Computer Engineering Commons^™

Investigating Effect Of Amount Of Augmented Data On Performance Of Convolutional Neural Network For Multiclass Image Classification, Shivam Khandelwal

Dissertations

This research project seeks to investigate the use of Image Data augmentation that generates synthetic data by adding distortions to original images, as a means of replacement to a large amount of real data used to train the Convolutional Neural Networks. The purpose of the research project is to assess the effectiveness of augmented data over the real data by comparing the performance of the model trained with various amounts of augmented training and validation data ratio. Deep learning tasks involving convolutional neural networks have difficulty in generalizing the models effectively for computer vision tasks when the training dataset is …

Investigating The Predictability Of A Chaotic Time-Series Data Using Reservoir Computing, Deep-Learning And Machine- Learning On The Short-, Medium- And Long-Term Pricing Of Bitcoin And Ethereum., Molly Kenny

Dissertations

This study will investigate the predictability of a Chaotic time-series data using Reservoir computing (Echo State Network), Deep-Learning(LSTM) and Machine- Learning(Linear, Bayesian, ElasticNetCV , Random Forest, XGBoost Regression and a machine learning Neural Network) on the short (1-day out prediction), medium (5-day out prediction) and long-term (30-day out prediction) pricing of Bitcoin and Ethereum Using a range of machine learning tools, to perform feature selection by permutation importance to select technical indicators on the individual cryptocurrencies, to ensure the datasets are the best for predictions per cryptocurrency while reducing noise within the models. The predictability of these two chaotic time-series …

Designing Shared Control Strategies For Teleoperated Robots Across Intrinsic User Qualities, Nancy Pham

School of Computing: Dissertations, Theses, and Student Research

Accounting for variance in human behavior is an integral part of interacting with robotic systems that share control between users and robots in order to reduce errors, improve performance, and maintain safety. In this work we focus on the shared control of a telepresence robot and how individual user traits may affect a person's performance while navigating the robot. This requires understanding which user qualities impact performance and cause conflicts -- with the ultimate goal of building shared controllers that adapt to those qualities. Toward this goal, we develop novel adaptive shared controllers and integrate the study of intrinsic user …

The Artificial University: Decision Support For Universities In The Covid-19 Era, Wesley J. Wildman, Saikou Y. Diallo, George Hodulik, Andrew Page, Andreas Tolk, Neha Gondal

VMASC Publications

Operating universities under pandemic conditions is a complex undertaking. The Artificial University (TAU) responds to this need. TAU is a configurable, open-source computer simulation of a university using a contact network based on publicly available information about university classes, residences, and activities. This study evaluates health outcomes for an array of interventions and testing protocols in an artificial university of 6,500 students, faculty, and staff. Findings suggest that physical distancing and centralized contact tracing are most effective at reducing infections, but there is a tipping point for compliance below which physical distancing is less effective. If student compliance is anything …

Automatic Chest X-Rays Analysis Using Statistical Machine Learning Strategies, Hermann Yepdjio Nkouanga

All Master's Theses

Tuberculosis (TB) is a disease responsible for the deaths of more than one million people worldwide every year. Even though it is preventable and curable, it remains a major threat to humanity that needs to be taken care of. It is often diagnosed in developed countries using approaches such as sputum smear microscopy and culture methods. However, since these approaches are rather expensive, they are not commonly used in poor regions of the globe such as India, Africa, and Bangladesh. Instead, the well known and affordable chest x-ray (CXR) interpretation by radiologists is the technique employed in those places. Nevertheless, …

Glaciernet: A Deep-Learning Approach For Debris-Covered Glacier Mapping, Zhiyuan Xie, Umesh K. Haritashya, Vijayan K. Asari, Brennan W. Young, Michael P. Bishop, Jeffrey S. Kargel

