Engineering Commons^™

Multi-Style Explainable Matrix Factorization Techniques For Recommender Systems., Olurotimi Nugbepo Seton

Electronic Theses and Dissertations

Black-box recommender system models are machine learning models that generate personalized recommendations without explaining how the recommendations were generated to the user or giving them a way to correct wrong assumptions made about them by the model. However, compared to white-box models, which are transparent and scrutable, black-box models are generally more accurate. Recent research has shown that accuracy alone is not sufficient for user satisfaction. One such black-box model is Matrix Factorization, a State of the Art recommendation technique that is widely used due to its ability to deal with sparse data sets and to produce accurate recommendations. Recent …

Deep Convolutional Spiking Neural Networks For Image Classification, Ruthvik Vaila

Boise State University Theses and Dissertations

Spiking neural networks are biologically plausible counterparts of artificial neural networks. Artificial neural networks are usually trained with stochastic gradient descent (SGD) and spiking neural networks are trained with bioinspired spike timing dependent plasticity (STDP). Spiking networks could potentially help in reducing power usage owing to their binary activations. In this work, we use unsupervised STDP in the feature extraction layers of a neural network with instantaneous neurons to extract meaningful features. The extracted binary feature vectors are then classified using classification layers containing neurons with binary activations. Gradient descent (backpropagation) is used only on the output layer to perform …

Quantitative Analysis Of Research On Artificial Intelligence In Retinopathy Of Prematurity, Ranjana Agrawal, Manasi Anup Agrawal, Sucheta Kulkarni, Ketan Kotecha, Rahee Walambe

Library Philosophy and Practice (e-journal)

Retinopathy of Prematurity (ROP) is a disease of the eye and a potential source of blindness in low birth weight preterm infants. It is preventable if diagnosed and treated on time. Artificial Intelligence (AI) has played an important role in developing automated screening systems to assist medical experts. There are many traditional literature review articles available that focus on the scientific content of ROP-AI. The researchers also require a bibliometric analysis to become acquainted with the competing groups and new trends in this field. This paper gives a brief overview of ROP and AI systems for ROP screening with a …

Mlatticeabc: Generic Lattice Constant Prediction Of Crystal Materials Using Machine Learning, Yuxin Li, Wenhui Yang, Rongzhi Dong, Jianjun Hu

Faculty Publications

Lattice constants such as unit cell edge lengths and plane angles are important parameters of the periodic structures of crystal materials. Predicting crystal lattice constants has wide applications in crystal structure prediction and materials property prediction. Previous work has used machine learning models such as neural networks and support vector machines combined with composition features for lattice constant prediction and has achieved a maximum performance for cubic structures with an average coefficient of determination (R2) of 0.82. Other models tailored for special materials family of a fixed form such as ABX3 perovskites can achieve much higher performance due …

An Inside Vs. Outside Classification System For Wi-Fi Iot Devices, Paul Gralla

Dartmouth College Undergraduate Theses

We are entering an era in which Smart Devices are increasingly integrated into our daily lives. Everyday objects are gaining computational power to interact with their environments and communicate with each other and the world via the Internet. While the integration of such devices offers many potential benefits to their users, it also gives rise to a unique set of challenges. One of those challenges is to detect whether a device belongs to one’s own ecosystem, or to a neighbor – or represents an unexpected adversary. An important part of determining whether a device is friend or adversary is to …

Machine Learning-Based Side-Channel Analysis On The Advanced Encryption Standard, Jack Edmonds, Tyler Moon

Electrical and Computer Engineering Senior Theses

Hardware security is essential in keeping sensitive information private. Because of this, it’s imperative that we evaluate the ability of cryptosystems to withstand cutting edge attacks. Doing so encourages the development of countermeasures and new methods of data protection as needed. In this thesis, we present our findings of an evaluation of the Advanced Encryption Standard, particularly unmasked and masked AES-128, implemented in software on an STM32F415 microcontroller unit (MCU), against machine learning-based side-channel analysis (MLSCA). 12 machine learning classifiers were used in combination with a side-channel leakage model in the context of four scenarios: profiling one device and key …

