Other Computer Engineering Commons^™

Quadratic Neural Network Architecture As Evaluated Relative To Conventional Neural Network Architecture, Reid Taylor

Senior Theses

Current work in the field of deep learning and neural networks revolves around several variations of the same mathematical model for associative learning. These variations, while significant and exceptionally applicable in the real world, fail to push the limits of modern computational prowess. This research does just that: by leveraging high order tensors in place of 2nd order tensors, quadratic neural networks can be developed and can allow for substantially more complex machine learning models which allow for self-interactions of collected and analyzed data. This research shows the theorization and development of mathematical model necessary for such an idea to …

Seabem: An Artificial Intelligence Powered Web Application To Predict Cover Crop Biomass, Aime Christian Tuyishime, Andrea Basche

Honors Program: Senior Projects (Public)

SEABEM, the Stacked Ensemble Algorithms Biomass Estimator Model, is a web application with a stacked ensemble of Machine Learning (ML) algorithms running on the backend to predict cover crop biomass for locations in Sub-Saharan. The SEABEM model was developed using a previously developed database of crop growth and yield that included site characteristics such as latitude, longitude, soil texture (sand, silt, and clay percentages), temperature, and precipitation. The goal of SEABEM is to provide global farmers, mainly small-scale African farmers, the knowledge they need before practicing and benefiting from cover crops while avoiding the expensive and time-consuming operations that come …

Efficacy Of Reported Issue Times As A Means For Effort Estimation, Paul Phillip Maclean

Graduate Theses, Dissertations, and Problem Reports (ETD)

Software effort is a measure of manpower dedicated to developing and maintaining and software. Effort estimation can help project managers monitor their software, teams, and timelines. Conversely, improper effort estimation can result in budget overruns, delays, lost contracts, and accumulated Technical Debt (TD). Issue Tracking Systems (ITS) have become mainstream project management tools, with over 65,000 companies using Jira alone. ITS are an untapped resource for issue resolution effort research. Related work investigates issue effort for specific issue types, usually Bugs or similar. They model their developer-documented issue resolution times using features from the issues themselves. This thesis explores a …

Machine Learning For Unmanned Aerial System (Uas) Networking, Jian Wang

Doctoral Dissertations and Master's Theses

Fueled by the advancement of 5G new radio (5G NR), rapid development has occurred in many fields. Compared with the conventional approaches, beamforming and network slicing enable 5G NR to have ten times decrease in latency, connection density, and experienced throughput than 4G long term evolution (4G LTE). These advantages pave the way for the evolution of Cyber-physical Systems (CPS) on a large scale. The reduction of consumption, the advancement of control engineering, and the simplification of Unmanned Aircraft System (UAS) enable the UAS networking deployment on a large scale to become feasible. The UAS networking can finish multiple complex …

Observation Of The Evolution Of Hide And Seek Ai, Anthony J. Catelani

Computer Science and Software Engineering

The purpose of this project is to observe the evolution of two artificial agents, a ‘Seeker’ and a ‘Hider’, as they play a simplified version of the game Hide and Seek. These agents will improve through machine learning, and will only be given an understanding of the rules of the game and the ability to navigate through the grid-like space where the game shall be played; they will not be taught or given any strategies, and will be made to learn from a clean slate. Of particular interest is observing the particular playstyle of hider and seeker intelligences as new …

Bibliometric Survey On Flood Prediction Using Machine Learning, Seema Patil Prof., Daksh Khurana Mr., Kartik Rao Mr, Priyanshu Meena Mr, Shivendra Singh Mr

Library Philosophy and Practice (e-journal)

Floods are one of the most devastating natural hazards, and modelling them is extremely difficult. Flood prediction model advancement study led to factors such as loss of human and animal life, property damage, and risk mitigation. The focus of this bibliometric survey is to recognise the few studies which have upheld on the factors affecting the floods. The analysis is done based on 254 documents such as articles, conference papers, article reviews and some reviews and notes. India contributes to the maximum number of documents followed by China and the United States of America. This bibliometric survey is conducted using …

Decentralized Aggregation Design And Study Of Federated Learning, Venkata Naga Surya Sameeraja Malladi

Master of Science in Software Engineering Theses

The advent of machine learning techniques has given rise to modern devices with built-in models for decision making and providing rich content to users. This typically involves processing huge volumes of data in central servers and sending updated models to end-user devices. There are two main concerns on this server architecture, one is the privacy of data that is being transferred to a central server and the other is volumes of data sent over the network for the model update. Federated Learning helps solve these problems by training models on local data within the device and aggregating the model with …

Synthesizer Parameter Approximation By Deep Learning, Daniel Faronbi, Alisa Gilmore

