Engineering Commons^™

Deep Learning Approaches With Optimum Alpha For Energy Usage Forecasting, Aji Prasetya Wibawa, Agung Bella Putra Utama, Ade Kurnia Ganesh Akbari, Akhmad Fanny Fadhilla, Alfiansyah Putra Pertama Triono, Andien Khansa’A Iffat Paramarta, Faradini Usha Setyaputri, Leonel Hernandez

Knowledge Engineering and Data Science

Energy use is an essential aspect of many human activities, from individual to industrial scale. However, increasing global energy demand and the challenges posed by environmental change make understanding energy use patterns crucial. Accurate predictions of future energy consumption can greatly influence decision-making, supply-demand stability and energy efficiency. Energy use data often exhibits time-series patterns, which creates complexity in forecasting. To address this complexity, this research utilizes Deep Learning (DL), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU) models. The main objective is to improve the accuracy of …

The Effect Of The Number Of Hidden Layers On The Performance Of Deep Q-Network For Traveling Salesman Problem, Benzfica Hanif, Aisyah Larasati, Rudi Nurdiansyah, Trung Le

Knowledge Engineering and Data Science

The Traveling Salesman Problem (TSP) effectively represents the complex distribution issues encountered by couriers, who must carefully plan a route that includes all customer addresses while minimizing the distance traveled. As the magnitude of deliveries and the range of destinations expand, the courier's responsibility becomes progressively challenging. In this particular context, the objective of our research is to expand the existing knowledge and explore the complete capabilities of Deep Q-Network (DQN) models in order to achieve the most efficient route determination. This endeavor can potentially bring about significant changes in the courier and delivery service sector. The foundation of our …

Stacked Lstm-Gru Long-Term Forecasting Model For Indonesian Islamic Banks, Yayat Sujatna, Adhitio Satyo Bayangkari Karno, Widi Hastomo, Nia Yuningsih, Dody Arif, Sri Setya Handayani, Aqwam Rosadi Kardian, Ire Puspa Wardhani, L.M Rasdi Rere

Knowledge Engineering and Data Science

The development of the Islamic banking industry in Indonesia has become a significant concern in recent years, with rapid growth in the number of banks operating based on Sharia principles. To face emerging challenges and opportunities, a deep understanding of the long-term financial behavior of Islamic banks is becoming increasingly important. This study aims to predict the share price of PT Bank Syariah Indonesia Tbk, over 28 days using the LSTM-GRU stack. The observation stage includes importing the dataset, data separation, model variations, the training process, output, and evaluation. Observations were conducted using 10 model variations from 4 stacks of …

Comparison Of Machine Learning Algorithms For Species Family Classification Using Dna Barcode, Lala Septem Riza, M Ammar Fadhlur Rahman, Yudi Prasetyo, Muhammad Iqbal Zain, Herbert Siregar, Topik Hidayat, Khyrina Airin Fariza Abu Samah, Miftahurrahma Rosyda

Knowledge Engineering and Data Science

Classifying plant species within the Liliaceae and Amaryllidaceae families presents inherent challenges due to the complex genetic diversity and overlapping morphological traits among species. This study explores the difficulties in accurate classification by comparing 11 supervised learning algorithms applied to DNA barcode data, aiming to enhance the precision of species family classification in these taxonomically intricate plant families. The ribulose-1,5-bisphosphate carboxylase-oxygenase large sub-unit (rbcL) gene, selected as a DNA barcode locus for plants, is used to represent species within the Amaryllidaceae and Liliaceae families. The experimental results demonstrate that nearly all tested models achieve accurate species classification into the appropriate …

Multivariate Analysis Approach To Factor-Affected Tuberculosis Disease, Zuli Agustina Gultom, Farid Akbar Siregar, Mahardika Abdi Prawira Tanjung, Al-Hamidy Hazidar

Knowledge Engineering and Data Science

Tuberculosis is a disease caused by infection with the mycobacterium tuberculosis complex. Tuberculosis attack organ besides the lung, such as the pleura, lining of the brain, lining of the heart, lymph gland, bones, joint, skin, intestines, kidney, urinary tract, and genital. This disease is found in densely populated settlements with poor sanitation, lack of ventilation and sunlight and lack of rest. Moreover, the factors that will be analyzed in this research are Population Density (X1), Number of HIV/AIDS (X2), number of toddlers who experience nutrition (X3), Number of toddlers who experience BCG immunization (X4), number of toddlers who get exclusive …