Data-Efficient Machine Learning With Focus On Transfer Learning, Shuteng Niu

Doctoral Dissertations and Master's Theses

Machine learning (ML) has attracted a significant amount of attention from the artificial intelligence community. ML has shown state-of-art performance in various fields, such as signal processing, healthcare system, and natural language processing (NLP). However, most conventional ML algorithms suffer from three significant difficulties: 1) insufficient high-quality training data, 2) costly training process, and 3) domain discrepancy. Therefore, it is important to develop solutions for these problems, so the future of ML will be more sustainable. Recently, a new concept, data-efficient ma- chine learning (DEML), has been proposed to deal with the current bottlenecks of ML. Moreover, transfer learning (TL) …

Artificial Intelligence Driven Infrastructure Management And Maintenance Plan, Julian Jesso

Doctoral Dissertations and Master's Theses

Rapid development in trucking technology and increasing demands in freight transportation has led to longer and heavier vehicles traveling on Florida’s highway system. Vehicles with gross vehicle weight (GVW) over 80,000 pounds, or permit vehicles, have significant effects on infrastructure, thus requiring an approved permit prior to departure. The combination of these increasing loads and harsh environmental conditions that Florida is subject to requires an enhanced infrastructure management program. Additionally, there is a need to eliminate inconsistencies in permit applications and derive a uniform maintenance practice for Florida’s infrastructure. In this research, the focus was to develop an analytical procedure …

Universal Image Segmentation For Optical Identification Of 2d Materials, Joshua Island, Randy M. Sterbentz, Kristine L. Haley

Physics & Astronomy Faculty Research

Machine learning methods are changing the way data is analyzed. One of the most powerful and widespread applications of these techniques is in image segmentation wherein disparate objects of a digital image are partitioned and classified. Here we present an image segmentation program incorporating a series of unsupervised clustering algorithms for the automatic thickness identification of two-dimensional materials from digital optical microscopy images. The program identifies mono- and few-layer flakes of a variety of materials on both opaque and transparent substrates with a pixel accuracy of roughly 95%. Contrasting with previous attempts, application generality is achieved through preservation and analysis …

Predicting Vasovagal Responses: A Model-Based And Machine Learning Approach, Theodore Raphan, Sergei B. Yakushi

Publications and Research

Vasovagal syncope (VVS) or neurogenically induced fainting has resulted in falls, fractures, and death. Methods to deal with VVS are to use implanted pacemakers or beta blockers. These are often ineffective because the underlying changes in the cardiovascular system that lead to the syncope are incompletely understood and diagnosis of frequent occurrences of VVS is still based on history and a tilt test, in which subjects are passively tilted from a supine position to 20◦ from the spatial vertical (to a 70◦ position) on the tilt table and maintained in that orientation for 10–15 min. Recently, is has been shown …

Machine Learning Methods To Map Stabilizer Effectiveness Based On Common Soil Properties, Amit Gajurel, Bhaskar Chittoori, Partha Sarathi Mukherjee, Mojtaba Sadegh

Civil Engineering Faculty Publications and Presentations

Most chemical stabilization guidelines for subgrade/base use unconfined compressive strength (UCS) of treated soils as the primary acceptance criteria for selecting optimum stabilizer in laboratory testing. Establishing optimal additive content to augment UCS involves a resource-intensive trial-and-error procedure. Also, samples collected from discrete locations for laboratory trials may not be representative of the overall site. This study aims to minimize the number of laboratory trials and help strategize sampling locations by developing spatial maps of UCS at different treatment levels for lime and cement. These spatial maps were developed using machine-learning techniques, and using a database compiled from various reported …

Contract Information Extraction Using Machine Learning, Zachary E. Butcher

Theses and Dissertations

The Air Force Sustainment Center assisted by the Data Analytics Resource Team and the Defense Logistics Agency collected four million contracts onto one of the Air Force Research Laboratory’s high power computers. This thesis focuses on the effort to determine if parts are available through those contracts. Some information is extracted using machine learning in combination with natural language processing. Where machine learning methods are unsuccessful or inappropriate, text mining techniques, such as pattern recognition and rules, are used. Upon completion, the information is combined into a Gantt chart for quick evaluation. Only 21% of the contracts have their information …

