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Articles 1 - 30 of 148

Full-Text Articles in Theory and Algorithms

Unique Combinations Of Packing Integer Squares, Keith M. Dreiling, Austin Leanna, William Mooney Apr 2026

Unique Combinations Of Packing Integer Squares, Keith M. Dreiling, Austin Leanna, William Mooney

SACAD: Scholarly Activities

This research investigates a function, informally named WAK(x), that describes the number of ways to divide an integer square into integer subsquares counting only the list of parts. Previous research has shown values up to 28, though finding these values is computationally complex and requires a long runtime using computer algorithms. We attempt to find patterns in the values and many aspects of the values, hoping to find a general solution. We are unsure if a solution exists, but we have ideas for how to move forward in finding a solution.

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Wearable Sensor-Based Phase Segmentation Analysis Of Front Crawl Swimming: A Scoping Review, Jonathan Simoes, Samuel Aylward, Daniel Hamze, Daniel James Goble, Daniel M. Russell, Joshua Haworth Jan 2026

Wearable Sensor-Based Phase Segmentation Analysis Of Front Crawl Swimming: A Scoping Review, Jonathan Simoes, Samuel Aylward, Daniel Hamze, Daniel James Goble, Daniel M. Russell, Joshua Haworth

Exercise Science Faculty Publications

Front crawl swimming stroke phase segmentation has historically relied on video analysis, but the development of wearable sensor technology has created new opportunities for automated phase segmentation. This scoping review mapped the available evidence on wearable sensor-based stroke phase segmentation methods in front crawl swimming, following PRISMA-ScR guidelines. A systematic search of SPORTDiscus, Web of Science, and IEEE Xplore conducted from January to June 2026, identified 15 eligible peer-reviewed studies published between 2000 and 2024. The review revealed an emerging field of research that has converged methodologically around inertial measurement units (IMUs) and the Chollet phase segmentation framework while remaining …

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Maxgrnet: A Multi-Axis Vision Transformer With Improved Generalization For Eye Disease Classification Using Explainable Ai With Insertion-Deletion Operations On Fundus Images, Md Mehedi Hasan Santo, Fuyad Hasan Bhoyan, Fuad Ibne Jashim Farhad, Fahmid Al Farid, Sovon Chakraborty, Md Humaion Kabir Mehedi, Jia Uddin, Hezerul Bin Abdul Karim Jan 2026

Maxgrnet: A Multi-Axis Vision Transformer With Improved Generalization For Eye Disease Classification Using Explainable Ai With Insertion-Deletion Operations On Fundus Images, Md Mehedi Hasan Santo, Fuyad Hasan Bhoyan, Fuad Ibne Jashim Farhad, Fahmid Al Farid, Sovon Chakraborty, Md Humaion Kabir Mehedi, Jia Uddin, Hezerul Bin Abdul Karim

Computer Science Faculty Publications

Eye diseases, including diabetic retinopathy (DR), glaucoma, and cataracts, represent a major global health concern and can lead to severe visual impairment or blindness if not identified in a timely manner. This study proposes a novel eye disease classification framework based on a multi-axis vision transformer (MaxViT) applied to color fundus images with Explainable Artificial Intelligence (XAI) techniques to enhance model transparency. The proposed architecture integrates transformer-based attention mechanisms with Global Response Normalization (GRN)-based multi-layer perceptron (MLP) layers to capture complex spatial and contextual relationships within fundus images effectively. The model was evaluated on a publicly available eye disease classification …

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Markov Chain Wave Generative Adversarial Network For Bee Bioacoustic Signal Synthesis, Kumudu Samarappuli, Iman Ardekani, Mahsa Mohaghegh, Abdolhossein Sarrafzadeh Jan 2026

Markov Chain Wave Generative Adversarial Network For Bee Bioacoustic Signal Synthesis, Kumudu Samarappuli, Iman Ardekani, Mahsa Mohaghegh, Abdolhossein Sarrafzadeh

Electrical & Computer Engineering Faculty Publications

This paper presents a framework for synthesizing bee bioacoustic signals associated with hive events. While existing approaches like WaveGAN have shown promise in audio generation, they often fail to preserve the subtle temporal and spectral features of bioacoustic signals critical for event-specific classification. The proposed method, MCWaveGAN, extends WaveGAN with a Markov Chain refinement stage, producing synthetic signals that more closely match the distribution of real bioacoustic data. Experimental results show that this method captures signal characteristics more effectively than WaveGAN alone. Furthermore, when integrated into a classifier, synthesized signals improved hive status prediction accuracy. These results highlight the potential …