Evidence Of Students’ Academic Performance At The Federal College Of Education Asaba Nigeria: Mining Education Data, Arnold Adimabua Ojugo, Christopher Chukwufunaya Odiakaose, Frances Emordi, Rita Erhovwo Ako, Winifred Adigwe, Kizito Eluemonor Anazia, Victor Geteloma

Knowledge Engineering and Data Science

One main objective of higher education is to provide quality education to its students. One way to achieve the highest level of quality in the higher education system is by discovering knowledge for prediction regarding enrolment of students in a particular course, alienation of traditional classroom teaching model, detection of unfair means used in online examination, detection of abnormal values in the result sheets of the students, and prediction about students’ performance. The knowledge is hidden among the educational data set and is extractable through data mining techniques. The present paper is designed to justify the capabilities of data mining …

Recurrent Session Approach To Generative Association Rule Based Recommendation, Tubagus Arief Armanda, Ire Puspa Wardhani, Tubagus M. Akhriza, Tubagus M. Adrie Admira

Knowledge Engineering and Data Science

This article introduces a generative association rule (AR)-based recommendation system (RS) using a recurrent neural network approach implemented when a user searches for an item in a browsing session. It is proposed to overcome the limitations of the traditional AR-based RS which implements query-based sessions that are not adaptive to input series, thus failing to generate recommendations. The dataset used is accurate retail transaction data from online stores in Europe. The contribution of the proposed method is a next-item prediction model using LSTM, but what is trained to develop the model is an associative rule string, not a string of …

Implementation Of Adas And Autonomy On Unlv Campus, Zillur Rahman

UNLV Theses, Dissertations, Professional Papers, and Capstones

The integration of Advanced Driving Assistance Systems (ADAS) and autonomous driving functionalities into contemporary vehicles has notably surged, driven by the remarkable progress in artificial intelligence (AI). These AI systems, capable of learning from real-world data, now exhibit the capability to perceive their surroundings via a suite of sensors, create optimal routes from source to destination, and execute vehicle control akin to a human driver.

Within the context of this thesis, we undertake a comprehensive exploration of three distinct yet interrelated ADAS and Autonomy projects. Our central objective is the implementation of autonomous driving(AD) technology at UNLV campus, culminating in …

Ensuring Non-Repudiation In Long-Distance Constrained Devices, Ethan Blum

Honors Theses

Satellite communication is essential for the exploration and study of space. Satellites allow communications with many devices and systems residing in space and on the surface of celestial bodies from ground stations on Earth. However, with the rise of Ground Station as a Service (GsaaS), the ability to efficiently send action commands to distant satellites must ensure non-repudiation such that an attacker is unable to send malicious commands to distant satellites. Distant satellites are also constrained devices and rely on limited power, meaning security on these devices is minimal. Therefore, this study attempted to propose a novel algorithm to allow …

Amorphous Boron Carbide-Amorphous Silicon Heterojunction Devices, Vojislav Medic

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

This dissertation will show successful development and characterization of amorphous boron carbide-amorphous silicon heterojunction device with potential for neutron detection. The amorphous hydrogenated boron carbide (a-BC:H) has been extensively researched as a semiconductor for neutron voltaic device fabrication. Naturally occurring boron contains 19.8% of boron isotope B¹⁰ that has a high absorption cross section of thermal neutrons at lower energies, and boron carbide contains 14.7% of that B¹⁰ isotope. Therefore, as a semiconductor compound of boron a-BC:H has the ability to absorb radiation, generate charge carriers, and collect those carriers. Previous work on a-BC:H devices investigated the fabrication …

An Investigation Of Match For Lossless Video Compression, Brittany Sullivan-Reicks

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

A new lossless video compression technique, Match, is investigated. Match uses the similarity between the frames of a video or the slices of medical images to find a prediction for the current pixel. A portion of the previous frame is searched to find a matching context, which is the pixels surrounding the current pixel, within some distance centered on the current location. The best distance to use for each dataset is found experimentally. The matching context refers to the neighborhood of w, nw, n, and ne, where the pixel in the previous frame with the closest matching context becomes the …

