Engineering Commons^™

Detection Of Various Dental Conditions On Dental Panoramic Radiography Using Faster R-Cnn, Shih Lun Chen, Tsung Yi Chen, Yi Cheng Mao, Szu Yin Lin, Ya Yun Huang, Chiung An Chen, Yuan Jin Lin, Mian Heng Chuang, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

The dental panoramic radiograph (DPR) is a pivotal diagnostic tool in dentistry. However, despite the growing prevalence of artificial intelligence (AI) across various medical domains, manual methods remain the prevailing means of interpreting DPR images. This study aims to introduce an advanced identification system for detecting seven dental conditions in DPR images by utilizing Faster R-CNN. The primary objectives are to enhance dentists' efficiency and evaluate the performance of various CNN models as foundational training networks. This study contributes significantly to the field in several notable ways. Firstly, including a Butterworth filter in the training process yielded an approximately 7% …

Performance Evaluation Of Face Mask Detection For Real-Time Implementation On An Rpi, Ivan George L. Tarun, Vidal Wyatt M. Lopez, Pamela Anne C. Serrano, Patricia Angela R. Abu, Rosula Reyes, Ma. Regina Justina Estuar

Department of Information Systems & Computer Science Faculty Publications

Mask-wearing remains to be one of the primary protective measures against COVID-19. To address the difficulty of manual compliance monitoring, face mask detection models considerate of both frontal and angled faces were developed. This study aimed to test the performance of the said models in classifying multi-face images and upon running on a Raspberry Pi device. The accuracies and inference speeds were measured and compared when inferencing images with one, two, and three faces and on the desktop and the Raspberry Pi. With an increasing number of faces in an image, the models’ accuracies were observed to decline, while their …

Towards The Development Of A Blockchain System For Philippine Government Processes For Enhanced Transparency And Verifiability, Karlo Angelo F. Cabugwang, Raphael Christen K. Enriquez, Bienvenido E. Villabroza, Christian E. Pulmano

Department of Information Systems & Computer Science Faculty Publications

There are cases of corruption and fraud within the Philippine government that have gone under the radar, often due to a lack of transparency and verifiability. The objective of this study is to prototype a blockchain network that can run a government process as a decentralized application such that it can enhance transparency and verifiability in the public sector. This can be accomplished by identifying a government process that would be converted into a decentralized application. One of these processes would be converted into a decentralized application. Afterwards, a blockchain framework should be identified one which can create a public …

Unsupervised-Based Distributed Machine Learning For Efficient Data Clustering And Prediction, Vishnu Vardhan Baligodugula

Browse all Theses and Dissertations

Machine learning techniques utilize training data samples to help understand, predict, classify, and make valuable decisions for different applications such as medicine, email filtering, speech recognition, agriculture, and computer vision, where it is challenging or unfeasible to produce traditional algorithms to accomplish the needed tasks. Unsupervised ML-based approaches have emerged for building groups of data samples known as data clusters for driving necessary decisions about these data samples and helping solve challenges in critical applications. Data clustering is used in multiple fields, including health, finance, social networks, education, and science. Sequential processing of clustering algorithms, like the K-Means, Minibatch K-Means, …

Anomaly Detection In Multi-Seasonal Time Series Data, Ashton Taylor Williams

Browse all Theses and Dissertations

Most of today’s time series data contain anomalies and multiple seasonalities, and accurate anomaly detection in these data is critical to almost any type of business. However, most mainstream forecasting models used for anomaly detection can only incorporate one or no seasonal component into their forecasts and cannot capture every known seasonal pattern in time series data. In this thesis, we propose a new multi-seasonal forecasting model for anomaly detection in time series data that extends the popular Seasonal Autoregressive Integrated Moving Average (SARIMA) model. Our model, named multi-SARIMA, utilizes a time series dataset’s multiple pre-determined seasonal trends to increase …

Path-Safe: Enabling Dynamic Mandatory Access Controls Using Security Tokens, James P. Maclennan

Browse all Theses and Dissertations

Deploying Mandatory Access Controls (MAC) is a popular way to provide host protection against malware. Unfortunately, current implementations lack the flexibility to adapt to emergent malware threats and are known for being difficult to configure. A core tenet of MAC security systems is that the policies they are deployed with are immutable from the host while they are active. This work looks at deploying a MAC system that leverages using encrypted security tokens to allow for redeploying policy configurations in real-time without the need to stop a running process. This is instrumental in developing an adaptive framework for security systems …