Electrical and Computer Engineering Faculty Publications

Rising global temperatures over the past decades is directly affecting glacier dynamics. To understand glacier fluctuations and document regional glacier-state trends, glacier-boundary detection is necessary. Debris-covered glacier (DCG) mapping, however, is notoriously difficult using conventional geospatial technology methods. Therefore, in this research for automated DCG mapping, we evaluate the utility of a convolutional neural network (CNN), which is a deep learning feed-forward neural network. The CNN inputs include Landsat satellite images, an Advanced Land Observation Satellite (ALOS) digital elevation model (DEM) and DEM-derived land-surface parameters. Our CNN based deep-learning approach named GlacierNet was designed by appropriately choosing the type, number …

Mitosisnet: End-To-End Mitotic Cell Detection By Multi-Task Learning, Md Zahangir Alom, Theus Aspiras, Tarek M. Taha, Tj Bowen, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

Mitotic cell detection is one of the challenging problems in the field of computational pathology. Currently, mitotic cell detection and counting are one of the strongest prognostic markers for breast cancer diagnosis. The clinical visual inspection on histology slides is tedious, error prone, and time consuming for the pathologist. Thus, automatic mitotic cell detection approaches are highly demanded in clinical practice. In this paper, we propose an end-to-end multi-task learning system for mitosis detection from pathological images which is named"MitosisNet". MitosisNet consist of segmentation, detection, and classification models where the segmentation, and detection models are used for mitosis reference region …

Deep Hashing For Image Similarity Search, Ali Al Kobaisi

Electronic Theses and Dissertations, 2020-2023

Hashing for similarity search is one of the most widely used methods to solve the approximate nearest neighbor search problem. In this method, one first maps data items from a real valued high-dimensional space to a suitable low dimensional binary code space and then performs the approximate nearest neighbor search in this code space instead. This is beneficial because the search in the code space can be solved more efficiently in terms of runtime complexity and storage consumption. Obviously, for this method to succeed, it is necessary that similar data items be mapped to binary code words that have small …

Extracting Data-Level Parallelism In High-Level Synthesis For Reconfigurable Architectures, Juan Andres Escobedo Contreras

Electronic Theses and Dissertations, 2020-2023

High-Level Synthesis (HLS) tools are a set of algorithms that allow programmers to obtain implementable Hardware Description Language (HDL) code from specifications written high-level, sequential languages such as C, C++, or Java. HLS has allowed programmers to code in their preferred language while still obtaining all the benefits hardware acceleration has to offer without them needing to be intimately familiar with the hardware platform of the accelerator. In this work we summarize and expand upon several of our approaches to improve the automatic memory banking capabilities of HLS tools targeting reconfigurable architectures, namely Field-Programmable Gate Arrays or FPGA's. We explored …

อัลกอริทึมการระบุการผันกลับของเซลลูลาร์ออโตมาตาหนึ่งมิติด้วยกราฟสับเซตย่อยภายใต้เงื่อนไขการกำหนดขอบเขตแบบไม่มีค่า, วรยุทธ วงศ์นิล

Chulalongkorn University Theses and Dissertations (Chula ETD)

