Digital Commons Network^™

Manifold Learning And Undersampling Approaches For Imbalanced Class Sentiment Classification, L.M. Risman Dwi Jumansyah, Agus Mohamad Soleh, Utami Dyah Syafitri

Knowledge Engineering and Data Science

Movie reviews are crucial in determining a film's success by influencing audience decisions. Automating sentiment classification is essential for efficient public opinion analysis. However, it faces challenges such as high-dimensional data and imbalanced class distributions. This study addresses these issues by applying manifold learning techniques, Principal Component Analysis (PCA) and Laplacian Eigenmaps (LE) to reduce data complexity and undersampling strategies (Random Undersampling (RUS) and EasyEnsemble) to balance data and improve predictions for both sentiment classes. On reviews of The Raid 2: Berandal, EasyEnsemble achieved the highest average G-Mean of 0.694 using Term Frequency-Inverse Document Frequency (TF IDF) features with a …

Deep Learning Approach For Dental Anomalies X-Ray Imaging Using Yolov8, Amelia Ritahani Ismail, Md Salim Sadman Taseen

Knowledge Engineering and Data Science

Dental X-ray imaging is a critical diagnostic tool for identifying various dental anomalies. However, manual interpretation is time-consuming, prone to human error, and requires specialized expertise. Deep learning models, particularly object detection frameworks like YOLO, have demonstrated promising results in automating medical image analysis. This study aims to develop and evaluate a YOLOv8-based deep learning model for automated detection and classification of 14 dental anomaly categories, including Caries, Crowns, Fillings, Implants, and Periapical lesions. The proposed approach addresses limitations in previous YOLO versions by leveraging anchor-free detection and enhanced feature extraction for improved accuracy. The model was trained on a …

Optimal Strategy For Handling Unbalanced Medical Datasets: Performance Evaluation Of K-Nn Algorithm Using Sampling Techniques, Yulita Salim, Aulia Putri Utami, Abdul Rachman Manga, Huzain Azis, Fadhila Tangguh Admojo

Knowledge Engineering and Data Science

This study addresses the critical role of medical image classification in enhancing healthcare effectiveness and tackling the challenges of imbalanced medical datasets. It focuses on optimizing classification performance by integrating Canny edge detection for segmentation and Hu-moment feature extraction and applying oversampling and undersampling techniques. Five diverse medical datasets were utilized, covering Alzheimer’s and Parkinson’s diseases, COVID-19, brain tumours, and lung cancer. The K-Nearest Neighbors (K-NN) algorithm was implemented to enhance classification accuracy, aiming to develop a more robust framework for medical image analysis. The evaluation, conducted using cross-validation, demonstrated notable improvements in key metrics. Specifically, oversampling significantly enhanced lung …

A Hierarchical Density-Based Spatial Clustering Of Applications With Noise (Hdbscan) Approach For Identifying Potential Villages In Buleleng Regency, Dina Nur Amalina, Achmad Fauzan

Knowledge Engineering and Data Science

Buleleng Regency, located in Bali Province, possesses diverse village potential, including agricultural production and tourist attractions. However, this potential has not been fully optimized. Therefore, it is important to enhance village potential by clustering villages based on their specific characteristics to identify and prioritize those requiring special attention. This approach aims to promote equitable village development and reduce poverty levels. This study clusters villages in Buleleng Regency based on their potential using the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) method. The data utilized in this study comprises village potential data obtained from the Buleleng Regency Statistics Office …

Classification Of Anxiety Levels Entering The World Of Work In Final Year Students Using The Neighbor Weighted K-Nearest Neighbor Method, Awang Hendrianto Pratomo, Muhammad Fahmi Adam, Dessyanto Boedi Prasetyo