Data-Driven Strategies For Disease Management In Patients Admitted For Heart Failure, Ankita Agarwal

Browse all Theses and Dissertations

Heart failure is a syndrome which effects a patient’s quality of life adversely. It can be caused by different underlying conditions or abnormalities and involves both cardiovascular and non-cardiovascular comorbidities. Heart failure cannot be cured but a patient’s quality of life can be improved by effective treatment through medicines and surgery, and lifestyle management. As effective treatment of heart failure incurs cost for the patients and resource allocation for the hospitals, predicting length of stay of these patients during each hospitalization becomes important. Heart failure can be classified into two types: left sided heart failure and right sided heart failure. …

Understanding And Enhancing The Efficiency And Efficacy Of Machine Learning-Assisted Software Vulnerability Detection, Daniel J. Grahn

Browse all Theses and Dissertations

As our world has become dependent upon software for nearly every aspect of modern society, software security has followed as an essential feature. The first line of defense against vulnerabilities is secure coding. While today’s programmers are carefully taught secure coding best practices, they can make mistakes or intentionally introduce vulnerable code. The traditional backstop to human errors and insider threats is the adoption of automated security analysis tools. These analysis tools have limitations. Static analysis suffers from high false positive rates that may cause annoyance and complacency among developers. Dynamic analysis can be difficult to set up and very …

Memory Optimizations For High-Throughput Computer Systems, Zhiyuan Lu

Dissertations, Master's Theses and Master's Reports

The emergence of new non-volatile memory (NVM) technology and deep neural network (DNN) inferences bring challenges related to off-chip memory access. Ensuring crash consistency leads to additional memory operations and exposes memory update operations on the critical execution path. DNN inference execution on some accelerators suffers from intensive off-chip memory access. The focus of this dissertation is to tackle the issues related to off-chip memory in these high performance computing systems.

The logging operations, required by the crash consistency, impose a significant performance overhead due to the extra memory access. To mitigate the persistence time of log requests, we introduce …

Interest-Based Recommendation System Using Gmail Topic Modelling, Pranav Ghaskadbi

Master's Projects

Emails are a fundamental part of modern communication. Much of communicative discourse in modern society occurs over email, resulting in personal collections for each mail user which are rich in latent user’s interests. Conventional recommendation systems require historical data of user activity and interactions to derive user interests. The absence of activity and interaction data poses an interesting challenge for generating relevant recommendations for users. We were motivated to investigate approaches to identify user interests in the absence of historical data to generate personalized content recommendations. There is opportunity to derive user interests from email data, which can be used …

Identifying Potential Alzheimer’S Disease Biomarkers Beyond Amyloid-Beta And Tau, Frank Cai

Master's Projects

Alzheimer's Disease (AD) and other forms of Mild Cognitive Impairment (MCI) affect millions of people around the world. The buildup of Amyloid-Beta (Aβ) and Tau proteins in the brain produced by amyloid precursor protein (APP) has been identified as an important cofactor in the onset and progression of AD. However, although patients diagnosed with AD exhibit Aβ and Tau buildup, about 40% of the subjects with Aβ and Tau buildup are not diagnosed with AD. In this project, we hypothesize the involvement of other epigenetic interactions between APP and related genes in addition to the buildup of Aβ and Tau …

A Flexible Photonic Reduction Network Architecture For Spatial Gemm Accelerators For Deep Learning, Bobby Bose

Theses and Dissertations--Electrical and Computer Engineering

As deep neural network (DNN) models increase significantly in complexity and size, it has become important to increase the computing capability of specialized hardware architectures typically used for DNN processing. The major linear operations of DNNs, which comprise the fully connected and convolution layers, are commonly converted into general matrix-matrix multiplication (GEMM) operations for acceleration. Specialized GEMM accelerators are typically employed to implement these GEMM operations, where a GEMM operation is decomposed into multiple vector-dot-product operations that run in parallel. A common challenge that arises in modern DNNs is the mismatch between the matrices used for GEMM operations and the …

Application Of Conventional Feedforward And Deep Neural Networks To Power Distribution System State Estimation And State Forecasting, James Paul Carmichael

