Physical Sciences and Mathematics Commons^™

A Fuzzy–Neutrosophic Suitability Index For Selecting An Appropriate Reasoning Model Under Vagueness, Incompleteness, And Conflict, Nada A. Nabeeh, Ahmed Samy

Neutrosophic Systems with Applications

Fuzzy reasoning and neutrosophic reasoning are both used to handle uncertainty, but they are not intended for the same uncertainty structure. Fuzzy reasoning is suitable when uncertainty appears mainly as gradual vagueness, where a value may belong to a concept such as ``high risk'' or ``good performance'' to a certain degree. In this case, a membership value is often sufficient. Neutrosophic reasoning is more suitable when the problem also contains incomplete information, undecided evidence, or conflict between sources. In such cases, one membership degree may be too limited because it cannot represent support, rejection, and indeterminacy separately. This study introduces …

Foundations Of Neutrosophic N-Semirings Theory And Structural Properties, Raja Muhammad Hashim, Muhammad Gulistan, Muhammad Shahzad

Neutrosophic Systems with Applications

In this paper the concept of neutrosophic n-semirings (S∪ I, ∗ ₁, ∗ _2, ∗ ₃,..., ∗ _n, ∗ _n+1) has been introduced. The substructure of n-semirings (S∪ I, ∗ ₁, ∗ _2, ∗ ₃,..., ∗ _n, ∗ _n+1) has been defined and some useful results have been proved. Moreover, in order to familiarize the readers with these concepts some worthy examples have been coined. The left, right and two sided ideals of neutrosophic n-semirings have been paid a special heed. Finally we have turned our discussion towards the compatible and congruence …

Evaluating Generative Ai-Based User Interfaces Using An Integrated Neutrosophic Multi-Criteria Decision-Making Framework, Nada Mohamed, Alshaimaa A. Tantawy

Neutrosophic Systems with Applications

User Interface (UI) design can be seen as an essential aspect of human-computer interaction (HCI) and makes communication easier between people and technology. In today's digital economy, interface quality has become one of the most important business concerns, since it has a direct impact on customer satisfaction and retention while affecting revenue. Although creating user-centered and accessible interfaces is crucial, doing so is a difficult and time-consuming process, which leads to burnout for many usability professionals. Although conventional artificial intelligence (AI) was utilized for design assessment and automation, the arrival of generative AI technology has created new possibilities for automated …

Digraphicsoft Sets And Bidigraphicsoft Sets: Directed And Bidirected Extensions Of Graphicsoft Modeling, Takaaki Fujita, Ajoy Kanti Das, Suman Das, Sankar Prasad Mondal, Volkan Duran, Mithun Datta

Neutrosophic Systems with Applications

Uncertainty has been modeled through a wide variety of mathematical frameworks, including fuzzy sets, neutrosophic sets, rough sets, and plithogenic sets. Among these approaches, soft sets offer a parameterized representation of uncertain information and have inspired numerous extensions, such as multisoft sets, double-framed soft sets, hypersoft sets, SuperHyperSoft sets, TreeSoft sets, ForestSoft sets, IndetermSoft sets, and IndetermHyperSoft sets.
This paper focuses on GraphicSoft Sets, which extend the classical soft-set framework by assigning a subset of the universe to each subgraph of an attribute graph. In this way, relationships among attributes are incorporated directly into the parameterized model. Building on …

Neutrosophic Time-Truncated Acceptance Sampling Plans Based On The Exponentiated Weibull Distribution For Reliability Applications, Divya P.R., Preethi John

Neutrosophic Systems with Applications

Classical acceptance sampling plans require precisely specified parameter values, an assumption routinely violated by measurement uncertainty and gauge imprecision in practice. This article develops Neutrosophic Time-Truncated Acceptance Sampling Plans (N-TTASP) for the Exponentiated Weibull (EW) distribution by representing the scale parameter as a neutrosophic interval. The neutrosophic sample size n_N ∈ [n_L, n_U] and acceptance number c_N ∈ [c_L, c_U] are obtained by minimizing n_U subject to dual producer and consumer risk constraints on the neutrosophic Operating Characteristic interval. A new indeterminacy ratio η is introduced as a …

Exploiting Uncertainty Of Computational Methodology In Optimizing User Interface In Human-Computer Interaction, Nada Mohamed, Alshaimaa A. Tantawy

