Engineering Commons^™

Ant Colony Optimization For Resistor Color Code Detection, Slamet Wibawanto, Kartika Candra Kirana, Hani Ramadhan

Knowledge Engineering and Data Science

In the early stages of learning resistors, introducing color-based values is needed. Moreover, some combinations require a resistor trip analysis to identify. Unfortunately, a resistor body color is considered a local solution, which often confuses resistor coloration. Ant Colony Optimization (ACO) is a heuristic algorithm that can recognize problems with traveling a group of ants. ACO is proposed to select commercial matrix values to be computed without preventing local solutions. In this study, each explores the matrix based on pheromones and heuristic information to generate local solutions. Global solutions are selected based on their high degree of similarity with other …

K-Means Clustering And Multilayer Perceptron For Categorizing Student Business Groups, Miftahul Walid, Norfiah Lailatin Nispi Sahbaniya, Hozairi Hozairi, Fajar Baskoro, Arya Yudhi Wijaya

Knowledge Engineering and Data Science

The research conducted in this study was driven by the East Java provincial government's requirement to assess the transaction levels of the Student Business Group (KUS) in the SMA Double Track program. These transaction levels are a basis for allocating supplementary financial aid to each business group. The system's primary objective is to assist the provincial government of East Java in making well-informed choices pertaining to the distribution of supplementary capital to the KUS. The classification technique employed in this study is the multilayer perceptron. However, the K-Means Clustering method is utilised to generate target data due to the limited …

Round-Robin Algorithm In Load Balancing For National Data Centers, I Kadek Wahyu Sudiatmika, Gede Indrawan, Sariyasa Sariyasa

Knowledge Engineering and Data Science

The Provincial Government of Bali assumes a crucial role in administering various public service applications to meet the requirements of its community, traditional villages, and regional apparatus. Nevertheless, the escalating magnitude of traffic and uneven distribution of requests have resulted in substantial server burdens, which may jeopardize the operation of applications and heighten the likelihood of downtime. Ensuring efficient load distribution is of utmost importance in tackling these difficulties, and the Round Robin algorithm is often utilized for this purpose. However, the current body of research has not extensively examined the distinct circumstances surrounding on-premise servers in the Bali Provincial …

Long-Term Traffic Prediction Based On Stacked Gcn Model, Atkia Akila Karim, Naushin Nower

Knowledge Engineering and Data Science

With the recent surge in road traffic within major cities, the need for both short and long-term traffic flow forecasting has become paramount for city authorities. Previous research efforts have predominantly focused on short-term traffic flow estimations for specific road segments and paths. However, applications of paramount importance, such as traffic management and schedule routing planning, demand a deep understanding of long-term traffic flow predictions. However, due to the intricate interplay of underlying factors, there exists a scarcity of studies dedicated to long-term traffic prediction. Previous research has also highlighted the challenge of lower accuracy in long-term predictions owing to …

Optimizing Random Forest Algorithm To Classify Player's Memorisation Via In-Game Data, Akmal Vrisna Alzuhdi, Harits Ar Rosyid, Mohammad Yasser Chuttur, Shah Nazir

Knowledge Engineering and Data Science

Assessment of a player's knowledge in game education has been around for some time. Traditional evaluation in and around a gaming session may disrupt the players' immersion. This research uses an optimized Random Forest to construct a non-invasive prediction of a game education player's Memorization via in-game data. Firstly, we obtained the dataset from a 3-month survey to record in-game data of 50 players who play 4-15 game stages of the Chem Fight (a test case game). Next, we generated three variants of datasets via the preprocessing stages: resampling method (SMOTE), normalization (min-max), and a combination of resampling and normalization. …

A Comparative Effectiveness Study On Opioid Use Disorder Prediction Using Artificial Intelligence And Existing Risk Models, Sajjad Fouladvand, Jeffery Talbert, Linda Phyliss Dwoskin, Heather M. Bush, Amy L. Meadows, Lars E. Peterson, Yash R. Mishra, Steven K. Roggenkamp, Fei Wang, Ramakanth Kavuluru, Jin Chen

Markey Cancer Center Faculty Publications

Opioid use disorder (OUD) is a leading cause of death in the United States placing a tremendous burden on patients, their families, and health care systems. Artificial intelligence (AI) can be harnessed with available healthcare data to produce automated OUD prediction tools. In this retrospective study, we developed AI based models for OUD prediction and showed that AI can predict OUD more effectively than existing clinical tools including the unweighted opioid risk tool (ORT). Data include 474,208 patients’ data over 10 years; 269,748 were females with an average age of 56.78 years. Cases are prescription opioid users with at least …

