Application Of Using Fault Detection Techniques In Different Components In Power Systems, Mohamed Yahia Abd-Alkader, Ahmed M. Ebid Dr., Ibrahim Mahdi, Ibrahim Abdelrashed Nosseir

Future Engineering Journal

Throughout the years, Fault detection has been one of the most frequently discussed topics in the scientific community at present. Consequently, it has been extensively studied and focused on in recent years. This scientific paper presents some of this research, what they have reached, and the methods used for their studies. The literature review provides that these problems are not covered yet. It needs more works to develop fault detection techniques for better performance.

Artificial Image Objects For Classification Of Breast Cancer Biomarkers With Transcriptome Sequencing Data And Convolutional Neural Network Algorithms, Xiangning Chen, Daniel G. Chen, Zhongming Zhao, Justin M. Balko, Jingchun Chen

School of Medicine Faculty Research

Background: Transcriptome sequencing has been broadly available in clinical studies. However, it remains a challenge to utilize these data effectively for clinical applications due to the high dimension of the data and the highly correlated expression between individual genes. Methods: We proposed a method to transform RNA sequencing data into artificial image objects (AIOs) and applied convolutional neural network (CNN) algorithms to classify these AIOs. With the AIO technique, we considered each gene as a pixel in an image and its expression level as pixel intensity. Using the GSE96058 (n = 2976), GSE81538 (n = 405), and GSE163882 (n = …

Impossibility Results In Ai: A Survey, Mario Brcic, Roman Yampolskiy

Faculty and Staff Scholarship

An impossibility theorem demonstrates that a particular problem or set of problems cannot be solved as described in the claim. Such theorems put limits on what is possible to do concerning artificial intelligence, especially the super-intelligent one. As such, these results serve as guidelines, reminders, and warnings to AI safety, AI policy, and governance researchers. These might enable solutions to some long-standing questions in the form of formalizing theories in the framework of constraint satisfaction without committing to one option. In this paper, we have categorized impossibility theorems applicable to the domain of AI into five categories: deduction, indistinguishability, induction, …

Innovative Computational Methods For Pharmaceutical Problem Solving A Review Part I: The Drug Development Process, Heather R. Campbell, Robert A. Lodder

Pharmaceutical Sciences Faculty Publications

Computational methods have provided pharmaceutical scientists and engineers a means to go beyond what's possible with experimental testing alone. Providing a means to study active pharmaceutical ingredients (API), excipients, and drug interactions at or near-atomic levels. This paper provides a review of this and other innovative computational methods used for solving pharmaceutical problems throughout the drug development process. Part one of two this paper will emphasize the role of computational methods and game theory in solving pharmaceutical challenges.

Towards Laparoscopic Visual Ai: Development Of A Visual Guidance System For Laparoscopic Surgical Palpation, Kerwin G. Caballas, Harold Jay M. Bolingot, Nathaniel Joseph C. Libatique, Gregory L. Tangonan

Electronics, Computer, and Communications Engineering Faculty Publications

Currently, there are numerous obstacles to performing palpation during laparoscopic surgery. The laparoscopic interface does not allow access into a patient's body anything other than the tools that are inserted through the trocars. Palpation is usually done with the surgeon's hands to detect lumps and certain anomalies underneath the skin, muscle, or tissues. It can be useful technique for augmenting surgical decision-making during laparoscopic surgery, especially when discerning operations involving cancerous tumors. Previous research demonstrated the use of tactile sensors and mechanical sensors placed at the end-effectors for palpating laparoscopically. In this study, a visual guidance system is proposed for …

Graphical Models In Reconstructability Analysis And Bayesian Networks, Marcus Harris, Martin Zwick

Complex Systems Faculty Publications and Presentations

Reconstructability Analysis (RA) and Bayesian Networks (BN) are both probabilistic graphical modeling methodologies used in machine learning and artificial intelligence. There are RA models that are statistically equivalent to BN models and there are also models unique to RA and models unique to BN. The primary goal of this paper is to unify these two methodologies via a lattice of structures that offers an expanded set of models to represent complex systems more accurately or more simply. The conceptualization of this lattice also offers a framework for additional innovations beyond what is presented here. Specifically, this paper integrates RA and …

Home Energy Management System For Coordinated Pv And Hvac Controls Based On Ai Forecasting, Huangjie Gong, Rosemary E. Alden, Dan M. Ionel

Power and Energy Institute of Kentucky Presentations

Introduction

• This HEMS serves to transform HVAC system demand into a schedulable load bank or “dispatchable load” through controls based on day ahead forecasts
• Within this poster, a complete structure from data acquisition to day-ahead load scheduling is proposed
• For the purpose of study, measured data is used in place of forecasts to showcase best case results.