Knowledge Engineering and Data Science

This study evaluates the accuracy of the Neighbor Weighted K-Nearest Neighbor (NWKNN) method in classifying the anxiety levels of final-year students as they prepare to enter the workforce, particularly in cases of unbalanced data distribution. The system was developed using the prototype method, and NWKNN was applied to classify anxiety levels into low, medium, and high categories. Testing using the Confusion Matrix demonstrated strong performance, achieving an accuracy of 94% based on a dataset of 1009 students, with a 90:10 ratio of training to test data. The results indicate that NWKNN effectively provides classification input values, making it a reliable …

Comparative Analysis Of Bpnn And Lvq For Sundanese Character Recognition, Haviluddin Haviluddin, Herman Santoso Pakpahan, Dinda Izmya Nurpadillah, Hario Jati Setyadi, Medi Taruk, Rayner Alfred

Knowledge Engineering and Data Science

The Sundanese script (Aksara Sunda), an essential part of Sundanese cultural heritage, has been used since the 14th century AD. However, recognizing handwritten Sundanese characters remains challenging due to variations in individual writing styles. This study compares the performance of Backpropagation Neural Network (BPNN) and Learning Vector Quantization (LVQ) for recognizing handwritten Sundanese vowel (Swara) characters. A dataset was collected from 15 individuals, each writing seven Sundanese vowel characters, which were then used for training and testing the recognition models. Experimental results show that BPNN outperforms LVQ, achieving a higher classification accuracy (95.23%), lower Mean Squared Error (MSE), and faster …

Impact Of Electrical Testing Strategies On The Performance Metrics Of Bio-Organic-Based Resistive Switching Memory, Muhammad Awais, Hao Zhe Leong, Nadras Othman, Mohamad Danial Shafiq, Feng Zhao, Kuan Yew Cheong

Electrical and Computer Engineering Faculty Research & Creative Works

Resistive Random-Access Memory (ReRAM) is considered as one of the most promising non-volatile memory technologies because of its high scalability, fast switching speed, and low power consumption. While many review papers are focused on investigating material types, material properties, device fabrication methods, and device structures, the influence of electrical testing strategies on ReRAM performance has yet been reviewed, particularly for bio-organic-based ReRAM. This review compiled, analyzed, and discussed how compliance current, voltage sweep rate, voltage sweep range, and voltage sweeping direction affect the ON/OFF ratio, read memory window, and both SET and RESET voltages of ReRAM.

Ohmic Contacts For Fabrication Of Sic Cmos Devices, Anthony Di Mauro

Graduate Theses and Dissertations

Today’s world of electronics is dominated by semiconductor devices which utilize silicon as their substrate material. Though, silicon is not ideal for semiconductor devices in high-voltage or high-temperature applications. Additionally, the efficiency of silicon devices becomes drastically reduced in nonideal operating conditions. Therefore, finding alternatives to silicon?based devices has been a topic for decades now. A few great candidates to replace silicon devices for said applications include Silicon Carbide (SiC), Gallium Nitride (GaN), and Aluminum Arsenide (AlAs). SiC has grown its reputation as the best candidate, when compared to other potential alternatives, due to its wide bandgap, high operating frequency, …

Design And Optimization Of Matrix Core Transformer For High Power Bidirectional Isolated Dc/Dc Converter, Zhe Zhao

Graduate Theses and Dissertations

Thanks to the advancement of semiconductor technology, the silicon carbide (SiC) devices with high voltage and power capability are developed and widely used in several industry applications, e.g., gird-connected photovoltaic (PV) system, data center power supply, and electrical vehicle (EV) charging station. After the concept of solid-state transformer (SST) was proposed as an important solution to the medium voltage (MV) power conversion systems, this bidirectional isolated DC/DC converter has attracted much attention in academia and industry. One of the main advantages of SST is to improve both the power density and efficiency of the power conversion system by replacing the …

Investigation Of Conductive Die-Top Thermal Capacitors During Short Circuit Operation Of Silicon Carbide Devices, Youssef Abotaleb

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Silicon carbide (SiC) power devices have garnered significant attention in recent years due to their superior thermal and electrical properties compared to traditional silicon devices. However, SiC power devices suffer from severe reliability issues arising from their mechanical properties. This is because the Young's modulus of SiC is about three times larger than that of silicon, which correspondingly raises the mechanical stresses acting on the bonding structure, often leading to device failure under power shock events.

