Learning (AutoML) powered by Bayesian Optimization to enhance the efficiency of machine learning across multiple disciplines. It focuses on algorithm optimization and data management through three interconnected studies. In the first study, we investigate how data management technique - active learning helps …

Multi-Case Study Of Left-Flank Boundaries Within Supercells, Peyton B. Stevenson

Department of Earth and Atmospheric Sciences: Dissertations, Theses, and Student Research

This study investigates the prevalence and significance of forward-flank convergence boundaries (FFCBs) and left-flank convergence boundaries (LFCBs) in shaping the structure and intensity of supercells, using observational data from various field projects. Unlike previous research focusing on individual cases, this study examines a diverse range of cases to provide comprehensive insights into the relationship between these boundaries and supercell characteristics such as intensity, longevity, and tornadogenesis. By analyzing high-resolution surface data, the research addresses the frequency, location, and intensity of these boundaries, and their impact on pseudo vertical vorticity, pseudo convergence, and density gradients. A total of 228 boundary identifications …

Configurable Mirror Descent : Towards A Unification Of Decision Making, Pengdeng Li, Shuxin Li, Chang Yang, Xinrun Wang, Hau Chan, Bo An

Research Collection School Of Computing and Information Systems

Decision-making problems, categorized as single-agent, e.g., Atari, cooperative multi-agent, e.g., Hanabi, competitive multi-agent, e.g., Hold’em poker, and mixed cooperative and competitive, e.g., football, are ubiquitous in the real world. Although various methods have been proposed to address the specific decision-making categories, these methods typically evolve independently and cannot generalize to other categories. Therefore, a fundamental question for decision-making is: Can we develop a single algorithm to tackle ALL categories of decision-making problems? There are several main challenges to address this question: i) different decision-making categories involve different numbers of agents and different relationships between agents, ii) different categories have different …

Foxann: A Method For Boosting Neural Network Performance, Mahmood A. Jumaah, Yossra H. Ali, Tarik A. Rashid, S. Vimal

Journal of Soft Computing and Computer Applications

Artificial neural networks play a crucial role in machine learning and there is a need to improve their performance. This paper presents FOXANN, a novel classification model that combines the recently developed Fox optimizer with ANN to solve ML problems. Fox optimizer replaces the backpropagation algorithm in ANN; optimizes synaptic weights; and achieves high classification accuracy with a minimum loss, improved model generalization, and interpretability. The performance of FOXANN is evaluated on three standard datasets: Iris Flower, Breast Cancer Wisconsin, and Wine. The results presented in this paper are derived from 100 epochs using 10-fold cross-validation, ensuring that all dataset …

Surveying Machine Learning In Cyberattack Datasets: A Comprehensive Analysis, Azhar F. Al-Zubidi, Alaa Kadhim Farhan, El-Sayed M. El-Kenawy

Journal of Soft Computing and Computer Applications

Cyberattacks have become one of the most significant security threats that have emerged in the last couple of years. It is imperative to comprehend such attacks; thus, analyzing various kinds of cyberattack datasets assists in constructing the precise intrusion detection models. This paper tries to analyze many of the available cyberattack datasets and compare them with many of the fields that are used to detect and predict cyberattack, like the Internet of Things (IoT) traffic-based, network traffic-based, cyber-physical system, and web traffic-based. In the present paper, an overview of each of them is provided, as well as the course of …

Addressing Social Inequalities Using Ai, Big Data, And Machine Learning, Erica L. Jensen, Lakell Archer, Sumaya Ali

Journal of Nonprofit Innovation

No abstract provided.

Assessment And Prediction Of Meteorological Drought Using Machine Learning Algorithms And Climate Data, Khalid En-Nagre, Mourad Aqnouy, Ayoub Ouarka, Syed Ali Asad Naqvi, Ismail Bouizrou, Jamal Eddine Stitou El Messari, Aqil Tariq, Walid Soufan, Wenzhao Li, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Monitoring drought in semi-arid regions due to climate change is of paramount importance. This study, conducted in Morocco’s Upper Drâa Basin (UDB), analyzed data spanning from 1980 to 2019, focusing on the calculation of drought indices, specifically the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) at multiple timescales (1, 3, 9, 12 months). Trends were assessed using statistical methods such as the Mann-Kendall test and the Sen’s Slope estimator. Four significant machine learning (ML) algorithms, including Random Forest, Voting Regressor, AdaBoost Regressor, and K-Nearest Neighbors Regressor, were evaluated to predict the SPEI values for both three …