Neutrosophic Systems with Applications

Human-computer interaction (HCI) evaluation and optimization of user interfaces (UIs) constitute a complex multi-criteria decision-making challenge, marked by conflicting evaluation dimensions, subjective expert judgments, and inherent uncertainty in user experience assessment. Traditional evaluation approaches, such as heuristic expert reviews and user satisfaction surveys, rely on sharp, binary classifications that fail to capture the gradual and overlapping nature of human cognitive and affective states. This limitation necessitates a more robust uncertainty-aware methodology that can model the true complexity of HCI evaluation. This paper proposes a hybrid mathematical model that integrates various Multi-Criteria Decision Making (MCDM) techniques of Entropy, and Simple Additive …

An Interactive Multi-Objective Programming Approach For Optimizing Fully Trapezoidal Spherical Fuzzy Linear Programming Problem With Application, Sultan S. Alodhaibi, Hissah Ibrahim Almuzini, Hamiden Abd El-Wahed Khalifa

Neutrosophic Systems with Applications

In this paper, a linear programming framework with completely uncertain parameters is investigated by employing trapezoidal spherical fuzzy numbers (TrSFNs). The proposed formulation incorporates a spherical fuzzy (SF) decision environment in which the optimization process simultaneously maximizes the degree of positive membership while minimizing the corresponding neutral and negative membership degrees. By utilizing the concept of the α -cut associated with TrSFNs, the original fully fuzzy linear programming problem is transformed into an interval-valued linear programming model with confidence levels. To rank and compare the resulting interval objective values, an interval ordering approach based on the decision maker's preferences—considering the …

Interval-Valued Neutrosophic Dombi Bonferroni Mean Aggregation Operators In Medical Diagnosis And Sustainable Energy, Maryam Faisal, Muhammad Nadeem, Muhammad Kamran

Neutrosophic Systems with Applications

Medical diagnosis is one of the most difficult fields in which decisions must be made due to the fact that medical information often has characteristics of uncertainty, incompleteness, imprecision and even contradiction. Traditional aggregation and decision-making methods are often not well suited to such complexities, and may result in less reliable diagnostic outcomes. In order to overcome these drawbacks, the authors propose a new approach using a novel representation of Interval-Valued Neutrosophic Sets (IVNSs), the Dombi operational laws, and Bonferroni Mean (BM) aggregation operators. The proposed framework is specifically aimed at coping with uncertainty, indeterminacy and falsity all at once …

Generative Endurance Logic: An Axiomatic Framework For Reasoning About Outcome-Generating Objects Under Constraints, Hafiz Burhan Ul Haq, Muhammad Nauman Irshad

Neutrosophic Systems with Applications

This paper introduces Generative Endurance Logic (GEL), a formal framework for studying objects through the outcomes they can produce. In many cases, an object cannot be judged only by a fixed truth value, score, or utility value. A rule, model, action, or strategy may behave well in one situation but fail when the context changes or when small perturbations occur. GEL addresses this issue by treating each object as a generator of outcomes. Each object a is linked to a generation map Ga:X×Ω→Y, where X is the context space, Ω is the …

Recursive Neutrosophic Superhypergraphs With Illustrative Applications, Takaaki Fujita, Ajoy Kanti Das, Suman Das, Sankar Prasad Mondal, Volkan Duran

Neutrosophic Systems with Applications

Finite hypergraphs generalize ordinary graphs by permitting each hyperedge to join any nonempty set of vertices, and thus provide a natural model for truly multiway interactions. To represent hierarchical and multi-layer structure, SuperHyperGraphs iterate the powerset operation so that set-valued entities created at one level can be treated as vertices at higher levels. Independently, recursive hypergraphs allow edge recursion: an edge may contain not only vertices but also lower-level edges, yielding nested (and possibly self-referential) incidence controlled by a specified recursion depth. In this work we introduce and axiomatize Recursive Neutrosophic SuperHyperGraphs, a unified framework that combines vertex …

Evaluation And Distillation Of Source Code Generation Tasks By Large Language Models, Danny Brahman

Electronic Theses and Dissertations

Large Language Models (LLMs) are predominantly assessed based on their common sense reasoning, language comprehension, and logical reasoning abilities. While models trained in specialized domains like mathematics or coding have demonstrated remarkable advancements in logical reasoning, there remains a significant gap in evaluating their code generation capabilities. Existing benchmark datasets fall short in pinpointing specific strengths and weaknesses, impeding targeted enhancements in models’ reasoning abilities to synthesize code.

