Engineering Commons^™

Exposure Assessment Of Emerging Contaminants: Rapid Screening And Modeling Of Plant Uptake, Majid Bagheri

Doctoral Dissertations

"With the advent of new chemicals and their increasing uses in every aspect of our life, considerable number of emerging contaminants are introduced to environment yearly. Emerging contaminants in forms of pharmaceuticals, detergents, biosolids, and reclaimed wastewater can cross plant roots and translocate to various parts of the plants. Long-term human exposure to emerging contaminants through food consumption is assumed to be a pathway of interest. Thus, uptake and translocation of emerging contaminants in plants are important for the assessment of health risks associated with human exposure to emerging contaminants. To have a better understanding over fate of emerging contaminants …

Predictability Improvement Of Scheduled Flights Departure Time Variation Using Supervised Machine Learning, Deepudev Sahadevan, Palanisamy Ponnusamy Dr, Manjunath K. Nelli Mr, Varun P. Gopi Dr

International Journal of Aviation, Aeronautics, and Aerospace

The departure time uncertainty exacerbates the inaccuracy of arrival time estimation and demand for arrival slots, particularly for movements to capacity constrained airports. The Estimated Take-Off Time (ETOT) or Estimated Departure Time(ETD) for each individual flight is currently derived from Air Traffic Flow Management System (ATFMS), which are solely determined based on individual flight plan Estimated Off Block Time(EOBT) or subsequent delays updated by Airline. Even if normal weather conditions prevail, aircraft departure times will differ from ETOTs determined by the ATFMS due to a number of factors such as congestion, early/delayed inbound flight (linked flights), reactionary delays and air …

Underestimation Bias And Underfitting In Machine Learning, Padraig Cunningham, Sarah Jane Delany

Conference papers

. Often, what is termed algorithmic bias in machine learning will be due to historic bias in the training data. But sometimes the bias may be introduced (or at least exacerbated) by the algorithm itself. The ways in which algorithms can actually accentuate bias has not received a lot of attention with researchers focusing directly on methods to eliminate bias - no matter the source. In this paper we report on initial research to understand the factors that contribute to bias in classification algorithms. We believe this is important because underestimation bias is inextricably tied to regularization, i.e. measures to …

Circuit Topology Estimation In An Adaptive Protection Platform, Daniel Ruiz

Electrical and Computer Engineering ETDs

Modern electric power systems, power system protections and controls have experienced a significant change, thanks to the introduction of new technologies, such as microprocessors, GPS, communication, etc. These technologies brought an increased amount of measured-data and information flow on power grids. Adaptive protection systems have been introduced to increase the reliability, selectivity, and sensitivity of the traditional protection systems. An adaptive protection system highly relies on the communication system infrastructure to identify the latest status of power grid (e.g., circuit topology or generation level of distributed energy resources). However, when the communication links to some of the equipment are outaged …

Well Oiled Machine: Classifying Machinery Performance Reductions Using Work Order Data, Jacob Brionez, Amber Burnett, Cho Kim, Scott M. Whitney, Thomas N. Anderson, Sumeet Treehan

SMU Data Science Review

Work Order (WO) data from System Applications and Products in Data Processing (SAP) software contains valuable information about what WOs intend to accomplish. Using SAP work order data, with time-series machinery sensor data combined into the same dataset, provides an opportunity to optimize prediction models to increase performance. Ideally, WO data can be utilized to help predict machinery's anticipated performance and can help prioritize a WO among others based on the anticipated machinery performance. It is possible to identify anomalies in pump sensor data using the Isolation Forest algorithm as the method for anomaly detection. The relationship between the sensor …

Representational Learning Approach For Predicting Developer Expertise Using Eye Movements, Sumeet Maan

School of Computing: Dissertations, Theses, and Student Research

The thesis analyzes an existing eye-tracking dataset collected while software developers were solving bug fixing tasks in an open-source system. The analysis is performed using a representational learning approach namely, Multi-layer Perceptron (MLP). The novel aspect of the analysis is the introduction of a new feature engineering method based on the eye-tracking data. This is then used to predict developer expertise on the data. The dataset used in this thesis is inherently more complex because it is collected in a very dynamic environment i.e., the Eclipse IDE using an eye-tracking plugin, iTrace. Previous work in this area only worked on …

