Computer Sciences Commons^™

A Machine Learning And Deep Learning Framework For Binary, Ternary, And Multiclass Emotion Classification Of Covid-19 Vaccine-Related Tweets, Aditya Dubey

Honors Scholar Theses

My research mines public emotion toward the Covid-19 vaccine based on Twitter data collected over the past 6-12 months. This project is centered around building and developing machine learning and deep learning models to perform natural language processing of short-form text, which in our case tweets. These tweets are all vaccine-related tweets and the goal of the classification task is for our models to accurately classify a tweet into one of four emotion groups: Apprehension/Anticipation, Sadness/Anger/Frustration, Joy/Humor/Sarcasm, and Gratitude/Relief. Given this data and the goal of the paper, we aim to answer the following questions: (1) Can a framework be …

Building An Artificial Intelligence Framework For Hypertension Diagnosis: A Use Case Of The Problem List Curation, Ketemwabi Yves Shamavu

Theses & Dissertations

Hypertension is the world's leading factor in cardiovascular disease. Forty-seven percent or close to one in two Americans aged 18 and older are affected. It predicts approximately a thousand deaths per day. Based on recent statistics from the Centers for Disease Control and Prevention, one in three patients with hypertension does not know they are hypertensive. Seventy-five percent of hypertensive patients have uncontrolled hypertension - meaning that they are not treated to target. While there is extensive literature on hypertension diagnosis and management, there is an apparent gap in understanding and acknowledging that a person is hypertensive. Moreover, blood pressure …

Reinforcement Learning With Deep Q-Networks, Caleb Cassady

Masters Theses & Specialist Projects

In the past decade, machine learning strategies centered on the use of Deep Neural Networks (DNNs) have caught the interest of researchers due to their success in complicated classification and prediction problems. More recently, these DNNs have been applied to reinforcement learning tasks with state of- the-art results using Deep Q-Networks (DQNs) based on the Q-Learning algorithm. However, the DQN training process is different from standard DNNs and poses significant challenges for certain reinforcement learning environments. This paper examines some of these challenges, compares proposed solutions, and offers novel solutions based on previous research. Experiment implementation available at https://github.com/caleb98/dqlearning.

Could Alexa Increase Your Social Worth?, Peter Tripp

Electronic Theses and Dissertations

People have historically used personal introductions to build social capital, which is the foundation of career networking and is perhaps the most effective way to advance a career (Lin, 2001). With societal changes, such as the pandemic (Venkatesh & Edirappuli, 2020), and the increasing capabilities of Artificial Intelligence (AI), new approaches may emerge that impact societal relationships. Social capital theory highlights the need for reciprocal agreements to establish the trust between parties (Gouldner, 1960). My theoretical prediction and focus of this research include two principles: The impact of reciprocity in evaluating trust of the source of the introduction and the …

Liability For Use Of Artificial Intelligence In Medicine, W. Nicholson Price, Sara Gerke, I. Glenn Cohen

Law & Economics Working Papers

While artificial intelligence has substantial potential to improve medical practice, errors will certainly occur, sometimes resulting in injury. Who will be liable? Questions of liability for AI-related injury raise not only immediate concerns for potentially liable parties, but also broader systemic questions about how AI will be developed and adopted. The landscape of liability is complex, involving health-care providers and institutions and the developers of AI systems. In this chapter, we consider these three principal loci of liability: individual health-care providers, focused on physicians; institutions, focused on hospitals; and developers.

Development Of Accurate And Efficient Computational Methodologies For Predicting Protein-Ligand And Protein-Protein Binding Free Energies, Alexander Hamilton Williams

Theses and Dissertations--Pharmacy

Computational modeling is an invaluable tool in the drug discovery process either for small ligand or protein therapeutics. The widespread availability of protein X-Ray Crystal and Cryo-Electron Microscopy (Cryo-EM) structures has allowed for more accurate molecular dynamics (MD) simulations that are not reliant on methods such as homology modeling, which may produce structures that require significant computational time to demonstrate their stability. In this thesis we describe several novel methodologies for the computationally efficient modeling of protein/ligand and protein/protein complexes that may be employed within both large-scale virtual screenings and lead compound optimization. These methodologies may also be utilized in …

Ascp-Iomt: Ai-Enabled Lightweight Secure Communication Protocol For Internet Of Medical Things, Mohammad Wazid, Jaskaran Singh, Ashok Kumar Das, Sachin Shetty, Muhammad Khurram Khan, Joel J.P.C. Rodrigues

VMASC Publications

The Internet of Medical Things (IoMT) is a unification of smart healthcare devices, tools, and software, which connect various patients and other users to the healthcare information system through the networking technology. It further reduces unnecessary hospital visits and the burden on healthcare systems by connecting the patients to their healthcare experts (i.e., doctors) and allows secure transmission of healthcare data over an insecure channel (e.g., the Internet). Since Artificial Intelligence (AI) has a great impact on the performance and usability of an information system, it is important to include its modules in a healthcare information system, which will be …

