Engineering Commons^™

The Use Of Artıfıcıal Intellıgence For Fake News Detectıon, Altina Salihu, Diellza Berisha

UBT International Conference

The phenomenon of fake news has become a global concern, directly influencing democratic processes, social perceptions, and public security. Detecting fake news represents a critical challenge for computer science, where Artificial Intelligence (AI) offers advanced solutions through Natural Language Processing (NLP) and text classification techniques. This paper analyzes and compares the main approaches used in this field, ranging from traditional machine learning models such as Naive Bayes and Support Vector Machines (SVM) to modern deep learning architectures like Long Short-Term Memory (LSTM), BERT, and GPT-based models. Furthermore, it discusses widely adopted datasets such as FakeNewsNet and LIAR, which serve as …

Insights On Ai-Supported Uncrewed And Autonomous Systems Education, Brent A. Terwilliger Ph. D, John Faraca

Publications

Artificial Intelligence (AI) related technology is reshaping the educational experience in programs focused on uncrewed and autonomous systems, aviation, robotics, and aerospace, with growing implications for workforce readiness and cross-sector innovation. Early survey data, capturing student, educator, and employer perspectives, reveals that AI-supported tools are notably changing student engagement, communication, and skills development. Initial indications underscores the importance of AI proficiency and technological familiarity in hiring and workforce development, particularly in technical and operational roles. Key areas of focus include the use of AI to strengthen outreach and interactivity; enrich instruction through intelligent simulations; inform curricular improvements using data analytics; …

The Method Of Power Networks Mode Optimization In Basis Of Genetic Algorithm, Tulkin Shernazarovich Gayibov, Gulnaz Makhmutovna Turmanova

Technical science and innovation

One of the main tasks solved in planning short-term and managing operational modes of electric power systems (EPS) is the optimization of their network modes on the adjustable parameters. For modern complex EPS, this task is often characterized by the multi-extremality of the objective function, the appearance of discontinuous functions, the presence of initial information of a probabilistic and partially uncertain nature. In such conditions, solving the problem by traditional algorithms using mainly linear and nonlinear programming methods, Lagrange, gradient, etc., is associated with a number of difficulties in simplifying them and bringing them to a convenient form for calculations. …

Nonlinear Design Scaling Of Electric Machines Based On Hybrid De And Meta-Modeling Application To Synchronous Motors With Combined Pm Stator And Reluctance Rotor Excitation, Oluwaseun A. Badewa, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

This paper presents an innovative method for nonlinear scaling of electric machines by integrating machine learning (ML)-based meta-modeling with a differential evolution (DE) algorithm. The technique is applied to high-performance combined-excitation synchronous electric motors which exhibit highly nonlinear characteristics, making performance scaling challenging. The proposed approach employs an ML meta-model trained on data obtained from finite element analysis (FEA), utilizing an experimentally validated model for nonlinear scaling and performance prediction at different power ratings. The accuracy of the meta-model in capturing the nonlinear relationships between design parameters and motor performance is first assessed using metrics such as R-squared (R2) and …

Soft Sensing Of Biological Oxygen Demand In Industrial Wastewater Using Machine Learning Models, Muhammad Hassnain, Sarada M.W. Lee, Muhammad Rizwan Azhar

Research outputs 2022 to 2026

Traditional methods for determining biological oxygen demand (BOD) from industrial water resource recovery facilities (WRRFs) are time-consuming and often impractical for real-time process control. This study explores the application of machine learning (ML) and artificial intelligence (AI) models for the prediction of final effluent BOD (F-BOD) based on physicochemical and operational parameters by leveraging nineteen years of historical laboratory and instrumentation data from the WRRF of an essential oil manufacturing plant. The predictions from these models are then used to simulate the process dynamics, assessing the optimal operational boundary conditions for all input parameters at which the target (F-BOD) falls …

Drawing On Uncertainty Methodologies Of Neutrosophic Hypersoft Sets In Cognitive Computing-Driven Healthcare Systems, Mona Mohamed, Nurhan Alaa