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Qubit Lattice Algorithm Simulations Of The Scattering Of A Bounded Two Dimensional Electromagnetic Pulse From The Infinite Planar Dielectric Interface, Min Soe, George Vahala, Linda Vahala, Efstratios Koukoutsis, Abhay K. Ram, Kyriakos Hizanidis Jan 2026

Qubit Lattice Algorithm Simulations Of The Scattering Of A Bounded Two Dimensional Electromagnetic Pulse From The Infinite Planar Dielectric Interface, Min Soe, George Vahala, Linda Vahala, Efstratios Koukoutsis, Abhay K. Ram, Kyriakos Hizanidis

Electrical & Computer Engineering Faculty Publications

Qubit lattice algorithm (QLA) simulations are performed for a two-dimensional spatially bounded pulse propagating onto a plane interface between two dielectric slabs. QLA is an initial value scheme that consists of a sequence of unitary collision and streaming operators, with appropriate potential operators, that recover Maxwell equations in inhomogeneous dielectric media to the second order in the lattice discreteness. For the case of total internal reflection, there is transient energy transfer into the second medium due to the evanescent fields as the Poynting unit vector of the pulse is rotated from its incident to reflected direction. Because of the finite …

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Pure And Strong Nash Equilibrium Computation In Compactly Representable Aggregate Games, Jared Soundy, Mohammad T. Irfan, Hau Chan Jul 2025

Pure And Strong Nash Equilibrium Computation In Compactly Representable Aggregate Games, Jared Soundy, Mohammad T. Irfan, Hau Chan

Research & Publications

Aggregate games model interdependent decision making when an agent’s utility depends on their own choice and the aggregation of everyone's choices. We define a compactly representable subclass of aggregate games we call additive aggregate games, which encompasses popular games like congestion games, anonymous games, Schelling games, etc. We study computational questions on pure Nash equilibrium (PNE) and pure strong Nash equilibrium (SNE). We show that PNE existence is NP-complete for very simple cases of additive aggregate games. We devise an efficient algorithmic scheme for deciding the existence of a PNE and computing one (if it exists) for bounded aggregate space. …

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Algorithms For Order Statistics In Farey Sequences: A Computational Study, Connor Weyers Apr 2025

Algorithms For Order Statistics In Farey Sequences: A Computational Study, Connor Weyers

School of Computing: Dissertations, Theses, and Student Research

Farey sequences are the sets of irreducible fractions in increasing order with denominator less or equal to some integer n. They are a well-known concept in number theory problems and are related to many other concepts in number theory including integer factoring, Fibonacci sequences, and Riemann’s Zeta function. In this paper, we investigate some known algorithms to solve certain problems in Farey sequences from a computational perspective. In particular, we implement established algorithms that have not been previously implemented with the goal of creating a package that can be used more broadly. We also develop a new algorithm for rational …

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Optiselect And Enshap: Integrating Machine Learning And Game Theory For Ischemic Stroke Prediction, Pritam Chakraborty, Anjan Bandyopadhyay, Sricheta Parul, Sujata Swain, Partha Sarathy Banerjee, Tapas Si, Hong Qin, Saurav Mallik Jan 2025

Optiselect And Enshap: Integrating Machine Learning And Game Theory For Ischemic Stroke Prediction, Pritam Chakraborty, Anjan Bandyopadhyay, Sricheta Parul, Sujata Swain, Partha Sarathy Banerjee, Tapas Si, Hong Qin, Saurav Mallik

Computer Science Faculty Publications

Stroke analysis using game theory and machine learning techniques. The study investigates the use of the Shapley value in predictive ischemic brain stroke analysis. Initially, preference algorithms identify the most important features in various machine learning models, including logistic regression, K-nearest neighbor, decision tree, support vector machine (linear kernel), support vector machine ( RBF kernel), neural networks, etc. For each sample, the top 3, 4, and 5 features are evaluated and selected to evaluate their performance. The Shapley value method was used to rank the models using their best four features based on their predictive capabilities. As a result, better-performing …