Artificial Intelligence-Based Short-Term Electric Load Forecasts For Experimental Smart Homes Including Hvac And Pv Components, Rosemary E. Alden, Cristinel Ababei, Dan M. Ionel

Power and Energy Institute of Kentucky Presentations

Problem Formulation

• To predict electric load of the total average power as well as individual components for two residencies from experimental data
• Individual residential forecasting is difficult due to high variability of appliance usage and random human behavior influences
• Separate the HVAC load from total load as a desired profile using weather relationship and minimum HVAC load at night
• Data driven approach to reduce the amount of information about the home required

Towards Intelligent Structural Health Monitoring Of Infrastructure Systems: An Interdisciplinary Study Of Acoustic Emission Monitoring, Numerical Simulation, And Artificial Intelligence, Li Ai

Theses and Dissertations

Some complex infrastructure systems, such as nuclear facilities and bridges, are subject to structural damage due to environmental erosion, material deterioration, and other factors after long periods of use. Stress corrosion cracking (SCC) and alkali-silica reaction (ASR) have been identified as the primary degradation mechanisms for steel and concrete structures in nuclear facilities and bridges. Ensuring the integrity and operational safety of structures during their lifetime is an important task. Nondestructive methods and structural health monitoring can be used to detect damage caused by SCC and ASR instead of conventional visual inspection. Among the nondestructive methods, acoustic emission (AE) is …

Enterprise Engineering And Intellectual Technologies For Lifecycle Management Of Industrial Production, Nodirbek Rustambekovich Yusupbekov, Valery Borisovich Tarasov, Shukhrat Manapovich Gulyamov, Fahritdin Raupovich Abdurasulov

Chemical Technology, Control and Management

The fundamental scientific problem of the development of the mathematical foundations of engineering for industrial enterprises and the development of mathematical methods of production management, as well as the creation of intelligent systems for coordinated management of the life cycles of products and production in the network of enterprises are discussed. The issues in demand in the development of a vast interdisciplinary field of enterprise engineering and the development of modern network enterprises and intelligent production using mathematical modeling methods are discussed.

Flying Free: A Research Overview Of Deep Learning In Drone Navigation Autonomy, Thomas Lee, Susan Mckeever, Jane Courtney

Articles

With the rise of Deep Learning approaches in computer vision applications, significant strides have been made towards vehicular autonomy. Research activity in autonomous drone navigation has increased rapidly in the past five years, and drones are moving fast towards the ultimate goal of near-complete autonomy. However, while much work in the area focuses on specific tasks in drone navigation, the contribution to the overall goal of autonomy is often not assessed, and a comprehensive overview is needed. In this work, a taxonomy of drone navigation autonomy is established by mapping the definitions of vehicular autonomy levels, as defined by the …

Artificial Intelligence (Ai) And Augmented Reality (Ar): Disambiguated In The Telemedicine / Telehealth Sphere, Sharon L. Burton

Publications

The world is navigating through unfamiliar and incomprehensible times – COVID-19, international economic crisis, and crumbling healthcare systems. The United States (US) healthcare industry is grappling with an increased workload and advancing digitization technological concerns. The failure of organizations to offer suitable cybersecurity controls within the critical infrastructure leads to advanced persistent threat (APT) that could have incapacitating effects on organizations. A keen understanding of cybersecurity is vital for leaders and the need is referenced in US policy that advances a national unity of effort to strengthen and maintain secure, functioning, and resilient critical infrastructure. Akin to the Presidential Policy …

A Framework For Assessing And Designing Human Annotation Practices In Human-Ai Teaming, Suzanne Ashley Stevens

Theses and Dissertations

This thesis work examines how people accomplish annotation tasks (i.e., labelling data based on content) while working with an artificial intelligence (AI) system. When people and AI systems work together to accomplish a task, this is referred to as human-AI teaming. This study reports on the results of an interview and observation study of 15 volunteers from the Washington DC area as the volunteers annotated Twitter messages (tweets) about the COVID-19 pandemic. During the interviews, researchers observed the volunteers as they annotated tweets, noting any needs, frustrations, or confusion that the volunteers expressed about the task itself or when working …