Theses and Dissertations--Electrical and Computer Engineering

Classical neural networks such as feedforward multilayer perceptron models (MLPs) are well established as universal approximators and as such, show promise in applications such as static state estimation in power transmission systems. This research investigates the application of conventional neural networks (MLPs) and deep learning based models such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) to mitigate challenges in power distribution system state estimation and forecasting based upon conventional analytic methods. The ability of MLPs to perform regression to perform power system state estimation will be investigated. MLPs are considered based upon their promise to learn …

Learning Outcomes And Learner Satisfaction: The Mediating Roles Of Self-Regulated Learning And Dialogues, Sean Eom, Nicholas Jeremy Ashill

Journal of International Technology and Information Management

The interdependent learning process is regarded as a crucial part of e-learning success, but it has been largely ignored in e-learning empirical research. Grounded in constructivist and social constructivist theory, we present and test an e-learning success model consisting of eight e-learning critical success factors (CSF) derived from constructivist and social constructivist models. Three hundred seventy-two on-line students from a Midwestern university in the United States participated in the survey. The data collected from the survey was used to examine the partial least squares structural equation model. The results highlight the importance of self-regulated learning and dialogical processes to explain …

Acceptance Of Interoperable Electronic Health Record (Ehrs) Systems: A Tanzanian E-Health Perspective, Emmanuel Mbwambo, Herman Mandari

Journal of International Technology and Information Management

The study assessed factors that influence the acceptance of interoperable electronic Health Records (EHRs) Systems in Tanzania Public Hospitals. The study applied a hybrid model that combined the Technology Acceptance Model (TAM) and Technology-Organization-Environment (TOE). Snowball sampling technique was applied and a total of 340 questionnaires were distributed to selected clinics, polyclinics and hospitals, of which 261 (77%) received questionnaires were considered to be valid and reliable for subsequent data analysis. IBM SPSS software version 27.0 was employed for data analysis. Findings indicated that relative advantage, compatibility, management support, organizational competency, training and education, perceived ease of use, perceived usefulness, …

Analysis Of The Impact Of Vaccinations On Pandemic Metrics In The New York Metropolitan Area, Oredola A. Soluade, Heechang Shin, Robert Richardson

Journal of International Technology and Information Management

This study evaluates the relationship between pandemic cases and vaccination usage, ICU bed utilization, hospitalizations, and deaths in the New York City metropolitan area. The study includes variables for the lockdown period and confirmed infections. The evaluation addresses three periods: (1) before vaccinations, (2) after vaccinations, and (3) the lockdown period. In addition, the number of vaccines per day for the manufacturers (Pfizer, Moderna, and Johnson & Johnson) are included in the study. Comparisons with New Jersey and Connecticut are used to validate that New York statistics are consistent with other states. The results provide a general model of the …

Enhanced Load Balancing Based On Hybrid Artificial Bee Colony With Enhanced Β-Hill Climbing In Cloud, Maha Zeedan, Gamal Attiya, Nawal El-Fishawy

Mansoura Engineering Journal

This paper proposes enhanced load balancer based artificial bee colony and β-Hill climbing for improving the performance metrics such as response time, processing cost, and utilization to avoid overloaded or under loaded situations of virtual machines. In this study, the suggested load balancer is called enhanced load balancing based on hybrid artificial bee colony with enhanced β-Hill climbing (ELBABCEβHC) to improve the response time, processing cost and the resource utilization. Our proposed approach starts by ranking the task then the greedy randomized adaptive search procedure (GRASP) is used in initializing populations. Further, the binary artificial bee colony (BABC) enhanced with …

Enhancing The Performance Of Federated Learning With Diffusion Models: Leveraging Synthetic Data To Address Non-Iid Data Challenges, Karin Huangsuwan

Chulalongkorn University Theses and Dissertations (Chula ETD)

In the context of machine learning in healthcare, federated learning (FL) is frequently seen as an effective approach to tackling issues of data privacy and distribution. Nonetheless, many real-world datasets exhibit non-identical and independently distributed (non-IID) characteristics, meaning that data features vary across different institutions. This non-IID nature presents challenges for FL model convergence, such as client drifting, where model weights lean towards local optima rather than global optimum. To address these issues, we introduce a new framework called "FedDrip (Federated Learning with Diffusion Reinforcement at Pseudo-site)," which leverages diffusion-generated synthetic data to mitigate data-related problems in non-IID settings. Our …