Imitation Improvement Learning For Large-Scale Capacitated Vehicle Routing Problems, The Viet Bui, Tien Mai

Research Collection School Of Computing and Information Systems

Recent works using deep reinforcement learning (RL) to solve routing problems such as the capacitated vehicle routing problem (CVRP) have focused on improvement learning-based methods, which involve improving a given solution until it becomes near-optimal. Although adequate solutions can be achieved for small problem instances, their efficiency degrades for large-scale ones. In this work, we propose a newimprovement learning-based framework based on imitation learning where classical heuristics serve as experts to encourage the policy model to mimic and produce similar or better solutions. Moreover, to improve scalability, we propose Clockwise Clustering, a novel augmented framework for decomposing large-scale CVRP into …

Characterization And Estimation Of Musculoskeletal Pain Using Machine Learning, Boluwatife Faremi

Master's Theses

Traditional scales utilized for recording pain are known to be highly subjective and biased due to inaccuracies in recollecting actual pain intensities. As a result, machine learning (ML) models that are trained using these scores as ground truth are reported to have low performance for objective pain classification because of the huge disparity between what was felt in moments of pain and the scores recorded afterward.

In the present study, two devices were designed for gathering real-time, continuous in-session subjective pain scores and the recording of the autonomic nervous system (ANS) altered endodermal (EDA) activity. 24 participants were recruited to …

Predicting Material Structures And Properties Using Deep Learning And Machine Learning Algorithms, Yuqi Song

Theses and Dissertations

Discovering new materials and understanding their crystal structures and chemical properties are critical tasks in the material sciences. Although computational methodologies such as Density Functional Theory (DFT), provide a convenient means for calculating certain properties of materials or predicting crystal structures when combined with search algorithms, DFT is computationally too demanding for structure prediction and property calculation for most material families, especially for those materials with a large number of atoms. This dissertation aims to address this limitation by developing novel deep learning and machine learning algorithms for effective prediction of material crystal structures and properties. Our data-driven machine learning …

An Ml Based Digital Forensics Software For Triage Analysis Through Face Recognition, Gaurav Gogia, Parag H. Rughani

Journal of Digital Forensics, Security and Law

Since the past few years, the complexity and heterogeneity of digital crimes has increased exponentially, which has made the digital evidence & digital forensics paramount for both criminal investigation and civil litigation cases. Some of the routine digital forensic analysis tasks are cumbersome and can increase the number of pending cases especially when there is a shortage of domain experts. While the work is not very complex, the sheer scale can be taxing. With the current scenarios and future predictions, crimes are only going to become more complex and the precedent of collecting and examining digital evidence is only going …

Extending The Convolution In Graph Neural Networks To Solve Materials Science And Node Classification Problems, Steph-Yves Mike Louis

Theses and Dissertations

The usage of graph to represent one's data in machine learning has grown in popularity in both academia and the industry due to its inherent benefits. With its flexible nature and immediate translation to real life observed objects, graph representation had a considerable contribution in advancing the state-of-the-art performance of machine learning in materials.

In this dissertation proposal, we discuss how machines can learn from graph encoded data and provide excellent results through graph neural networks (GNN). Notably, we focus our adaptation of graph neural networks on three tasks: predicting crystal materials properties, nullifying the negative impact of inferior graph …

Learning Deep Time-Index Models For Time Series Forecasting, Jiale Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven Hoi

Research Collection School Of Computing and Information Systems

Deep learning has been actively applied to time series forecasting, leading to a deluge of new methods, belonging to the class of historicalvalue models. Yet, despite the attractive properties of time-index models, such as being able to model the continuous nature of underlying time series dynamics, little attention has been given to them. Indeed, while naive deep timeindex models are far more expressive than the manually predefined function representations of classical time-index models, they are inadequate for forecasting, being unable to generalize to unseen time steps due to the lack of inductive bias. In this paper, we propose DeepTime, a …

Accurate Covariance Estimation For Pose Data From Iterative Closest Point Algorithm, Rick H. Yuan, Clark N. Taylor, Scott L. Nykl