To bridge this gap, this thesis introduces two novel contributions: CodeEval and CodeQual. CodeEval is an innovative, pedagogical benchmarking method that mirrors the evaluation processes encountered in academic programming courses. It comprises …

Ms110 Syllabus: Introduction To Computers, Information Systems, And Artificial Intelligence, Wei Zhang

Management Science and Information Systems Faculty Publication Series

This is a syllabus for Professor Wei Zhang's MS110: Introduction to Computers, Information Systems and Artificial Intelligence Course within UMass Boston's College of Management. This is an Open Educational Resource and can be remixed, copied, redistributed, altered and reused as long as permission is given to the original creator.

On Some Properties Of Generalised Regular Anti-Topological Space, Bhimraj Basumatary, Gugu Narzary, Jeevan Krishna Khaklary

Neutrosophic Systems with Applications

This article studies Regular Anti-open sets and Generalised Regular Anti-open sets within a topological framework, addressing the limited development of anti-open structures in generalised topology. We introduce Regular Anti-open sets and establish their fundamental properties. The study is extended by defining Generalised Regular Anti-open sets, providing a broader and more flexible class of sets. Furthermore, the notions of GR-interior and GR-closure are introduced and analyzed, and their essential properties are obtained. The results contribute to a clearer understanding of generalized anti-open structures and provide a basis for further research in topology.

A Goal Programming Approach For Finding The Best Compromise Solution Of Multiobjective Linear Programming Under Neutrosophic Environment, Sultan S. Alodhaibi, Hamiden Abd El- Wahed Khalifa

Neutrosophic Systems with Applications

Decision problems often involve multiple objectives that conflict, making simultaneous optimization impossible. These problems require trade-offs to achieve a solution that balances the competing goals. In this paper, the detailed discussion that is related to linear programming (SVTrNFMOLP) with single valued trapezoidal neutrosophic numbers is made. Furthermore, this study deals with all the related parameters and discussion. As rank function and due to its definition, the SVTrNFMOLP is transformed in the crisp MOLP. It is noticed that goal programming is best to get the best compromise solution. The advantages of the proposed approach are: The use of Tr allows the …

Classes Of Analytic Functions Defined By Salagean Derivative Operator Associated With Neutrosophic Generalized Poisson Distribution, Soliu O. Opeyemi Okunola, Olushola Adeyemo, Sayo A. Abidemi Gbangbala, Folorunso I. Isola Akinwale

Neutrosophic Systems with Applications

This study introduces and analyses new subclasses of analytic functions by applying the Salagean derivative operator to the Neutrosophic Generalized Poisson Distribution (NGPD) series. We develop a model where the mean parameter is treated as an interval or set to account for indeterminacy in complex systems. By employing Stirling numbers of the second kind and decreasing factorials, we derive necessary and sufficient coefficient inequalities and inclusion relations for these new subclasses. Numerical results and graphical illustrations demonstrate the sensitivity of these functions to orientation and the neutrosophic parameter, providing a framework for applications in fields like medical imaging and network …

Enhanced Population Mean Estimation Using An Exponential Estimator With Known Medians Of Dual Auxiliary Variables Within A Neutrosophic Approach: Applications In Agricultural Yield Prediction, Anchal Yadav, Mukesh Kumar

Neutrosophic Systems with Applications

In classical statistical theory, estimation of population parameters is generally carried out under the assumption that all observed data are precise, complete, and free from ambiguity. However, in many practical and real-world situations, data often deviate from these ideal conditions and instead appear in vague, uncertain, or interval-valued forms. Such imperfections reduce the effectiveness of traditional estimation techniques and motivate the development of more flexible and robust methodologies. To address these challenges, several improved estimators, particularly neutrosophic ratio-type estimators and their advanced extensions, have been proposed in recent literature. In this study, a new estimator known as the two auxiliary …

Optimization Of A Multi-Item Supply Chain Model With Shortages Under Parametric Type-2 Interval Environments, Sourav Kumar Kumar Giri, Totan Garai, Sahidul Islam, Haridas Mondal, Shariful Alam

Neutrosophic Systems with Applications

Modern supply chain systems frequently operate in environments where demand, costs and inventory-related parameters are uncertain and difficult to estimate accurately. These uncertainties become more critical in multi-objective decision-making situations, where decision makers must simultaneously balance several conflicting goals. Conventional optimization techniques often fail to represent the ambiguity and vagueness present in practical decision environments. To overcome these limitations, this study develops a multi-item supply chain model for a single supplier and a single buyer by incorporating Type-2 interval representations into the modelling framework. The proposed approach introduces a structured set of arithmetic operations for Type-2 intervals to manage uncertain …