Traditional packaging materials and designs tend to constrain SiC performance, as hot-spot temperature resulting from a power shock is one of the key factors …

Intelligent Transportation System With 5g Vehicle-To-Everything (V2x): Architectures, Vehicular Use Cases, Emergency Vehicles, Current Challenges, And Future Directions, Vaishali Pawar, Nilima Zade, Deepali Vora, Vaishali Khairnar, Aurenice M. Oliveira, Ketan Kotecha, Ambarish Kulkarni

Michigan Tech Publications

In recent years, connected vehicle technologies emerged to provide a safer and more connected environment for transportation systems, which has further evolved into cellular vehicle-to-everything (C-V2X) technology. The current study contributes in the direction of future research of smart India @2047 in connected vehicles. The study presents the diverse aspects of 5G V2X as a comprehensive overview of Vehicle-to-Everything (V2X) technology that covers the significant scope of V2X technology, including its development, components, functionality, use cases, and future potential. It is observed that few surveys are available that cover limited aspects of 5G V2X communication technologies, with one or two …

Data Security In The Apple Ecosystem: An Evaluation, Kayrene Woods

Cybersecurity Undergraduate Research Showcase

This study provides a comprehensive evaluation of data security within the Apple ecosystem, focusing on the company’s privacy policies, user perceptions, and the effectiveness of its App Store review processes. Employing an interdisciplinary methodology, the research examines Apple’s commitment to data protection, emphasizing transparency and user trust. A survey of user experiences revealed varying levels of engagement and understanding of Apple’s privacy practices, with only 32.8% of respondents having read the Privacy Policy and mixed opinions on its clarity. Additionally, concerns persist about third-party app security, with 39.7% of users expressing apprehension and skepticism about Apple’s App Store review process. …

An Improved Conditional Integrator Sliding Mode Controller Based On Swarm Intelligence For A Magnetic Levitation System, Abdelkader Kerraci, Mohamed Fayçal Khelfi, Zoubir Ahmed-Foitih

Turkish Journal of Electrical Engineering and Computer Sciences

This paper proposes an enhanced Conditional Integrator Sliding Mode Controller using Particle Swarm Optimization (CISMCPSO) for a magnetic levitation system (MLS). The main advantage of this controller is its robustness to uncertainties and disturbances, which also avoids chattering and ensures zero static steady-state error. The main idea of CISMCPSO is to activate its integral action only when the sliding surface reaches the boundary layer while it is reduced to zero or close to zero elsewhere, which avoids destroying the transient response caused by the conventional integral sliding-mode controller. A particle swarm optimization algorithm schedules the conditional integral term parameter of …

A New Dxccdita Based Meminductor Emulator And Its Application In Chaotic Oscillator, Bhawna Aggarwal, Shireesh Kumar Rai, Harsh Jain

Turkish Journal of Electrical Engineering and Computer Sciences

This work introduces a new dual-X current conveyor differential input transconductance amplifier (DXCCDITA) based meminductor emulator, alongside its application in chaotic oscillator has also been presented. To realize the designed meminductor emulator, one DXCCDITA, two resistors, and two capacitors are employed. Pinched hysteresis loops are achieved across a wide frequency range spanning from 100 Hz to 1.5 MHz, encompassing both decremental and incremental topologies. Additionally, the proposed circuit offers the flexibility to switch between incremental and decremental configurations using a simple switch. Through examination of non-volatility and transient responses, the efficiency of the presented emulator is evidently demonstrated. To further …

