Computer Sciences Commons^™

Enhanced Iot-Based Electrocardiogram Monitoring System With Deep Learning, Jian Ni

UNLV Theses, Dissertations, Professional Papers, and Capstones

Due to the rapid development of computing and sensing technologies, Internet of Things (IoT)-based cardiac monitoring plays a crucial role in providing patients with cost-efficient solutions for long-term, continuous, and pervasive electrocardiogram (ECG) monitoring outside a hospital setting. In a typical IoT-based ECG monitoring system, ECG signals are picked up by sensors located on the edge, and then uploaded to the remote cloud servers. ECG interpretation is performed for the collected ECGs in the cloud servers and the analysis results can be made instantly available to the patients as well as their healthcare providers.In this dissertation, we first examine the …

Algorithmic Approaches To Battery Consolidation In A Smart Grid, Dara Nyknahad

UNLV Theses, Dissertations, Professional Papers, and Capstones

The concept of the battery exchange station (BES) as a part of the battery consolidation system (BCS) has certain criteria that make it a significant player in the better adaptation plan of electric vehicles (EVs) in the grid toward the smart grid. In this dissertation, we study the BES optimization problem as a promising approach for EV adaptation in a smart grid. We introduce the concept of BCS, which focuses on optimizing the incorporation and transaction of all of the components in a smart grid. Accordingly, we address three BES optimization problems in the BCS. In the first problem, we …

Towards Explainable Ai: Predicting Linear And Nonlinear Feature Relations Of Datasets, Nimmy Chhaganbhai Patel

Theses and Dissertations

Artificial Intelligence (AI) makes critical decisions in an opaque way without explaining the reasoning behind them. Decision support systems are often built as black boxes. This has generated interest in Explainable AI (XAI), an area of research that explains AI algorithms and provides more insight into their internal decision-making process. Advancements in XAI have enhanced machine learning (ML) models’ interpretability, explainability, and transparency. Additionally, there has been speculation on whether it is possible to predict the type of dataset used in the model by analyzing the results. In this research, we proposed a methodology for determining the linearity or non-linearity …

A Comprehensive Review On Machine Learning In Healthcare Industry: Classification, Restrictions, Opportunities And Challenges, Qi An, Saifur Rahman, Jingwen Zhou, James Jin Kang

Research outputs 2022 to 2026

Recently, various sophisticated methods, including machine learning and artificial intelligence, have been employed to examine health-related data. Medical professionals are acquiring enhanced diagnostic and treatment abilities by utilizing machine learning applications in the healthcare domain. Medical data have been used by many researchers to detect diseases and identify patterns. In the current literature, there are very few studies that address machine learning algorithms to improve healthcare data accuracy and efficiency. We examined the effectiveness of machine learning algorithms in improving time series healthcare metrics for heart rate data transmission (accuracy and efficiency). In this paper, we reviewed several machine learning …

A Review On Deep-Learning-Based Cyberbullying Detection, Md Tarek Hasan, Md Al Emran Hossain, Md Saddam Hossain Mukta, Arifa Akter, Mohiuddin Ahmed, Salekul Islam

Research outputs 2022 to 2026

Bullying is described as an undesirable behavior by others that harms an individual physically, mentally, or socially. Cyberbullying is a virtual form (e.g., textual or image) of bullying or harassment, also known as online bullying. Cyberbullying detection is a pressing need in today’s world, as the prevalence of cyberbullying is continually growing, resulting in mental health issues. Conventional machine learning models were previously used to identify cyberbullying. However, current research demonstrates that deep learning surpasses traditional machine learning algorithms in identifying cyberbullying for several reasons, including handling extensive data, efficiently classifying text and images, extracting features automatically through hidden layers, …

Determinants Of Cloud Computing Integration And Its Impact On Sustainable Performance In Smes: An Empirical Investigation Using The Sem-Ann Approach, Mohammed A. Al-Sharafi, Mohammad Iranmanesh, Mostafa Al-Emran, Ahmed I. Alzahrani, Fadi Herzallah, Norziana Jamil

Research outputs 2022 to 2026

Although extant literature has thoroughly investigated the incorporation of cloud computing services, examining their influence on sustainable performance, particularly at the organizational level, is insufficient. Consequently, the present research aims to assess the factors that impact the integration of cloud computing within small and medium-sized enterprises (SMEs) and its subsequent effects on environmental, financial, and social performance. The data were collected from 415 SMEs and were analyzed using a hybrid SEM-ANN approach. PLS-SEM results indicate that relative advantage, complexity, compatibility, top management support, cost reduction, and government support significantly affect cloud computing integration. This study also empirically demonstrated that SMEs …