Indoor Navigation Using Convolutional Neural Networks And Floor Plans, Ricky D. Anderson

Theses and Dissertations

The goal of this thesis is to evaluate a new indoor navigation technique by incorporating floor plans along with monocular camera images into a CNN as a potential means for identifying camera position. Building floor plans are widely available and provide potential information for localizing within the building. This work sets out to determine if a CNN can learn the architectural features of a floor plan and use that information to determine a location. In this work, a simulated indoor data set is created and used to train two CNNs. A classification CNN, which breaks up the floor plan into …

Machine Learning Approaches To Historic Music Restoration, Quinn Coleman

Master's Theses

In 1889, a representative of Thomas Edison recorded Johannes Brahms playing a piano arrangement of his piece titled “Hungarian Dance No. 1”. This recording acts as a window into how musical masters played in the 19th century. Yet, due to years of damage on the original recording medium of a wax cylinder, it was un-listenable by the time it was digitized into WAV format. This thesis presents machine learning approaches to an audio restoration system for historic music, which aims to convert this poor-quality Brahms piano recording into a higher quality one. Digital signal processing is paired with two machine …

Behavior Modeling For Computer Generated Forces Based On Machine Learning, Zhang Qi, Junjie Zeng, Xu Kai, Qin Long, Quanjun Yin

Journal of System Simulation

Abstract: With the rapid development of Machine Learning, especially deep learning, it has become an important way of modeling Computer Generated Force (CGF) behavior by ML methods, which can overcome the challenges of traditional methods. The existing research and application of three typical learning methods in CGF behavior modeling are discussed, and the effects of introducing learning into different stages of the typical CGF applications are analyzed, and the function and performance requirements of CGF behavior modeling using machine learning are proposed. Four potential research directions in the field for future are proposed.

Liver Segmentation And Liver Cancer Detection Based On Deep Convolutional Neural Network: A Brief Bibliometric Survey, Kiran Malhari Napte Mr., Anurag Mahajan Dr.

Library Philosophy and Practice (e-journal)

Background: This study analyzes liver segmentation and cancer detection work, with the perspectives of machine learning and deep learning and different image processing techniques from the year 2012 to 2020. The study uses different Bibliometric analysis methods.

Methods: The articles on the topic were obtained from one of the most popular databases- Scopus. The year span for the analysis is considered to be from 2012 to 2020. Scopus analyzer facilitates the analysis of the databases with different categories such as documents by source, year, and county and so on. Analysis is also done by using different units of analysis such …

A Bibliometric Analysis Of Plant Disease Classification With Artificial Intelligence Based On Scopus And Wos, Shivali Amit Wagle, Harikrishnan R

Library Philosophy and Practice (e-journal)

The maneuver of Artificial Intelligence (AI) techniques in the field of agriculture help in the classification of diseases. Early prediction of the disease benefits in taking relevant management steps. This is an important step towards controlling the disease growth that will yield good quality products to fulfill the global food demand. The main objective of this paper is to study the extent of research work done in this area of plant disease classification. The paper discusses the bibliometric analysis of plant disease classification with AI in Scopus and Web of Science core collection (WOS) database in analyzing the research by …

Holistic Control For Cyber-Physical Systems, Yehan Ma

McKelvey School of Engineering Graduate Student Theses & Dissertations

The Industrial Internet of Things (IIoT) are transforming industries through emerging technologies such as wireless networks, edge computing, and machine learning. However, IIoT technologies are not ready for control systems for industrial automation that demands control performance of physical processes, resiliency to both cyber and physical disturbances, and energy efficiency. To meet the challenges of IIoT-driven control, we propose holistic control as a cyber-physical system (CPS) approach to next-generation industrial automation systems. In contrast to traditional industrial automation systems where computing, communication, and control are managed in isolation, holistic control orchestrates the management of cyber platforms (networks and computing platforms) …

Price Optimization For Revenue Maximization At Scale, Nikhil Gupta, Massimiliano Moro, Kailey A. Ayala, Bivin Sadler