Neutrosophic Systems with Applications

A new paradigm called cognitive computing simulates human reasoning and decision-making through integrating advanced techniques such as artificial intelligence (AI) and natural language processing (NLP). Cognitive computing systems, in contrast to traditional systems, can handle both structured and unstructured data, adjust to new information, and offer context-sensitive insights. This study examines how cognitive computing improves decision-making, personalization, and human-machine collaboration in various fields. Cognitive computing in the healthcare sector processes clinical notes, imaging data, and electronic health records to help physicians with diagnosis, treatment planning, and patient engagement. This study examines key applications, including their role in diagnostic support, where …

Sustainable Additive Manufacturing Of Polymer Composites: A Review, Wan Sharuzi Wan Harun, Faiz Ahmad, Farhana Mohd Foudzi, Fujio Tsumori, Arun Kumar Thirugnanasambandam, Mohammed Saber

The Nexus of Sustainability and Energy Technology Journal

Additive Manufacturing (AM) of polymer composites has matured from a prototyping tool into a viable process for functional components. This review critically assesses this progress, focusing on the intersection of performance, sustainability, and industrial readiness. Advancements in AM processes now accommodate diverse reinforcements, from nanoscale fillers to continuous fibres, yielding parts with exceptional mechanical properties. Concurrently, a sustainability strategy has emerged, prioritising bio-based and recycled feedstocks, enhanced process energy efficiency through novel out-of-oven curing, and circular economy principles. However, widespread industrial adoption is hindered by persistent challenges. These include managing fibre-matrix interfacial integrity, the inherent anisotropy of printed parts, complex …

Research And Development Of Intelligent Measurement Systems, Odil Abdujalilovich Jumaev, Mahmudov Giyosjon Baqoyevich

Chemical Technology, Control and Management

The article discusses modern methods for developing intelligent measuring systems. Intelligent measuring systems are systems based on intelligent technologies that not only accurately measure physical or chemical quantities, but also have the ability to self-analyze, diagnose and make management decisions. The article comprehensively examines the architecture, components of such systems, the organization of their software and hardware, the relationship of sensors and artificial intelligence algorithms. It also analyzes the practical application and prospects of intelligent measuring systems in such areas as industry, medicine, energy, ecology, transport.

Digital Testing And Evaluation: Current Status, Challenges, And Prospects, Bo Sun, Kai Zheng

Journal of System Simulation

Abstract: Digital testing and evaluation (DTE) represents a novel paradigm in the evolution of testing and evaluation methodologies within the digital era. It is achieved through the integration of multiple digital theories and technologies to conduct testing and evaluations in the digital domain. This paper analyzed the characteristics of test objects across different historical periods, reviewed the core features of testing and evaluation techniques in each stage, and unveiled the paradigm shifts within the testing and evaluation technology system. Building upon this foundation, it explored the new demands placed on testing by test objects in the information age, clarifying the …

Depaul Digest

DePaul Magazine

College of Communication faculty Matthew Ragas and Ron Culp mentor students on gaining access to executive-level administration. News briefs on exciting developments at DePaul University’s 10 colleges and schools, from nursing students studying public health protocols in Prague to a new DePaul-hosted conference exploring AI in filmmaking. DePaul alumni volunteers share their experiences spreading the Vincentian mission nationwide.

Machine Learning And Clinical Eeg Data For Multiple Sclerosis: A Systematic Review, Badr Mouazen, Ahmed Bendaouia, El Hassan Abdelwahed, Giovanni De Marco

Manufacturing & Industrial Engineering Faculty Publications

Multiple Sclerosis (MS) is a chronic neuroinflammatory disease of the Central Nervous System (CNS) in which the body’s immune system attacks and destroys the myelin sheath that protects nerve fibers, leading to a wide range of debilitating symptoms and causing disruption of axonal signal transmission. Accurate prediction, diagnosis, monitoring and treatment (PDMT) of MS are essential to improve patient outcomes. Recent advances in neuroimaging technologies, particularly electroencephalography (EEG), combined with machine learning (ML) techniques — including Deep Learning (DL) models — offer promising avenues for enhancing MS management. This systematic review synthesizes existing research on the application of ML and …

Digital Twins, Ai, And Cybersecurity In Additive Manufacturing: A Comprehensive Review Of Current Trends And Challenges, Md Sazol Ahmmed, Laraib Khan, Muhammad Arif Mahmood, Frank Liou