Decision Support Issues In Automated Driving Systems, William N. Caballero, David Ríos Insua, David Banks

Faculty Publications

Machine learning and computational processing have advanced such that automated driving systems (ADSs) are no longer a distant reality. Many automobile manufacturers have developed prototypes; however, there exist numerous decision support issues requiring resolution to ensure mass ADS adoption. In the coming decades, it is likely that production ADSs will only be partially autonomous. Such ADSs operate within predetermined conditions and require driver intervention when they are violated. Since forecasts of their 20-year market penetration are relatively low, ADSs will likely operate in heterogeneous traffic characterized by vehicles of varying autonomy levels. Under these conditions, effective decision support must consider …

Portable Sparse Polyhedral Framework Code Generation Using Multi Level Intermediate Representation, Aaron St. George

Boise State University Theses and Dissertations

The Sparse Polyhedral Framework (SPF) provides vital support to scientific applications, but is limited in portability. SPF extends the Polyhedral Model to non-affine codes. Scientific applications need the optimizations SPF enables, but current SPF tools don't support GPUs or other heterogeneous hardware targets. As clock speeds continue to stagnate, scientific applications need the performance enhancements enabled by both SPF and newer heterogeneous hardware.

The MLIR (Multi-Level Intermediate Representation) ecosystem offers a large, extensible, and cooperating set of intermediate representations (called dialects). A typical compiler has one main intermediate representation, whereas an MLIR based compiler will have many. Because of this …

Severity Measures For Assessing Error In Automatic Speech Recognition, Ryan Whetten

Boise State University Theses and Dissertations

A common metric for evaluating Automatic Speech Recognition (ASR) is Word Error Rate (WER) which solely takes into account discrepancies at the word-level. Although WER is useful, it is not guaranteed to correlate well with intelligibility or performance on downstream tasks that make use of ASR. Meaningful assess- ment of ASR mistakes becomes even more important in high-stake scenarios such as health-care. I propose 2 general measures to evaluate the quality or severity of mistakes made by ASR systems, one based on sentiment analysis and another based on text embeddings. Both have the potential to overcome the limitations of WER. …

Breath Analysis For Detection Of Lung Cancer With Hybrid Sensor-Based Electronic Nose, Ümi̇t Özsandikcioğlu, Ayten Atasoy

Turkish Journal of Electrical Engineering and Computer Sciences

Lung cancer has the highest death rates among all types of cancer worldwide. Detection of lung cancer in its early stages significantly increases the survival rate. In this study, the aim is to improve the lung cancer detection performance of electronic noses (e-noses) with breath analysis by using two different types of gas sensor-based e-nose. The developed e-nose system consists of 14 quartz crystal microbalance (QCM) sensors and 8 metal oxide semiconductor (MOS) sensors. Breath samples were collected from a total of 100 volunteers, including 60 patients with lung cancer, 20 healthy nonsmokers, and 20 healthy smokers, and were classified …

Analysis And Implementation Of A New High-Buck Dc-Dc Converter With Interleaved Output Inductors And Soft Switching Capability, Sajad Ghabeli Sani, Mohamad Reza Banaei, Seyed Hossein Hosseini

Turkish Journal of Electrical Engineering and Computer Sciences

This paper proposes an innovative structure for DC-DC converters with high buck gain by using a lower number of elements. The converter provides highly efficient output power and an extended output voltage range. In addition, the distribution of output current between two inductors and the soft-switching capability of the power switches have made the converter suitable for applications that require high output current. All power switches accomplish the ZVZCS (zero-voltage and zero-current switching) condition with the aid of a small auxiliary inductor (Lx), which charges and discharges parallel capacitors of main switches to provide soft-switching conditions. Thus, the switching losses …

Integration Of Neural Network And Distance Relay To Improve The Fault Localization On Transmission Lines, Linh Tran