Faculty Publications

One of the fundamental problems of robotics and navigation is the estimation of the relative pose of an external object with respect to the observer. A common method for computing the relative pose is the iterative closest point (ICP) algorithm, where a reference point cloud of a known object is registered against a sensed point cloud to determine relative pose. To use this computed pose information in downstream processing algorithms, it is necessary to estimate the uncertainty of the ICP output, typically represented as a covariance matrix. In this paper, a novel method for estimating uncertainty from sensed data is …

Freestanding Graphene Heat Engine Analyzed Using Stochastic Thermodynamics, J. Durbin, J. M. Mangum, M. N. Gikunda, F. Harerimana, P. Kumar, L. L. Bonilla, P. M. Thibado

Physics Faculty Publications and Presentations

We present an Ito-Langevin model for freestanding graphene connected to an electrical circuit. The graphene is treated as a Brownian particle in a double-well potential and is adjacent to a fixed electrode to form a variable capacitor. The capacitor is connected in series with a battery and a load resistor. The capacitor and resistor are given separate thermal reservoirs. We have solved the coupled Ito-Langevin equations for a broad range of temperature differences between the two reservoirs. Using ensemble averages, we report the rate of change in energy, heat, and work using stochastic thermodynamics. When the resistor is held at …

Kinetic Particle Simulations Of Plasma Charging At Lunar Craters Under Severe Conditions, David Lund, Xiaoming He, Daoru Frank Han

Mathematics and Statistics Faculty Research & Creative Works

This paper presents fully kinetic particle simulations of plasma charging at lunar craters with the presence of lunar lander modules using the recently developed Parallel Immersed-Finite-Element Particle-in-Cell (PIFE-PIC) code. The computation model explicitly includes the lunar regolith layer on top of the lunar bedrock, taking into account the regolith layer thickness and permittivity as well as the lunar lander module in the simulation domain, resolving a nontrivial surface terrain or lunar lander configuration. Simulations were carried out to study the lunar surface and lunar lander module charging near craters at the lunar terminator region under mean and severe plasma environments. …

Estimation Of Recursive Route Choice Models With Incomplete Trip Observations, Tien Mai, The Viet Bui, Quoc Phong Nguyen, Tho V. Le

Research Collection School Of Computing and Information Systems

This work concerns the estimation of recursive route choice models in the situation that the trip observations are incomplete, i.e., there are unconnected links (or nodes) in the observations. A direct approach to handle this issue could be intractable because enumerating all paths between unconnected links (or nodes) in a real network is typically not possible. We exploit an expectation–maximization (EM) method that allows dealing with the missing-data issue by alternatively performing two steps of sampling the missing segments in the observations and solving maximum likelihood estimation problems. Moreover, observing that the EM method could be expensive, we propose a …

A Hierarchical Optimization Approach For Dynamic Pickup And Delivery Problem With Lifo Constraints, Jianhui Du, Zhiqin Zhang, Xu Wang, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We consider a dynamic pickup and delivery problem (DPDP) where loading and unloading operations must follow a last in first out (LIFO) sequence. A fleet of vehicles will pick up orders in pickup points and deliver them to destinations. The objective is to minimize the total over-time (that is the amount of time that exceeds the committed delivery time) and total travel distance. Given the dynamics of orders and vehicles, this paper proposes a hierarchical optimization approach based on multiple intuitive yet often-neglected strategies, namely what we term as the urgent strategy, hitchhike strategy and packing-bags strategy. These multiple strategies …

Cost Estimation Trends For Major Defense Acquisition Programs, Sammantha Jones, Edward D. White, Jonathan D. Ritschel, Shawn M. Valentine

Faculty Publications

The authors use both descriptive and inferential techniques to investigate average and standard deviation trends in cost estimates for major defense acquisition programs (MDAPs) grouped into decades from the 1970s to 2010s. For total program-cost-growth factors (CGFs), the 2010s exhibited lower CGFs compared to the 1990s. For the program-acquisition-unit cost (PAUC) CGFs, the 2010s appear lower than the 1990s and borderline lower than the 1970s. A statistically significant decreasing trend in the standard deviations of total program CGFs throughout the decades was identified. This lowering variability trend also appeared for PAUC CGFs from the 1980s onward. This finding appears to …

Localized Surface Plasmon Resonance Of Palladium Parallelepiped Nanoparticles, Salem Marhaba, Nourhan El Kawni