Mechanical and Aerospace Engineering Faculty Research & Creative Works

The development of Industry 4.0 has accelerated the adoption of sophisticated technologies, including Digital Twins (DTs), Artificial Intelligence (AI), and cybersecurity, within Additive Manufacturing (AM). Enabling real-time monitoring, process optimization, predictive maintenance, and secure data management can redefine conventional manufacturing paradigms. Although their individual importance is increasing, a consistent understanding of how these technologies interact and collectively improve AM procedures is lacking. Focusing on the integration of digital twins (DTs), modular AI, and cybersecurity in AM, this review presents a comprehensive analysis of over 137 research publications from Scopus, Web of Science, Google Scholar, and ResearchGate. The publications are categorized …

Advancement Of Biocarbon Materials In Sustainable Thermal And Electrochemical Energy Storage With Future Outlooks, Md Shahriar Mohtasim, Barun K. Das

Research outputs 2022 to 2026

Biocarbon exhibits significant potential in thermal and electrochemical energy storage owing to its higher surface area, flexible porosity, and superior thermal stability, facilitating effective energy absorption, storage, and conversion. Additionally, its sustainable and cost-effective nature, derived from renewable biomass, aligns with the growing demand for eco-friendly solutions. There is not enough review work that offers comprehensive details on the unique properties of biocarbon (both animal and plant-derived), synthesis and characterization methods, the root level mechanism of biocarbon's interaction with phase change materials (PCMs) matrix, the generalization of thermal and electrochemical regulation with an emphasis on the benefits to the environment …

Investigating The Impact Of Agent Openness On Planning In Multi-Agent Systems, Bala Subramanyam Duggirala

School of Computing: Dissertations, Theses, and Student Research

Multi-agent systems (MAS) possess significant potential for modeling real-world scenarios requiring coordinated actions (like wildfire fighting or ridesharing) among autonomous entities or agents (e.g., wildfire fighting agents) in complex, dynamic environments. Effective decision-theoretic planning (where each agent must carefully consider both the immediate and the future situations or states, and coordinate with the other agents (neighbors) to evaluate what needs to be done at present) within MAS, especially multiagent planning, where the planning agent directly models its neighbors in order to estimate their optimal actions, is critical, yet challenged by factors like partial observability, openness, and diverse agent types with …

Thinking On Simulation Science And Engineering In The Era Of Artificial Intelligence, Wenhui Fan, Yuan Jiang

Journal of System Simulation

Abstract: With the rapid advancement of artificial intelligence and computing technologies, simulation technologies have leapfrogged, propelling the discipline of simulation toward greater maturity. Research progress in computer simulation technologies both in China and abroad was reviewed, and the definition and connotation of simulation were clarified. It was proposed that the Chinese terms "仿真" "仿效"and " 模拟" be unified under a single term " 仿真" with corresponding "Simulation" "Emulation" and "Analog" in English translated uniformly as "Simulation". Simulation science and engineering discipline was delineated, which was grounded in three core theoretical foundations: analogical theory,computational theory, and model validation theory. The first-level …

Type-2 Neutrosophic Numbers For Artificial Intelligence Software Choice For Cybersecurity Testing, O.M. Akash, We’Am Adel Talafha, Mamdouh Gomaa

Neutrosophic Systems with Applications

The choice of artificial intelligence (AI) software for cybersecurity testing is a multi-criteria decision-making approach (MCDM) due to it including different criteria. Evaluation decision making problems include uncertainty and vague information. So, the neutrosophic set is used in this study to overcome this uncertainty and vague information. It has three functions such as truth, indeterminacy, and falsity functions. Type-2 neutrosophic numbers is a type of neutrosophic set that includes nine membership functions. This study uses the average method of computing the criteria weights. The CoCoSo method is used to rank alternatives. Six experts and decision makers created the decision makers …

Enhancing Art History Education With Ai: Accessibility, Engagement, And Skill Development, Analisa Soverns-Reed

International Journal of Emerging and Disruptive Innovation in Education : VISIONARIUM

The integration of Artificial Intelligence (AI) in the art history classroom offers transformative opportunities for students through accessibility, engagement, and skill development. This paper explores how AI can democratize access to art history education by providing cost-effective tools that eliminate traditional barriers, such as expensive textbooks, inaccessible archives, or costly field trips. AI-powered platforms like visual recognition software and adaptive learning tools enable students to engage with material more deeply, allowing for personalized learning pathways and interactive explorations of art and its contexts. Beyond content delivery, incorporating AI fosters critical digital literacy, equipping students with the skills to navigate and …