Motivating Sustainability Through The State Of Biologically Inspired Design, Bryan Watson

Sustainability Conference

There are multiple arguements for sustainability, but one that resonates with environmentalists and the public alike is the need for preservation to all us to discovery natural solutions to our problems. Common examples often given include medical discoveries, unique mechanisms, and new materials. This presentation focuses on two ideas to motivate sustainability. First, what is the current state of biologically inspired design? Is there more to learn from nature? To answer these questions, recent research is presented which examined 660 Biologically Inspired Design samples from three data sources: Google Scholar, Google News, and the Asknature.org “Innovations” database. The data were …

Model-Based Navigation And Control Of Multirotor Uavs: A Machine Learning Approach, Serhat Sönmez

Electronic Theses and Dissertations

In recent decades, unmanned systems, particularly Unmanned Aerial Vehicles (UAVs), have seen significant advancement and unprecedented growth in military, civilian and public domain applications. Scientists have focused on enhancing UAV navigation and control through cutting-edge technologies and support tools. UAVs find applications in many fields, except military, such as agriculture, infrastructure inspection, wildlife monitoring, search and rescue, emergency response, border protection, to name but a few relevant civilian applications. Given the faster-than-exponential increase of available computational power, learning-based algorithms have emerged as a prominent tool for (real-time) multirotor UAV navigation and control. This dissertation centers around the fusion of conventional …

It's Not As Bad As You Think: Detecting Ai-Generated Voices, Yong Qin Xu

Undergraduate Research Symposium Lightning Talks

Advances in machine learning have opened up the world to a brand new frontier of fraudulent phone calls which the average person may not be in any way prepared for. From imitations of a loved one's voice to lifelike mimicry of human callers, telephone scams may become harder than ever to anticipate or prevent now that criminals have the help of AI on their side. This is why in my research paper, I aim to analyze and compare two existing methods of detecting the authenticity of human voice recordings in order to demonstrate and explain currently available technology that's capable …

Strategies To Alleviate Flickering: Bayesian And Smoothing Methods For Deep Learning Classification In Video, Noah Miller, Glen Ryan Drumm, Lance Champagne, Bruce A. Cox, Trevor Bihl

Faculty Publications

Excerpt: Increasing reliance on autonomous systems requires confidence in the accuracies produced from computer vision classification algorithms. Computer vision (CV) for video classification provides phenomenal abilities, but it often suffers from “flickering” of results. Flickering occurs when the CV algorithm switches between declared classes over successive frames. Such behavior causes a loss of trust and confidence in their operations.

Soc-Based Control System Performance Analysis For Trapped-Ion Quantum Computing, Tiamike Dudley

Electrical and Computer Engineering ETDs

Scatter-gather dynamic-memory-access (SG-DMA) is utilized in applications that require high bandwidth and low latency data transfers between memory and peripherals, where data blocks, described using buffer descriptors (BDs), are distributed throughout the memory system. The data transfer organization and requirements of a Trapped-Ion Quantum Computer (TIQC) possess characteristics similar to those targeted by SG-DMA. In particular, the ion qubits in a TIQC are manipulated by applying control sequences consisting primarily of modulated laser pulses. These optical pulses are defined by parameters that are (re)configured by the electrical control system. Variations in the operating environment and equipment make it necessary to …

Automated Solutions For Hydroponic Plant Growth, Sydney Mcclure, Adam Lachguar

Sustainability Conference

Within the past year, Project H.O.M.E. has been focusing on the design and development of a semi-automatic hydroponic system specifically for sustaining plant life in Martian-like conditions. Given the significance of extended space-based travel, where the duration of human life in space is a crucial factor, growing food becomes imperative. This project has integrated electrical engineering and computer science, with features like automated pH testing and sensor-based evaluations. Key functionalities, including timed watering and automatic adjustments, were coded to enhance plant care. Initially, the project’s comprehensive research and strategic planning resulted in detailed blueprints and computer-aided design models for the …