BAU Journal - Science and Technology

Localized surface plasmon resonance (LSPR) of palladium parallelepiped nanoparticle have been investigated using Finite Element Method (FEM) in the frame of COMSOL Multiphysics Software. Spectral characteristics (magnitude, peak position and width) of LSPR of a single Pd nanoparticle having the shape of a parallelepiped are investigated in two different cases. The first case studied the size effect on LSPR by fixing the height with varying both length and width. However, the second case studied the shape effect on the LSPR by rounding the corners of the parallelepiped for different radii while fixing its dimensions. The extinction cross-section spectra of these …

Three-Dimensional Higher-Order Compressive Sensing For Raypath Separation In An Acoustic Waveguide, Fengyan Zhong

Journal of Marine Science and Technology–Taiwan

In an acoustic waveguide, the separation of multiple raypaths, especially close raypaths, is a challenging task in the presence of colored noise due to interference in the acoustic eld. In this paper, a three-dimensional higher-order compressive sensing method for raypath separation is proposed. The method is applied on an array-to-array experimental conguration where both emitter and reception arrays are used. With the array-to-array conguration, the raypaths are characterized by three parameters including arrival direction, departure direction, and arrival time. Furthermore, the performance of the proposed algorithm is illustrated by simulations and experiments. The numerical results based on simulation illustrate that …

Projection Of Sea Surface Temperature Based On Empirical Mode Decomposition And Its Application In Typhoon Number Forecast, Wen-Cheng Huang, Jung-Chen Lee

Journal of Marine Science and Technology–Taiwan

The primary objective of this paper is to demonstrate the applicability of empirical mode decomposition (EMD) in projecting non-stationary daily sea surface temperature (SST) series, and the other is to apply the projected series of SST to estimate the number of typhoons. The study focuses on an area ranging from 5°N~25°N and 110°E~170°E, collecting daily sea temperature data and typhoons records within this region. The findings conclusively indicate that the EMD-based data generation method proposed in this paper can effectively addresses the limitations of previous hydrological models in generating non-stationary series, signifying a significant advancement in hydrological time series modeling …

Neutrosophic Decision Making Model For Investment Portfolios Selection And Optimizing Based On Wide Variety Of Investment Opportunities And Many Criteria In Market, Ayman H. Abdel-Aziem, Hoda K. Mohamed, Ahmed Abdelhafeez

Neutrosophic Systems with Applications

Investment portfolio selection is a difficult subject due to the presence of competing factors. Choosing a portfolio for one's investments is a major choice that may have far-reaching effects on one's financial well-being. Risk tolerance, time horizon, investing objectives, asset allocation, and investment selection are only a few of the factors that will be studied in this article. The Stable Preference Ordering Towards Ideal Solution (SPOTIS) technique is the basis for our proposed integrated multi-criteria decision-making (MCDM) model. This paper used the single-valued neutrosophic set as a framework to deal with uncertain data. The purpose of the suggested SPOTIS–Neutrosophic model …

Adaptive Plasmonic Metasurfaces For Radiative Cooling And Passive Thermoregulation, Azadeh Didari-Bader, Nooshin M. Estakhri, Nasim Mohammadi Estrakhri

Engineering Faculty Articles and Research

In this work, we investigate a class of planar photonic structures operating as passive thermoregulators. The radiative cooling process is adjusted through the incorporation of a phase change material (Vanadium Dioxide, VO₂) in conjunction with a layer of transparent conductive oxide (Aluminum-doped Zinc Oxide, AZO). VO₂ is known to undergo a phase transition from the “dielectric” phase to the “plasmonic” or “metallic” phase at a critical temperature close to 68°C. In addition, AZO shows plasmonic properties at the long-wave infrared spectrum, which, combined with VO₂, provides a rich platform to achieve low reflections across the …

Optimization Techniques For Machine Learning Inference And Near Memory Image Processing In Hardware For Highly Constrained Iot Edge Nodes, Rajeev Joshi

USF Tampa Graduate Theses and Dissertations

The growing demand for fast and energy-efficient hardware for resource-constrained Internet of Things (IoT) edge devices has highlighted the limitations of conventional computing architectures. This research focuses on addressing the demand for fast, optimized, and energy-efficient machine learning inference engines as well as image processing in IoT edge applications. In this work, we address three challenging research problems and devise efficient solutions. Our investigation involved comprehensive exploration and analysis, leading to the proposal of effective approaches for overcoming these demanding issues. Through our work, we contribute novel solutions that offer improved efficiency and effectiveness in handling these research problems.