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Motion Artifacts (Ma) At-Rest In Measured Arterial Pulse Signals: Time-Varying Amplitude In Each Harmonic And Non-Flat Harmonic-Ma Coupled Baseline, Md Mahfuzur Rahman, Mamun Hasan, Zhili Hao Jan 2025

Motion Artifacts (Ma) At-Rest In Measured Arterial Pulse Signals: Time-Varying Amplitude In Each Harmonic And Non-Flat Harmonic-Ma Coupled Baseline, Md Mahfuzur Rahman, Mamun Hasan, Zhili Hao

Mechanical & Aerospace Engineering Faculty Publications

Motion artifacts (MA) cause great variability in a measured arterial pulse signal, and treatment of MA solely as a baseline drift (BD) fails to eliminate its effect on the measured signal. This paper presents a study on the effect of MA at rest (< 0.7 Hz) on measured arterial pulse signals using a microfluidic-based tactile sensor. By taking full account of the dynamic behavior of the transmission path from the true pulse signal in an artery to a measured pulse signal at the sensor, the tissue-contact-sensor (TCS) stack, an analytical model of MA in a measured pulse signal is developed. In this model, the TCS stack is treated as a 1DOF system for its dynamic behavior; MA is quantified as the displacement (i.e., BD) and time-varying system parameters (TVSP) of the TCS stack. The mathematical expression of MA in a measured pulse signal reveals that while BD remains as low-frequency additive noise, TVSP causes time-varying harmonics in a measured pulse signal. Further time-frequency analysis (TFA) of measured pulse signals validates the existence of TVSP and, for the first time, reveals its effect on a measured pulse signal: time-varying amplitude in each harmonic and non-flat harmonic-MA-coupled baseline.

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Long-Term Traffic Prediction Using Deep Learning Long Short-Term Memory, Ange-Lionel Toba, Sameer Kulkarni, Wael Khallouli, Timothy Pennington Jan 2025

Long-Term Traffic Prediction Using Deep Learning Long Short-Term Memory, Ange-Lionel Toba, Sameer Kulkarni, Wael Khallouli, Timothy Pennington

School of Cybersecurity Faculty Publications

Traffic conditions are a key factor in our society, contributing to quality of life and the economy, as well as access to professional, educational, and health resources. This emphasizes the need for a reliable road network to facilitate traffic fluidity across the nation and improve mobility. Reaching these characteristics demands good traffic volume prediction methods, not only in the short term but also in the long term, which helps design transportation strategies and road planning. However, most of the research has focused on short-term prediction, applied mostly to short-trip distances, while effective long-term forecasting, which has become a challenging issue …

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Automating The Amino Acid Identification In Elliptical Dichroism Spectrometer With Machine Learning, Ridhanya Sree Balamurugan, Yusuf Asad, Tommy Gao, Dharmakeerthi Nawarathna, Umamaheswara Rao Tida, Dali Sun Jan 2025

Automating The Amino Acid Identification In Elliptical Dichroism Spectrometer With Machine Learning, Ridhanya Sree Balamurugan, Yusuf Asad, Tommy Gao, Dharmakeerthi Nawarathna, Umamaheswara Rao Tida, Dali Sun

Electrical & Computer Engineering Faculty Publications

Amino acid identification is crucial across various scientific disciplines, including biochemistry, pharmaceutical research, and medical diagnostics. However, traditional methods such as mass spectrometry require extensive sample preparation and are time-consuming, complex and costly. Therefore, this study presents a pioneering Machine Learning (ML) approach for automatic amino acid identification by utilizing the unique absorption profiles from an Elliptical Dichroism (ED) spectrometer. Advanced data preprocessing techniques and ML algorithms to learn patterns from the absorption profiles that distinguish different amino acids were investigated to prove the feasibility of this approach. The results show that ML can potentially revolutionize the amino acid analysis …

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A Fast Framework For Generating Radioactive Mixture Spectra And Its Application To Remote High-Performance Mixture Identification, Chiman Kwan, Bulent Ayhan, Adam Stavola, Kazi Aminul Islam, Hongfang Zhang, Jiang Li Jan 2025

A Fast Framework For Generating Radioactive Mixture Spectra And Its Application To Remote High-Performance Mixture Identification, Chiman Kwan, Bulent Ayhan, Adam Stavola, Kazi Aminul Islam, Hongfang Zhang, Jiang Li