Painting With Ai: Enhancing Creativity And Understanding In The Arts Classroom, Erica Blum

International Journal of Emerging and Disruptive Innovation in Education : VISIONARIUM

In the context of contemporary art and design education, the rapid advancement of generative artificial intelligence has introduced profound shifts in both creative practice and pedagogical responsibility. Art and design professors now encounter students who approach AIdriven tools with a mixture of skepticism and curiosity, often perceiving such technologies as antithetical to authentic artistic development or as expedient alternatives to disciplined practice. This article articulates an instructional approach that leverages Adobe Photoshop’s generative capabilities—specifically neural filters, generative fill, and the manipulation of alpha channels— to position artificial intelligence as a collaborative, rather than substitutive, element within digital art-making. The 2025 …

Writing As Curation: Empowering Authorial Agency In Ai-Assisted Composition Through Style Prompting And Quotation Glosses, Daniel Plate

International Journal of Emerging and Disruptive Innovation in Education : VISIONARIUM

As artificial intelligence writing tools become increasingly prevalent in educational settings, students often report feeling alienated from AI-generated content, describing it as lacking their "authentic voice." This article presents a pedagogical approach that addresses this challenge by repositioning students as curators of their authorial identities rather than passive consumers of AI-generated text. Through a dual curation methodology combining quotation glosses with style prompting, students develop multiple authorial voices for different rhetorical situations while maintaining agency over their writing process. The approach builds on classical rhetorical traditions while leveraging contemporary AI capabilities, creating a framework where students collaborate with AI tools …

Internet Of Things In Sustainable Agriculture Systems, Ataguba E. Hillary, Ayodeji A. Okubanjo, Nurudeen. S. Lawal, Abisola A. Olayiwola

AUIQ Technical Engineering Science

The agriculture industry has evolved toward intelligent, data-driven processes due to the growing need for food worldwide, environmental sustainability, and effective resource use. A thorough analysis of smart agriculture as a game-changing element of the industry 4.0 revolution is provided in this study, with a focus on the incorporation of Internet of Things (IoT)-based technologies for sustainable farming. To optimize agricultural processes including irrigation, crop health monitoring, climate and weather tracking, animal management, and disease detection, it investigates the functions and uses of smart sensors and IoT devices. The study demonstrates how smart agriculture may meet important issues like food …

Internet Of Things For Sustainable Transportation Systems, Ayodeji Akinsoji Okubanjo, Ignatius Kema Okakwu, Oluyinka Esther Olaifa, Matthew Babatunde Olajide, Olufemi Peter Alao, Olayiwola Abisola

Al-Mustaqbal Journal of Sustainability in Engineering Sciences

This paper highlights the opportunities for the Internet of Things in the transportation industry. The need for the Internet of Things and its architecture to address various complex challenges in the transportation sector are discussed. Various smart applications of the Internet of Things and its noticeable benefits over the existing technology are well articulated. In addition, the role of new and emerging technology such as artificial intelligence, machine learning, big data, cloud, data storage, and analysis for future sustainable transportation are highlighted with specific cases. Furthermore, smart areas of the Internet of Things in transportation are pictorially discussed. In addition, …

الأمن السيبراني والذكاء الاصطناعي: حلول لإدارة أزمات البنية التحتية الرقمية, عبدالله سعد الغامدي

Journal of the Association of Arab Universities for Research in Higher Education مجلة اتحاد الجامعات العربية للبحوث في التعليم العالي

في ظل التحول الرقمي المتسارع، أصبحت إدارة الأزمات التقنية تحديًا استراتيجيًا يستوجب تبني حلول مبتكرة للحفاظ على استمرارية الأعمال وحماية البنية التحتية الرقمية. ناقشت هذه الورقة دور الأمن السيبراني والذكاء الاصطناعي في تعزيز قدرات المنظمات والجهات على التنبؤ بالأزمات والاستجابة لها بفعالية ، وتعتمد على منهجية تحليلية تجمع بين دراسة الحالات الواقعية وتحليل البيانات باستخدام تقنيات التعلم العميق Deep Learning ونظم الأمن السيبراني المتقدمة مثل SIEM وSOAR ومدى الاستفادة من دمج هذه التقنيات لتحسين زمن الاستجابة وتقليل معدل الهجمات الناجحة، مع التدليل على أمثلة من المملكة العربية السعودية والتي سجلت أكثر من 38 مليون محاولة هجوم سيبراني في عام 2024. …