First, …

Assessment Of Spatial Variability Of Groundwater Levels In Moroto District, Uganda, Augustina Clara Alexander Dr

Tanzania Journal of Engineering and Technology (TJET)

Globally, the variation of groundwater levels is increasingly overwhelming due to over exploitation resulting from population growth. The dynamic nature of socio-economic activities in Moroto District such as agriculture, settlements and increased trends in irrigation technology has been well-known worldwide as common parameters triggering groundwater variability. Nevertheless, determination of groundwater levels for groundwater management and development purpose is a challenge due to spatial variability in levels across Moroto District. In this study, geostatistical technique in Geographical Information System (GIS) was used to analyze spatial variability of water levels in the study area. The analysis utilized ordinary Kriging method to predict …

Performance Evaluation Of Full Array, Sub-Array And Overlapped Sub-Array Hybrid Beamforming Architectures For Massive Mimo Systems, Godwin Mruma Gadiel

Tanzania Journal of Engineering and Technology (TJET)

The technological advancement in wireless communication, promises high data rate for end users. This has led to the possibility of smart cities, inter connected vehicles, and virtual reality applications. One of the recent technologies in wireless communication is massive MIMO where large number of antennas are deployed at the transmitter or receiver. This is possible due to the use of mmWave in wireless communication. With massive MIMO, beamforming technique can be employed in the communication system. Beamforming is the ability of communication system to direct power to the intended users and to cancel power at non-intended users and thus significantly …

Product Costing And Pricing In Small And Medium Enterprises In Tanzania, Cosmas F Kindole

Tanzania Journal of Engineering and Technology (TJET)

metalwork small and medium enterprises located in Dar es Salaam with the aim of establishing methods used by SMEs in Tanzania in setting selling prices of their products. The study was necessitated by the apparent discrepancy of prices for similar products from SMEs. The approach used to gather information was mainly through direct interview and the use of questionnaires. Direct observation and literature review were also used to establish the whole costing and pricing practices in metal manufacturing SMEs. The findings of the study established that product selling practices of the surveyed SMEs were based on instinct and appearance of …

On The Use Of Wireless Technologies For Wildlife Monitoring: Wireless Sensor Network Routing Protocols, Lilian Mutalemwa

Tanzania Journal of Engineering and Technology (TJET)

Traditional methods for wildlife monitoring are labor-intensive and time-consuming. Therefore, advanced technologies and remote monitoring methods are becoming increasingly popular. This paper presents a study on wireless technologies for wildlife monitoring. In the study, a review of the literature was done to identify the most commonly used wireless technologies. Various technologies were explored including unmanned aerial vehicles (UAVs), Internet of Things (IoT), wireless sensor networks (WSNs), artificial intelligence (AI), global positioning system (GPS), and very high frequency (VHF) radio. Then, a more detailed study was done on WSN technology. Investigations were done to observe the performance of routing protocols in …

Neutrosophic Decision Making Model For Investment Portfolios Selection And Optimizing Based On Wide Variety Of Investment Opportunities And Many Criteria In Market, Ayman H. Abdel-Aziem, Hoda K. Mohamed, Ahmed Abdelhafeez

Neutrosophic Systems with Applications

Investment portfolio selection is a difficult subject due to the presence of competing factors. Choosing a portfolio for one's investments is a major choice that may have far-reaching effects on one's financial well-being. Risk tolerance, time horizon, investing objectives, asset allocation, and investment selection are only a few of the factors that will be studied in this article. The Stable Preference Ordering Towards Ideal Solution (SPOTIS) technique is the basis for our proposed integrated multi-criteria decision-making (MCDM) model. This paper used the single-valued neutrosophic set as a framework to deal with uncertain data. The purpose of the suggested SPOTIS–Neutrosophic model …

Status And Prospects Of Fully Mechanized Mining Technology In Vietnam Coal Mines From 2005 To 2020, Hai Duong Duc, Quang Dao Hong, Viet Phan Van, Thang Ngo Van

Journal of Sustainable Mining

This paper analyzes the achievement and the failures of applying the mechanized mining method in underground coal mines by the Vietnam National Coal – Mineral Industries Holding Corporation Ltd. (VINACOMIN) for 15 years (from 2005 to 2020). It also proposes the recommendations for developing mechanized mining in order to improve coal production, productivity and safety management for the sustainable development of Vietnam’s underground coal mining in the period 2021-2025