เซลลูลาร์ออโตมาตาถือเป็นโมเดลทางคณิตศาสตร์ที่สามารถทำงานแบบระบบพลวัต ซึ่งประกอบไปด้วยสถานะจำกัดที่เรียงตัวกันอย่างเป็นระบบเรียกเซลล์ แต่ละเซลล์จะเปลี่ยนสถานะไปยังสถานะใหม่พร้อมกันด้วยการอาศัยกฎการส่งผ่านที่ขึ้นอยู่กับเซลล์รอบ ๆ ด้วยเวลาแบบเต็มหน่วย แม้ว่าเซลลูลาร์ออโตมาตามีโครงสร้างและนิยามในแบบพื้นฐาน แต่สามารถสร้างระบบที่พฤติกรรมมีความซับซ้อนได้ สมบัติในการผันกลับได้ของเซลลูลาร์ออโตมาตาถือเป็นสมบัติสำคัญที่ได้รับความสนใจในหลายงานวิจัยและสามารถนำไปประยุกต์ใช้ได้ในงานหลาย ๆ ด้านในทางวิทยาศาสตร์ แต่สำหรับเซลลูลาร์ออโตมาตาหนึ่งมิติภายใต้เงื่อนไขการกำหนดขอบเขตแบบไม่มีค่ายังถือมีข้อจำกัดของจำนวนกฎที่มีไม่มากที่มีสมบัติดังกล่าว ในงานวิจัยนี้ศึกษาและเสนออัลกอริทึมการระบุการผันกลับของเซลลูลาร์ออโตมาตาหนึ่งมิติด้วยกราฟสับเซตย่อยภายใต้เงื่อนไขการกำหนดขอบเขตแบบไม่มีค่านิยามเซลล์เพื่อนบ้านด้วยเวกเตอร์ ด้วยการแทนเซลลูลาร์ออโตมาตาด้วยกราฟสับเซตย่อยเราเสนอวิธีในการระบุสมบัติการผันกลับได้ในกราฟโดยการพิจารณาเส้นเชื่อมและจุดยอดที่เชื่อมถึงกัน นอกจากนี้งานวิจัยนี้ยังเสนอวิธีในการคำนวณสถานะก่อนหน้าสำหรับสถานะใด ๆ ของเซลลูลาร์ออโตมาตาหนึ่งมิติที่มีสมบัติผันกลับได้ภายใต้เงื่อนไขการกำหนดขอบเขตแบบไม่มีค่า ซึ่งวิธีที่ได้เสนออยู่บนพื้นฐานของการพิจารณาลักษณะของเซลล์เพื่อนบ้านด้วยการคำนวณทางเดินบนกราฟด้วยการดำเนินการของเมตริกซ์

การวิเคราะห์ข้อความภาษาธรรมชาติตามประมวลกฎหมายอาญา, วีรยุทธ ครั่งกลาง

Chulalongkorn University Theses and Dissertations (Chula ETD)

วิทยานิพนธ์นี้วิเคราะห์การบังคับใช้กฎหมายอาญาของประเทศไทย ในภาค1 บทบัญญัติทั่วไป และภาค2 เฉพาะความผิดเกี่ยวกับชีวิต มาตรา 288 และมาตรา 289 ในลักษณะ10 ความผิดเกี่ยวกับชีวิตและร่างกาย ตามประมวลกฎหมายอาญาของไทย ส่วนแรกของวิทยานิพนธ์นี้ใช้ความรู้ด้านกฎหมายอาญาและคำพิพากษาของศาลฎีกาในการสร้างกฎในการพิจารณาที่มนุษย์สามารถเข้าใจได้ และส่วนที่สองคือการฝึกฝนแบบจำลองด้วยชุดข้อมูลจากคำพิพากษาด้วยเทคนิคการเรียนรู้เชิงลึก โดยแก้ปัญหาความไม่สมดุลของกลุ่มข้อมูลฝึกสอนด้วยการสังเคราะห์ตัวอย่างข้อมูลในกลุ่มอื่น ๆ ให้มีจำนวนเท่ากับกลุ่มที่มากที่สุด และฝึกสอนด้วยโครงข่ายหน่วยความจำระยะสั้นแบบยาวทิศทางเดียวและสองทิศทาง ซึ่งเป็นโครงข่ายประสาทเทียมแบบวกกลับประเภทหนึ่ง และเมื่อวัดประสิทธิภาพแบบจำลองด้วยค่าเฉลี่ยมหภาคเอฟวัน พบว่าแบบจำลองของหน่วยความจำระยะสั้นแบบยาวสองทิศทางให้ประสิทธิภาพสูงกว่าแบบทิศทางเดียว และการใช้ค่าถ่วงน้ำหนักเริ่มต้นจากเรียนรู้ด้วยคลังข้อมูลขนาดใหญ่อื่น ให้ประสิทธิภาพที่สูงกว่าการใช้เฉพาะข้อมูลฝึกสอน และท้ายสุดทำการทดสอบความแม่นยำของแบบจำลองจากข่าวอาชญากรรมด้วยเทคนิคการหาค่าเฉลี่ยความน่าจะเป็น เพื่อใช้เป็นข้อมูลขาเข้าของกฎการพิจารณา พบว่าสอดคล้องกับความเห็นของนักกฎหมาย 59 %