Evaluating The Performance Of Extreme Learning Machine Technique For Ore Grade Estimation, Clara Akalanya Abuntori, Sulemana Al-Hassan, Daniel Mireku-Gyimah, Yao Yevenyo Ziggah

Journal of Sustainable Mining

Due to the complex geology of vein deposits and their erratic grade distributions, there is the tendency of overestimating or underestimating the ore grade. These estimated grade results determine the profitability of mining the ore deposit or otherwise. In this study, five Extreme Learning Machine (ELM) variants based on hard limit, sigmoid, triangular basis, sine and radial basis activation functions were applied to predict ore grade. The motive is that the activation function has been identified to play a key role in achieving optimum ELM performance. Therefore, assessing the extent of influence the activation functions will have on the final …

Characterization Of Time-Variant And Time-Invariant Assessment Of Suicidality On Reddit Using C-Ssrs, Manas Gaur, Vamsi Aribandi, Amanuel Alambo, Ugur Kursuncu, Krishnaprasad Thirunarayan, Jonathan Beich, Jyotishman Pathak, Amit Sheth

Publications

Suicide is the 10th leading cause of death in the U.S (1999-2019). However, predicting when someone will attempt suicide has been nearly impossible. In the modern world, many individuals suffering from mental illness seek emotional support and advice on well-known and easily-accessible social media platforms such as Reddit. While prior artificial intelligence research has demonstrated the ability to extract valuable information from social media on suicidal thoughts and behaviors, these efforts have not considered both severity and temporality of risk. The insights made possible by access to such data have enormous clinical potential - most dramatically envisioned as a trigger …

Designing Ai For Explainability And Verifiability: A Value Sensitive Design Approach To Avoid Artificial Stupidity In Autonomous Vehicles, Steven Umbrello, Roman V. Yampolskiy

Faculty and Staff Scholarship

One of the primary, if not most critical, difficulties in the design and implementation of autonomous systems is the black-boxed nature of the decision-making structures and logical pathways. How human values are embodied and actualised in situ may ultimately prove to be harmful if not outright recalcitrant. For this reason, the values of stakeholders become of particular significance given the risks posed by opaque structures of intelligent agents. This paper explores how decision matrix algorithms, via the belief-desire-intention model for autonomous vehicles, can be designed to minimize the risks of opaque architectures. Primarily through an explicit orientation towards designing for …

Artificial Intelligence And The Ethics Behind It, Isaac Johnston

Senior Honors Theses

Artificial intelligence (AI) has been a widely used buzzword for the past couple of years. If there is a technology that works without human interaction, it is labeled as AI. But what is AI, and should individuals be concerned? The following research aims to define what artificial intelligence is, specifically machine learning (ML) and neural networks. It is important to understand how AI is used today in cars, image recognition, ad marketing, and other areas. Although AI has many benefits, there are areas of ethical concerns such as autonomous cars, military applications, social media marketing, and others. This paper helps …

Real-Time Material State Assessment Of Composites Using Artificial Intelligence And Its Challenges, Muthu Ram Prabhu Elenchezhian

Mechanical and Aerospace Engineering Dissertations - Archive

Over several decades of careful experimental investigation and exhaustive development of discrete damage analysis methods including integrated computational mechanics methods, our community knows a great deal about how discrete defects such as matrix cracks and defect growth (e.g. delamination) can be predicted in structural composites. For many practical situations controlled by laminated multiaxial composite structures, the loss of performance and “sudden death” end of life is controlled by defect coupling which becomes a precursor to fracture plane development. These interaction sequences are highly dependent on local details of manufacture, design configurations, and loading for a given application material and influenced …

The Future Of Artificial Intelligence, Alex Guerra

Emerging Writers

Whether we like it or not Artificial Intelligence (AI) is coming, and we are not ready for it. AI has unimaginable potential and will revolutionize the world over the next few decades, but with this great potential we are faced with choices that could prove detrimental to humanity. This article examines the challenges AI presents and explores possible solutions to make AI align with human interests.

Machine Learning-Based Recognition On Crowdsourced Food Images, Aditya Kulkarni

Honors Scholar Theses

With nearly a third of the world’s population suffering from food-induced chronic diseases such as obesity, the role of food in community health is required now more than ever. While current research underscores food proximity and density, there is a dearth in regard to its nutrition and quality. However, recent research in geospatial data collection and analysis as well as intelligent deep learning will help us study this further.

