Digital Commons Network^™

Application Of Some Artificial Intelligence Optimization Methods To Determine The Freshness Of Eggs, Hasan Alp Şahi̇n, Hasan Önder

Turkish Journal of Veterinary & Animal Sciences

Egg quality, can be divided into two groups as internal and external, is evaluated using various methods whether breaking eggs. Image processing makes digital images usable for various purposes such as image compression, image editing, object recognition, face recognition, medical imaging, and many other areas such as the automotive industry. This study aimed to determine the freshness of eggs using different artificial intelligence optimization methods with image processing without breaking the eggs. Artificial neural networks (ANNs), artificial bee colony, particle swarm optimization, and genetic algorithms were compared using classification coefficients. As a result of the study, it was determined that …

Artificial Intelligence Chatbot As A Mathematics Curriculum Developer: Discovering Preservice Teachers’ Overconfidence In Chatgpt, Amanda G. Sawyer

International Journal on Responsibility

Instructors in many colleges and universities are responsible for supporting their preservice teachers’ understanding of mathematics curriculum to best serve elementary students’ needs. As such, preservice teachers are taught how to critically analyze curriculum materials. However, with the advent of technologies like ChatGPT, preservice teachers are utilizing artificial intelligence (AI) tools in new ways to collate and construct mathematical curricula. As of now, little is known about how to adapt these new resources for the classroom. ChatGPT, an AI chatbot, can create a mathematics curriculum based on user questions, which teachers can then adapt to their classrooms. Although this AI …

Can The Automated State Be Trusted? The Role Of Rule Of Law Safeguards For Governing Automated Decision-Making And Artificial Intelligence, Joanna Mazur, Maciej Bernatt

Georgia Law Review

The goal of this Article is to take a broader perspective on the lessons which can be learned from the implementation of automated decision-making in the public sector—specifically in regard to how procedural rule of law mechanisms can make such projects trustworthy. The underlying idea behind this Article is to posit the debate about the ADM within the context of rule of law as a key notion in liberal democracies. At the same time, we rely on a law and political economy framework. We look through the lens of power relations and assess to what extent selected procedural solutions governing …

Perceptions And Aspirations Of Undergraduate Computer Science Students Towards Generative Ai: A Qualitative Inquiry, James Hutson, Theresa Jeevanjee

Faculty Scholarship

This article presents a comprehensive study conducted during the spring semester of 2024, aimed at exploring undergraduate computer science students’ perceptions, awareness, and understanding of generative artificial intelligence (GAI) tools within the context of their Artificial Intelligence (AI) courses. The research methodology employed qualitative techniques, including human-subject research and focus groups, to delve into students’ insights on the evolution of AI as delineated in the seminal textbook by Russell and Norvig. The study-initiated discussions on the historical development of AI, prompting students to reflect on the aspects that intrigued them the most, and to identify which historical concepts and methodologies, …

Predictive Power Of Machine Learning Models On Degree Completion Among Adult Learners, Emily Barnes, James Hutson, Karriem Perry

Faculty Scholarship

The integration of machine learning (ML) into higher education has been recognized as a transformative force for adult learners, a growing demographic facing unique educational challenges. This study evaluates the predictive power of three ML models—Random Forest, Gradient-Boosting Machine, and Decision Trees—in forecasting degree completion among this group. Utilizing a dataset from the academic years 2013-14 to 2021-22, which includes demographic and academic performance metrics, the study employs accuracy, precision, recall, and F1 score to assess the efficacy of these models. The results indicate that the Gradient-Boosting Machine model outperforms others in predicting degree completion, suggesting that ML can significantly …

Architectural Elements Contributing To Interpretability Of Deep Neural Networks (Dnns), Emily Barnes, James Hutson

Faculty Scholarship

The interpretability of Deep Neural Networks (DNNs) has become a critical focus in artificial intelligence and machine learning, particularly as DNNs are increasingly used in high-stakes applications like healthcare, finance, and autonomous driving. Interpretability refers to the extent to which humans can understand the reasons behind a model's decisions, which is essential for trust, accountability, and transparency. However, the complexity and depth of DNN architectures often compromise interpretability as these models function as "black boxes." This article reviews key architectural elements of DNNs that affect their interpretability, aiming to guide the design of more transparent and trustworthy models. The primary …