Integration Of Machine Learning In Structural Health Monitoring For Damage Identification And Response Prediction In Bridges, Naga Lakshmi Chittitalli Ravuri

Theses and Dissertations

Machine learning-based structural health monitoring (ML-SHM) plays a pivotal role in enhancing structural resilience. By recognizing potential hazards, implementing resistance measures, facilitating swift recovery, and continuously monitoring structural health, ML-SHM ensures proactive maintenance and minimizes recovery delays post-events. Leveraging machine learning algorithms and sensor data, ML-SHM enables early detection of anomalies, prediction of failures, and adaptive responses, enhancing the structure's ability to withstand and recover from adverse conditions. This integrated approach not only improves the structure's performance and adaptability but also contributes to overall safety and longevity. This thesis presents a comprehensive exploration of structural health monitoring (SHM) techniques for …

A Meta-Ensemble Predictive Model For The Risk Of Lung Cancer, Sideeqoh Oluwaseun Olawale-Shosanya, Olayinka Olufunmilayo Olusanya, Adeyemi Omotayo Joseph, Kabir Oluwatobi Idowu, Oyelade Babatunde Eriwa, Adedeji Oladimeji Adebare, Morufat Adebola Usman

Al-Bahir

The lungs play a vital role in supplying oxygen to every cell, filtering air to prevent harmful substances, and supporting defense mechanisms. However, they remain susceptible to the risk of diseases such as infections, inflammation, and cancer that affect the lungs. Meta-ensemble techniques are prominent methods used in machine learning to enhance the accuracy of classifier learning systems in making predictions. This work proposes a robust predictive model using a meta-ensemble method to identify high-risk individuals with lung cancer, thereby taking early action to prevent long-term problems benchmarked upon the Kaggle Machine Learning practitioners' Lung Cancer Dataset. Three machine learning …

How To Propagate Uncertainty Via Ai Algorithms, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Any data processing starts with measurement results. Measurement results are never absolutely accurate. Because of this measurement uncertainty, the results of processing measurement results are, in general, somewhat different from what we would have obtained if we knew the exact values of the measured quantities. To make a decision based on the result of data processing, we need to know how accurate is this result, i.e., we need to propagate the measurement uncertainty through the data processing algorithm. There are many techniques for uncertainty propagation. Usually, they involve applying the same data processing algorithm several times to appropriately modified data. …

How To Make Ai More Reliable, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

One of the reasons why the results of the current AI methods (especially deep-learning-based methods) are not absolutely reliable is that, in contrast to more traditional data processing techniques which are based on solid mathematical and statistical foundations, modern AI techniques use a lot of semi-heuristic methods. These methods have been, in many cases, empirically successful, but the absence of solid justification makes us less certain that these methods will work in other cases as well. To make AI more reliable, it is therefore necessary to provide mathematical foundations for the current semi-heuristic techniques. In this paper, we show that …

Toward Generalist Anomaly Detection Via In-Context Residual Learning With Few-Shot Sample Prompts, Jiawen Zhu, Guansong Pang

Research Collection School Of Computing and Information Systems

This paper explores the problem of Generalist Anomaly Detection (GAD), aiming to train one single detection model that can generalize to detect anomalies in diverse datasets from different application domains without any further training on the target data. Some recent studies have showed that large pre-trained Visual-Language Models (VLMs) like CLIP have strong generalization capabilities on detecting industrial defects from various datasets, but their methods rely heavily on handcrafted text prompts about defects, making them difficult to generalize to anomalies in other applications, e.g., medical image anomalies or semantic anomalies in natural images. In this work, we propose to train …

Anomaly Heterogeneity Learning For Open-Set Supervised Anomaly Detection, Jiawen Zhu, Choubo Ding, Yu Tian, Guansong Pang

Research Collection School Of Computing and Information Systems

Open-set supervised anomaly detection (OSAD) - a recently emerging anomaly detection area - aims at utilizing a few samples of anomaly classes seen during training to detect unseen anomalies (i.e., samples from open-set anomaly classes), while effectively identifying the seen anomalies. Benefiting from the prior knowledge illustrated by the seen anomalies, current OSAD methods can often largely reduce false positive errors. However, these methods are trained in a closed-set setting and treat the anomaly examples as from a homogeneous distribution, rendering them less effective in generalizing to unseen anomalies that can be drawn from any distribution. This paper proposes to …

