Digital Commons Network^™

Neutrosophic Stance Detection And Fsqca-Based Necessary Condition Analysis For Causal Hypothesis Assessment In Ai-Enhanced Learning, Jesús Rafael Hechavarría-Hernández, Maikel Y. Leyva Vazquez, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

This study addresses the ambiguities in empirical findings on artificial intelligence (AI) in education by proposing a new methodological framework that combines neutrosophic stance detection and Fuzzy Set Qualitative Comparative Analysis (fsQCA). This approach explicitly models truth, indeterminacy, and falsity, allowing for the synthesis of contradictory research. The authors evaluated four causal hypotheses related to AI-based learning, using a survey of 24 university participants to explore the necessary conditions for perceived learning improvements. The results indicate that the digital divide is a perfectly necessary condition for effective AI-enhanced learning. The findings also reveal that AI feedback and AI platform use …

Neutrosophic Twofold Superhyperalgebra And Anti Superhyperalgebra, Takaaki Fujita, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

Neutrosophic Sets are conceptual frameworks designed to address uncertainty. A Neutrosophic TwoFold Algebra is a hybrid algebraic structure defined over a neutrosophic set, combining classical algebraic operations with neutrosophic components. Concepts such as Hyperalgebra and Superhyperalgebra extend classical Algebra using Power Sets and 𝑛-th powersets. Additionally, structures such as NeutroAlgebra and AntiAlgebra have been defined in recent y ears. This paper explores several related concepts, including TwoFold SuperhyperAlgebra and Anti SuperhyperAlgebra.

Ai-Generated Messaging For Life Events Using Structured Prompts: A Comparative Study Of Gpt With Human Experts And Machine Learning, Christopher Lynch, Erik Jensen, Ross Gore, Virginia Zamponi, Kevin O'Brien, Brandon Feldhaus, Katherine Smith, Joseph Martínez, Madison H. Munro, Timur E. Ozkose, Tugce B. Gundogdu, Ann Marie Reinhold, Hamdi Kavak, Barry Ezell

VMASC Publications

Large Language Models (LLMs) play an increasingly integrated and pivotal role in generating diverse types of texts, such as social media messages, emails, narratives, and technical reports, among other textual communication forms. As AI-generated messaging filters into human communication, a systematic exploration of their effectiveness for mimicking human-like communication of life events is needed. In this study, we employ a zero-shot structured narrative prompt to generate 24,000 life event messages for birth, death, hiring, and firing events using OpenAI's GPT-4. From this dataset, we manually classify 2880 messages and evaluate their validity in conveying these life events through the form …

A Hybrid Soft Voting And Stacking-Based Meta-Learning Approach For Sentiment Analysis Of Bangkalan Batik, Moh. Imron Wahyudi, Lailil Muflikhah, Rizal Setya Perdana

Knowledge Engineering and Data Science

Sentiment analysis is an important field in Natural Language Processing (NLP) that focuses on processing consumer opinions to gain useful insights. The information generated from sentiment analysis can be used as a basis for business decision-making, service quality evaluation, and the formulation of more effective marketing strategies. In the local context, Bangkalan Batik, as one of Madura's distinctive cultural products, has high economic value and cultural identity. However, consumer reviews available online, for example through Google Maps, are still rarely utilized optimally by MSMEs as a source of strategic information. Therefore, this study was conducted to develop a sentiment classification …

A Comparative Study Of Machine Learning Models For Javanese Wuku Classification: Exploring Svm, Naïve Bayes, And Cnn For Cultural Texts, Danang Arbian Sulistyo, Aji Prasetya Wibawa, Didik Dwi Prasetya, Fadhli Almu'iini Ahda, Agung Bella Putra Utama