Deep Learning For Digitized Histology Image Analysis, Sudhir Sornapudi

Doctoral Dissertations

“Cervical cancer is the fourth most frequent cancer that affects women worldwide. Assessment of cervical intraepithelial neoplasia (CIN) through histopathology remains as the standard for absolute determination of cancer. The examination of tissue samples under a microscope requires considerable time and effort from expert pathologists. There is a need to design an automated tool to assist pathologists for digitized histology slide analysis. Pre-cervical cancer is generally determined by examining the CIN which is the growth of atypical cells from the basement membrane (bottom) to the top of the epithelium. It has four grades, including: Normal, CIN1, CIN2, and CIN3. In …

Data Entry Voice Assistant For Healthcare Providers, Sajad Hussain M Alhamada

EWU Masters Thesis Collection

No abstract provided.

Image Forgery Detection With Machine Learning, Lubna Alzamil

All Master's Theses

The issue of forged images is currently a global issue that spreads mainly via social networks. Image forgery has weakened Internet users’ confidence in digital images. In recent years, extensive research has been devoted to the development of new techniques to combat various image forgery attacks. Detecting fake images prevents counterfeit photos from being used to deceive or cause harm to others. In this thesis, we propose methods using the error level analysis algorithm to detect manipulated images. We show that our combination of image pre-processing and machine learning techniques is an efficient approach to detecting image forgery attacks.

Ambiqual: Towards A Quality Metric For Headphone Rendered Compressed Ambisonic Spatial Audio, Miroslaw Narbutt, Jan Skoglund, Andrew Allen, Michael Chinen, Dan Barry, Andrew Hines

Articles

Spatial audio is essential for creating a sense of immersion in virtual environments. Efficient encoding methods are required to deliver spatial audio over networks without compromising Quality of Service (QoS). Streaming service providers such as YouTube typically transcode content into various bit rates and need a perceptually relevant audio quality metric to monitor users’ perceived quality and spatial localization accuracy. The aim of the paper is two-fold. First, it is to investigate the effect of Opus codec compression on the quality of spatial audio as perceived by listeners using subjective listening tests. Secondly, it is to introduce AMBIQUAL, a full …

A Comparative Study Of Text Summarization On E-Mail Data Using Unsupervised Learning Approaches, Tijo Thomas

Dissertations

Over the last few years, email has met with enormous popularity. People send and receive a lot of messages every day, connect with colleagues and friends, share files and information. Unfortunately, the email overload outbreak has developed into a personal trouble for users as well as a financial concerns for businesses. Accessing an ever-increasing number of lengthy emails in the present generation has become a major concern for many users. Email text summarization is a promising approach to resolve this challenge. Email messages are general domain text, unstructured and not always well developed syntactically. Such elements introduce challenges for study …

Evaluating Bert Embeddings For Text Classification In Bio-Medical Domain To Determine Eligibility Of Patients In Clinical Trials, Saurabh Khodake

Dissertations

Clinical Trials are studies conducted by researchers in order to assess the impact of new medicine in terms of its efficacy and most importantly safety on human health. For any advancement in the field of medicine it is very important that clinical trials are conducted with right ethics supported by scientific evidence. Not all people who volunteer or participate in clinical trials are allowed to undergo the trials. Age, comorbidity and other health issues present in a patient can be a major factor to decide whether the profile is suitable or not for the trial. Profiles selected for clinical trials …

Customer Churn Prediction, Deepshikha Wadikar

Dissertations

Churned customers identification plays an essential role for the functioning and growth of any business. Identification of churned customers can help the business to know the reasons for the churn and they can plan their market strategies accordingly to enhance the growth of a business. This research is aimed at developing a machine learning model that can precisely predict the churned customers from the total customers of a Credit Union financial institution. A quantitative and deductive research strategies are employed to build a supervised machine learning model that addresses the class imbalance problem handled feature selection and efficiently predict the …