Employing the efficiency and interconnection of computer vision and geospatial technology, we want to study whether healthy food in the community is attainable. Specifically, with the help of deep learning in …

A Brief Bibliometric Survey Of Explainable Ai In Medical Field, Nilkanth Mukund Deshpande, Shilpa Shailesh Gite

Library Philosophy and Practice (e-journal)

_{^{Background: This study aims to analyze the work done in the field of explainability related to artificial intelligence, especially in the medical field from 2004 onwards using the bibliometric methods.}}

_{^{Methods: different articles based on the topic leukemia detection were retrieved using one of the most popular database- Scopus. The articles are considered from 2004 onwards. Scopus analyzer is used for different types of analysis including documents by year, source, county and so on. There are other different analysis tools such as VOSviewer Version 1.6.15. This is used for the analysis of different units such as co-authorship, co-occurrences, citation analysis …}}

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 …

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 …

Analysis Of Consistency Of Similarity Predicates To Measures Of Similarity And Advantages Of Their Application In Digital Systems For Solving Intellectual Problems, Makhmudjon Abdullaev, Nodira Batirdjanovna Alimova

Chemical Technology, Control and Management

The results of the analysis of the correspondence of similarity predicates to similarity measures used in various metric methods used for signal classification and the feasibility of using similarity predicates in the construction of digital systems for solving intellectual problems, for which the simplification of computational operations is of no small importance, are presented. Some general and distinctive features of the similarity predicate are considered in comparison with the euclidean metric in relation to one-dimensional and two-dimensional spaces, and generalized to the case of n-dimensional metric spaces. The expediency of using methods based on the calculation of similarity predicates, which …

Blast-Induced Noise Level Prediction Model Based On Brain Inspired Emotional Neural Network, Victor Amoako Temeng, Yao Yevenyo Ziggah, Clement Kweku Arthur

Journal of Sustainable Mining

Although a major portion of the emitted energy from mine blast is sub-audible (lower frequency), there exist a component that is audible (high frequencies from 20 Hz to 20 KHz) and as such within the range of human hearing as noise. Unlike blast air overpressure (low frequency occurrence), noise prediction from mine blasting has received little scholarly attention in mining sciences. Noise from mine blast is considered a major detrimental blasting effect and can be a menace to nearby residents and workers in the mine. In this paper, a blast-induced noise level prediction model based on Brain Inspired Emotional Neural …

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 …

Role Of Artificial Intelligence In The Internet Of Things (Iot) Cybersecurity, Murat Kuzlu, Corinne Fair, Ozgur Guler

Engineering Technology Faculty Publications

In recent years, the use of the Internet of Things (IoT) has increased exponentially, and cybersecurity concerns have increased along with it. On the cutting edge of cybersecurity is Artificial Intelligence (AI), which is used for the development of complex algorithms to protect networks and systems, including IoT systems. However, cyber-attackers have figured out how to exploit AI and have even begun to use adversarial AI in order to carry out cybersecurity attacks. This review paper compiles information from several other surveys and research papers regarding IoT, AI, and attacks with and against AI and explores the relationship between these …

A Bibliometric Analysis Of The Tea Quality Evaluation Using Artificial Intelligence, Amruta Bajirao Patil Research Scholar, Mrinal Rahul Bachute Ph.D Guide And Associate Professor

Library Philosophy and Practice (e-journal)

ABSTRACT: In this study, we have carried the bibliometric review of the “Tea quality evaluation using artificial intelligence”. Only the Scopus database is under consideration for this analysis. To coat all possible research approaches here we have generated the valid search queries which excludes irrelevant literature. The result analysis shows overall 602 useful papers are available on the tea quality evaluation out of which 12 papers are specifically on artificial taste perception of tea. This survey illustrates the emerging trend of quality evaluation and assurance (QEA) in tea industry and its importance. As the production of tea is huge, storage …

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 …

Application Of Analogical Reasoning For Use In Visual Knowledge Extraction, Kara Lian Combs

Browse all Theses and Dissertations

There is a continual push to make Artificial Intelligence (AI) as human-like as possible; however, this is a difficult task because of its inability to learn beyond its current comprehension. Analogical reasoning (AR) has been proposed as one method to achieve this goal. Current literature lacks a technical comparison on psychologically-inspired and natural-language-processing-produced AR algorithms with consistent metrics on multiple-choice word-based analogy problems. Assessment is based on “correctness” and “goodness” metrics. There is not a one-size-fits-all algorithm for all textual problems. As contribution in visual AR, a convolutional neural network (CNN) is integrated with the AR vector space model, Global …