Low-Power, Event-Driven System On A Chip For Charge Pulse Processing Applications, Joseph A. Schmitz

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

This dissertation presents an electronic architecture and methodology capable of processing charge pulses generated by a range of sensors, including radiation detectors and tactile synthetic skin. These sensors output a charge signal proportional to the input stimulus, which is processed electronically in both the analog and digital domains. The presented work implements this functionality using an event-driven methodology, which greatly reduces power consumption compared to standard implementations. This enables new application areas that require a long operating time or compact physical dimensions, which would not otherwise be possible. The architecture is designed, fabricated, and tested in the aforementioned applications to …

Sc-Fuse: A Feature Fusion Approach For Unpaved Road Detection From Remotely Sensed Images, Aniruddh Saxena

School of Computing: Dissertations, Theses, and Student Research

Road network extraction from remote sensing imagery is crucial for numerous applications, ranging from autonomous navigation to urban and rural planning. A particularly challenging aspect is the detection of unpaved roads, often underrepresented in research and data. These roads display variability in texture, width, shape, and surroundings, making their detection quite complex. This thesis addresses these challenges by creating a specialized dataset and introducing the SC-Fuse model.

Our custom dataset comprises high resolution remote sensing imagery which primarily targets unpaved roads of the American Midwest. To capture the diverse seasonal variation and their impact, the dataset includes images from different …

A Modular Framework For Surface-Embedded Actuation And Optical Sensing In Soft Robots., Paul Bupe Jr

Electronic Theses and Dissertations

This dissertation explores the development and integration of modular technologies in soft robotics, with a focus on the OptiGap sensor system. OptiGap serves as a simple, flexible, cost-effective solution for real-time sensing of bending and deformation, validated through simulation and experimentation. Working as part of an emerging category of soft robotics called Soft, Curved, Reconfigurable, Anisotropic Mechanisms, or SCRAMs, this research also introduces the Thermally-Activated SCRAM Limb (TASL) technology, which employs shape-memory alloy (SMA) wire embedded in curved sheets for surface actuation and served as the initial inspiration for OptiGap. In addition, the EneGate system is presented as a complementary …

Cybersecurity In Critical Infrastructure Systems: Emulated Protection Relay, Mitchell Bylak

Computer Science and Computer Engineering Undergraduate Honors Theses

Cyber-attacks on Critical Systems Infrastructure have been steadily increasing across the world as the capabilities of and reliance on technology have grown throughout the 21st century, and despite the influx of new cybersecurity practices and technologies, the industry faces challenges in its cooperation between the government that regulates law practices and the private sector that owns and operates critical infrastructure and security, which has directly led to an absence of eas- ily accessible information and learning resources on cybersecurity for use in public environments and educational settings. This honors research thesis addresses these challenges by submitting the development of an …

Docai, Riley Badnin, Justin Brunings

Computer Science and Software Engineering

DocAI presents a user-friendly platform for recording, transcribing, summarizing, and classifying doctor-patient consultations. The application utilizes AssemblyAI for conversational transcription, and the user interface allows users to either live-record consultations or upload an existing MP3 file. The classification process, powered by 'ml-classify-text,' organizes the consultation transcription into SOAP (Subjective, Objective, Assessment, and Plan) format – a widely used method of documentation for healthcare providers. The result of this development is a simple yet effective interface that effectively plays the role of a medical scribe. However, the application is still facing challenges of inconsistent summarization from the AssemblyAI backend. Future work …

Renovating The Ipmu Via Internet Of Things For Pollutant Emission Estimations In Poultry Facilities, Joshua Dotto

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

The emissions of ammonia (NH₃), particulate matter (PM_2.5), and carbon dioxide (CO₂) are major concerns in poultry facilities. They can pose environmental concerns and nuances. Robust and affordable measurement systems are needed to accurately measure in-barn concentrations and quantify the emissions.