Security Concerns On Machine Learning Solutions For 6g Networks In Mmwave Beam Prediction, Ferhat Ozgur Catak, Murat Kuzlu, Evren Catak, Umit Cali, Devrim Unal

Engineering Technology Faculty Publications

6G – sixth generation – is the latest cellular technology currently under development for wireless communication systems. In recent years, machine learning (ML) algorithms have been applied widely in various fields, such as healthcare, transportation, energy, autonomous cars, and many more. Those algorithms have also been used in communication technologies to improve the system performance in terms of frequency spectrum usage, latency, and security. With the rapid developments of ML techniques, especially deep learning (DL), it is critical to consider the security concern when applying the algorithms. While ML algorithms offer significant advantages for 6G networks, security concerns on artificial …

Security And Privacy In Iot-Based Healthcare Systems: A Review, Omar Ibrahim Obaid, Saba Abdul-Baqi Salman

Mesopotamian Journal of Computer Science

IoT-based healthcare solutions have transformed patient care and improved results. These technologies capture and store sensitive patient data, raising security and privacy concerns. The paper first discusses IoT-based healthcare system security issues. It evaluates cyber threats, system unavailability, and vulnerabilities attackers can use to get patient data. The report also tackles IoT-based healthcare system privacy issues such illegal data access and sharing and patient data breach concerns. The study proposes numerous frameworks and solutions to protect patient data in IoT-based healthcare systems. Blockchain technology can increase data security and privacy by offering a transparent, decentralized, tamperresistant database. The report also …

Binary Classifiers For Noisy Datasets: A Comparative Study Of Existing Quantum Machine Learning Frameworks And Some New Approaches, Nikolaos Schetakis, Davit Aghamalyan, Paul Robert Griffin, Michael Boguslavsky

Research Collection School Of Computing and Information Systems

This technology offer is a quantum machine learning algorithm applied to binary classification models for noisy datasets which are prevalent in financial and other datasets. By combining hybrid-neural networks, quantum parametric circuits, and data re-uploading we have improved the classification of non-convex 2-dimensional figures by understanding learning stability as noise increases in the dataset. The metric we use for assessing the performance of our quantum classifiers is the area under the receiver operator curve (ROC AUC). We are interested to collaborate with partners with use cases for binary classification of noisy data. Also, as quantum technology is still insufficient for …

The Ratio Method: Addressing Complex Tort Liability In The Fourth Industrial Revolution, Harrison C. Margolin, Grant H. Frazier

St. Mary's Law Journal

Emerging technologies of the Fourth Industrial Revolution show fundamental promise for improving productivity and quality of life, though their misuse may also cause significant social disruption. For example, while artificial intelligence will be used to accelerate society’s processes, it may also displace millions of workers and arm cybercriminals with increasingly powerful hacking capabilities. Similarly, human gene editing shows promise for curing numerous diseases, but also raises significant concerns about adverse health consequences related to the corruption of human and pathogenic genomes.

In most instances, only specialists understand the growing intricacies of these novel technologies. As the complexity and speed of …

Deep Fakes: The Algorithms That Create And Detect Them And The National Security Risks They Pose, Nick Dunard

James Madison Undergraduate Research Journal (JMURJ)

The dissemination of deep fakes for nefarious purposes poses significant national security risks to the United States, requiring an urgent development of technologies to detect their use and strategies to mitigate their effects. Deep fakes are images and videos created by or with the assistance of AI algorithms in which a person’s likeness, actions, or words have been replaced by someone else’s to deceive an audience. Often created with the help of generative adversarial networks, deep fakes can be used to blackmail, harass, exploit, and intimidate individuals and businesses; in large-scale disinformation campaigns, they can incite political tensions around the …

Research On The Issues Of Next Generation Wargame System Model Engine, Yubo Tang, Bilong Shen, Shi Lei, Yi Xing

Journal of System Simulation

Abstract: Aiming at the more and more complex war systems, widely used artificial intelligence technology is needed to make up the human deficiencies in future wargame deduction, which is necessary for the next generation wargame system model engine. To address these challenges, a framework prototype of the next generation wargame model engine based on the experience of the long-term development and application is proposed. The decoupling method for the complexity of structure and computation is researched. The human-computer integration architecture on digital twinning technology is studied. Some new modeling techniques which the threshold of model development is reduced and the …

The Role Of Trust In Advice Acceptance From Non-Human Actors, Rahul Banerjee

Dissertations and Theses Collection (Open Access)

Advancements in technology are now allowing non-human actors in the form of robot-advisors, driverless cars, medical assistants to perform increasingly complex tasks. While technological change is as old as civilization, these non-human actors can do novel tasks. One such task is that they provide advice which is a credence service (Dulleck, & Kerschbamer, 2006). Using a financial services context this thesis studies the role trust plays in advice acceptance.