Electrical & Computer Engineering Faculty Publications

Remote detection of radioactive materials in mixtures using handheld or portal detectors remains a challenge because of factors such as low concentration, environmental interference, sensor noise, and other complications. This work introduces a fast framework for generating realistic mixture spectra. Moreover, we present mixture isotope identification using data generated by the fast framework. Researchers have examined a range of conventional and recent algorithms within the fields of machine learning and deep learning. An application to uranium enrichment-level prediction has been included. Extensive simulation experiments validated the efficacy of the proposed framework.

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Time-Marching Quantum Algorithm For Simulation Of Nonlinear Lorenz Dynamics, Efstratios Koukoutsis, George Vahala, Min Soe, Kyriakos Hizanidis, Linda Vahala, Abhay K. Ram Jan 2025

Time-Marching Quantum Algorithm For Simulation Of Nonlinear Lorenz Dynamics, Efstratios Koukoutsis, George Vahala, Min Soe, Kyriakos Hizanidis, Linda Vahala, Abhay K. Ram

Electrical & Computer Engineering Faculty Publications

Simulating nonlinear classical dynamics on a quantum computer is an inherently challenging task due to the linear operator formulation of quantum mechanics. In this work, we provide a systematic approach to alleviate this difficulty by developing an explicit quantum algorithm that implements the time evolution of a second-order time-discretized version of the Lorenz model. The Lorenz model is a celebrated system of nonlinear ordinary differential equations that has been extensively studied in the contexts of climate science, fluid dynamics, and chaos theory. Our algorithm possesses a recursive structure and requires only a linear number of copies of the initial state …

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An Analytical Model Of Motion Artifacts In A Measured Arterial Pulse Signal—Part I: Accelerometers And Ppg Sensors, Md Mahfuzur Rahman, Subodh Toraskar, Mamun Hasan, Zhili Hao Jan 2025

An Analytical Model Of Motion Artifacts In A Measured Arterial Pulse Signal—Part I: Accelerometers And Ppg Sensors, Md Mahfuzur Rahman, Subodh Toraskar, Mamun Hasan, Zhili Hao

Mechanical & Aerospace Engineering Faculty Publications

This paper, the first of two parts, presents an analytical model of motion artifacts (MAs) in measured pulse signals by accelerometers and photoplethysmography (PPG) sensors. As the transmission path from the true pulse signal in an artery to the sensor output (measured pulse signal), the tissue-contact-sensor (TCS) stack is modeled as a 1DOF (degree-of-freedom) system. MAs cause baseline drift of the mass and simultaneously time-varying system parameters (TVSPs) of the TCS stack. With arterial wall displacement and pulsatile pressure serving separately as the true pulse signal, an analytical model is developed to mathematically relate baseline drift and TVSP to a …

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Motion Artifacts Removal From Measured Arterial Pulse Signals At Rest: A Generalized Sdof-Model-Based Time-Frequency Method, Zhili Hao Jan 2025

Motion Artifacts Removal From Measured Arterial Pulse Signals At Rest: A Generalized Sdof-Model-Based Time-Frequency Method, Zhili Hao

Mechanical & Aerospace Engineering Faculty Publications

Motion artifacts (MA) are a key factor affecting the accuracy of a measured arterial pulse signal at rest. This paper presents a generalized time–frequency method for MA removal that is built upon a single-degree-of-freedom (SDOF) model of MA, where MA is manifested as time-varying system parameters (TVSPs) of the SDOF system for the tissue–contact-sensor (TCS) stack between an artery and a sensor. This model distinguishes the effects of MA and respiration on the instant parameters of harmonics in a measured pulse signal. Accordingly, a generalized SDOF-model-based time–frequency (SDOF-TF) method is developed to obtain the instant parameters of each harmonic in …

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Faithful Narratives From Complex Conceptual Models: Should Modelers Or Large Language Models Simplify Causal Maps, Tyler J. Gandee, Philippe J. Giabbanelli Jan 2025

Faithful Narratives From Complex Conceptual Models: Should Modelers Or Large Language Models Simplify Causal Maps, Tyler J. Gandee, Philippe J. Giabbanelli