Navigating The Complexities Of Ai: The Critical Role Of Interpretability And Explainability In Ensuring Transparency And Trust, Emily Barnes, James Hutson

Faculty Scholarship

The interpretability and explainability of deep neural networks (DNNs) are paramount in artificial intelligence (AI), especially when applied to high-stakes fields such as healthcare, finance, and autonomous driving. The need for this study arises from the growing integration of AI into critical areas where transparency, trust, and ethical decision-making are essential. This paper explores the impact of architectural design choices on DNN interpretability, focusing on how different architectural elements like layer types, network depth, connectivity patterns, and attention mechanisms affect model transparency. Methodologically, the study employs a comprehensive review of case studies and experimental results to analyze the balance between …

Evaluating Methods For Assessing Interpretability Of Deep Neural Networks (Dnns), Emily Barnes, James Hutson

Faculty Scholarship

The interpretability of deep neural networks (DNNs) is a critical focus in artificial intelligence (AI) and machine learning (ML), particularly as these models are increasingly deployed in high-stakes applications such as healthcare, finance, and autonomous systems. In the context of these technologies, interpretability refers to the extent to which a human can understand the cause of a decision made by a model. This article evaluates various methods for assessing the interpretability of DNNs, recognizing the significant challenges posed by their complex and opaque nature. The review encompasses both quantitative metrics and qualitative evaluations, aiming to identify effective strategies that enhance …

Navigating The Ethical Terrain Of Ai In Higher Education: Strategies For Mitigating Bias And Promoting Fairness, Emily Barnes, James Hutson

Faculty Scholarship

Artificial intelligence (AI) and machine learning (ML) are transforming higher education by enhancing personalized learning and academic support, yet they pose significant ethical challenges, particularly in terms of inherent biases. This review critically examines the integration of AI in higher education, underscoring the dual aspects of its potential to innovate educational paradigms and the essential need to address ethical implications to avoid perpetuating existing inequalities. The researchers employed a methodological approach that analyzed case studies and literature as primary data collection methods, focusing on strategies to mitigate biases through technical solutions, diverse datasets, and strict adherence to ethical guidelines. Their …

Strategic Integration Of Ai In Higher Education And Industry: The Ai8-Point Model, Emily Barnes, James Hutson

Faculty Scholarship

The AI8-Point Model, derived from extensive experience in technology, AI, and higher education administration, addresses the critical need for cost-effective, high-impact strategies tailored to higher education. Despite the transformative potential of AI in enhancing student engagement, optimizing processes, and improving educational outcomes, institutions often struggle with practical implementation. The AI8-Point Model fills this gap by offering strategies that balance cost and impact. Visualized as a circle divided into four quadrants, the model encompasses phases of student engagement and institutional interaction: pre-enrollment beyond institutional control, pre-enrollment within institutional control, post-enrollment within institutional control, and post-enrollment beyond institutional control. Each quadrant contains …

Drake Or Droid?: A.I.-Generated Music And The Legal Challenges In Safeguarding Artist Rights, Nicoletta Gasparis

Hofstra Law Review

The article explores the growing use of artificial intelligence (A.I.) in music production and its legal implications. Topics include the rise of generative A.I. in music, exemplified by the creation of the song "Heart on My Sleeve," the ethical and legal concerns regarding A.I.'s ability to replicate artists' voices and styles, and the challenges in safeguarding artist rights amidst this new technological landscape.

Present Case Studies Highlighting Practical Implications Of Architectural Design Choices, Emily Barnes, James Hutson

Faculty Scholarship

The interpretability of deep neural networks (DNNs) has become a crucial focus within artificial intelligence and machine learning, particularly as these models are increasingly used in high-stakes applications such as healthcare, finance, and autonomous driving. This article explores the impact of architectural design choices on the interpretability of DNNs, emphasizing the importance of transparency, trust, and accountability in AI systems. By presenting case studies and experimental results, the article highlights how different architectural elements—such as layer types, network depth, connectivity patterns, and attention mechanisms—affect model interpretability and performance. The discussion is structured into three main sections: real-world applications, architectural trade-offs, …

Advances In The Integration Of Microalgal Communities For Biomonitoring Of Metal Pollution In Aquatic Ecosystems Of Sub-Saharan Africa, Mary Mulenga, Concillia Monde, Todd Johnson, Kennedy O Ouma, Stephen Syampungani