Theses/Capstones/Creative Projects

Synthesizers have been an essential tool for composers of any style of music including computer generated sound. They allow for an expansion in timbral variety to the orchestration of a piece of music or sound scape. Sound designers are trained to be able to recreate a timbre in their head using a synthesizer. This works well for simple sounds but becomes more difficult as the number of parameters required to produce a specific timbre increase. The goal of this research project is to formulate a method for synthesizers to approximate a timbre given an input audio sample using deep learning. …

How The Power Of Machine – Machine Learning, Data Science And Nlp Can Be Used To Prevent Spoofing And Reduce Financial Risks, Sasibhushan Rao Chanthati

Harrisburg University Other Works

This paper discusses the potential of machine learning, data science, and natural language processing (NLP) in mitigating the incidence of spoofing and financial risks hinged on cyber threats. Another one is spoofing; it is the act of impersonating legitimate entities to gain unauthorized information and it is indeed a threat to the public and companies to some extent. The research introduces two primary methodologies to combat spoofing: an email filtering system using a machine learning algorithm and an encryption and decryption system using a Caesar Cipher and Python programming language. It distinguishes between approved domains and unapproved domains by using …

Time Series Data Analysis Using Machine Learning-(Ml) Approach, Mvv Prasad Kantipudi Dr., Pradeep Kumar N.S Dr., S.Sreenath Kashyap Dr., Ss Anusha Vemuri Ms

Library Philosophy and Practice (e-journal)

Healthcare benefits related to continuous monitoring of human movement and physical activity can potentially reduce the risk of accidents associated with elderly living alone at home. Based on the literature review, it is found that many studies focus on human activity recognition and are still active towards achieving practical solutions to support the elderly care system. The proposed system has introduced a joint approach of machine learning and signal processing technology for the recognition of human's physical movements using signal data generated by accelerometer sensors. The framework adopts the concept of DSP to select very descriptive feature sets and uses …

Visualization For Solving Non-Image Problems And Saliency Mapping, Divya Chandrika Kalla

All Master's Theses

High-dimensional data play an important role in knowledge discovery and data science. Integration of visualization, visual analytics, machine learning (ML), and data mining (DM) are the key aspects of data science research for high-dimensional data. This thesis is to explore the efficiency of a new algorithm to convert non-images data into raster images by visualizing data using heatmap in the collocated paired coordinates (CPC). These images are called the CPC-R images and the algorithm that produces them is called the CPC-R algorithm. Powerful deep learning methods open an opportunity to solve non-image ML/DM problems by transforming non-image ML problems into …

Evaluation Of Supervised Deep-Learning For Improved Pneumonia Diagnosis, Andrew Kalaani

College of Graduate Studies: Theses & Dissertations

Pneumonia is one of the leading causes of infections in the lung area and deaths worldwide. The mortality rate is 24.8% for patients over 70 years of age due to other health complications present along with it. In least fortunate countries, pneumonia can often times go untreated because of how cost extensive it is to diagnose, especially severe cases that cannot be seen by a plain X-ray. Other scanning methods can find the lung abnormality but are time-extensive and not cost effective. An autonomous approach however can help aid diagnosing pneumonia with a plain X-ray scan due to the structural …

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 …

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 …

Data Science Methods For Standardization, Safety, And Quality Assurance In Radiation Oncology, Khajamoinuddin Syed

Theses and Dissertations

Radiation oncology is the field of medicine that deals with treating cancer patients through ionizing radiation. The clinical modality or technique used to treat the cancer patients in the radiation oncology domain is referred to as radiation therapy. Radiation therapy aims to deliver precisely measured dose irradiation to a defined tumor volume (target) with as minimal damage as possible to surrounding healthy tissue (organs-at-risk), resulting in eradication of the tumor, high quality of life, and prolongation of survival. A typical radiotherapy process requires the use of different clinical systems at various stages of the workflow. The data generated in these …

Personality Prediction Through Curriculam Vitae Analysis Involving Password Encryption And Prediction Analysis, Gagandeep Kaur, Shruti Maheshwari

Library Philosophy and Practice (e-journal)

A recruitment process requires an eligibility check, an aptitude evaluation and a psychometric analysis of prospective candidates. The work puts forward an application where the system allows employers to post new job offerings and registered candidates can apply. The application estimates applicant’s emotional aptitude through a psychometric analysis based on a test whereas the professional standard is verified via a technical aptitude test. OCEAN Model is used to assess emotional quotient and predict the personality traits. Machine learning techniques such as Logistic Regression are used for modelling the personality predictor. The details of the candidates are kept secure by using …