An Analytical Framework For Quantifying Urban And Community Resilience To Natural Hazards From Cell-Phone Gps-Location And Traffic-Flow Data, Georgios Chatzikyriakidis

Civil and Environmental Engineering Theses and Dissertations

Urban areas are increasingly exposed to natural hazards while accommodating a growing share of the global population, yet a consistent science-based framework for quantifying urban and community resilience remains lacking. This dissertation develops a physics-based analytical framework grounded in statistical mechanics and the quantitative theory of Brownian motion. A city is conceptualized as a complex medium in which citizens move analogously to Brownian particles within a viscoelastic environment, influenced by socioeconomic interactions and infrastructure functionality.

A central premise is that urban resilience, interpreted as engineering resilience (an outcome), can be quantified through a single metric: the mean-square displacement MSD=⟨r²(t)⟩, of …

A View Under The Hood: Duquesne Kline's Law And Computing Program, Wesley M. Oliver, Katherine L.W. Norton, Martin Mckown, David Horrigan

West Virginia Law Review

No abstract provided.

Fisheasy, Jake Ryan Rankin

Posters - 2026

The purpose of FishEasy is to create an all-in-one fishing application that supports both beginner and experienced anglers through education, recommendations, and data tracking. Many new anglers struggle with understanding gear, knots, bait, and locations, while existing tools often limit access through paid features. FishEasy addresses these gaps by providing:

• Educational Tutorials for knots, rigs, and beginner guidance • Smart Recommendations based on location, species, and conditions • Catch Logging & Analytics to track performance • Regulation Awareness for legal fishing practices


Overall, FishEasy aims to make fishing more accessible, efficient, and easy to learn for all users.

Addressing The Problems Of Data Variations, Quality, And Scarcity In Training Deep Neural Networks, Jian Sun

Electronic Theses and Dissertations

The performance of deep neural networks (DNNs) is strongly influenced by the characteristics and quality of the underlying datasets. This Ph.D. dissertation addresses three pervasive data challenges-imbalance, quality degradation, and scarcity-that commonly hinder the effectiveness of DNNs in computer vision (CV) and natural language processing (NLP) applications.

Class imbalance remains one of the most frequent causes of degraded model generalization. While Focal Loss effectively mitigates inter-class imbalance by assigning higher weights to minority classes, it struggles with intra-class imbalance, particularly in video datasets where longer clips dominate feature representation. To address this, I implement and utilize …

Task-Optimized Brain Parcellations Reveal Latent Functional Organization For Enhanced Connectivity-Based Neuroimaging Classification, Andrew Hannum, Mario A. Lopez

Computer Science: Faculty Scholarship

Brain parcellation schemes are fundamental to neuroimaging, yet general-purpose atlases may obscure the specific functional architecture relevant to a given cognitive task or clinical condition. This reflects a growing consensus that the “optimal” brain map is context-dependent. Here, we introduce a novel framework that validates this principle by generating task-optimized human brain parcellation maps directly from supervised learning objectives. Our method defines functional parcels by grouping brain regions based on the similarity of their contributions to a classifier's decision boundary for a specific goal (e.g., cognitive state decoding or clinical group separation). This approach prioritizes a region's discriminative role over …

Llm-Driven Weekly Newsletter To Assess Open Source Software Project Github Health, Christian Novalski, Christopher Chavez, Ghalian Fayyadh, Kostadin Damevski

Undergraduate Research Posters

Open Source Software (OSS) projects increasingly depend on a diverse set of contributors, including episodic participants who contribute intermittently. Episodic contributors represent a large portion of OSS communities, yet projects often struggle to retain them, leading to decreased project health and continuity. While dashboards and real-time communication tools support continuously active contributors, they often fail to serve the unique needs of episodic participants, who may struggle to remain informed and re-engage with project activity after periods of absence. In this study, we examine the effect of a weekly, email-based newsletter intervention designed to improve awareness and engagement among episodic OSS …

Utilizing Computer Modeling To Optimize Electric Fields Within Xenon Time Projection Chambers, Miles Meloni