Knowledge Engineering and Data Science

This study rigorously evaluates machine learning models for classifying culturally significant Javanese Wuku texts from the “Keagamaan atau Spiritual” category, a domain challenged by unique linguistic nuances and limited digitized resources. We compared Support Vector Machine (SVM), Naïve Bayes, and Convolutional Neural Network (CNN) on texts from five pivotal Wuku types (Sinta, Galungan, Kuningan, Sungsang, Warigalit) sourced from sastra.org, aiming to identify the most effective computational approach. The dataset comprises N = 1419 documents (T = 751.290 tokens), with per-class document counts reported for all five Wuku types. Our evaluation uses accuracy, precision, recall, F1-score, and …

Stable Numerical Solution Of An Elliptic Pde Inverse Problem Subject To Incomplete Boundary Conditions, Qasim Abd Ali Tayyeh

Knowledge Engineering and Data Science

This study addresses the challenging problem of solving inverse elliptic Partial Differential Equations (PDE) with incomplete boundary data, data available only on a part of the domain boundary. The aim is to develop a robust, effective numerical framework that consistently recovers parameters and/or sources from incomplete, ill-posed data. In the case of a variational problem discretized by the Finite Element Method (FEM) and solved by an adjoint-based optimization strategy, the framework uses Tikhonov regularization. Morozov's Discrepancy Principle is used to determine regularization parameters that achieve the best balance between accuracy and stability. Even with 5% noise in the measurement data, …

Video Comprehension Score (Vcs): A Metric For Long-Form Video Description Evaluation, Harsh Dubey

Electronic Theses and Dissertations

Existing video description evaluation metrics fail to capture the long-range chronology and semantic alignment essential for long-form descriptions. An effective evaluation metric for long-form descriptions must (i) assess global thematic alignment, (ii) measure local semantic alignment, and (iii) evaluate chronological alignment while detecting corrupted content. We introduce Video Comprehension Score (VCS), a reference-based metric, which directly addresses these evaluation requirements through three components: Global Alignment Score for thematic alignment, Local Alignment Score for local semantic alignment, and Narrative Alignment Score for chronological alignment with adjustable tolerance. We evaluate VCS on two large-scale synthetic datasets designed to test corruption detection and …

Unwritten Rules And Oral Transmission In Architectural Proportion Systems: Case Study Of Bubungan Tinggi House, Huzairin. Muhammad Deddy, Dila Nadya Andini, Rudi Hartono, Anna Oktaviana, Dahliani Dahliani

ASEAN Journal on Science and Technology for Development

The proportional systems in vernacular architecture often exist without formal written codification, raising questions about their transmission across generations. Bubungan Tinggi House which is a replica of the Banjar Kingdom palace was built by Banjarese merchants and aristocrats from the 16th to the 19th centuries, showing a uniform aesthetic quality, which indicates a regularity in the proportions of its form. However, no historical written manuals detailing these proportions are known to exist. To prove the existence of regularity in the Bubungan Tinggi House proportion system, research was conducted by taking 7 Bubungan Tinggi House samples spread across South Kalimantan province, …

Enhancing Fake News And Rumor Detection Using Metadata, Context, And User Interaction With A Hybrid Gcn-Transformer., Lucas Romulo Zuin Gigli

ICT

The increasing influence of social media has significantly impacted how news spreads among users, making the detection of misinformation, including fake news and rumours, a critical task. Previous research has explored content-based and metadata-driven approaches, while recent advancements have leveraged Graph Neural Networks (GNNs) such as GCNs, GATs, and SAGE to analyse user interactions and propagation patterns. This thesis investigates the effectiveness of a Hybrid Graph-Transformer Convolutional Neural Network (GCN-Transformer) for fake news detection, combining graph-based learning with Transformer attention mechanisms. Through in-depth data analysis and advanced detection techniques, this model aims to enhance predictive performance and mitigate the spread …

Discovering Latent Themes: Mixed-Methods Comparative Analysis Of Topic Extraction And Clustering., Laura Byrne