Turkish Journal of Electrical Engineering and Computer Sciences

Power transmission lines are integral and very important components of power systems. Because of the length of these lines and the complexity of the power grids, the lines may encounter various incidents such as lightning strike, shortage, and breakage. When an incident or a fault occurs, a fast process of identification, localization, and isolation of the fault is desired. An accurate fault localization would have a great impact in reducing the restoration time of the system. One of the most popular solutions for fault detection and localization is the distance relays using the impedance-based algorithms. However, these relays are still …

Quadratic Programming Based Partitioning For Block Cimmino With Correct Value Representation, Zuhal Taş, Fahreddi̇n Şükrü Torun

Turkish Journal of Electrical Engineering and Computer Sciences

The block Cimmino method is successfully used for the parallel solution of large linear systems of equations due to its amenability to parallel processing. Since the convergence rate of block Cimmino depends on the orthogonality between the row blocks, advanced partitioning methods are used for faster convergence. In this work, we propose a new partitioning method that is superior to the state-of-the-art partitioning method, GRIP, in several ways. Firstly, our proposed method exploits the Mongoose partitioning library which can outperform the state-of-the-art methods by combining the advantages of classical combinatoric methods and continuous quadratic programming formulations. Secondly, the proposed method …

Unbiased Federated Learning In Energy Harvesting Error-Prone Channels, Zeynep Çakir, Eli̇f Tuğçe Ceran Arslan

Turkish Journal of Electrical Engineering and Computer Sciences

Federated learning (FL) is a communication-efficient and privacy-preserving learning technique for collaborative training of machine learning models on vast amounts of data produced and stored locally on the distributed users. This paper investigates unbiased FL methods that achieve a similar convergence as state-of-the-art methods in scenarios with various constraints like an error-prone channel or intermittent energy availability. For this purpose, we propose FL algorithms that jointly design unbiased user scheduling and gradient weighting according to each user's distinct energy and channel profile. In addition, we exploit a prevalent metric called the age of information (AoI), which quantifies the staleness of …

Efficient Modelling Of Random Access Memory Cell: An Approach Using Qca Nanocomputing, Ali Newaz Bahar, Angshuman Khan

Turkish Journal of Electrical Engineering and Computer Sciences

Quantum-dot cellular automata (QCA) is innovative and potentially fruitful nanotechnology that provides a solution for transistor-based circuits with enhanced switching frequency, large-scale integration, and low power consumption. The random-access memory (RAM) cell is a fundamental component that is designed to operate quickly and effectively since memory is a core part of the semiconductor industry, thus the QCA family. The RAM cell design in this work is based on a multiplexer structure and is implemented without using coplanar crossovers of QCA technology. QCADesigner-2.0.3, a standard QCA layout design and verification tool, is used in the simulation and validation processes for the …

An Analytical Solution Of Fractional Order Pi Controller Design For Stable/Unstable/Integrating Processes With Time Delay, Erdal Çökmez, İbrahi̇m Kaya

Turkish Journal of Electrical Engineering and Computer Sciences

This paper aims to put forward an analytical solution for tuning parameters of a fractional order PI (FOPI) controller for stable, unstable, and integrating processes with time delay. Following this purpose, the analytical weighted geometrical center (AWGC) method has been extended to the design of fractional order PI controllers. To apply AWGC, the stability equations of the closed-loop system are written in terms of process and fractional order PI controller parameters. With the proposed method, the centroid can be calculated analytically, and the controller parameters can be easily calculated without the need of repetitive drawings of the stability boundary regions. …

More Wifi For Everyone: Increasing Spectral Efficiency In Wifi6 Networks Using A Distributed Obss/Pd Mechanism, Ali̇ Karakoç, Hüseyi̇n Bi̇rkan Yilmaz, Mehmet Şükrü Kuran

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, we propose a distributed algorithm that determines effective Overlapping Basic Service Set/Preamble Detection (OBSS/PD) threshold levels in each WiFi6 device to maximize the total throughput by increasing the spectral efficiency. Within WiFi6 standard, OBSS/PD mechanism is introduced to increase the overall efficiency of WiFi networks by tuning the receiver sensitivity as well as the transmission power. In a nutshell, the proposed algorithm, RACEBOT, tunes the hearing (i.e. reception) and speaking (i.e. transmission) parameters of each WiFi device individually for the betterment of the WiFi experience of all WiFi networks in a neighborhood. WiFi experience is not only …

An Efficient Deep Learning Architecture For Turkish Lira Recognition And Counterfeit Detection, Burak İyi̇kesi̇ci̇, Ergun Erçelebi̇