D-Hacking, Emily Black, Talia B. Gillis, Zara Hall

Faculty Scholarship

Recent regulatory efforts, including Executive Order 14110 and the AI Bill of Rights, have focused on mitigating discrimination in AI systems through novel and traditional application of anti-discrimination laws. While these initiatives rightly emphasize fairness testing and mitigation, we argue that they pay insufficient attention to robust bias measurement and mitigation — and that without doing so, the frameworks cannot effectively achieve the goal of reducing discrimination in deployed AI models. This oversight is particularly concerning given the instability and brittleness of current algorithmic bias mitigation and fairness optimization methods, as highlighted by growing evidence in the algorithmic fairness literature. …

Sensing With Integrity: Responsible Sensor Systems In An Era Of Ai, David Eisenberg

Dissertations

Deep and machine learning now offer immense benefits for consumer choice, decision-making, medicine, mental health and education, smart cities, and intelligent transportation and driver safety. However, as communication and Internet technology further advances, these benefits have the potential to be outweighed by compromises to privacy, personal freedom, consumer trust, and discrimination. While ethical consequences for personal freedom and equity rise from these technological advances, the issue may not be the technology itself but a lack of regulation and policy that allow abuses to occur. A first study examines how emerging sensor-based technologies, limited to only accelerometer and gyroscope data from …

A Machine Learning-Assisted Steering And Scheduling Framework For Big-Data Scientific Workflows On Heterogeneous Computing Platforms, Yijie Zhang

Dissertations

In next-generation scientific applications, the exponential growth of big data necessitates advanced techniques for efficient data storage, processing, and analysis. This has led to the construction of intricate computing workflows, managed and orchestrated by powerful engines in big data systems as exemplified by Hadoop. As scientific applications increasingly shift towards simulation-centric approaches, traditional methodologies face new challenges in accommodating the complexity of extreme-scale numerical modeling with numerous tunable parameters. To address these challenges, this dissertation propose to develop a machine learning-assisted framework that enables autonomous computational steering of scientific simulations and optimized execution of big-data workflows on heterogeneous platforms. This …

The Next Strike: Pioneering Forward-Thinking Attack Techniques With Rowhammer In Dram Technologies, Nakul Kochar

Theses

In the realm of DRAM technologies this study investigates RowHammer vulnerabilities in DDR4 DRAM memory across various manufacturers, employing advanced multi-sided fault injection techniques to impose attack strategies directly on physical memory rows. Our novel approach, diverging from traditional victim-focused methods, involves strategically allocating virtual memory rows to their physical counterparts for more potent attacks. These attacks, exploiting the inherent weaknesses in DRAM design, are capable of inducing bit flips in a controlled manner to undermine system integrity. We employed a strategy that compromised system integrity through a nuanced approach of targeting rows situated at a distance of two rows …

Missing Wedge Completion Via Unsupervised Learning With Coordinate Networks, Dave Van Veen, Jesús G Galaz-Montoya, Liyue Shen, Philip Baldwin, Akshay S Chaudhari, Dmitry Lyumkis, Michael F Schmid, Wah Chiu, John Pauly

Faculty, Staff and Students Publications

Cryogenic electron tomography (cryoET) is a powerful tool in structural biology, enabling detailed 3D imaging of biological specimens at a resolution of nanometers. Despite its potential, cryoET faces challenges such as the missing wedge problem, which limits reconstruction quality due to incomplete data collection angles. Recently, supervised deep learning methods leveraging convolutional neural networks (CNNs) have considerably addressed this issue; however, their pretraining requirements render them susceptible to inaccuracies and artifacts, particularly when representative training data is scarce. To overcome these limitations, we introduce a proof-of-concept unsupervised learning approach using coordinate networks (CNs) that optimizes network weights directly against input …

High-Dimensional Data Analysis Using Parameter Free Algorithm Data Point Positioning Analysis, S. M. F. D. Syed Mustapha