Modeling W/V-Band Satellite Communications In The Presence Of Noise Jamming, Ryan Michael Eckman

Electrical and Computer Engineering ETDs

Understanding the performance of wideband modulated signals under noise jamming conditions is important for advancing W/V-band military satellite communications. This research developed and validated models to predict performance degradation of a communication channel in the presence of wideband noise jamming. Channel models were developed that included both modulated communication signals and jammer signals. Integrated models included environmental link factors and practical implementation factors. Integrated models were verified and validated using outdoor radio frequency environments utilizing two W/V-band transceivers, a portable W-band jammer, and software defined radios. Experimental results demonstrated excellent agreement with integrated model predictions. Channel degradation due to the …

Trust-Me: Resource Allocation And Server Selection Based On Trust In Multi-Access Edge Computing, Sean Tsikteris

Electrical and Computer Engineering ETDs

Multi-access Edge Computing (MEC) is crucial for Internet of Things (IoT) applications by optimizing data processing and reducing latency. This thesis presents contributions to resource allocation and decision-making in edge computing environments. The TRUST-ME model is introduced, involving multiple edge servers and IoT devices (users) offloading computing tasks to MEC servers. A utility function is designed to assess latency and cost benefits for IoT devices using server resources. The core innovation is a novel trust model that evaluates IoT devices’ confidence in MEC servers by integrating both direct and indirect trust, based on interactions and feedback from other devices. In …

Electrochemical Copper Printing For Wearable Electronics, Nirmita Roy

USF Tampa Graduate Theses and Dissertations

Wearable electronics have become a transformative force across industries like healthcare, aerospace, and military applications. However, a significant challenge persists in directly integrating electronic circuits onto fabrics. Addressing this challenge, the aim of this research is to introduce a novel sequential manufacturing process. Initially, a fabric is coated with a customized ink containing lignin, establishing a conductive template through laser burning. Subsequently, a localized Hydrogen Evolution Assisted (HEA) copper electroplating method is applied, resulting in a low-resistive circuit layout. Our investigations encompassed nanostructure analyses using Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDX), Raman Spectroscopy, and Fourier Transform Infrared …

Sustainable Mobility: Machine Learning-Driven Deployment Of Ev Charging Points In Dublin, Ruairí De Fréin, Alexander Mutiso Mutua Mr

Articles

Electric vehicle (EV) drivers in urban areas face range anxiety due to the fear of running out of charge without timely access to charging points (CPs). The lack of sufficient numbers of CPs has hindered EV adoption and negatively impacted the progress of sustainable mobility. We propose a CP distribution algorithm that is machine learning-based and leverages population density, points of interest (POIs), and the most used roads as input parameters to determine the best locations for deploying CPs. The objects of the following research are as follows: (1) to allocate weights to the three parameters in a $6$ km …

Reducing Carbon Footprint In Ai: A Framework For Sustainable Training Of Large Language Models, Sunbal Iftikhar, Steven Davy

Conference papers

In the world of artificial intelligence (AI), large language models (LLMs) are leading the way, transforming how people understand and use language. These models have significantly impacted various domains, from natural language processing (NLP) to content generation, sparking a wave of innovation and exploration. However, this rapid progress brings to light the environmental implications of LLMs, particularly the significant energy consumption and carbon emissions during their training and operational phases. This requires a shift towards more energy-efficient practices in training and deploying LLMs, balancing AI innovation with environmental responsibility. This paper emphasizes the need for improving the energy efficiency of …

Digital Health Intervention For Children With Adhd To Improve Mental Health Intervention, Patient Experiences, And Outcomes: A Study Protocol, Nancy Herrera, Franceli L. Cibrian, Lucas M. Silva, Jesus Armando Beltran, Sabrina E. B. Schuck, Gillian R. Hayes, Kimberley D. Lakes