Faculty, Staff and Student Publications

This review elucidated the recent advances in integrating microalgal communities in monitoring metal pollution in aquatic ecosystems of sub-Saharan Africa (SSA). It also highlighted the potential of incorporating microalgae as bioindicators in emerging technologies, identified research gaps, and suggested directions for further research in biomonitoring of metal pollution. Reputable online scholarly databases were used to identify research articles published between January 2000 and June 2023 for synthesis. Results indicated that microalgae were integrated either individually or combined with other bioindicators, mainly macroinvertebrates, macrophytes, and fish, alongside physicochemical monitoring. There was a significantly low level of integration (< 1%) of microalgae for biomonitoring aquatic metal pollution in SSA compared to other geographical regions. Microalgal communities were employed to assess compliance (76%), in diagnosis (38%), and as early-warning systems (38%) of aquatic ecological health status. About 14% of biomonitoring studies integrated microalgal eDNA, while other technologies, such as remote sensing, artificial intelligence, and biosensors, are yet to be significantly incorporated. Nevertheless, there is potential for the aforementioned emerging technologies for monitoring aquatic metal pollution in SSA. Future monitoring in the region should also consider the standardisation and synchronisation of integrative biomonitoring and embrace the "Citizen Science" concept at national and regional scales.

Ethical Considerations Toward Protestware, Marc Cheong, Raula Kula, Christoph Treude

Research Collection School Of Computing and Information Systems

This article looks into possible scenarios where developers might consider turning their free and open source software into protestware. Using different frameworks commonly used in artificial intelligence (AI) ethics, we extend the applications of AI ethics to the study of protestware.

The Next 'Deep' Thing In X To Z Marketing: An Artificial Intelligence Driven Approach, Vincent Charles, Nripendra Rana, Ilias O. Pappas, Morten Kamphaug, Keng Siau, Kenth Engo-Monsen

Research Collection School Of Computing and Information Systems

The existing body of literature indicates a growing interest in research pertaining to the influence of artificial intelligence (AI) on marketing strategies, processes, and practices. However, further studies are required to fully unravel its complete potential and the implications it holds for practical application. The aim of this special issue on “The Next ‘Deep’ Thing in X to Z Marketing: An Artificial Intelligence-Driven Approach” is to explore the next frontiers and delve into the various facets of AI-driven marketing, shedding light on cutting-edge research and practical insights that can shape the future of the field. It also focuses on novel …

Ai Employment Decision-Making: Integrating The Equal Opportunity Merit Principle And Explainable Ai, Gary Kok Yew Chan

Research Collection Yong Pung How School Of Law

Artificial intelligence (AI) tools used in employment decision-making cut across the multiple stages of job advertisements, shortlisting, interviews and hiring, and actual and potential bias can arise in each of these stages. One major challenge is to mitigate AI bias and promote fairness in opaque AI systems. This paper argues that the equal opportunity merit principle is an ethical approach for fair AI employment decision-making. Further, explainable AI can mitigate the opacity problem by placing greater emphasis on enhancing the understanding of reasonable users (employing organisations) and affected persons (employees and job candidates) as to the AI output. Both the …

Sensing With Integrity: Responsible Sensor Systems In An Era Of Ai, David Eisenberg

Dissertations

Deep and machine learning now offer immense benefits for consumer choice, decision-making, medicine, mental health and education, smart cities, and intelligent transportation and driver safety. However, as communication and Internet technology further advances, these benefits have the potential to be outweighed by compromises to privacy, personal freedom, consumer trust, and discrimination. While ethical consequences for personal freedom and equity rise from these technological advances, the issue may not be the technology itself but a lack of regulation and policy that allow abuses to occur. A first study examines how emerging sensor-based technologies, limited to only accelerometer and gyroscope data from …

Charting A Path To The Quintuple Aim: Harnessing Ai To Address Social Determinants Of Health, Yash Shah, Zachary Goldberg, Erika Harness, David Nash