Proportional Voting Based Semi-Unsupervised Machine Learning Intrusion Detection System, Yang G. Kim, Ohbong Kwon, John Yoon

Publications and Research

Feature selection of NSL-KDD data set is usually done by finding co-relationships among features, irrespective of target prediction. We aim to determine the relationship between features and target goals to facilitate different target detection goals regardless of the correlated feature selection. The unbalanced data structure in NSL-KDD data can be relaxed by Proportional Representation (PR). However, adopting PR would deny the notion of winner-take-all by attracting a majority of the vote and also provide a fairly proportional share for any grouping of like-minded data. Furthermore, minorities and majorities would get a fair share of power and representation in data structure …

A Big Data Approach For Improving Nebraska Cycling Routes, Fadi Alsaleem, Ali Al-Ramini, Mohammad Ali Takallou, Daniel P. Piatkowski

Nebraska Department of Transportation: Research Reports

More people are becoming interested in creating healthy lifestyle habits for themselves. It has been proved that cycling has several health benefits. Therefore, governments are planning towards more cycling-friendly infrastructures and environments. To understand the cycling activities in Nebraska, we analyze Strava data for the last three years, providing valuable insight into cycling activities for urban planning purposes.

We analyze commute and recreational cycling patterns varying between weekdays and weekends to identify peak hours. Moreover, we study the effect of weather on cycling activity patterns for the last three years. Also, we use the impact of the outside temperature as …

A Python-Based Brain-Computer Interface Package For Neural Data Analysis, Md Hasan Anowar

Theses and Dissertations

Anowar, Md Hasan, A Python-based Brain-Computer Interface Package for Neural Data Analysis. Master of Science (MS), December, 2020, 70 pp., 4 tables, 23 figures, 74 references.

Although a growing amount of research has been dedicated to neural engineering, only a handful of software packages are available for brain signal processing. Popular brain-computer interface packages depend on commercial software products such as MATLAB. Moreover, almost every brain-computer interface software is designed for a specific neuro-biological signal; there is no single Python-based package that supports motor imagery, sleep, and stimulated brain signal analysis. The necessity to introduce a brain-computer interface package that …

Emocolor : Fine-Grained Emotion Recognition From Skin Color Information, Maria Guadalupe Jimenez Velasco

Open Access Theses & Dissertations

In everyday human-to-human communication, emotions play a fundamental role. Emotions represent the affective behavior of humans that is multi-modal, subtle, and complex. Previous approaches based on conventional computer vision explicitly used shape information. Modern approaches based on deep learning implicitly exploit all information available in the image, but by their nature make it difficult to assess the contributions of each source of information. In addition, skin color as a unimodal technique to recognize emotions has been explored to recognize only three coarse-grained emotions in valence space.To the best of our knowledge, this work presents the first approach to fine-grained emotion …

Comparison Of Classification Algorithms And Undersampling Methods On Employee Churn Prediction: A Case Study Of A Tech Company, Heather Cooper

Master's Theses

Churn prediction is a common data mining problem that many companies face across industries. More commonly, customer churn has been studied extensively within the telecommunications industry where there is low customer retention due to high market competition. Similar to customer churn, employee churn is very costly to a company and by not deploying proper risk mitigation strategies, profits cannot be maximized, and valuable employees may leave the company. The cost to replace an employee is exponentially higher than finding a replacement, so it is in any company’s best interest to prioritize employee retention.