Brexit Election: Forecasting A Conservative Party Victory Through The Pound Using Arima And Facebook's Prophet, James Usher, Pierpaolo Dondio

Conference papers

On the 30th October, 2019, the markets watched as British Prime Minister, Boris Johnson, took a massive political gamble to call a general election to break the Withdrawal Agreement stalemate in the House of Commons to “Get BREXIT Done”. The pound had been politically sensitive owing to BREXIT uncertainty. With the polls indicating a Conservative win on 4thDecember, 2019, the margin of victory could be observed through increases in the pound. The outcome of a Conservative party victory would benefit the pound by removing the current market turbulence. We look to provide a short-term forecast of the pound. Our approach …

Android Compcache Based On Graphics Processing Unit, Muder Almi'ani, Abdu Razaque, Saleh Atiewi, Mohammed Alweshah, Ayman Al-Dmour, Basel Magableh

Conference papers

Android systems have been successfully developed to meet the demands of users. The following four methods are used in Android systems for memory management: backing swap, CompCache, traditional Linux swap, and low memory killer. These memory management methods are fully functioning.
However, Android phones cannot swap memory into solid-state drives, thus slowing the processor and reducing storage lifetime. In addition, the compression and decompression processes consume additional energy and latency. Therefore, the CompCache requires an extension. An extended Android CompCache using a graphics processing unit to compress and decompress memory pages on demand and reduce the latency is introduced in …

A Direct Data-Cluster Analysis Method Based On Neutrosophic Set Implication, Florentin Smarandache, Sudan Jha, Gyanendra Prasad Joshi, Lewis Nkenyereya, Dae Wan Kim

Branch Mathematics and Statistics Faculty and Staff Publications

Raw data are classified using clustering techniques in a reasonable manner to create disjoint clusters. A lot of clustering algorithms based on specific parameters have been proposed to access a high volume of datasets. This paper focuses on cluster analysis based on neutrosophic set implication, i.e., a k-means algorithm with a threshold-based clustering technique. This algorithm addresses the shortcomings of the k-means clustering algorithm by overcoming the limitations of the threshold-based clustering algorithm. To evaluate the validity of the proposed method, several validity measures and validity indices are applied to the Iris dataset (from the University of California, Irvine, Machine …

Topological Analysis Of Averaged Sentence Embeddings, Wesley J. Holmes

Browse all Theses and Dissertations

Sentence embeddings are frequently generated by using complex, pretrained models that were trained on a very general corpus of data. This thesis explores a potential alternative method for generating high-quality sentence embeddings for highly specialized corpora in an efficient manner. A framework for visualizing and analyzing sentence embeddings is developed to help assess the quality of sentence embeddings for a highly specialized corpus of documents related to the 2019 coronavirus epidemic. A Topological Data Analysis (TDA) technique is explored as an alternative method for grouping embeddings for document clustering and topic modeling tasks and is compared to a simple clustering …

Design Of A Novel Wearable Ultrasound Vest For Autonomous Monitoring Of The Heart Using Machine Learning, Garrett G. Goodman

Browse all Theses and Dissertations

As the population of older individuals increases worldwide, the number of people with cardiovascular issues and diseases is also increasing. The rate at which individuals in the United States of America and worldwide that succumb to Cardiovascular Disease (CVD) is rising as well. Approximately 2,303 Americans die to some form of CVD per day according to the American Heart Association. Furthermore, the Center for Disease Control and Prevention states that 647,000 Americans die yearly due to some form of CVD, which equates to one person every 37 seconds. Finally, the World Health Organization reports that the number one cause of …

Understanding Depression During The Covid-19 Pandemic Through Social Media Data, Nusrat Armin

Electronic Theses and Dissertations

The COVID-19 pandemic has dramatically affected peoples’ daily lives all over theworld - physically, economically, and emotionally. Due to the virus, many people have died, and many hospitalized. A record number of people have lost their job, and many businesses have closed. The global economy is at risk. People are facing new realities of their lives. Studies have shown that the level of depression is three times higher than before this pandemic. Previous studies have shown that people use social media to express their emotions and feelings. The purpose of this study is to understand the depression during this COVID-19 …