College of Population Health Faculty Papers

The Quintuple Aim seeks to improve healthcare by addressing social determinants of health (SDOHs), which are responsible for 70-80% of medical outcomes. SDOH-related concerns have traditionally been addressed through referrals to social workers and community-based organizations (CBOs), but these pathways have had limited success in connecting patients with resources. Given that health inequity is expected to cost the United States nearly USD 300 billion by 2050, new artificial intelligence (AI) technology may aid providers in addressing SDOH. In this commentary, we present our experience with using ChatGPT to obtain SDOH management recommendations for archetypal patients in Philadelphia, PA. ChatGPT identified …

From The Editor In Chief, Antulio J. Echevarria Ii

The US Army War College Quarterly: Parameters

Welcome to the Summer 2024 issue of Parameters. We open this issue with a special “In Memoriam” by General Charles A. Flynn, Commander US Army Pacific, honoring the life and legacies of our director and consummate colleague, Carol V. Evans. We dedicate this issue to her. General Flynn’s memoriam is followed by an In Focus commentary on China’s Belt and Road Initiative. We then feature three forums covering the Russia-Ukraine War, the Middle East, and Professional Development. This issue also contains special essays on the role of professional writing, the US Army War College’s Civil-Military Relations Center, …

Parameters Summer 2024, Usawc Press

The US Army War College Quarterly: Parameters

No abstract provided.

Automatic Measurement Of Dialogue Engagingness In Multilingual Settings, Amila Ferron

Dissertations and Theses

Expansive use of large language models (LLMs) as dialogue systems brings increased importance to the evaluation of the responses they generate. Although evaluation of qualities such as coherence and fluency are readily possible with well-established automatic metrics, engagingness is often measured with human evaluation -- a process that can be costly and slows the pace of development. Existing automatic metrics for engagingness have low to moderate correlation with human annotations, evaluate the response without the conversation history, are complicated to implement, or are designed for a specific dataset. Moreover, they have been tested exclusively on English conversations. Given that dialogue …

Looking Ahead: Incorporating Ai In Mlis Competencies, Souvick Ghosh, Denise Mccoy

School of Information Student Research Journal

Libraries have long been essential for democratizing knowledge and providing reliable information, extending their services to meet diverse community needs, including educational programs and internet access (Pawley, 2022; Freudenberger, 2022). Librarians, as custodians of information and culture, possess core competencies in information organization, digital literacy, and research skills. The San José State University (SJSU) School of Information (iSchool) aims to educate professionals who significantly impact global communities through high-quality education, research, and technology innovation. This article examines the Master of Library and Information Science (MLIS) program at SJSU iSchool, focusing on the evolution of its 14 core competencies to incorporate …

Synthetic Realities And Artificial Intelligence-Generated Contents, Daniel Moreira, Sebastien Marcel, Anderson Rocha

Computer Science: Faculty Publications and Other Works

Welcome to the IEEE Security & Privacy Special Issue on Synthetic Realities and Artificial Intelligence-Generated Contents! In this edition, we delve into the topic of synthetic realities, where generative artificial intelligence (GAI) is revolutionizing the construction of narratives, blurring the boundaries between fact and fiction, for the good and the bad. Indeed, content created or enabled by GAI spans a wide spectrum of usage and intentions, from fostering positive experiences, such as entertainment, training, and education, to more questionable utilization, such as deception, propaganda, and manipulation.

Bibliometric Insights Into Indian Artificial Intelligence Research In Library And Information Science, Khadeeja Mohammed Nizam Ansari, Dr. Sunil Bhatt

Library Philosophy and Practice (e-journal)

The study aims to evaluate the research productivity of Indian researchers on artificial intelligence (AI) in the Library and information science (LIS) field. The study employed a bibliometric approach to analyze the yearly growth, prolific authors, organization, country, pattern of Authorship, keyword analysis, and most cited papers on AI in LIS. The researcher used the Web of Science database to retrieve literature related to Indian AI researchers in LIS between 1995 and 2023. Four hundred forty publications were downloaded and analyzed using MS Excel, Biblioshiny, and Vosviewer software. The study reveals that Indian researchers published 440 publications and 6227 citations …

Missing Wedge Completion Via Unsupervised Learning With Coordinate Networks, Dave Van Veen, Jesús G Galaz-Montoya, Liyue Shen, Philip Baldwin, Akshay S Chaudhari, Dmitry Lyumkis, Michael F Schmid, Wah Chiu, John Pauly