VMASC Publications

(1) Background: Comprehensive conceptual models can result in complex artifacts, consisting of many concepts that interact through multiple mechanisms. This complexity can be acceptable and even expected when generating rich models, for instance to support ensuing analyses that find central concepts or decompose models into parts that can be managed by different actors. However, complexity can become a barrier when the conceptual model is used directly by individuals. A ‘transparent’ model can support learning among stakeholders (e.g., in group model building) and it can motivate the adoption of specific interventions (i.e., using a model as evidence base). Although advances in …

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Flux-Weakening Control Methods For Permanent Magnet Synchronous Machines In Electric Vehicles At High Speed, Samer Alwaqfi, Mohamad Alzayed, Hicham Chaoui Jan 2025

Flux-Weakening Control Methods For Permanent Magnet Synchronous Machines In Electric Vehicles At High Speed, Samer Alwaqfi, Mohamad Alzayed, Hicham Chaoui

Electrical & Computer Engineering Faculty Publications

Permanent magnet synchronous motors (PMSMs) are widely favored by manufacturers for use in electric vehicles (EVs) because of their many benefits, which include high power density at high speeds, ruggedness, potential for high efficiency, and reduced control complexity. However, since the Back Electromotive Force (EMF) increases proportionally with the motor’s rotational speed, it must be carefully controlled at high speeds. Flux-weakening (FW) control is required to avoid excessive electromagnetic flux beyond the power source and inverter’s voltage restrictions. This paper aims to compare various FW control strategies and analyze their effectiveness in maximizing the speed of PMSMs in EV applications …

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Land Target Detection Algorithm In Remote Sensing Images Based On Deep Learning, Wenyi Hu, Xiaomeng Jiang, Jiawei Tian, Shitong Ye, Shan Liu Jan 2025

Land Target Detection Algorithm In Remote Sensing Images Based On Deep Learning, Wenyi Hu, Xiaomeng Jiang, Jiawei Tian, Shitong Ye, Shan Liu

Electrical & Computer Engineering Faculty Publications

Remote sensing technology plays a crucial role across various sectors, such as meteorological monitoring, city planning, and natural resource exploration. A critical aspect of remote sensing image analysis is land target detection, which involves identifying and classifying land-based objects within satellite or aerial imagery. However, despite advancements in both traditional detection methods and deep-learning-based approaches, detecting land targets remains challenging, especially when dealing with small and rotated objects that are difficult to distinguish. To address these challenges, this study introduces an enhanced model, YOLOv5s-CACSD, which builds upon the YOLOv5s framework. Our model integrates the channel attention (CA) mechanism, CARAFE, and …

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Differentiating Opioid Use Disorder From Healthy Controls Via Ml Analysis Of Rs-Fmri Networks, Ahmed Temtam, Megan A. Witherow, Liangsuo Ma, M. Shibly Sadique, F. Gerard Moeller, C. Kenneth, Dianne Wright, Khan M. Iftekharuddin Jan 2025

Differentiating Opioid Use Disorder From Healthy Controls Via Ml Analysis Of Rs-Fmri Networks, Ahmed Temtam, Megan A. Witherow, Liangsuo Ma, M. Shibly Sadique, F. Gerard Moeller, C. Kenneth, Dianne Wright, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Objectives/Goals: This work aims to identify functional brain networks that differentiate opioid use disorder (OUD) subjects from healthy controls (HC) using machine learning (ML) analysis of resting-state fMRI (rs-fMRI). We investigate the default mode network (DMN), salience network (SN), and executive control network (ECN), as well as demographic features. Methods/Study Population: This work uses high-resolution rs-fMRI data from a National Institute on Drug Abuse study (IRB #HM20023630) with 31 OUD and 45 HC subjects. We extract rs-fMRI blood oxygenation level-dependent (BOLD) features from the DMN, SN, and ECN. The Boruta ML algorithm identifies statistically significant features and brain activity mapping …

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A Large Scale Multi Institutional Study For Radiomics Driven Machine Learning For Meningioma Grading, Mert Karabacak, Shiv Patil, Rui Feng, Raj K. Shrivastava, Konstantinos Margetis Oct 2024

A Large Scale Multi Institutional Study For Radiomics Driven Machine Learning For Meningioma Grading, Mert Karabacak, Shiv Patil, Rui Feng, Raj K. Shrivastava, Konstantinos Margetis