All Works

Clustering is an effective statistical data analysis technique; it has several applications, including data mining, pattern recognition, image analysis, bioinformatics, and machine learning. Clustering helps to partition data into groups of objects with distinct characteristics. Most of the methods for clustering use manually selected parameters to find the clusters from the dataset. Consequently, it can be very challenging and time-consuming to extract the optimal parameters for clustering a dataset. Moreover, some clustering methods are inadequate for locating clusters in high-dimensional data. To address these concerns systematically, this paper introduces a novel selection-free clustering technique named data point positioning analysis (DPPA). …

Systematic Comparison Of Reservoir Computing Frameworks, Nihar S. Koppolu, Christof Teuscher

Student Research Symposium

In this poster, we present a systematic evaluation and comparison of five Reservoir computing (RC) software simulation frameworks, namely reservoirpy, RcTorch, pyRCN, pytorch-esn, and ReservoirComputing.jl. RC is a specific machine learning approach that leverages fixed, nonlinear systems to map signals into higher dimensions. Its unique strength lies in training only the readout layer, which reduces the training complexity. RC excels in temporal signal processing and is also well suited for various physical implementations. The increasing interest in RC has led to the proliferation of various RC simulation frameworks. Our RC simulation framework evaluation focuses on a feature comparison, documentation quality, …

Behavioral Intention For Ai Usage In Higher Education, Isaac A. Odai, Elliot Wiley

Student Research Symposium

This study sought to further understand the cognitive factors that influence undergraduate students' behavioral intention to use generative AI. Generative AI's presence in academic spaces opens the door for ethical and pedagogical questions. This study surveyed 51 undergraduate communication students to measure their attitudes, subjective norms, self efficacy and their behavioral intention to use GenAI for school work. The results of this study showed behavioral intent had a positive relationship with attitudes and subjective norms. The implications of these findings show that personal beliefs and the perceived beliefs of others are correlated to undergraduate students’ intent to use GenAI for …

Accuracy Of Machine Learning To Predict The Outcomes Of Shoulder Arthroplasty: A Systematic Review, Amir H. Karimi, Joshua Langberg, Ajith Malige, Omar Rahman, Joseph A. Abboud, Michael A. Stone

Department of Orthopaedic Surgery Faculty Papers

BACKGROUND: Artificial intelligence (AI) uses computer systems to simulate cognitive capacities to accomplish goals like problem-solving and decision-making. Machine learning (ML), a branch of AI, makes algorithms find connections between preset variables, thereby producing prediction models. ML can aid shoulder surgeons in determining which patients may be susceptible to worse outcomes and complications following shoulder arthroplasty (SA) and align patient expectations following SA. However, limited literature is available on ML utilization in total shoulder arthroplasty (TSA) and reverse TSA.

METHODS: A systematic literature review in accordance with PRISMA guidelines was performed to identify primary research articles evaluating ML's ability to …

Advancing Learning Models For High-Dimensional Data: From Molecular Modeling To Motion Planning, Tuan Nguyen Anh Tran

Legacy Theses & Dissertations (2009 - 2024)

Processing and analyzing high-dimensional data, particularly in domains like protein research and robotics, introduces unique challenges. In protein analysis, high-dimensional data often arises from complex molecular structures and their interactions. Analyzing protein data involves intricate computational models and algorithms that must deal with the large size of molecular datasets and the need to extract biologically relevant information. This makes it challenging to discern significant protein structure-activity relationships, understand complex biological interactions, or predict protein behavior accurately. Similarly, in robotics, high-dimensional data is prevalent when dealing with sensory inputs, the dimensionality of the robot, or the complexity of the operational environments. …

Cctfv2: Modeling Cyber Competitions, Basheer Qolomany, Tristan J. Calay, Liaquat Hossain, Aos Mulahuwaish, Jacques Bou Abdo

School of Computing Faculty Scholarship and Creative Works

Cyber competitions are usually team activities, where team performance not only depends on the members’ abilities but also on team collaboration. This seems intuitive, especially given that team formation is a well-studied discipline in competitive sports and project management, but unfortunately, team performance and team formation strategies are rarely studied in the context of cybersecurity and cyber competitions. Since cyber competitions are becoming more prevalent and organized, this gap becomes an opportunity to formalize the study of team performance in the context of cyber competitions. This work follows a cross-validating two-approach methodology. The first is the computational modeling of cyber …

Enhancing Monthly Streamflow Prediction Using Meteorological Factors And Machine Learning Models In The Upper Colorado River Basin, Saichand Thota, Ayman Nassar, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi, Pouya Hosseinzadeh