SMU Data Science Review

This study presents a novel approach to price optimization in order to maximize revenue for the distribution market of non-perishable products. Data analysis techniques such as association mining, statistical modeling, machine learning, and an automated machine learning platform are used to forecast the demand for products considering the impact of pricing. The techniques used allow for accurate modeling of the customer’s buying patterns including cross effects such as cannibalization and the halo effect. This study uses data from 2013 to 2019 for Super Premium Whiskey from a large distributor of alcoholic beverages. The expected demand and the ideal pricing strategy …

Hybrid Modelling For Stroke Care: Review And Suggestions Of New Approaches For Risk Assessment And Simulation Of Scenarios, Tilda Herrgårdh, Vince I. Madai, John Kelleher, Rasmus Magnusson, Mika Gustafsson, Lili Milani, Peter Gennemark, Gunnar Cedersund

Articles

Stroke is an example of a complex and multi-factorial disease involving multiple organs, timescales, and disease mechanisms. To deal with this complexity, and to realize Precision Medicine of stroke, mathematical models are needed. Such approaches include: 1) machine learning, 2) bioinformatic network models, and 3) mechanistic models. Since these three approaches have complementary strengths and weaknesses, a hybrid modelling approach combining them would be the most beneficial. However, no concrete approach ready to be implemented for a specific disease has been presented to date. In this paper, we both review the strengths and weaknesses of the three approaches, and propose …

Developing An Open-Book Online Exam For Final Year Students, Keith Quille, Keith Nolan, Brett Becker, Sean Mchugh

Conference Papers

Like many others, our institution had to adapt our traditional proctored, written examinations to open-book online variants due to the COVID-19 pandemic. This paper describes the process applied to develop open-book online exams for final year (undergraduate) students studying Applied Machine Learning and Applied Artificial Intelligence and Deep Learning courses as part of a four-year BSc in Computer Science. We also present processes used to validate the examinations as well as plagiarism detection methods implemented. Findings from this study highlight positive effects of using open-book online exams, with 85% of students reporting that they either prefer online open-book examinations or …

Wind Turbine Parameter Calibration Using Deep Learning Approaches, Rebecca Mccubbin

Electronic Theses and Dissertations

The inertia and damping coefficients are critical to understanding the workings of a wind turbine, especially when it is in a transient state. However, many manufacturers do not provide this information about their turbines, requiring people to estimate these values themselves. This research seeks to design a multilayer perceptron (MLP) that can accurately predict the inertia and damping coefficients using the power data from a turbine during a transient state. To do this, a model of a wind turbine was built in Matlab, and a simulation of a three-phase fault was used to collect realistic fault data to input into …

Are We In The Digital Dark Times? How The Philosophy Of Hannah Arendt Can Illuminate Some Of The Ethical Dilemmas Posed By Modern Digital Technologies, Damian Gordon, Anna Becevel

Conference Papers

Philosophers are not generally credited with being clairvoyant, and yet because they recognise, record and reflect on trends in their society, their observations can often appear prescient. In the field of the ethics of technology, there is, perhaps, no philosopher whose perspective on these issues is worth examining in detail more than that of Hannah Arendt, who can offer real perspective on the challenges we are facing with technologies in the twenty-first century. Arendt, a thinker of Jewish-German origin, student of Martin Heidegger and Karl Jaspers, encountered her life turning point when she was forced into becoming a refugee as …

An Improved Version Of Multi-View K-Nearest Neighbors (Mvknn) For Multipleview Learning, Eli̇fe Öztürk Kiyak, Derya Bi̇rant, Kökten Ulaş Bi̇rant

Turkish Journal of Electrical Engineering and Computer Sciences

Multi-view learning (MVL) is a special type of machine learning that utilizes more than one views, where views include various descriptions of a given sample. Traditionally, classification algorithms such as k-nearest neighbors (KNN) are designed for learning from single-view data. However, many real-world applications involve datasets with multiple views and each view may contain different and partly independent information, which makes the traditional single-view classification approaches ineffective. Therefore, this article proposes an improved MVL algorithm, called multi-view k-nearest neighbors (MVKNN), based on the existing KNN algorithm. The experimental results conducted in this research show that a significant improvement is achieved …