Robo-advisors are rapidly replacing human financial advisors as the agent-provider for portfolio investment services. For centuries, it was the banker (human financial advisor) who was responsible for providing his investors with …

Application Of Artificial Intelligence And Machine Learning In Libraries: A Systematic Review, Rajesh Kumar Das, Mohammad Sharif Ul Islam

Library Philosophy and Practice (e-journal)

As the concept and implementation of cutting-edge technologies like artificial intelligence and machine learning has become relevant, academics, researchers and information professionals involve research in this area. The objective of this systematic literature review is to provide a synthesis of empirical studies exploring application of artificial intelligence and machine learning in libraries. To achieve the objectives of the study, a systematic literature review was conducted based on the original guidelines proposed by Kitchenham et al. (2009). Data was collected from Web of Science, Scopus, LISA and LISTA databases. Following the rigorous/ established selection process, a total of thirty-two articles were …

Machine Learning In Stock Price Prediction Using Long Short-Term Memory Networks And Gradient Boosted Decision Trees, Carl Samuel Cederborg

Honors Projects

Quantitative analysis has been a staple of the financial world and investing for many years. Recently, machine learning has been applied to this field with varying levels of success. In this paper, two different methods of machine learning (ML) are applied to predicting stock prices. The first utilizes deep learning and Long Short-Term Memory networks (LSTMs), and the second uses ensemble learning in the form of gradient tree boosting. Using closing price as the training data and Root Mean Squared Error (RMSE) as the error metric, experimental results suggest the gradient boosting approach is more viable.

Honors Symposium: ML is …

Recommender App Development For Essential Health Products : Covid And Beyond, Jessica Lourenco

Theses, Dissertations and Culminating Projects

The COVID-19 pandemic has affected all of our lives in many ways and as a result people have become more health conscious. Now more than ever it is critical to take cautious steps to prevent being infected and spreading the virus. It is important to be supplied with the right products that maintain us all safe and healthy. Although many stores have health related products, sometimes it is a hassle to find them and even to pick out the best ones. With that, the Health Essentials app was developed to facilitate the findings of health products. The app is solely …

The Power Of The "Internet Of Things" To Mislead And Manipulate Consumers: A Regulatory Challenge, Kate Tokeley

Notre Dame Journal on Emerging Technologies

The “Internet of Things” revolution is on its way, and with it comes an unprecedented risk of unregulated misleading marketing and a dramatic increase in the power of personalized manipulative marketing. IoT is a term that refers to a growing network of internet-connected physical “smart” objects accumulating in our homes and cities. These include “smart” versions of traditional objects such as refrigerators, thermostats, watches, toys, light bulbs, cars, and Alexa-style digital assistants. The corporations who develop IoT are able to utilize a far greater depth of data than is possible from merely tracking our web browsing in regular online environments. …

Human-Ai Teaming For Dynamic Interpersonal Skill Training, Xavian Alexander Ogletree

Browse all Theses and Dissertations

In almost every field, there is a need for strong interpersonal skills. This is especially true in fields such as medicine, psychology, and education. For instance, healthcare providers need to show understanding and compassion for LGBTQ+ and BIPOC (Black, Indigenous, and People of Color), or individuals with unique developmental or mental health needs. Improving interpersonal skills often requires first-person experience with expert evaluation and guidance to achieve proficiency. However, due to limited availability of assessment capabilities, professional standardized patients and instructional experts, students and professionals currently have inadequate opportunities for expert-guided training sessions. Therefore, this research aims to demonstrate leveraging …

Playing Pong Using Q-Learning, Akash Kumar

West Chester University Master’s Theses

This thesis involves the use of a reinforcement learning algorithm (RL) called Q-learning to train a Q-agent to play a game of Pong against a near-perfect opponent. Compared to previously related work which trained Pong RL agents by combining Q-learning with deep learning in an algorithm known as Deep Q-Networks, the work presented in this paper takes advantage of known environment constraints of the custom-made Pong environment to train the agent using one-step Q-learning alone. In addition, the thesis explores ways of making the Q-learning more efficient by converting Markov Decision Processes (MDPs) to Partially Observable Markov Decision Processes (POMDPs), …

Law Library Blog (January 2021): Legal Beagle's Blog Archive, Roger Williams University School Of Law

Law Library Newsletters/Blog

No abstract provided.

Algorithmic Responsibility, Algorithmic Bias, Darla Jackson

Other Faculty Publications

No abstract provided.