ICT

This study investigates whether clustering and topic modeling can uncover themes within the 167 verses of the King James Version of the Book of Esther. A standardized preprocessing pipeline was applied, and TF-IDF and sentence-embedding feature spaces were used to evaluate topic extraction (NMF, GSDMM, BERTopic, Top2Vec) and clustering (HDBSCAN, DBSCAN, Gaussian Mixtures, Agglomerative) using coherence, cluster validity, lexical distinctiveness, and cross-model similarity metrics. NMF provided the most interpretable topics, GSDMM yielded compact low-overlap topics, BERTopic offered entity-centered groupings, and Top2Vec highlighted broad thematic regions. Cross-model analysis revealed overlapping motifs, notably decree and banquet themes, but clusters and topics did …

Agentic Ai Systems In Professional Domains: A Probabilistic Framework For Role Suitability And Legal Accountability, Matthew Veach

Master's Theses and Doctoral Dissertations

This thesis presents a cross-sector analysis of agentic AI systems deployed in STEM, education, healthcare, and enterprise domains, with a focus on role suitability, orchestration maturity, and legal accountability. It introduces the Bounded Agentic Suitability Envelope, a dual-bound scoring framework that evaluates deployment viability using weighted assessments of agent capability and decomposed role complexity. Through case studies from Fujitsu, Cleveland Clinic, Carnegie Learning, and Duolingo, the thesis demonstrates measurable gains in efficiency, personalization, and compliance. It argues for an augmentation-first strategy, preserving human roles in high-context domains while enabling targeted replacement in low-complexity workflows. To mitigate risk, the thesis formalizes …

Multimodal Deception Detection Via Audio-Text Fusion With Deep Learning And Asr, Xiangyi Li

Master's Projects

Emotion detection plays a crucial role in human-computer interaction, enabling machines to recognize and respond appropriately to human emotional states. This project explores a two-stage approach to emotion detection using multimodal data, first predicting dimensional values (Arousal, Valence, Dominance) from textual and audio inputs, then mapping these representations to discrete emotion categories. We compare this approach with direct categorical classification using transformer-based language models like BERT, RoBERTa, and DeBERTa for text processing, alongside various audio feature extraction methods, including MFCCs and spectrograms. Using the IEMOCAP dataset, we evaluate both approaches across text-only, audio-only, and multimodal configurations. Our findings reveal that …

World As Other, World As Self: Beyond Dualism In Environmental Narratives, Tamara Wallace Ramirez

CGU Theses & Dissertations

Stories of place matter to the way we live with the Earth. A dualistic perspective that pits "culture" against "nature" enables environmental degradation on small and large scales. Conversely, understanding humans and nonhumans as entwined allows us to think and act differently with our planetary community. This view entails integrating the "self" and the "other." Instead of being antithetical to the other, the self arises through engagement with others, human and nonhuman. Understanding our mutuality with others while respecting their alterity and agency fosters caring and dedication. Our sense of connection with the more-than-human world inspires our compassion. However, such …

Method Entity And Relation Extraction Based On Automatically Generated Syntactic Templates: A Case Study Of Csdn Artificial Intelligence Blog, Kuiliang Li, Bolin Huang

Journal of Scientific Information Research

[Purpose/significance]There are many relationships between method entities and application scenarios, problems,organizations and other entities. Extracting these entity relationships helps to capture the development trend of technology and promote the improvement of innovation ability.[Method/process]This paper discusses a method for extracting method entities and relations based on automatically generated syntactic templates. By designing a new adaptive template, the method improves flexibility and adaptability, reducing dependence on large-scale labeled data. Using a small number of seed triples, the method iteratively generates syntactic templates and extracts method entities and relations for the CSDN artificial intelligence topic blog. It also improves the extraction quality using …

Pave Outer Space And Put Up A Parking Lot: Lagrange Points Should Be The Common Heritage Of Mankind, David A. Koplow

Michigan Journal of International Law

Outer space offers a vast array of opportunities, with different locations or regions available for exploitation by diverse users for a growing variety of satellite functions. But not all sectors of space are equally valuable for all applications, and the most desirable venues can become crowded, affording a premium for those who gain access first and impeding the development of a fair and efficient all-inclusive international legal regime.