Department of Medicine Faculty Papers

This study aims to develop and evaluate radiomics-based machine learning (ML) models for predicting meningioma grades using multiparametric magnetic resonance imaging (MRI). The study utilized the BraTS-MEN dataset's training split, including 698 patients (524 with grade 1 and 174 with grade 2-3 meningiomas). We extracted 4872 radiomic features from T1, T1 with contrast, T2, and FLAIR MRI sequences using PyRadiomics. LASSO regression reduced features to 176. The data was split into training (60%), validation (20%), and test (20%) sets. Five ML algorithms (TabPFN, XGBoost, LightGBM, CatBoost, and Random Forest) were employed to build models differentiating low-grade (grade 1) from high-grade …

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A Feasibility-Preserved Quantum Approximate Solver For The Capacitated Vehicle Routing Problem, Ningyi Xie, Xinwei Lee, Dongsheng Cai, Yoshiyuki Saito, Nobuyoshi Asai, Hoong Chuin Lau Jul 2024

A Feasibility-Preserved Quantum Approximate Solver For The Capacitated Vehicle Routing Problem, Ningyi Xie, Xinwei Lee, Dongsheng Cai, Yoshiyuki Saito, Nobuyoshi Asai, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

The Capacitated Vehicle Routing Problem (CVRP) is an NP-optimization problem (NPO) that arises in various fields including transportation and logistics. The CVRP extends from the Vehicle Routing Problem (VRP), aiming to determine the most efficient plan for a fleet of vehicles to deliver goods to a set of customers, subject to the limited carrying capacity of each vehicle. As the number of possible solutions increases exponentially with the number of customers, finding high-quality solutions remains a significant challenge. Recently, the Quantum Approximate Optimization Algorithm (QAOA), a quantum–classical hybrid algorithm, has exhibited enhanced performance in certain combinatorial optimization problems, such as …

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Asteroidal Sets And Dominating Targets In Graphs, Oleksiy Al-Saadi May 2024

Asteroidal Sets And Dominating Targets In Graphs, Oleksiy Al-Saadi

School of Computing: Dissertations, Theses, and Student Research

The focus of this PhD thesis is on various distance and domination properties in graphs. In particular, we prove strong results about the interactions between asteroidal sets and dominating targets. Our results add to or extend a plethora of results on these properties within the literature. We define the class of strict dominating pair graphs and show structural and algorithmic properties of this class. Notably, we prove that such graphs have diameter 3, 4, or contain an asteroidal quadruple. Then, we design an algorithm to to efficiently recognize chordal hereditary dominating pair graphs. We provide new results that describe the …

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Trading Cloud Computing Stocks Using Sma, Xianrong Zheng, Lingyu Li Jan 2024

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 …

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Integrative Machine Learning Approaches For Enhanced Classification Of Genomic Sequences: A Next-Generation Sequencing Perspective, Sujatha Alla, Nagesh Bheesetty, Sai Gireesh Komaragiri, Prasanthi Chidipudi, Joshit Mohanty, Sathish Kumar Chintala, Jubin Thomas, Jayapal Vummadi, Hemanth Volikatla, Navin Kamuni Jan 2024

Integrative Machine Learning Approaches For Enhanced Classification Of Genomic Sequences: A Next-Generation Sequencing Perspective, Sujatha Alla, Nagesh Bheesetty, Sai Gireesh Komaragiri, Prasanthi Chidipudi, Joshit Mohanty, Sathish Kumar Chintala, Jubin Thomas, Jayapal Vummadi, Hemanth Volikatla, Navin Kamuni

Engineering Management & Systems Engineering Faculty Publications

The advent of Next-Generation Sequencing (NGS) techniques has revolutionized genomic research by enabling the rapid sequencing of DNA and RNA. This data can be used for various applications, including genome sequencing, transcriptome profiling, metagenomics, and epigenetics studies. For this study, DNA classifier dataset was extracted from UCI repository of machine learning databases. This vast amount of genomic data necessitates the development of sophisticated machine learning (ML) models for effective classification and analysis. This study presents a comprehensive comparison of various ML models, including Support Vector Machines (SVM), Random Forests (RF), and Neural Networks (NNs), approaches, in classifying genomic data. We …