Fast Decision-Making Under Time And Resource Constraints, Kyle Gabriel Lassak

Graduate Theses, Dissertations, and Problem Reports (ETD)

Practical decision makers are inherently limited by computational and memory resources as well as the time available in which to make decisions. To cope with these limitations, humans actively seek methods which limit their resource demands by exploiting structure within the environment and exploiting a coupling between their sensing and actuation to form heuristics for fast decision-making. To date, such behavior has not been replicated in artificial agents. This research explores how heuristics may be incorporated into the decision-making process to quickly make high-quality decisions through the analysis of a prominent case study: the outfielder problem. In the outfielder problem, …

Deep Learning Based Face Detection And Recognition In Mwir And Visible Bands, Suha Reddy Mokalla

Graduate Theses, Dissertations, and Problem Reports (ETD)

In non-favorable conditions for visible imaging like extreme illumination or nighttime, there is a need to collect images in other spectra, specifically infrared. Mid-Wave infrared (3-5 microm) images can be collected without giving away the location of the sensor in varying illumination conditions. There are many algorithms for face detection, face alignment, face recognition etc. proposed in visible band till date, while the research using MWIR images is highly limited. Face detection is an important pre-processing step for face recognition, which in turn is an important biometric modality. This thesis works towards bridging the gap between MWIR and visible spectrum …

Minding Morality: Ethical Artificial Societies For Public Policy Modeling, Saikou Y. Diallo, F. Leron Shults, Wesley J. Wildman

VMASC Publications

Public policies are designed to have an impact on particular societies, yet policy-oriented computer models and simulations often focus more on articulating the policies to be applied than on realistically rendering the cultural dynamics of the target society. This approach can lead to policy assessments that ignore crucial social contextual factors. For example, by leaving out distinctive moral and normative dimensions of cultural contexts in artificial societies, estimations of downstream policy effectiveness fail to account for dynamics that are fundamental in human life and central to many public policy challenges. In this paper, we supply evidence that incorporating morally salient …

A Tutorial And Future Research For Building A Blockchain-Based Secure Communication Scheme For Internet Of Intelligent Things, Mohammad Wazid, Ashok Kumar Das, Sachin Shetty, Minho Jo

Computational Modeling & Simulation Engineering Faculty Publications

The Internet of Intelligent Things (IoIT) communication environment can be utilized in various types of applications (for example, intelligent battlefields, smart healthcare systems, the industrial internet, home automation, and many more). Communications that happen in such environments can have different types of security and privacy issues, which can be resolved through the utilization of blockchain. In this paper, we propose a tutorial that aims in desiging a generalized blockchain-based secure authentication key management scheme for the IoIT environment. Moreover, some issues with using blockchain for a communication environment are discussed as future research directions. The details of different types of …

(Φ, Ψ)-Weak Contractions In Neutrosophic Cone Metric Spaces Via Fixed Point Theorems, Florentin Smarandache, Wadei F. Al-Omeri

Branch Mathematics and Statistics Faculty and Staff Publications

In this manuscript, we obtain common fixed point theorems in the neutrosophic cone metric space. Also, notion of (Φ, Ψ)-weak contraction is defined in the neutrosophic cone metric space by using the idea of altering distance function. Finally, we review many examples of cone metric spaces to verify some properties.

Jc Drain And Sewer Website, Jarod Pichler, Nathan Houman

Capstone Showcase

1. A website for a small plumbing business in Scranton, Pennsylvania. The website includes a Home, About Us, Services, Contact Us, and Testimonials page. The home page introduces the company to the website viewer. The About Us page provides information about the company and owner, to the website viewer. The Services page provides the website viewer with all of the services that the company can provide. The Contact Us page allows the website viewer to send the company an email. Finally, the Testimonials page will allow the website viewer to leave a comment about the company’s services. The website also includes …

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