The Intelligent Portable Monitoring Unit (iPMU or PMU3) developed in 2016 was reconstructed into PMU4 to include upgraded NH₃ and PM_2.5 sensors and wireless connectivity for a low-cost, robust, and accurate air quality monitoring device with contactless data transfer using the concept of Internet of Things (IoT). In addition, a user-friendly …

Communication Security In Smart Home Iot And Scada Applications, Ismael Holguin

Open Access Theses & Dissertations

The advancements in technology have enabled continuous integration of consumer electronicdevices and sensors to the Internet, creating Internet of Things (IoT) environments for various applications. IoT communication protocols play a significant role in these environments and greatly vary depending on the type of application. In this work, we consider communication protocols in Smart Home IoT and SCADA environments, focus on their security features, and investigate methods to enhance their security. Most of these communication protocols were designed to meet specific communication goals, where security was not a requirement or priority during design. However, due to a shift in the security …

Addressing Current Security And Privacy Limitations In Iot Applications With Differential Privacy, Nicholas Anthony Lopez

Open Access Theses & Dissertations

Over the last decade, billions of devices have been developed to run on the Internet of Things (IoT) domain. IoT has been embedded into everyday tasks so much that the number of devices has outpaced the global population. Some of the main areas of IoT include healthcare, industry, communications, transportation, logistics, and environmental awareness, to name a few. With this greater demand for devices comes an even greater demand for privacy and security. The three main principles of security include confidentiality, integrity, and availability. However, there is no “one size fits all” solution regarding privacy or security. When applied across …

Assessing Blockchain’S Potential To Ensure Data Integrity And Security For Ai And Machine Learning Applications, Aiasha Siddika

Master of Science in Information Technology Theses

The increasing use of data-centric approaches in the fields of Machine Learning and Artificial Intelligence (ML/AI) has raised substantial issues over the security, integrity, and trustworthiness of data. In response to this challenge, Blockchain technology offered a promising and practical solution, as its inherent characteristics as a decentralized distributed ledger, coupled with cryptographic processes, offer an unprecedented level of data confidentiality and immutability. This study examines the mutually beneficial connection between Blockchain technology and ML/AI, using Blockchain's inherent capacity to protect against unauthorized alterations of data during the training phase of ML models. The method involves building valid blocks of …

Melanoma Detection Based On Deep Learning Networks, Sanjay Devaraneni

Electronic Theses, Projects, and Dissertations

Our main objective is to develop a method for identifying melanoma enabling accurate assessments of patient’s health. Skin cancer, such as melanoma can be extremely dangerous if not detected and treated early. Detecting skin cancer accurately and promptly can greatly increase the chances of survival. To achieve this, it is important to develop a computer-aided diagnostic support system. In this study a research team introduces a sophisticated transfer learning model that utilizes Resnet50 to classify melanoma. Transfer learning is a machine learning technique that takes advantage of trained models, for similar tasks resulting in time saving and enhanced accuracy by …

Automated Medical Notes Labelling And Classification Using Machine Learning, Akhil Prabhakar Thota

Electronic Theses, Projects, and Dissertations

The amount of data generated in medical records, especially in a modern context, is growing significantly. As the amount of data grows, it is very useful to classify the data into relevant classes for further interventions. Different methods that are not automated are very time-consuming and require manual effort have been tried for this before.

Recently deep learning has been used for this task but due to the complexity of the dataset, specifically due to inter-class similarities in the dataset and specific terminology having different meanings in medical contexts has caused significant problems in having a definitive approach to medical …

Improving Credit Card Fraud Detection Using Transfer Learning And Data Resampling Techniques, Charmaine Eunice Mena Vinarta

Electronic Theses, Projects, and Dissertations

This Culminating Experience Project explores the use of machine learning algorithms to detect credit card fraud. The research questions are: Q1. What cross-domain techniques developed in other domains can be effectively adapted and applied to mitigate or eliminate credit card fraud, and how do these techniques compare in terms of fraud detection accuracy and efficiency? Q2. To what extent do synthetic data generation methods effectively mitigate the challenges posed by imbalanced datasets in credit card fraud detection, and how do these methods impact classification performance? Q3. To what extent can the combination of transfer learning and innovative data resampling techniques …

Quiz Web Application, Dipti Rathod

Electronic Theses, Projects, and Dissertations

The Quiz web application is designed to facilitate the process of quiz creation and participation. This web application mainly consists of three roles: Admin, Instructor, and Student. Each role has specific features, functionalities, and permissions. With a user-friendly interface, the admin role can handle the departments, courses, and instructors. This web application also ensures smooth quiz management, allowing the instructors to schedule the upcoming quizzes, create the questions, and manage the students with ease. Student roles have features like taking quizzes and seeing their results. Additionally, this web application includes a significant feature to prevent cheating during online tests, ensuring …