This article focuses on Lagrange points, a finite series of special locations in space where the gravitational forces from a pair of large celestial bodies interact in unusual ways. These points afford …

Generative Ai And Finding The Law, Paul D. Callister

Faculty Works

Legal information science requires, among other things, principles and theories. The article states six principles or considerations that any discussion of generative AI large language models and their role in finding the law must include. The article concludes that law librarianship will increasingly become legal information science and require new paradigms. In addition to the six principles, the article applies ecological holistic media theory to understand the relationship of the legal community’s cognitive authority, institutions, techné (technology, medium and method), geopolitical factors, and the past and future to understand the changes in this information milieu. The article also explains generative …

Developing Mathematical Maturity By Solving A Given Quadratic Equation, Armando A. Amador, Nieves Angulo, Juan B. Lacay

Publications and Research

This article explores the instructional challenges faced by mathematics educators when addressing students’ limited background knowledge and underdeveloped mathematical maturity, two critical barriers to success in college-level algebra. Using the quadratic equation 2x2+x−3=0, which is reducible over the field of rational numbers ℚ ⊂ ℝ, we analyze how diverse solution methods—factoring, completing the square, and the quadratic formula—can support learning. An additional strategy, “Slide and Divide,” was introduced to promote procedural fluency and conceptual flexibility. To gain insight into students’ evolving mathematical maturity, an online survey was conducted to students at various levels of mathematics. The survey captured their perspectives …

Improving Chinese Hate Speech Detection With Bert-Fasttext Fusion And Bert-Bilstm Fusion, Methini Ma

Chulalongkorn University Theses and Dissertations (Chula ETD)

Hate speech detection is an essential technique in the online environment, especially on social media platforms. This technique helps to create a safer space and reduce the risk of real-world harm. In Chinese, this task is particularly challenging because of unique linguistic structures and the frequent use of indirect expressions, sarcasm, homophones, character variants, and abbreviations. This study investigates how to improve Chinese hate speech detection by combining BERT with FastText and BERT with BiLSTM. There are six model variants that are configured: frozen BERT and fine-tuned BERT, each further extended with either FastText sentence embeddings or a clause-level BiLSTM. …

A Retrieval Augmented Approach To Improving Accuracy Of Biomedical Term Normalization By Large Language Models, Thanh Son Do

Graduate Theses/Dissertations

Ontology normalization is crucial in biomedical text processing, as it enables the mapping of medical expressions to standardized ontology terms and their corresponding identifiers. This thesis explores the feasibility of using large language models (LLMs) for ontology normalization, with a specific focus on the Human Phenotype Ontology and Gene Ontology. Prior research studies indicated that LLMs employing zero-shot learning tend to exhibit low accuracy and are prone to frequent hallucinations. We propose a retrieval augmented generation (RAG) approach to address these limitations and enhance normalization accuracy. We generated synthetic test sets of ontology-derived synonyms to evaluate normalization performance and developed …

Uncertain Labeling Graphs And Uncertain Graph Classes (With Survey For Various Uncertain Sets), Takaaki Fujita, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

Graph theory, a branch of mathematics, studies the relationships between entities using vertices and edges. Uncertain Graph Theory has emerged within this field to model the uncertainties present in real-world networks. Graph labeling involves assigning labels, typically integers, to the vertices or edges of a graph according to specific rules or constraints. This paper introduces the concept of the Turiyam Neutrosophic Labeling Graph, which extends the traditional graph framework by incorporating four membership values—truth, indeterminacy, falsity, and a liberal state—at each vertex and edge. This approach enables a more nuanced representation of complex relationships. Additionally, we discuss the Single-Valued Pentapartitioned …