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Using Feature Selection Enhancement To Evaluate Attack Detection In The Internet Of Things Environment, Khawlah Harahsheh, Rami Al-Naimat, Chung-Hao Chen Jan 2024

Using Feature Selection Enhancement To Evaluate Attack Detection In The Internet Of Things Environment, Khawlah Harahsheh, Rami Al-Naimat, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

The rapid evolution of technology has given rise to a connected world where billions of devices interact seamlessly, forming what is known as the Internet of Things (IoT). While the IoT offers incredible convenience and efficiency, it presents a significant challenge to cybersecurity and is characterized by various power, capacity, and computational process limitations. Machine learning techniques, particularly those encompassing supervised classification techniques, offer a systematic approach to training models using labeled datasets. These techniques enable intrusion detection systems (IDSs) to discern patterns indicative of potential attacks amidst the vast amounts of IoT data. Our investigation delves into various aspects …

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Scene Classification Of Remote Sensing Image Based On Multi-Path Reconfigurable Neural Network, Wenyi Hu, Chunjie Lan, Tian Chen, Shan Liu, Lirong Yin, Lei Wang Jan 2024

Scene Classification Of Remote Sensing Image Based On Multi-Path Reconfigurable Neural Network, Wenyi Hu, Chunjie Lan, Tian Chen, Shan Liu, Lirong Yin, Lei Wang

Electrical & Computer Engineering Faculty Publications

Land image recognition and classification and land environment detection are important research fields in remote sensing applications. Because of the diversity and complexity of different tasks of land environment recognition and classification, it is difficult for researchers to use a single model to achieve the best performance in scene classification of multiple remote sensing land images. Therefore, to determine which model is the best for the current recognition classification tasks, it is often necessary to select and experiment with many different models. However, finding the optimal model is accompanied by an increase in trial-and-error costs and is a waste of …

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The Manifesto For Teaching And Learning In A Time Of Generative Ai: A Critical Collective Stance To Better Navigate The Future, Aras Bozkurt, Junhong Xiao, Robert Farrow, John Y. H. Bai, Chrissi Nerantzi, Stephanie Moore, Jon Dron, Christian M. Stracke, Lenandlar Singh, Helen Crompton, Apostolos Koutropoulos, Evgenii Terentev, Angelica Pazurek, Mark Nichols, Alexander M. Sidorkin, Eamon Costello, Steven Watson, Dónal Mulligan, Sarah Honeychurch, Charles B. Hodges, Mike Sharples, Andrew Swindell, Isak Frumin, Ahmed Tlili, Patricia J. Slagter Van Tryon, Melissa Bond, Maha Bali, Jing Leng, Kai Zhang, Mutlu Cukurnova, Thomas K. F. Chiu, Kyungmee Lee, Stefan Hrastinski, Manuel B. Garcia, Ramesh Chander Sharma, Bryan Alexander, Olaf Zawacki-Richter, Henk Huijser, Petar Jandrić, Chanjin Zheng, Peter Shea, Josep M. Duart, Chryssa Themeli, Anton Vorochkov, Sunagül Sani-Bozkurt, Robert L. Moore, Tutaleni Iita Asino Jan 2024

The Manifesto For Teaching And Learning In A Time Of Generative Ai: A Critical Collective Stance To Better Navigate The Future, Aras Bozkurt, Junhong Xiao, Robert Farrow, John Y. H. Bai, Chrissi Nerantzi, Stephanie Moore, Jon Dron, Christian M. Stracke, Lenandlar Singh, Helen Crompton, Apostolos Koutropoulos, Evgenii Terentev, Angelica Pazurek, Mark Nichols, Alexander M. Sidorkin, Eamon Costello, Steven Watson, Dónal Mulligan, Sarah Honeychurch, Charles B. Hodges, Mike Sharples, Andrew Swindell, Isak Frumin, Ahmed Tlili, Patricia J. Slagter Van Tryon, Melissa Bond, Maha Bali, Jing Leng, Kai Zhang, Mutlu Cukurnova, Thomas K. F. Chiu, Kyungmee Lee, Stefan Hrastinski, Manuel B. Garcia, Ramesh Chander Sharma, Bryan Alexander, Olaf Zawacki-Richter, Henk Huijser, Petar Jandrić, Chanjin Zheng, Peter Shea, Josep M. Duart, Chryssa Themeli, Anton Vorochkov, Sunagül Sani-Bozkurt, Robert L. Moore, Tutaleni Iita Asino