Predicting Newcomer’S Turnover Using Predictive Analytics : A Case Study Of Thai Financial Firm In Bangkok, Thailand, Meena Kittikunsiri

Chulalongkorn University Theses and Dissertations (Chula ETD)

Employee turnover, a critical issue impacting workplace productivity, has prompted organizations to leverage machine learning techniques for predictive analysis. This study specifically targets the prediction of turnover among new employees, utilizing data obtained from a survey conducted at a Thai financial firm in Bangkok, Thailand. Through an evaluation of various machine learning models, the results indicate that the Random Forest model surpasses others. Furthermore, this research highlights crucial factors influencing newcomer turnover, such as comfort with workplace culture, work-from-home policies, onboarding programs, and satisfaction with the recruitment process. These findings offer actionable insights for HR professionals to focus on these …

Use Of Bioheat Modeling To Characterize And Optimize Implantable Medical Devices And Neuromodulation Technologies, Adantchede Louis Zannou

Dissertations and Theses

Medical device development includes prototyping, benchtop characterization, preclinical studies, and clinical trials. Understanding the limitations and potential adverse effects of medical devices prior to their administration in humans is a crucial first step. Optimizing medical devices is essential to employing technology and improving patients care. Computational modeling is widely adopted as a powerful tool to predict stimulation/recording parameter optimization, rapid electrode/device prototyping, investigating novel mechanism of action, and testing working principles of any medical devices. Many implantable neuromodulation technologies including Spinal Cord Stimulation (SCS), which provide substantial therapeutic benefit for patient population with lower back pain, produces heat via the …

Classifying Sidewalk Materials Using Multi-Modal Data, Jiawei Liu

Dissertations and Theses

Navigating safely and independently presents considerable challenges for people who are blind or have low vision (BLV), as it requires a comprehensive understanding of their neighborhood environment. Our user study reveals that materials and objects on sidewalks play a crucial role in navigation tasks. Unfortunately, current methods for assessing sidewalk materials are suboptimal, often relying on labor-intensive and expensive manual assessments that fail to capture the full range of sidewalk features critical to individuals with BLV.

In response to this problem, this master’s thesis investigates deep learning approaches specifically designed for the classification of multi-modal sidewalk materials. The proposed framework …

Anomaly Based Intrusion Detection System Through Remote Virtual Machine Introspection, Huseyn Huseynov

Dissertations and Theses

Research on identifying malicious applications is an important direction in information security, especially when it comes to detection of evasive malware such as keyloggers, trojans, rootkits and their derivatives. Inspired by a biological immune system and based on negative selection algorithm approach to detect various types of malwares is proposed in this paper.

By deeply studying Linux kernel, understanding links behind different internal system processes, examining, and experimenting with hundreds of various keyloggers we propose a single Artificial Intelligence based solution as a comprehensive protection against wide range of malwares. Developed Intrusion Detection System (IDS) can be deployed in the …

Are Ride-Hailing Services Safer Than Taxis? A Multivariate Spatial Approach With Accomodation Of Exposure Uncertainty, Guocong Zhai, Kun Xie, Hong Yang, Di Yang

Civil & Environmental Engineering Faculty Publications

Despite many research efforts on ride-hailing services and taxis, limited studies have compared the safety performance of the two modes. A major challenge is the need for reliable mode-specific exposure data to model their safety outcomes. Moreover, crash frequencies of the two modes by injury severities tend to be spatially and inherently correlated. To fully address these issues, this study proposes a novel multivariate conditional autoregressive model considering measurement errors in mode-specific exposures (MVCARME). More specially, a classical measurement error structure is used to accommodate the uncertainty of mode-specific exposures estimated, and a multivariate spatial specification is adopted to capture …

Energy-Efficient Hmac For Wireless Communications, Cesar Enrique Castellon Escobar

UNF Graduate Theses and Dissertations

This thesis introduces the Farming Lightweight Protocol (FLP) optimized for energy-restricted environments that depend upon secure communication, such as multi-robot information gathering systems within the vision of ``smart'' agriculture. FLP uses a hash-based message authentication code (HMAC) to achieve data integrity. HMAC implementations, resting upon repeated use of the SHA256 hashing operator, impose additional resource requirements and thus also impact system availability. We address this particular integrity/availability trade-off by proposing an energy-saving algorithmic engineering method on the internal SHA256 hashing operator. The energy-efficient hash is designed to maintain the original security benefits yet reduce the negative effects on system availability. …