Combining Virtual Reality And Machine Learning For Enhancing The Resiliency Of Transportation Infrastructure In Extreme Events, Supratik Mukhopadhyay, Yimin Zhu, Ravindra Gudishala

Data

Corresponding data set for Tran-SET Project No. 18ITSLSU09. Abstract of the final report is stated below for reference:

"Traffic management models that include route choice form the basis of traffic management systems. High-fidelity models that are based on rapidly evolving contextual conditions can have significant impact on smart and energy efficient transportation. Existing traffic/route choice models are generic and are calibrated on static contextual conditions. These models do not consider dynamic contextual conditions such as the location, failure of certain portions of the road network, the social network structure of population inhabiting the region, route choices made by other drivers, …

Combining Virtual Reality And Machine Learning For Enhancing The Resiliency Of Transportation Infrastructure In Extreme Events, Supratik Mukhopadhyay, Yimin Zhu, Ravindra Gudishala

Publications

Traffic management models that include route choice form the basis of traffic management systems. High-fidelity models that are based on rapidly evolving contextual conditions can have significant impact on smart and energy efficient transportation. Existing traffic/route choice models are generic and are calibrated on static contextual conditions. These models do not consider dynamic contextual conditions such as the location, failure of certain portions of the road network, the social network structure of population inhabiting the region, route choices made by other drivers, extreme conditions, etc. As a result, the model’s predictions are made at an aggregate level and for a …

Identifying Hourly Traffic Patterns With Python Deep Learning, Christopher L. Leavitt

Computer Engineering

This project was designed to explore and analyze the potential abilities and usefulness of applying machine learning models to data collected by parking sensors at a major metro shopping mall. By examining patterns in rates at which customer enter and exit parking garages on the campus of the Bellevue Collection shopping mall in Bellevue, Washington, a recurrent neural network will use data points from the previous hours will be trained to forecast future trends.

Comparative Study Of Sentiment Analysis With Product Reviews Using Machine Learning And Lexicon-Based Approaches, Heidi Nguyen, Aravind Veluchamy, Mamadou Diop, Rashed Iqbal

SMU Data Science Review

In this paper, we present a comparative study of text sentiment classification models using term frequency inverse document frequency vectorization in both supervised machine learning and lexicon-based techniques. There have been multiple promising machine learning and lexicon-based techniques, but the relative goodness of each approach on specific types of problems is not well understood. In order to offer researchers comprehensive insights, we compare a total of six algorithms to each other. The three machine learning algorithms are: Logistic Regression (LR), Support Vector Machine (SVM), and Gradient Boosting. The three lexicon-based algorithms are: Valence Aware Dictionary and Sentiment Reasoner (VADER), Pattern, …

Exploring Age-Related Metamemory Differences Using Modified Brier Scores And Hierarchical Clustering, Chelsea Parlett-Pelleriti, Grace C. Lin, Masha R. Jones, Erik Linstead, Susanne M. Jaeggi

Engineering Faculty Articles and Research

Older adults (OAs) typically experience memory failures as they age. However, with some exceptions, studies of OAs’ ability to assess their own memory functions—Metamemory (MM)— find little evidence that this function is susceptible to age-related decline. Our study examines OAs’ and young adults’ (YAs) MM performance and strategy use. Groups of YAs (N = 138) and OAs (N = 79) performed a MM task that required participants to place bets on how likely they were to remember words in a list. Our analytical approach includes hierarchical clustering, and we introduce a new measure of MM—the modified Brier—in order to adjust …

Localization Using Convolutional Neural Networks, Shannon D. Fong

Computer Engineering

With the increased accessibility to powerful GPUs, ability to develop machine learning algorithms has increased significantly. Coupled with open source deep learning frameworks, average users are now able to experiment with convolutional neural networks (CNNs) to solve novel problems. This project sought to train a CNN capable of classifying between various locations within a building. A single continuous video was taken while standing at each desired location so that every class in the neural network was represented by a single video. Each location was given a number to be used for classification and the video was subsequently titled locX. These …

Automatic Identification Of Animals In The Wild: A Comparative Study Between C-Capsule Networks And Deep Convolutional Neural Networks., Joel Kamdem Teto, Ying Xie

Master of Science in Computer Science Theses

The evolution of machine learning and computer vision in technology has driven a lot of

improvements and innovation into several domains. We see it being applied for credit decisions, insurance quotes, malware detection, fraud detection, email composition, and any other area having enough information to allow the machine to learn patterns. Over the years the number of sensors, cameras, and cognitive pieces of equipment placed in the wilderness has been growing exponentially. However, the resources (human) to leverage these data into something meaningful are not improving at the same rate. For instance, a team of scientist volunteers took 8.4 years, …

Training Machine Learning Agents In A 3d Game Engine, Diego Calderon

Computer Science and Computer Engineering Undergraduate Honors Theses

Artificial intelligence (AI) and video games benefit from each other. Games provide a challenging domain for testing learning algorithms, and AI provides a framework to designing and implementing intelligent behavior, which reinforces meaningful play. Medium and small studios, and independent game developers, have limited resources to design, implement, and maintain agents with reactive behavior. In this research, we trained agents using machine learning (ML), aiming to find an alternative to expensive traditional algorithms for intelligent behavior used in video games. We use Unity as a game engine to implement the environments and TensorFlow for the neural network training.