This research combines machine learning techniques with …

Health Monitoring Using Deep Learning Of Acoustic And Speech Signals, Eric E. Hamke

Electrical and Computer Engineering ETDs

The focus of the research is to identify stress markers in a firefighter's speech. These markers include changes in breathing patterns and changes in the fundamental frequency of an individual’s voice. The breathing patterns are characterized using the number of breaths taken in a minute and the time spent inhaling. These measures are estimated using a Restricted Boltzmann Machine to process a firefighters’ SCBA regulator sounds, as open and closed. The classifications are then combined into continuous intervals. Observing the length of the intervals and the number of interval-starts represents time spent inhaling and the breathing rates (breaths per minute). …

A Bibliometric Survey Of Smart Wearable In The Health Insurance Industry, Apeksha Shah, Swati Ahirrao, Shraddha Phansalkar, Ketan Kotecha

Library Philosophy and Practice (e-journal)

Smart wearables help real-time and remote monitoring of health data for effective diagnostic and preventive health care services. Wearable devices have the ability to track and monitor healthcare vitals such as heart rate, physical activities, BMI (Body Mass Index), blood pressure, and keeps an individual notified about the health status. Artificial Intelligence-enabled wearables show an ability to transform the health insurance sector. This would not only enable self-management of individual health but also help them focus from treatments to the preventions of health hazards. With this customer-centric approach to health care, it will enable the insurance companies to track the …

Using Spatial Analysis And Machine Learning Techniques To Develop A Comprehensive Highway-Rail Grade Crossing Consolidation Model, Samira Soleimani

LSU Doctoral Dissertations

The safety of highway-railroad grade crossings (HRGC) is still an issue in the United States of America (USA). The grade crossing is where a railroad crosses a road at the same level without any over or underpass. To improve the safety of crossings, the crossings’ condition should be explored from several aspects such as engineering design (speed limit, warning signs, etc.), road condition (number of lanes, surface markings, etc.), rail design (the type of track, ballast, etc.), temporal variables (weather, visibility, time of day, lightning, etc.), social variables (population, race, etc.), and last but not least, spatial variables (the type …

Forecasting Vegetation Health In The Mena Region By Predicting Vegetation Indicators With Machine Learning Models, Sachi Perera, Wenzhao Li, Erik Linstead, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Machine learning (ML) techniques can be applied to predict and monitor drought conditions due to climate change. Predicting future vegetation health indicators (such as EVI, NDVI, and LAI) is one approach to forecast drought events for hotspots (e.g. Middle East and North Africa (MENA) regions). Recently, ML models were implemented to predict EVI values using parameters such as land types, time series, historical vegetation indices, land surface temperature, soil moisture, evapotranspiration etc. In this work, we collected the MODIS atmospherically corrected surface spectral reflectance imagery with multiple vegetation related indices for modeling and evaluation of drought conditions in the MENA …

Machine Learning Techniques For Credit Card Fraud Detection, Hossam Eldin Mohammed Abd El-Hamid Ahmed Abdou, Wael Khalifa, Mohamed Ismail Roushdy, Abdel-Badeeh M. Salem

Future Computing and Informatics Journal

The term “fraud”, it always concerned about credit card fraud in our minds. And after the significant increase in the transactions of credit card, the fraud of credit card increased extremely in last years. So the fraud detection should include surveillance of the spending attitude for the person/customer to the determination, avoidance, and detection of unwanted behavior. Because the credit card is the most payment predominant way for the online and regular purchasing, the credit card fraud raises highly. The Fraud detection is not only concerned with capturing of the fraudulent practices, but also, discover it as fast as they …

Intelligent Technique For Automating The Conversion Between Major And Minor Melodies, Nermin N. J. Siphocly, El-Sayed M. El-Horbaty, Abd El-Badea Mohamed Salem Prof

Future Computing and Informatics Journal

Nowadays, computers are extremely beneficial to music composers. Computer music generation tools are developed for aiding composers in producing satisfying musical pieces. The automation of music composition tasks is a challenging research point, specially to the field of Artificial Intelligence. Converting melodies that are played on a major scale to minor (or vice versa) is interesting to both composers and music listeners. Newly converted melodies of famous songs, either from major to minor or the opposite, are becoming blockbusters on the social media. In this paper we propose an intelligent method for automating the conversion between major and minor melodies …

Unsupervised Learning Methods For Identification Of Defects In Heterogeneous Materials, Muthu Ram Prabhu Elenchezhian, Vamsee Vadlamudi, Rassel Raihan, Kenneth Reifsnider