Interpretable, Not Black-Box, Artificial Intelligence Should Be Used For Embryo Selection, Michael Anis Mihdi Afnan, Yanhe Liu, Vincent Conitzer, Cynthia Rudin, Abhishek Mishra, Julian Savulescu, Masoud Afnan

Research outputs 2014 to 2021

Artificial intelligence (AI) techniques are starting to be used in IVF, in particular for selecting which embryos to transfer to the woman. AI has the potential to process complex data sets, to be better at identifying subtle but important patterns, and to be more objective than humans when evaluating embryos. However, a current review of the literature shows much work is still needed before AI can be ethically implemented for this purpose. No randomized controlled trials (RCTs) have been published, and the efficacy studies which exist demonstrate that algorithms can broadly differentiate well between ‘good-’ and ‘poor-’ quality embryos but …

Lidar Object Detection Utilizing Existing Cnns For Smart Cities, Vinay Ponnaganti

Master's Projects

As governments and private companies alike race to achieve the vision of a smart city — where artificial intelligence (AI) technology is used to enable self-driving cars, cashier-less shopping experiences and connected home devices from thermostats to robot vacuum cleaners — advancements are being made in both software and hardware to enable increasingly real-time, accurate inference at the edge. One hardware solution adopted for this purpose is the LiDAR sensor, which utilizes infrared lasers to accurately detect and map its surroundings in 3D. On the software side, developers have turned to artificial neural networks to make predictions and recommendations with …

Transfer Learning For Decision Support In Covid-19 Detection From A Few Images In Big Data, Divydharshini Karthikeyan, Aparna S. Varde, Weitian Wang

Department of Computer Science Faculty Scholarship and Creative Works

The novel coronavirus (Covid-19) has spread rapidly amongst countries all around the globe. Compared to the rise in cases, there are few Covid-19 testing kits available. Due to the lack of testing kits for the public, it is useful to implement an automated AI-based E-health decision support system as a potential alternative method for Covid-19 detection. As per medical examinations, the symptoms of Covid-19 could be somewhat analogous to those of pneumonia, though certainly not identical. Considering the enormous number of cases of Covid-19 and pneumonia, and the complexity of the related images stored, the data pertaining to this problem …

Development Of Computational To Ols To Target Microrna, Luo Song

Dissertations and Theses (Open Access)

MicroRNAs (a.k.a, miRNAs) play an important role in disease development. However, few of their structures have been determined and structure-based computational methods remain challenging in accurately predicting their interactions with small molecules. To address this issue, my thesis is to develop integrated approaches to screening for novel inhibitors by targeting specific structure motifs in miRNAs. The project starts with implementing a tool to find potential miRNA targets with desired motifs. I combined both sequence information of miRNAs and known RNA structure data from Protein Data Bank (PDB) to predict the miRNA structure and identify the motif to target, then I …

New Methods For Deep Learning Based Real-Valued Inter-Residue Distance Prediction, Jacob Barger

Theses

Background: Much of the recent success in protein structure prediction has been a result of accurate protein contact prediction--a binary classification problem. Dozens of methods, built from various types of machine learning and deep learning algorithms, have been published over the last two decades for predicting contacts. Recently, many groups, including Google DeepMind, have demonstrated that reformulating the problem as a multi-class classification problem is a more promising direction to pursue. As an alternative approach, we recently proposed real-valued distance predictions, formulating the problem as a regression problem. The nuances of protein 3D structures make this formulation appropriate, allowing predictions …

Law Library Blog (November 2020): Legal Beagle's Blog Archive, Roger Williams University School Of Law

Law Library Newsletters/Blog

No abstract provided.

The Future Of Work Now: The Multi-Faceted Mall Security Guard At A Multi-Faceted Jewel, Thomas H. Davenport, Steven M. Miller

Research Collection School Of Computing and Information Systems

One of the most frequently-used phrases at business events these days is “the future of work.” It’s increasingly clear that artificial intelligence and other new technologies will bring substantial changes in work tasks and business processes. But while these changes are predicted for the future, they’re already present in many organizations for many different jobs. The job and incumbents described below are an example of this phenomenon. Steve Miller of Singapore Management University and I co-authored the story.

Person Identification And Tinetti Score Assessment Using Balance Parameters To Determine Fall Risk, Varsha Rani Chawan

Computer Science and Engineering Theses - Archive

This thesis is aimed at a substantial health problem among the elderly population that is “Fall”, a major cause of accidental home deaths. Studies show approximately one-third of community-dwelling people over 65 years of age will experience one or more falls each year. The balance and walking pattern are useful to determine the risk of fall in an individual and is highly influenced by several parameters and conditions. The deterioration in the balance and walking stability of an individual can occur because of the natural processes related to aging or as a result of various underlying health conditions, fatigue, muscle …