STEMPS Faculty Publications

This manifesto critically examines the unfolding integration of Generative AI (GenAI), chatbots, and algorithms into higher education, using a collective and thoughtful approach to navigate the future of teaching and learning. GenAI, while celebrated for its potential to personalize learning, enhance efficiency, and expand educational accessibility, is far from a neutral tool. Algorithms now shape human interaction, communication, and content creation, raising profound questions about human agency and biases and values embedded in their designs. As GenAI continues to evolve, we face critical challenges in maintaining human oversight, safeguarding equity, and facilitating meaningful, authentic learning experiences. This manifesto emphasizes that …

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Sccad: Cluster Decomposition-Based Anomaly Detection For Rare Cell Identification In Single-Cell Expression Data, Yunpei Xu, Shaokai Wang, Qilong Feng, Jiazhi Xia, Yaohang Li, Hong-Dong Li, Jianxin Wang Jan 2024

Sccad: Cluster Decomposition-Based Anomaly Detection For Rare Cell Identification In Single-Cell Expression Data, Yunpei Xu, Shaokai Wang, Qilong Feng, Jiazhi Xia, Yaohang Li, Hong-Dong Li, Jianxin Wang

Computer Science Faculty Publications

Single-cell RNA sequencing (scRNA-seq) technologies have become essential tools for characterizing cellular landscapes within complex tissues. Large-scale single-cell transcriptomics holds great potential for identifying rare cell types critical to the pathogenesis of diseases and biological processes. Existing methods for identifying rare cell types often rely on one-time clustering using partial or global gene expression. However, these rare cell types may be overlooked during the clustering phase, posing challenges for their accurate identification. In this paper, we propose a Cluster decomposition-based Anomaly Detection method (scCAD), which iteratively decomposes clusters based on the most differential signals in each cluster to effectively separate …

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Machine-Learning-Enabled Diagnostics With Improved Visualization Of Disease Lesions In Chest X-Ray Images, Md. Fashiar Rahman, Tzu-Liang (Bill) Tseng, Michael Pokojovy, Peter Mccaffrey, Eric Walser, Scott Moen, Alex Vo, Johnny C. Ho Jan 2024

Machine-Learning-Enabled Diagnostics With Improved Visualization Of Disease Lesions In Chest X-Ray Images, Md. Fashiar Rahman, Tzu-Liang (Bill) Tseng, Michael Pokojovy, Peter Mccaffrey, Eric Walser, Scott Moen, Alex Vo, Johnny C. Ho

Mathematics & Statistics Faculty Publications

The class activation map (CAM) represents the neural-network-derived region of interest, which can help clarify the mechanism of the convolutional neural network’s determination of any class of interest. In medical imaging, it can help medical practitioners diagnose diseases like COVID-19 or pneumonia by highlighting the suspicious regions in Computational Tomography (CT) or chest X-ray (CXR) film. Many contemporary deep learning techniques only focus on COVID-19 classification tasks using CXRs, while few attempt to make it explainable with a saliency map. To fill this research gap, we first propose a VGG-16-architecture-based deep learning approach in combination with image enhancement, segmentation-based region …

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A Generative Approach For Document Enhancement With Small Unpaired Data, Mohammad Shahab Uddin, Wael Khallouli, Andres Sousa-Poza, Samuel Kovacic, Jiang Li Jan 2024

A Generative Approach For Document Enhancement With Small Unpaired Data, Mohammad Shahab Uddin, Wael Khallouli, Andres Sousa-Poza, Samuel Kovacic, Jiang Li

Engineering Management & Systems Engineering Faculty Publications

Shipbuilding drawings, crafted manually before the digital era, are vital for historical reference and technical insight. However, their digital versions, stored as scanned PDFs, often contain significant noise, making them unsuitable for use in modern CAD software like AutoCAD. Traditional denoising techniques struggle with the diverse and intense noise found in these documents, which also does not adhere to standard noise models. In this paper, we propose an innovative generative approach tailored for document enhancement, particularly focusing on shipbuilding drawings. For a small, unpaired dataset of clean and noisy shipbuilding drawing documents, we first learn to generate the noise in …

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