Faculty, Staff and Students Publications

Cryogenic electron tomography (cryoET) is a powerful tool in structural biology, enabling detailed 3D imaging of biological specimens at a resolution of nanometers. Despite its potential, cryoET faces challenges such as the missing wedge problem, which limits reconstruction quality due to incomplete data collection angles. Recently, supervised deep learning methods leveraging convolutional neural networks (CNNs) have considerably addressed this issue; however, their pretraining requirements render them susceptible to inaccuracies and artifacts, particularly when representative training data is scarce. To overcome these limitations, we introduce a proof-of-concept unsupervised learning approach using coordinate networks (CNs) that optimizes network weights directly against input …

Computationally Assessing Suspicion, Wesley M. Oliver, Morgan A. Gray, Jaromir Savelka, Kevin D. Ashley

University of Cincinnati Law Review

Law enforcement officers performing drug interdiction on interstate highways have to decide nearly every day whether there is reasonable suspicion to detain motorists until a trained dog can sniff for the presence of drugs. The officers’ assessments are often wrong, however, and lead to unnecessary detentions of innocent persons and the suppression of drugs found on guilty ones. We propose a computational method of evaluating suspicion in these encounters and offer experimental results from early efforts demonstrating its feasibility. With the assistance of large language and predictive machine learning models, it appears that judges, advocates, and even police officers could …

Companionship, Romance, And Self-Perception With Conversational Chatbots, Jonathan Windsor

Departmental Honors & Graduate Capstone Projects

Serving as a metaphorical gateway transcending the communicative barriers of physical relationships in interpersonal dialogues, artificial imators of human behavior and speech, also known as conversational chatbots; a simulation of human knowledge and existence in a bi-directional conversation, functions as a rhetor of expression. Spanning from contexts of professional to romantic, I serve to dissect and critically analyze the nuances of human-machine relationships based on pre-established literature, inviting ethical considerations and biases in their design and marketing. Corporate influences spark pre-established servitude-esque relationships with conversational agents. Professional applications, both task-oriented and emotionally based alike, paint a mixed picture of …

Going Down An Incline With Chatgpt, Corey R. Sissons

Student Research Symposium

In our Large Language Model (LLM) research, examining ChatGPT 4, we devised a physics problem involving an object descending an inclined plane. Through variations in terminology such as "rolling," "sliding," "solid sphere," "hollow sphere," "wooden ramp," "no-slip ramp," and more, we sought to evaluate LLM responses for different scenarios. Our analysis aimed to discern whether the LLM’s answers exhibited expertise in the field of physics. This experiment sheds light on LLM’s ability to give accurate and precise physics answers as well as variation in responses to nuanced changes in problem formulation. This provides valuable insights into its proficiency and potential …

Behavioral Intention For Ai Usage In Higher Education, Isaac A. Odai, Elliot Wiley

Student Research Symposium

This study sought to further understand the cognitive factors that influence undergraduate students' behavioral intention to use generative AI. Generative AI's presence in academic spaces opens the door for ethical and pedagogical questions. This study surveyed 51 undergraduate communication students to measure their attitudes, subjective norms, self efficacy and their behavioral intention to use GenAI for school work. The results of this study showed behavioral intent had a positive relationship with attitudes and subjective norms. The implications of these findings show that personal beliefs and the perceived beliefs of others are correlated to undergraduate students’ intent to use GenAI for …

Accuracy Of Machine Learning To Predict The Outcomes Of Shoulder Arthroplasty: A Systematic Review, Amir H. Karimi, Joshua Langberg, Ajith Malige, Omar Rahman, Joseph A. Abboud, Michael A. Stone

Department of Orthopaedic Surgery Faculty Papers

BACKGROUND: Artificial intelligence (AI) uses computer systems to simulate cognitive capacities to accomplish goals like problem-solving and decision-making. Machine learning (ML), a branch of AI, makes algorithms find connections between preset variables, thereby producing prediction models. ML can aid shoulder surgeons in determining which patients may be susceptible to worse outcomes and complications following shoulder arthroplasty (SA) and align patient expectations following SA. However, limited literature is available on ML utilization in total shoulder arthroplasty (TSA) and reverse TSA.

METHODS: A systematic literature review in accordance with PRISMA guidelines was performed to identify primary research articles evaluating ML's ability to …