Institute of Predictive Performance Methodologies (IPPM-UTARI)-Archive

The complexity of composite materials due to the nature of their numerous laminated layers, stacking sequences, type of fibers, resin, and other external factors has challenged the world of structural health monitoring (SHM) and non-destructive inspection (NDI). Post-processing of these SHM and NDI methods has been mostly a manual time-consuming process, with human inspection causing errors associated with the bias decision made by NDI inspectors. Recent advances also call for Cure On The Fly (COTF), by integrating NDI with advanced technologies and analysis techniques such as Artificial Intelligence (AI) and Machine Learning (ML) for on-line real-time predictions of a defect, …

Evaluation Of Standard And Semantically-Augmented Distance Metrics For Neurology Patients, Daniel B. Hier, Jonathan Kopel, Steven U. Brint, Donald C. Wunsch, Gayla R. Olbricht, Sima Azizi, Blaine Allen

Electrical and Computer Engineering Faculty Research & Creative Works

Background: Patient distances can be calculated based on signs and symptoms derived from an ontological hierarchy. There is controversy as to whether patient distance metrics that consider the semantic similarity between concepts can outperform standard patient distance metrics that are agnostic to concept similarity. The choice of distance metric can dominate the performance of classification or clustering algorithms. Our objective was to determine if semantically augmented distance metrics would outperform standard metrics on machine learning tasks.

Methods: We converted the neurological findings from 382 published neurology cases into sets of concepts with corresponding machine-readable codes. We calculated patient distances by …

Routing Optimization In Heterogeneous Wireless Networks For Space And Mission-Driven Internet Of Things (Iot) Environments, Sara El Alaoui

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

As technological advances have made it possible to build cheap devices with more processing power and storage, and that are capable of continuously generating large amounts of data, the network has to undergo significant changes as well. The rising number of vendors and variety in platforms and wireless communication technologies have introduced heterogeneity to networks compromising the efficiency of existing routing algorithms. Furthermore, most of the existing solutions assume and require connection to the backbone network and involve changes to the infrastructures, which are not always possible -- a 2018 report by the Federal Communications Commission shows that over 31% …

Bacteria Analysis By Using A Supervised Machine Learning Algorithm Based On Droplet Microfluidics, Yulder Daniel Angarita

Electronic Theses and Dissertations

Sepsis is a major medical problem and massive resources have been invested in developing and evaluating alternative treatments. Statistics indicate that sepsis causes between one third and one half of all hospital deaths in the United States. Sepsis has a high impact on health care in the US, with direct sepsis costs in 2009 exceeding $15.4 billion. A research study found that a 1-hour delay in appropriate antimicrobial care resulted in a 7% - 10% rise in mortality. Several professional societies seek to reduce sepsis mortality by targeting the timely use of diagnostic tests and antimicrobial therapy. The diagnostic instruments …

An Investigation Into Multi-View Error Correcting Output Code Classifiers Applied To Organ Tissue Classification, Daniel Alvarez

UNLV Theses, Dissertations, Professional Papers, and Capstones

Large amounts of data is being generated constantly each day, so much data that it is difficult to find patterns in order to predict outcomes and make decisions for both humans and machines alike. It would be useful if this data could be simplified using machine learning techniques. For example, biological cell identity is dependent on many factors tied to genetic processes. Such factors include proteins, gene transcription, and gene methylation. Each of these factors are highly complex mechanism with immense amounts of data. Simplifying these can then be helpful in finding patterns in them. Error-Correcting Output Codes (ECOC) does …

Combining Machine Learning And Empirical Engineering Methods Towards Improving Oil Production Forecasting, Andrew J. Allen

Master's Theses

Current methods of production forecasting such as decline curve analysis (DCA) or numerical simulation require years of historical production data, and their accuracy is limited by the choice of model parameters. Unconventional resources have proven challenging to apply traditional methods of production forecasting because they lack long production histories and have extremely variable model parameters. This research proposes a data-driven alternative to reservoir simulation and production forecasting techniques. We create a proxy-well model for predicting cumulative oil production by selecting statistically significant well completion parameters and reservoir information as independent predictor variables in regression-based models. Then, principal component analysis (PCA) …

Machine Learning For The Internet Of Things: Applications, Implementation, And Security, Vishalini Laguduva Ramnath