Honors Theses

XENONnT is a physics experiment designed with the goal of detecting dark matter particles. The detector is a time projection chamber; a series of charged electrodes creates an electric field, surrounding a central body filled with liquid and gaseous xenon. Photomultiplier tubes (PMTs), positioned on either end of the chamber, serve to detect light signals. We seek to minimize the root mean square of the electric field norms experienced by the PMTs. This quantity corresponds to the variance in the electric field observed by the PMTs. Establishing a consistent electric field is important to maintaining these sensitive components. The electric …

An Llm-Driven System For Doctor-Patient Simulation, Akilan Amithasagaran

Computer Science Theses

Effective physician-patient communication is fundamental to clinical competence, yet traditional simulation-based training methods using standardized patients and high-fidelity manikins are costly, resource-intensive, and difficult to scale. This dissertation presents CLiVR (Conversational Learning system in Virtual Reality), an LLM-driven system that integrates large language models and 3D avatars to simulate doctor-patient interactions for medical communication training.

CLiVR addresses three key limitations in existing virtual reality medical training platforms. First, the system operates on standalone Meta Quest 3 hardware with realistic 3D patient avatars featuring synchronized lip movements and speech-based interaction. Second, CLiVR grounds LLM responses using a curated syndrome-symptom database, constraining …

Visualizing And Evaluating Binary Classifier Performance With Contingency Space, Colin D. Kehoe, Azim Ahmadzadeh

Undergraduate Research Symposium

Traditional metrics for evaluating binary classifiers, such as Accuracy, F1 Score, and True Skill Statistic (TSS), often obscure the underlying tradeoffs between true positive and true negative performance—particularly in imbalanced or high-stakes domains. This poster introduces the Contingency Space, a two-dimensional representation of classifier behavior defined by true positive rate (TPR) and true negative rate (TNR). Within this space, scalar performance metrics become geometric surfaces, revealing how scores vary across the entire landscape of possible classifier outputs.

We present a Python package that implements this framework, enabling users to map model predictions into the Contingency Space, visualize metric surfaces …

Efficient Self-Supervised Representation Learning For Large-Scale Time Series Classification, Kevin Garcia

Theses and Dissertations

Recently, there has been a significant advancement in designing Self-Supervised Learning (SSL) frameworks for time series data to reduce the dependency on data labels. Among these works, hierarchical contrastive learning-based SSL frameworks, which learn representations by contrasting data embeddings at multiple resolutions, have gained considerable attention. Due to their ability to gather more information, they exhibit better generalization in various downstream tasks. However, when the time series data length is significant long, the computational cost is often significantly higher than that of other SSL frameworks. In this paper, to address this challenge, we propose an efficient way to train hierarchical …

High Frequency Dc-Dc Converter Design Optimization, Modeling And Control Based-Wbg Technology., Salah Ahmed Abdullah Eltief

Electronic Theses and Dissertations

The performance of DC-DC power converters is a cornerstone of modern electric vehicle (EV) powertrains, directly governing overall system efficiency, size, cost, and reliability. This dissertation presents a comprehensive performance analysis and optimization of DC-DC converter topologies to determine the most suitable design for high voltage EV applications. The evaluation rigorously compares efficiency, power losses, and physical size under a range of harsh operating conditions. A primary objective is to leverage Wide Bandgap (WBG) semiconductors, specifically Silicon Carbide (SiC), to push the performance boundaries of power conversion. While SiC devices are known for their superior material properties, a clear understanding …

Archaeological Predictive Meta-Modeling In Pre-Columbian Mexico, Peter Stamm

Electronic Theses and Dissertations

Archaeological Predictive Modeling stands firmly as an important tool for Archaeologists to predict undiscovered sites from civilizations all across the globe. While powerful, this methodology is not without its own set of qualms. Striking a balance between pure a data-driven approach while also observing leading expert theories can be a complicated task. Going further, deciding on the specific domain of features to emphasize or overlook can be a challenge within itself, as one misstep can drastically change the output of model, sometimes for the worst. In addition, creating models that can expose their reasoning process can be rather difficult to …

“The Role Of Machine Learning In Social Media Content Moderation”, Bethaney A. Mallory-Smothers

Science University Research Symposium (SURS)

The majority of the world's over one billion active users depend on large-scale machine learning based social media platforms to personalize content through various methods of curation. Recommendation systems use many types of machine learning model including collaborative filtering, content-based filtering and deep learning to find what a specific user is likely to be interested in, and therefore increase user engagement. As beneficial as this is to the user experience, it has also generated a significant amount of concern about both bias in recommendations and the dissemination of false information on the web, as well as issues related to the …