Predicting The Vote Using Legislative Speech, Aditya Budhwar

Master's Theses

As most dedicated observers of voting bodies like the U.S. Supreme Court can attest, it is possible to guess vote outcomes based on statements made during deliberations or questioning by the voting members. In most forms of representative democracy, citizens can actively petition or lobby their representatives, and that often means understanding their intentions to vote for or against an issue of interest. In some U.S. state legislators, professional lobby groups and dedicated press members are highly informed and engaged, but the process is basically closed to ordinary citizens because they do not have enough background and familiarity with the …

Demand Side Management In Smart Grid Using Big Data Analytics, Sidhant Chatterjee

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

Smart Grids are the next generation electrical grid system that utilizes smart meter-ing devices and sensors to manage the grid operations. Grid management includes the prediction of load and and classification of the load patterns and consumer usage behav-iors. These predictions can be performed using machine learning methods which are often supervised. Supervised machine learning signifies that the algorithm trains the model to efficiently predict decisions based on the previously available data.

Smart grids are employed with numerous smart meters that send user statistics to a central server. The data can be accumulated and processed using data mining and machine …

Computer-Aided Detection Of Pathologically Enlarged Lymph Nodes On Non-Contrast Ct In Cervical Cancer Patients For Low-Resource Settings, Brian M. Anderson, Laurence E. Court, Ann Klopp, Stephen F. Kry, Jennifer Johnson, Erik Cressman, Arvind Rao, Jinzhong Yang

Dissertations and Theses (Open Access)

The mortality rate of cervical cancer is approximately 266,000 people each year, and 70% of the burden occurs in Low- and Middle- Income Countries (LMICs). Radiation therapy is the primary modality for treatment of locally advanced cervical cancer cases. In the absence of high quality diagnostic imaging needed to identify nodal metastasis, many LMIC sites treat standard pelvic fields, failing to include node metastasis outside of the field and/or to boost lymph nodes in the abdomen and pelvis. The first goal of this project was to create a program which automatically identifies positive cervical cancer lymph nodes on non-contrast daily …

Prediction Of Graduation Delay Based On Student Characterisitics And Performance, Tushar Ojha

Electrical and Computer Engineering ETDs

A college student's success depends on many factors including pre-university characteristics and university student support services. Student graduation rates are often used as an objective metric to measure institutional effectiveness. This work studies the impact of such factors on graduation rates, with a particular focus on delay in graduation. In this work, we used feature selection methods to identify a subset of the pre-institutional features with the highest discriminative power. In particular, Forward Selection with Linear Regression, Backward Elimination with Linear Regression, and Lasso Regression were applied. The feature sets were selected in a multivariate fashion. High school GPA, ACT …

Classification Of Images Based On Pixels That Represent A Small Part Of The Scene. A Case Applied To Microaneurysms In Fundus Retina Images, Pablo F. Ordonez, Pablo F. Ordonez

Master of Science in Computer Science Theses

Convolutional Neural Networks (CNNs), the state of the art in image classification, have proven to be as effective as an ophthalmologist, when detecting Referable Diabetic Retinopathy (RDR). Having a size of less than 1\% of the total image, microaneurysms are early lesions in DR that are difficult to classify. The purpose of this thesis is to improve the accuracy of detection of microaneurysms using a model that includes two CNNs with different input image sizes, 60x60 and 420x420 pixels. These models were trained using the Kaggle and Messidor datasets and tested independently against the Kaggle dataset, showing a sensitivity of …

Feature Selection And Improving Classification Performance For Malware Detection, Carlos A. Cepeda Mora

Master of Science in Computer Science Theses

The ubiquitous advance of technology has been conducive to the proliferation of cyber threats, resulting in attacks that have grown exponentially. Consequently, researchers have developed models based on machine learning algorithms for detecting malware. However, these methods require significant amount of extracted features for correct malware classification, making that feature extraction, training, and testing take significant time; even more, it has been unexplored which are the most important features for accomplish the correct classification.

In this Thesis, it is created and analyzed a dataset of malware and clean files (goodware) from the static and dynamic features provided by the online …