Chatgpt: Open Possibilities, Mohammad Aljanabi, Mohanad Ghazi, Ahmed Hussein Ali, Saad Abas Abed

Iraqi Journal for Computer Science and Mathematics

ChatGPT-3 is a powerful language model developed by OpenAI that has the potential to revolutionize the way we interact with technology. This model has been trained on a massive amount of data, allowing it to understand and generate human-like text with remarkable accuracy.One of the most exciting possibilities of ChatGPT-3 is its potential to improve natural language processing (NLP) and natural language understanding (NLU) in a wide range of applications. In particular, ChatGPT-3 can be used to power chatbots, virtual assistants, and other conversational interfaces. These types of systems are becoming increasingly important as more and more people use voice …

A Platform For In-Situ Creation Of Markerless, Location-Based Augmented Reality Content, Brett Kidman

Dartmouth College Master’s Theses

Augmented reality (AR) renders virtual objects over a real-world physical environment. Currently, the majority of the digital content for AR is created by professional developers with knowledge of AR frameworks such as ARKit and ARCore. User-Generated Content (UGC) is critical for the future of AR, as it will not only increase the number of AR experiences to match the projected rapid growth in the user base, but also democratize content creation. However, there is a current lack of UGC authoring tools for Augmented Reality (AR) to enable users to create, save, and share location-based, markerless AR content. Location-based AR persistently …

A Robust Model For Spot Virtual Machine Bidding In The Cloud Market Using Information Gap Decision Theory (Igdt), Mona Naghdehforoushha, Mehdi Dehghan Takht Fooladi, Mohammed Hossein Rezvani, Mohammad Mehdi Gilanian Sadeghi

Turkish Journal of Electrical Engineering and Computer Sciences

The spot market is one of the most common cloud markets where cloud providers, such as Amazon EC2, rent their surplus computing resources at lower prices in the form of spot virtual machines (SVMs). In this market, which is often managed through an auction mechanism, users seek optimal bidding strategies for renting SVMs to minimize cost and risk. Uncertainty in the price of SVMs and their low availability/reliability is a challenging issue to bid on the user side. In this paper, we present a robust model for minimizing the cost of executing tasks by considering the uncertainty of the price …

Wifi Sensing At The Edge Towards Scalable On-Device Wireless Sensing Systems, Steven M. Hernandez

Theses and Dissertations

WiFi sensing offers a powerful method for tracking physical activities using the radio-frequency signals already found throughout our homes and offices. This novel sensing modality offers continuous and non-intrusive activity tracking since sensing can be performed (i) without requiring wearable sensors, (ii) outside the line-of-sight, and even (iii) through the wall. Furthermore, WiFi has become a ubiquitous technology in our computers, our smartphones, and even in low-cost Internet of Things devices. In this work, we consider how the ubiquity of these low-cost WiFi devices offer an unparalleled opportunity for improving the scalability of wireless sensing systems. Thus far, WiFi sensing …

Portable Robotic Navigation Aid For The Visually Impaired, Lingqiu Jin

Theses and Dissertations

This dissertation aims to address the limitations of existing visual-inertial (VI) SLAM methods - lack of needed robustness and accuracy - for assistive navigation in a large indoor space. Several improvements are made to existing SLAM technology, and the improved methods are used to enable two robotic assistive devices, a robot cane, and a robotic object manipulation aid, for the visually impaired for assistive wayfinding and object detection/grasping. First, depth measurements are incorporated into the optimization process for device pose estimation to improve the success rate of VI SLAM's initialization and reduce scale drift. The improved method, called depth-enhanced visual-inertial …

Real-Time Motion Controller For Human-Robot Teams Utilizing Artificial Potential Fields, Gabriella Graziani

Theses and Dissertations

This thesis presents a real-time robotic motion control system for human-robot teams. The framework utilizes artificial potential fields (APFs) to guide robotic agents towards a “goal” agent while navigating around “obstacle” agents; these goals and obstacles are also dynamic agents with their own set of tasks. This system is also developed for a live-programming environment, where a controlling agent updates the tasks of all agents within the system at any time during the system’s runtime. This motion controller was created and tested for a human-robot choreographic team. After the controller was fully integrated on a Trossen Robotic LoCobot Wx200 robotic …