Computer Science Student Research

Streamflow prediction is crucial for planning future developments and safety measures along river basins, especially in the face of changing climate patterns. In this study, we utilized monthly streamflow data from the United States Bureau of Reclamation and meteorological data (snow water equivalent, temperature, and precipitation) from the various weather monitoring stations of the Snow Telemetry Network within the Upper Colorado River Basin to forecast monthly streamflow at Lees Ferry, a specific location along the Colorado River in the basin. Four machine learning models—Random Forest Regression, Long short-term memory, Gated Recurrent Unit, and Seasonal AutoRegresive Integrated Moving Average—were trained using …

Ethical Imperatives And Challenges: Review Of The Use Of Machine Learning For Predictive Analytics In Higher Education, Emily Barnes, James Hutson, Karriem Perry

Faculty Scholarship

The escalating integration of machine learning (ML) in higher education necessitates a critical examination of its ethical implications. This article conducts a comprehensive review of the application of ML for predictive analytics within higher education institutions (HEIs), emphasizing the technology's potential to enhance student outcomes and operational efficiency. The study identifies significant ethical concerns, such as data privacy, informed consent, transparency, and accountability, that arise from the use of ML. Through a detailed analysis of current practices, this review underscores the need for HEIs to develop robust ethical frameworks and technological infrastructures to navigate these challenges effectively. The findings reveal …

Identifying Temporomandibular Disorder Morphological Risk Factors Via Explainable Deep Learning And Multiscale Biomechanical Modeling, Shuchun Sun

All Dissertations

Clarifying multifactorial musculoskeletal disorder etiologies supports risk analysis and development of targeted prevention and treatment modalities. Deep learning enables comprehensive risk factor identification through systematic analysis of disease datasets but does not provide sufficient context for mechanistic understanding, limiting clinical applicability for etiological investigations. Conversely, multiscale biomechanical modeling can evaluate mechanistic etiology within the relevant biomechanical and physiological context. We propose a hybrid approach combining 3D explainable deep learning and multiscale biomechanical modeling; we applied this approach to investigate temporomandibular joint (TMJ) disorder etiology by systematically identifying risk factors and elucidating mechanistic relationships between risk factors and TMJ biomechanics and …

Next-Generation Crop Monitoring Technologies: Case Studies About Edge Image Processing For Crop Monitoring And Soil Water Property Modeling Via Above-Ground Sensors, Nipuna Chamara

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Artificial Intelligence (AI) has advanced rapidly in the past two decades. Internet of Things (IoT) technology has advanced rapidly during the last decade. Merging these two technologies has immense potential in several industries, including agriculture.

We have identified several research gaps in utilizing IoT technology in agriculture. One problem was the digital divide between rural, unconnected, or limited connected areas and urban areas for utilizing images for decision-making, which has advanced with the growth of AI. Another area for improvement was the farmers' demotivation to use in-situ soil moisture sensors for irrigation decision-making due to inherited installation difficulties. As Nebraska …

Escaping Saddle Points In Heterogeneous Federated Learning Via Distributed Sgd With Communication Compression, Sijin Chen, Zhize Li, Yuejie Chi

Research Collection School Of Computing and Information Systems

We consider the problem of finding second-order stationary points in the optimization of heterogeneous federated learning (FL). Previous works in FL mostly focus on first-order convergence guarantees, which do not rule out the scenario of unstable saddle points. Meanwhile, it is a key bottleneck of FL to achieve communication efficiency without compensating the learning accuracy, especially when local data are highly heterogeneous across different clients. Given this, we propose a novel algorithm PowerEF-SGD that only communicates compressed information via a novel error-feedback scheme. To our knowledge, PowerEF-SGD is the first distributed and compressed SGD algorithm that provably escapes saddle points …

Artificial Intelligence's Ability To Detect Online Predators, Olatilewa Osifeso

Electronic Theses, Projects, and Dissertations

Online child predators pose a danger to children who use the Internet. Children fall victim to online predators at an alarming rate, based on the data from the National Center of Missing and Exploited Children. When making online profiles and joining websites, you only need a name, an email and a password without identity verification. Studies have shown that online predators use a variety of methods and tools to manipulate and exploit children, such as blackmail, coercion, flattery, and deception. These issues have created an opportunity for skilled online predators to have fewer obstacles when it comes to contacting and …