Analysis Of Classifier Weaknesses Based On Patterns And Corrective Methods, Nicholas Skapura

Browse all Theses and Dissertations

Classification is an important branch of machine learning that impacts many areas of modern life. Many classification algorithms (classifiers for short) have been developed. They have highly different levels of sophistication and classification accuracy. Classification problems often have highly different levels of hardness and complexity. Practitioners of classification modeling need better understanding of those algorithms in order to select the optimal algorithm for given classification problems. Researchers of classification need new insight on how given classifiers are weak and how they can be improved by correcting their classification errors. This dissertation introduces new tools and concepts to analyze classifier weakness …

Unobtrusive Assessment Of Student Engagement Levels In Online Classroom Environment Using Emotion Analysis, Sasirekha Anbusegaran

College of Graduate Studies: Theses & Dissertations

Measuring student engagement has emerged as a significant factor in the process of learning and a good indicator of the knowledge retention capacity of the student. As synchronous online classes have become more prevalent in recent years, gauging a student's attention level is more critical in validating the progress of every student in an online classroom environment. This paper details the study on profiling the student attentiveness to different gradients of engagement level using multiple machine learning models. Results from the high accuracy model and the confidence score obtained from the cloud-based computer vision platform - Amazon Rekognition were then …

Machine Learning Model Optimization With Hyper Parameter Tuning Approach, Md. Riyad Hossain, Douglas Timmer

Manufacturing & Industrial Engineering Faculty Publications

Hyper-parameters tuning is a key step to find the optimal machine learning parameters. Determining the best hyper-parameters takes a good deal of time, especially when the objective functions are costly to determine, or a large number of parameters are required to be tuned. In contrast to the conventional machine learning algorithms, Neural Network requires tuning hyperparameters more because it has to process a lot of parameters together, and depending on the fine tuning, the accuracy of the model can be varied in between 25%-90%. A few of the most effective techniques for tuning hyper-parameters in the Deep learning methods are: …

Information Architecture For A Chemical Modeling Knowledge Graph, Adam R. Luxon

Theses and Dissertations

Machine learning models for chemical property predictions are high dimension design challenges spanning multiple disciplines. Free and open-source software libraries have streamlined the model implementation process, but the design complexity remains. In order better navigate and understand the machine learning design space, model information needs to be organized and contextualized. In this work, instances of chemical property models and their associated parameters were stored in a Neo4j property graph database. Machine learning model instances were created with permutations of dataset, learning algorithm, molecular featurization, data scaling, data splitting, hyperparameters, and hyperparameter optimization techniques. The resulting graph contains over 83,000 nodes …

Determining And Evaluating New Store Locations Using Remote Sensing Andmachine Learning, Berkan Höke, Zeynep Zerri̇n Turgay, Cem Ünsalan, Hande Küçükaydin

Turkish Journal of Electrical Engineering and Computer Sciences

Decision making for store locations is crucial for retail companies as the profit depends on the location. The key point for correct store location is profit approximation, which is highly dependent on population of the corresponding region, and hence, the volume of the residential area. Thus, estimating building volumes provides insight about the revenue if a new store is about to be opened there. Remote sensing through stereo/tri-stereo satellite images provides wide area coverage as well as adequate resolution for three dimensional reconstruction for volume estimation. We reconstruct 3D map of corresponding region with the help of semiglobal matching and …

A Case Study On Player Selection And Team Formation In Football With Machinelearning, Di̇dem Abi̇di̇n

Turkish Journal of Electrical Engineering and Computer Sciences

Machine learning has been widely used in different domains to extract information from raw data. Sports is one of the popular domains for researchers to work on recently. Although score prediction for matches is the most preferred application area for artificial intelligence, player selection, and team formation is also an application area worth working on. There are some studies in the literature about player selection and team formation which are examined in this study. The study has two important contributions: First one is to apply seven different machine learning algorithms on our dataset to find the best player combination for …