Turkish Journal of Electrical Engineering and Computer Sciences

Banknote counterfeiting is a common practice worldwide. Due to the recent developments in technology, banknote imitation has become easier than before. There are different kinds of algorithms developed for the detection of counterfeit banknotes for different countries in the literature. The earlier algorithms utilized classical image processing techniques where the implementations of machine learning and deep learning algorithms appeared with the developments in the artificial intelligence field as well as the computer hardware. In this study, a novel convolutional neural networks-based deep learning algorithm has been developed that detects counterfeit Turkish Lira banknotes and their denominations using the banknote images …

Fake News Detection Using Narrative Content And Discourse, Hongmin Kim

Boise State University Theses and Dissertations

With the growth of modern technology, we are living in a world where anyone can share news with the tap of a finger. The simplified process of news sharing has brought an inundation of information on the Internet, along with a vast amount of fake news. Researchers have been working to understand the characteristics of fake news, in order to accurately identify them through automated text analysis.

In this thesis, we propose the Narrative Content and Narrative Discourse features brought from the ideas by van Laer et al., Berger et al., and Aleti et al. We performed various experiments including …

Verifying Data Provenance During Workflow Execution For Scientific Reproducibility, Rizbanul Hasan

Boise State University Theses and Dissertations

Reproducibility is essential in scientific research to ensure that any findings or conclusions are accurate. The reproducibility crisis around scientific studies and experiments is a significant concern. Several strategies and technologies have been introduced to share and exchange research data. However, very few address scientific reproducibility issues when interacting with vast amounts of data that may be manually altered during workflow execution.

This research focuses on verifying data provenance using the principles of blockchain. This technique stores the hashes of research data in a database along with user information. It allows the workflow to verify the data against the hashes …

Exploring The Capability Of A Self-Supervised Conditional Image Generator For Image-To-Image Translation Without Labeled Data: A Case Study In Mobile User Interface Design, Hailee Kiesecker

Boise State University Theses and Dissertations

This research investigates the effectiveness of a conditional image generator trained on a restricted number of unlabeled images for image-to-image translation in computer vision. While previous research has focused on using labeled data for image labeling in conditional image generation, this study proposes an original framework that utilizes self-supervised classification on generated images. The proposed approach, which combines Conditional GAN and Semantic Clustering, showed promising results. However, this study has several limitations, including a limited dataset and the need for significant computational power to generate a single UI design. Further research is needed to optimize the performance of the proposed …

High-Performance Domain-Specific Library For Hydrologic Data Processing, Kalyan Bhetwal

Boise State University Theses and Dissertations

Hydrologists must process many gigabytes of data for hydrologic simulations, which takes time and resources degrading performance. The performance issues are caused mainly by domain scientists’ preference for using Python, which trades performance for productivity. In my thesis, I demonstrate that using the static compilation technique to compile Python to generate C code along with several optimizations reduces time and resources for hydrologic data processing. I developed a Domain Specific Library (DSL) which is a subset of Python and compiles to Sparse Polyhedral Framework - Intermediate Representation (SPF-IR), which allows opportunities for optimizations like read reduction fusion which are not …

Anomaly Detection Using Graph Neural Network, Bishal Lakha

Boise State University Theses and Dissertations

Detecting malicious behavior is becoming increasingly crucial as the internet becomes more prevalent. This problem can be formulated as an anomaly detection task on provenance data, where attacks are detectable as anomalies in the behavior of the system. The availability of system-level data in comparison to network data is quite limited and so is the research carried out on system-level logs. However, monitoring the operating system's processes during program execution and identifying anomalous behavior in system calls can be beneficial since it can provide broad coverage and generality, as a variety of malicious applications could be identified. Furthermore, logs like …

Sparse Format Conversion And Code Synthesis, Tobi Goodness Popoola

Boise State University Theses and Dissertations

Sparse computations are important in scientific computing. Many scientific applications compute on sparse data. Data is said to be sparse if it has a relatively small number of non-zeros. Sparse formats use auxiliary arrays to store non-zeros, as a result, the contents of auxiliary arrays are not known until run-time. The Inspector/Executor (I/E) paradigm uses run-time information for compiler optimizations. An inspector computes information at run-time to drive transformations. The executor---a compile-time transformation of the original code--- uses information computed by the inspector. The sparse polyhedral framework (SPF) encompasses a series of tools to support I/E run-time transformations. This work …