Analysis Of Vision Transformers And Domain Adaptation In Long-Range Facial Recognition, Zachary Michael Swanson

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Atmospheric turbulence presents a significant barrier to long-range facial recognition, introducing severe geometric distortions and blur that degrade image quality. This thesis investigates deep learning approaches for mitigating these effects, with a focus on transformer based architectures and domain adaptation strategies.

An in-depth benchmarking study was performed using convolutional neural networks (CNNs) and vision transformers (ViTs) on the Husker BRIAR Research Collection from up to 500m (HBRC-500) face dataset. The results demonstrated that vision transformers, particularly hierarchical vision transformers like the shifted-window (Swin) transformer, outperform CNN-based models at long distances due to their ability to model global spatial relationships and …

Artificial Intelligence In Everyday Life, Sally Brown

Artificial Intelligence Exhibit

This section explores using AI in everyday life including decision making.

Artificial Intelligence: The Twilight Zone And Conclusion, Sally Brown

Artificial Intelligence Exhibit

This section concludes the exhibit with an exploration of the deepfake dilemma, existential risks and societal impacts, and takeaway inquiries.

Ai Questions; "The Ai Tea", Sally Brown

Artificial Intelligence Exhibit

This section includes a series of questions related to AI and the exhibit content, as well as button designs by WVU students and Art in the Libraries committee members, a list of the exhibition sponsors, and information on the exhibition launch panel.

Artificial Intelligence Introduction Section, Seth Newell, Sally Brown

Artificial Intelligence Exhibit

The introduction gives an overview of the exhibition, along with an explanation of AI literacy, AI vs. Google, and a basic AI timeline.

Artificial Intelligence In Education, Sally Brown, Jill Woods, Mohamed Hefeida, Jennifer Sano-Franchini, Megan Vendemia, Erin Brock Carlson, Megan Leight, Gangqing Hu, Nicole Fuller

Artificial Intelligence Exhibit

This section explores the intersection of AI and education, including an overview of WVU's AI statement and submissions from WVU faculty, classes, students, and staff on AI related projects.

Designing A Portable And Accessible Diffuse Reflectance Spectroscopy System For Real-Time Detection Of Cervical Intraepithelial Neoplasia, Allison Scarbrough, Diana Moses, Tongtong Lu, Bing Yu

Biomedical Engineering Faculty Research and Publications

Significance: Over 80% of cervical cancer cases occur in lower-to-middle income countries (LMIC’s). This is partly because current screening techniques lack affordability, accessibility, and/or reliability for use in LMIC’s.

Aim: To develop an optical technique for cervical cancer screening that is affordable, accessible, and reliable for use in LMIC’s.

Approach: We developed a portable diffuse reflectance spectroscopy (DRS) system, which costs < $2500 USD to manufacture, and employs a Raspberry Pi to extract the absorption (μ_a) and reduced scattering (μ'_s) coefficients of biological tissue. The system was subject to travel and intentional rough handling. It was further used to capture 320 DRS spectra taken from 64 tissue-mimicking phantoms. Two …

Smart Traffic Management At Intersections, Gezim Hoxha, Piotr Gorzelanczyk, Rame Likaj, Shaban Thaqi, Flori Cikaqi

Journal of Sustainable Construction Materials and Technologies

Effective traffic control methods, whether at intersections or other nodes within the road network, play a critical role in ensuring traffic quality and safety. An inappropriate choice of traffic control techniques or poorly optimized light signal programming at intersections often leads to unsatisfactory service levels. This study specifically addresses the issues related to intersections controlled by traffic lights. It aims to identify the key factors influencing the regulation and programming of traffic lights, with a focus on using different microcontrollers, particularly the Raspberry Pi. The primary objective of this research is to develop a methodology and an adapted programming approach …