Some Types Of Hyperneutrosophic Set (4): Cubic, Trapozoidal, Q-Rung Orthopair, Overset, Underset, And Offset, Florentin Smarandache, Takaaki Fujita

Branch Mathematics and Statistics Faculty and Staff Publications

This paper builds upon the foundational work presented in [38–40]. The Neutrosophic Set provides a comprehensive mathematical framework for managing uncertainty, defined by three membership functions: truth, indeterminacy, and falsity. Recent advancements have introduced extensions such as the Hyperneutrosophic Set and the SuperHyperneutrosophic Set, which are specifically designed to address increasingly complex and multidimensional problems. The formal definitions of these sets are available in [30]. In this paper, we extend the Neutrosophic Cubic Set, Trapezoidal Neutrosophic Set, q-Rung Orthopair Neutrosophic Set, Neutrosophic Overset, Neutrosophic Underset, and Neutrosophic Offset using the frameworks of the Hyperneutrosophic Set and the SuperHyperneutrosophic Set. Furthermore, …

Some Types Of Hyperneutrosophic Set (3): Dynamic, Quadripartitioned, Pentapartitioned, Heptapartitioned, M-Polar, Takaaki Fujita, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

This paper builds upon the foundation established in [50, 51]. The Neutrosophic Set provides a robust mathematical framework for handling uncertainty, defined by three membership functions: truth, indeterminacy, and falsity. Recent developments have introduced extensions such as the Hyperneutrosophic Set and SuperHyperneutrosophic Set to tackle increasingly complex and multidimensional problems. In this study, we explore further extensions, including the Dynamic Neutrosophic Set, Quadripartitioned Neutrosophic Set, Pentapartitioned Neutrosophic Set, Heptapartitioned Neutrosophic Set, and m-Polar Neutrosophic Set, to address advanced challenges and applications.

Plithogenic Duplets And Plithogenic Triplets, Takaaki Fujita, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

A Neutrosophic Set is a mathematical framework that represents degrees of truth, indeterminacy, and falsehood to address uncertainty in membership values [41, 42]. In contrast, a Plithogenic Set extends this concept by incorporating attributes, their possible values, and the corresponding degrees of appurtenance and contradiction [50]. Among the related concepts of Neutrosophic Sets, Neutrosophic Duplets and Neutrosophic Triplets are well-known. This paper defines Plithogenic Duplets and Plithogenic Triplets as extensions of these concepts using the Plithogenic Set framework and briefly examines their relationship with existing concepts.

Some Types Of Hyperneutrosophic Set (2): Complex, Single-Valued Triangular, Fermatean, And Linguistic Sets, Takaaki Fujita, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

This paper is a continuation of the work presented in [35]. The Neutrosophic Set provides a mathematical framework for managing uncertainty, characterized by three membership functions: truth, indeterminacy, and falsity. Recent advancements have introduced extensions such as the Hyperneutrosophic Set and SuperHyperneutrosophic Set to address more complex and multidimensional challenges. In this study, we extend the Complex Neutrosophic Set, Single-Valued Triangular Neutrosophic Set, Fermatean Neutrosophic Set, and Linguistic Neutrosophic Set within the frameworks of Hyperneutrosophic Sets and SuperHyperneutrosophic Sets. Furthermore, we investigate their mathematical structures and analyze their connections with other set-theoretic concepts.

Beyond Dialectics, Paradoxes, And Binary Logic, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

Philosophy, long defined by its pursuit of truth, has historically been a battleground for dichotomies: truth vs. falsehood, materialism vs. idealism, reason vs. emotion. These oppositions often provide a framework for understanding philosophical discourse, but they fail to capture the full nuances of reality. To challenge these binary oppositions, I introduced the neutrosophic perspective in philosophy, rooted in Mathematics, and Many-Valued Logics.1 By emphasizing the interrelation of affirmation, negation, and neutrality, neutrosophy allows for the reconciliation of seemingly irreconcilable viewpoints, providing a new lens through which to reinterpret age-old philosophical questions.