USF Tampa Graduate Theses and Dissertations

Artificial intelligence and ubiquitous sensor systems have seen tremendous advances in recent times, resulting in groundbreaking impact across domains such as healthcare, entertainment, and transportation through a collective ecosystem called the Internet of Things. The advent of 5G and improved wireless networks will further accelerate the research and development of tools in deep learning, sensor systems, and computing platforms by providing improved network latency and bandwidth. While tremendous progress has been made in the Internet of Things, current work has largely focused on building robust applications that leverage the data collected through ubiquitous sensor nodes to provide actionable rules and …

Algorithmic Robot Design: Label Maps, Procrustean Graphs, And The Boundary Of Non-Destructiveness, Shervin Ghasemlou

Theses and Dissertations

This dissertation is focused on the problem of algorithmic robot design. The process of designing a robot or a team of robots that can reliably accomplish a task in an environment requires several key elements. How the problem is formulated can play a big role in the design process. The ability of the model to correctly reflect the environment, the events, and different pieces of the problem is crucial. Another key element is the ability of the model to show the relationship between different designs of a single system. These two elements can enable design algorithms to navigate through the …

Identification Of Users Via Ssh Timing Attack, Thomas J. Flucke

Master's Theses

Secure Shell, a tool to securely access and run programs on a remote machine, is an important tool for both system administrators and developers alike. The technology landscape is becoming increasingly distributed and reliant on tools such as Secure Shell to protect information as a user works on a system remotely. While Secure Shell accounts for the abuses the security of older tools such as telnet overlook, it still has fundamental vulnerabilities which leak information about both the user and their activities through timing attacks. The OpenSSH client, the implementation included in all Linux, Mac, and Windows computers, sends each …

Electricity Price Prediction With Computational Intelligence, Toan Hoang

Electrical Engineering

Electricity price depends on numerous factors including the weather, location, time of year/month/day (summer, holidays, day/night, etc.), consumption, and market changes or disruptions. The power loss in the transmission lines and the use of electricity also affect the load's price. Forecasting about the electricity price provides future trends and patterns consumption of the users. Forecasting electricity price is crucially important for producers and consumers in the energy trading markets. It is a complicated task because of the uncertainty behaviors and demand fluctuation. The maximization of profit for participants is highly associated with the bidding strategies. Multiple forecasting electricity price tools …

Temporal Decomposition For Multi-Interval Optimization In Power Systems, Farnaz Safdarian

LSU Doctoral Dissertations

Large optimization problems are frequently solved for power systems operation and analysis of electricity markets. Many of these problems are multi-interval optimization with intertemporal constraints. The size of optimization problems depends on the size of the system and the length of the considered scheduling horizon. Growing the length of the scheduling horizon increases the computational burden significantly and might make solving the problem in a required time span impossible. Many simplifications and approximation techniques are applied to reduce the computational complexity of multi-interval scheduling problems and make them solvable in a reasonable time span. Geographical decomposition is presented in the …

Characterizing Statin Use Among Prediabetic Patients With Predictive Analytics, Alexandra Gentile

Industrial Engineering Undergraduate Honors Theses

Diabetes is one of the leading causes of death in the United States and can cause severe impairments to those diagnosed. Prediabetes is a state when a patient has higher fasting plasma glucose levels than a non-diabetic person but is not quite high enough to be considered diabetes. Both diabetic and prediabetic patients are at higher risk for cardiovascular diseases (CVD), which is the leading cause of death in the United States. The primary form for prevention and treatment of CVD is through statin therapy. Statins are a class of medications used to treat and prevent CVD by limiting cholesterol …

A Machine Learning Approach For Allocating Route Cost To Customers For Transportation And Logistics Services., Alison Davis

Electronic Theses and Dissertations

Advancements in big data enabled management practices inspire logistics companies to study deeper into their transportation operations with a data driven approach. One such question asks: How can a logistics firm identify high-cost customers in their service network? In the presence of rich data on routes involving many customers, this thesis develops a framework to allocate a route cost among customers that the route serves, where each route is associated with multiple route features related to the transportation cost. Cost is allocated using the proportional allocation approach in combination with the random forest method in machine learning. First, this framework …