Enhancing Accident Investigation Using Traffic Cctv Footage, Aksharapriya Peddi

Electronic Theses, Projects, and Dissertations

This Culminating Experience Project investigated how the densenet-161 model will perform on accident severity prediction compared to proposed methods. The research questions are: (Q1) What is the impact of usage of augmentation techniques on imbalanced datasets? (Q2) How will the hyper parameter tuning affect the model performance? (Q3) How effective is the proposed model compared to existing work? The findings are: Q1. The effectiveness of our model depends on the implementation of augmentation techniques that pay attention to handling imbalanced datasets. Our dataset poses a challenge due to distribution of classes in terms of accident severity. To address this challenge …

Detection Of Myofascial Trigger Points With Ultrasound Imaging And Machine Learning, Benjamin Formby

All Theses

Myofascial Pain Syndrome (MPS) is a common chronic muscle pain disorder that affects a large portion of the global population, seen in 85-93% of patients in specialty pain clinics [10]. MPS is characterized by hard, palpable nodules caused by a stiffened taut band of muscle fibers. These nodules are referred to as Myofascial Trigger Points (MTrPs) and can be classified by two states: active MTrPs (A-MTrPs) and latent MtrPs (L-MTrPs). Treatment for MPS involves massage therapy, acupuncture, and injections or painkillers. Given the subjectivity of patient pain quantification, MPS can often lead to mistreatment or drug misuse. A deterministic way …

Convolution And Autoencoders Applied To Nonlinear Differential Equations, Noah Borquaye

Electronic Theses and Dissertations

Autoencoders, a type of artificial neural network, have gained recognition by researchers in various fields, especially machine learning due to their vast applications in data representations from inputs. Recently researchers have explored the possibility to extend the application of autoencoders to solve nonlinear differential equations. Algorithms and methods employed in an autoencoder framework include sparse identification of nonlinear dynamics (SINDy), dynamic mode decomposition (DMD), Koopman operator theory and singular value decomposition (SVD). These approaches use matrix multiplication to represent linear transformation. However, machine learning algorithms often use convolution to represent linear transformations. In our work, we modify these approaches to …

Qasm-To-Hls: A Framework For Accelerating Quantum Circuit Emulation On High-Performance Reconfigurable Computers, Anshul Maurya

Theses and Dissertations

High-performance reconfigurable computers (HPRCs) make use of Field-Programmable Gate Arrays (FPGAs) for efficient emulation of quantum algorithms. Generally, algorithm-specific architectures are implemented on the FPGAs and there is very little flexibility. Moreover, mapping a quantum algorithm onto its equivalent FPGA emulation architecture is challenging. In this work, we present an automation framework for converting quantum circuits to their equivalent FPGA emulation architectures. The framework processes quantum circuits represented in Quantum Assembly Language (QASM) and derives high-level descriptions of the hardware emulation architectures for High-Level Synthesis (HLS) on HPRCs. The framework generates the code for a heterogeneous architecture consisting of a …

Accelerating Machine Learning Inference For Satellite Component Feature Extraction Using Fpgas., Andrew Ekblad

Theses and Dissertations

Running computer vision algorithms requires complex devices with lots of computing power, these types of devices are not well suited for space deployment. The harsh radiation environment and limited power budgets have hindered the ability of running advanced computer vision algorithms in space. This problem makes running an on-orbit servicing detection algorithm very difficult. This work proposes using a low powered FPGA to accelerate the computer vision algorithms that enable satellite component feature extraction. This work uses AMD/Xilinx’s Zynq SoC and DPU IP to run model inference. Experiments in this work centered around improving model post processing by creating implementations …

Towards Enabling Usable Batteryless Wearables, Arwa Alsubhi

All Dissertations

Wearable devices have become ubiquitous in modern technology, providing a convenient way for a large number of users to monitor their health and track their daily activities. However, despite their numerous benefits, wearable devices are not without their challenges. One of the most significant issues is the need to recharge their batteries, which can disrupt users’ daily routines and might result in the discontinuation of device use.

Replacing batteries with capacitors and charging them using the available harvested energy from the surrounding environment (e.g., kinetic, thermal, or solar energy) eliminates the burden of recharging or replacing batteries, resulting in convenient, …