Streamlining Digital Elevation Model Construction From Historical Aerial Photographs: The Impact Of Reference Elevation Data On Spatial Accuracy, Xin Hong, Christopher H. Roosevelt

All Works

This study proposes a streamlined workflow for producing historical digital elevation models (hDEMs) from scanned 1950s aerial photographs using structure-from-motion and multi-view-stereo (SfM-MVS) techniques along with co-registration methods. We also conducted a sensitivity analysis to assess the impact of DEM references with varying spatial resolutions on the SfM-MVS process and co-registration accuracy. The DEM references included a 30 m SRTM DEM (low resolution), a 12 m TanDEM-X DEM (medium resolution), and a 5 m DEM provided by the General Directorate of Mapping of the Ministry of National Defense, Republic of T & uuml;rkiye (high resolution). Results indicate that higher resolution …

Customer Service Support. Utilizing Machine Learning To Classify, Prioritize And Summarize Issues., Hoai Nhan Nguyen

ICT

This capstone project investigates the application of machine learning and natural language processing (NLP) to enhance customer support operations through automated ticket classification, prioritization, and summarization. Using the multilingual Customer Support Emails dataset from Kaggle, the project follows the CRISP-DM methodology, performing extensive data cleaning, preprocessing, feature engineering, and class balancing. Five machine learning models—Decision Tree, KNN, LinearSVC, Naive Bayes, and Random Forest—were evaluated using hyperparameter tuning, cross-validation, confusion matrix analysis, and learning curves. LinearSVC demonstrated the strongest performance for both queue and priority classification, achieving accuracies of 89.8% and 81.2% respectively, with consistent generalization across folds. For summarization, extractive …

Impact Of Graph Structures For Rag Outcomes In Llms, Chris Davis Jaldi

Browse all Theses and Dissertations

Explainability, interpretability and adaptability (EIA) remain three central motivations for next-generation Artificial Intelligence (AI), especially as Large Language Models (LLMs) continue to engage with ever-increasing knowledge bodies. As the landscape pushes toward controllable agentic Retrieval-Augmented Generation (RAG) systems where AI agents engage in iterative, guided reasoning, a critical question arises as to the extent to which the knowledge design itself shapes these models' reasoning behavior. This work conducts a systematic evaluation of how different conceptualizations and representation of the identical knowledge affect an LLM's path-based reasoning capabilities. Through the introduction of controlled variations along graph structural complexity, linguistic and semantic …

Pca Text Sentiment Analysis Tool, Luke Gegick

Williams Honors College, Honors Research Projects

This project applies principal component analysis (PCA) to sentiment analysis of text to identify complex emotional responses from plain text. Existing sentiment analysis tools often rely on large language models or struggle to achieve high accuracy when processing large collections of short inputs, such as social media comments. By contrast, this project uses PCA as a lightweight, mathematically grounded alternative that can scale efficiently while still capturing meaningful emotional structure in text data.

PCA has shown strong effectiveness in text analysis, particularly when supported by a sufficiently large dataset and a robust preprocessing pipeline. To create consistent, information-rich input vectors, …

Personalized Physics Learning Through Ai: Insights From Problem Generation, Chatbot Dialogues, And Intelligent Tutoring Systems, Atharva Dange

Physics Dissertations - Archive

Artificial intelligence (AI) is poised to transform science education, yet questions remain on how best to integrate these technologies into teaching and learning. This dissertation investigates the use of AI-driven tools in university physics courses through three complementary studies. In the first study, a generative language model (ChatGPT) was used to create novel physics homework problems aligned with course objectives. Analysis showed that, after expert vetting, AI-generated questions can foster higher-order problem-solving and reduce student reliance on solution memorization, though careful instructor oversight is required to ensure accuracy. The second study embedded an AI chatbot as a learning aid in …