Low-Complexity Three-Dimensional Aoa-Cross Geometric Center Localization Methods Via Multi-Uav Network, Baihua Shi, Yifan Li, Guilu Wu, Riqing Chen, Shihao Yan, Feng Shu

Research outputs 2022 to 2026

The angle of arrival (AOA) is widely used to locate a wireless signal emitter in unmanned aerial vehicle (UAV) localization. Compared with received signal strength (RSS) and time of arrival (TOA), AOA has higher accuracy and is not sensitive to the time synchronization of the distributed sensors. However, there are few works focusing on three-dimensional (3-D) scenarios. Furthermore, although the maximum likelihood estimator (MLE) has a relatively high performance, its computational complexity is ultra-high. Therefore, it is hard to employ it in practical applications. This paper proposed two center of inscribed sphere-based methods for 3-D AOA positioning via multiple UAVs. …

A Generative Neural Network For Discovering Near Optimaldynamic Inductive Power Transfer Systems, Md Shain Shahid Chowdhury Oni

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

An urgent need is to electrify transportation to lower carbon emissions into the atmosphere. Wireless charging makes electrical vehicles (EVs) more convenient and cheaper because energy is transferred to the vehicle without the need to plug it in. Dynamic wireless charging is particularly interesting, where the vehicle does not need to stop to receive the energy. This technology requires the EV and the roadway to include coils of wire, where the roadway coil is energized as the vehicle passes over it to induce an electrical current in the EV coil through electromagnetic induction. However, the problem of designing the two …

Coding Bootcamps - Perceptions And Outcomes, Logan L. Hendricks

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

This thesis is focused on gathering, aggregating and analysing data related to software development coding bootcamps. It comprises of three major research initiatives: A coding bootcamp outcomes meta-analysis, a study on perspectives regarding white-label coding bootcamps, and the data analysis of a survey gathering long-term outcomes of coding bootcamp and certificate program graduates.

The first study aggregates graduate outcome data from the three main organizations that review coding bootcamp outcomes: CourseReport.com, SwitchUp.com and the Council on Integrity in Results Reporting (CIRR). The purpose of this meta-review is to establish a baseline dataset which is immediately utilized in my further research. …

Adversarial Swarming: A Groundwork For Multi-Drone Independent Interception Exercises Through Ma-Poca In Unity, Johnathan D. Kunz

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

As drones become more popular and easier to use, air spaces are becoming more congested. Airports, hospitals, and similar structures require controlled, safe airspaces and drones are increasingly a threat. Locally controlled airspace requires efficient removal of airborne threats to continue sensitive operations. Many methods have been investigated for removing drones from contested airspace. Generally these methods involve ground-based signal disruption, physical contact, or drone interception of a single intruder. In this work we present a drone interception model with a low-cost, low-capability group of short-range drones intercepting an incoming drone.

Generative Neural Network Approach To Designing And Optimizing Dynamic Inductive Power Transfer Systems, Andrew Pond Curtis

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Electric vehicles (EVs) offer many improvements over traditional combustion engines including increasing efficiency, while decreasing cost of operation and emissions. There is a need for the development of cheap and efficient charging systems for the future success of EVs. Most EVs currently utilize static plug-in charging systems. An alternative charging method of significant interest is dynamic inductive power transfer systems (DIPT). These systems utilize two coils, one placed in the vehicle and one in the roadway to wirelessly charge the vehicle as it passes over. This method removes the current limitations on EVs where they must stop and statically charge …

Sustainable Grain Transportation In Ukraine Amidst War Utilizing Knarm And Knowwheregraph, Yinglun Zhang, Antonina Broyaka, Jude Kastens, Allen M. Featherstone, Cogan Shimizu, Pascal Hitzler, Hande Küçük Mcginty

Computer Science and Engineering Faculty Publications

In this work, we propose a sustainable path-finding application for grain transportation during the ongoing Russian military invasion in Ukraine. This application is to build a suite of algorithms to find possible optimal paths for transporting grain that remains in Ukraine. The application uses the KNowledge Acquisition and Representation Methodology(KNARM) and the KnowWhereGraph to achieve this goal. Currently, we are working towards creating an ontology that will allow for a more effective heuristic approach by incorporating the lessons learned from the KnowWhereGraph. The aim is to enhance the path-finding process and provide more accurate and efficient results. In the future, …