Engineering Faculty Articles and Research

Background

Attention Deficit Hyperactivity Disorder (ADHD) is the most prevalent childhood psychiatric condition with profound public health, personal, and family consequences. ADHD requires comprehensive treatment; however, lack of communication and integration across multiple points of care is a substantial barrier to progress. Given the chronic and pervasive challenges associated with ADHD, innovative approaches are crucial. We developed the digital health intervention (DHI)—CoolTaCo [Cool Technology Assisting Co-regulation] to address these critical barriers. CoolTaCo uses Patient-Centered Digital Healthcare Technologies (PC-DHT) to promote co-regulation (child/parent), capture patient data, support efficient healthcare delivery, enhance patient engagement, and facilitate shared decision-making, thereby improving access to …

Advancing Drug Discovery And Disease Understanding Through Knowledge Graphs And Machine Learning Techniques, Swastika Tenkila Purushotham

Electrical and Computer Engineering ETDs

This thesis investigates knowledge graphs with particular reference to the NIH-funded Common Fund Data Ecosystem Data Distillery project. By combining data from nine Common Fund projects and other sources, this project has created a large knowledge graph using Neo4j. To find new and undiscovered drug targets, UNM’s Illuminating the Druggable Genome (IDG) Data Coordinating Center has supplied data and use cases. Condensed Knowledge Graph, a condensed version that is based on the Data Distillery Knowledge Graph, improves usability for IDG applications. Condensed Knowledge Graph research endeavors to enhance data organization through the categorization of disease terms, examination of Cerebellar Stroke …

A 3d Memristor Architecture For In-Memory Computing Demonstrated With Sha3, Muayad J. Aljafar, Rasika Joshi, John M. Acken

Electrical and Computer Engineering Faculty Publications and Presentations

Security is a growing problem that needs hardware support. Memristors provide an alternative technology for hardware-supported security implementation. This paper presents a specific technique that utilizes the benefits of hybrid CMOS-memristors technology demonstrated with SHA3 over implementations that use only memristor technology. In the proposed technique, SHA3 is implemented in a set of perpendicular crossbar arrays structured to facilitate logic implementation and circular bit rotation (Rho operation), which is perhaps the most complex operation in SHA3 when carried out in memristor arrays. The Rho operation itself is implemented with CMOS multiplexers (MUXs). The proposed accelerator is standby power-free and circumvents …

Development Of A Non-Intrusive Load Monitoring Technique Using Phase-Space-Reconstruction And 2-D Fourier Series Current Waveform Features, Motaz Abu Sbeitan

Thesis/ Dissertation Defenses

The growing need for energy and efficient energy control has emphasized the importance of tracking appliance-level energy usage. The capability of Non-Intrusive Load Monitoring (NILM) to separate energy usage data per appliance from a single measurement provides a practical solution. This thesis explores developing a novel NILM method using Phase-Space Reconstruction (PSR) and 2-D Fourier Series to enhance feature extraction from the steady-state current waveforms. Existing NILM techniques frequently encounter accuracy challenges caused by overlapping power signatures of appliances and complex operational states. The proposed method is designed to efficiently capture the steady-state characteristics of electrical appliances. It is evaluated …

Smart Systems For Employing Iot Devices For Monitoring And Control Of Electric Vehicle Residential Charging, Grant M. Fischer, Steven B. Poore, Rosemary E. Alden, Donovin D. Lewis, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

As the number of electric vehicles (EVs) on the road continues to increase, the rise in power demand may pose challenges, especially for the localized grid capacity during peak load events. This paper briefly reviews methods of power monitoring and load shedding such as smart charging and load management systems. An internet-of-things (IoT) power monitoring system is proposed for high-resolution power monitoring and control of J1772 standard level 2 EV charging systems to provide detailed data to enable future studies on EV grid integration. Additionally, a hardware test bench (HTB) including a DC battery emulator, oscilloscope, and commercial EV charger …