Physical Sciences and Mathematics Commons^™

Multi-Agent System-Based Framework For An Intelligent Management Of Competency Building, Fatma Outay, Nafaa Jabeur, Fahmi Bellalouna, Tasnim Al Hamzi

All Works

To measure the effectiveness of learning activities, intensive research works have focused on the process of competency building through the identification of learning stages as well as the setup of related key performance indictors to measure the attainment of specific learning objectives. To organize the learning activities as per the background and skills of each learner, individual learning styles have been identified and measured by several researchers. Despite their importance in personalizing the learning activities, these styles are difficult to implement for large groups of learners. They have also been rarely correlated with each specific learning stage. New approaches are, …

Enhancing Resilience And Reducing Waste In Food Supply Chains: A Systematic Review And Future Directions Leveraging Emerging Technologies, Asmaa Seyam, May Ei Barachi, Cheng Zhang, Bo Du, Jun Shen, Sujith Samuel Mathew

All Works

The sustainability of food supply chains is gaining increasing attention, particularly after the COVID-19 pandemic. A food supply system that simultaneously prioritises resilience and minimises wastage is crucial. It is found that many studies have explored reducing food waste and increasing supply chain resilience as separate objectives, but research is scarce investigating both objectives in conjunction. This paper presents a comprehensive systematic review focusing on existing solutions to reducing food waste and enhancing resilience. It discusses future directions, particularly leveraging emerging technologies such as the Internet of Things, blockchain, artificial intelligence, and machine learning. The studies are categorised into three …

How State Universities Are Addressing The Shortage Of Cybersecurity Professionals In The United States, Gary Harris

Journal of Cybersecurity Education, Research and Practice

Cybersecurity threats have been a serious and growing problem for decades. In addition, a severe shortage of cybersecurity professionals has been proliferating for nearly as long. These problems exist in the United States and globally and are well documented in literature. This study examined what state universities are doing to help address the shortage of cybersecurity professionals since higher education institutions are a primary source to the workforce pipeline. It is suggested that the number of cybersecurity professionals entering the workforce is related to the number of available programs. Thus increasing the number of programs will increase the number of …

Enhancing Dtc Control Of Im Using Fuzzy Logic And Three-Level Inverter: A Comparative Study, Siham Mencou, Majid Benyakhlef, Elbachir Tazi

Turkish Journal of Electrical Engineering and Computer Sciences

Direct torque control is the most appropriate strategy for induction motor drive systems, due to its considerable ability to reduce the impact of of machine parameter variations, while offering fast dynamic response and simplified control implementation. However, persistent problems associated with high torque ripple and variable switching frequencies prevent its widespread adoption. To overcome these limitations, several techniques have been developed, in particular the use of multi-level inverters and fuzzy logic algorithms. This article proposes an in-depth evaluation of these techniques in a MATALB/Simulink environment, under various operational conditions. The main objective is to provide a detailed performance analysis of …

Power Quality Enhancement In Hybrid Pv-Bes System Based On Ann-Mppt, Heli̇n Bozkurt, Özgür Çeli̇k, Ahmet Teke

Turkish Journal of Electrical Engineering and Computer Sciences

Battery energy systems (BESs) assisted photovoltaic (PV) plants are among the popular hybrid power systems in terms of energy efficiency, energy management, uninterrupted power supply, grid-connected and off-grid availability. The primary objective of this study is to enhance the power quality of a grid-tied PV-BES hybrid system by developing an operation strategy based on Artificial Neural Network (ANN) based maximum power point tracking (MPPT) method. A test system comprising a 10-kWh BES and a 12.4 kW PV plant is structured and simulated on the MATLAB/Simulink platform. The hybrid system is validated with three different cases: constant radiation, rapid changing radiation, …

Finger Movement Recognition Using Machine Learning Algorithms With Tree-Seed Algorithm, Muhammed Sami̇ Karakul, Ahmet Gökçen

Turkish Journal of Electrical Engineering and Computer Sciences

Electromyography (EMG) signals have been used to recognize various actions of hand movements, finger movements, and hand gestures. This paper aims to improve the classification accuracy of EMG signals while decreasing the number of features using the Tree-Seed Algorithm. The dataset containing EMG signals utilized in this investigation is derived from a publicly accessible source. The rationale for selecting the Tree-Seed Algorithm centers on its ability to enhance classification accuracy while minimizing the dimensionality of feature sets. The object function and Tree-Seed Algorithm's nature avoids the results to have low accuracy with fewer features. The aim is not just to …

Mention Detection In Turkish Coreference Resolution, Şeni̇z Demi̇r, Hani̇fi̇ İbrahi̇m Akdağ

Turkish Journal of Electrical Engineering and Computer Sciences

A crucial step in understanding natural language is detecting mentions that refer to real-world entities in a text and correctly identifying their boundaries. Mention detection is commonly considered a preprocessing step in coreference resolution which is shown to be helpful in several language processing applications such as machine translation and text summarization. Despite recent efforts on Turkish coreference resolution, no standalone neural solution to mention detection has been proposed yet. In this article, we present two models designed for detecting Turkish mentions by using feed-forward neural networks. Both models extract all spans up to a fixed length from input text …

A Single Operational Amplifier-Based Grounded Meminductor Mutators And Their Applications, Shalini Gupta, Kunwar Singh, Shireesh Kumar Rai

Turkish Journal of Electrical Engineering and Computer Sciences

In this work, three simple configurations of meminductor mutator are presented. The first two configurations of meminductor mutator have been implemented utilizing one CMOS-based operational amplifier, one memristor, one capacitor, and five resistors, while the third configuration of meminductor mutator is implemented utilizing one CMOS based operational amplifier, two memristors, one capacitor, and four resistors. The implementation and simulation of the proposed configurations are done by using LTspice tool. The viability of the proposed circuits is demonstrated by utilizing TSMC 180 nm CMOS technology parameters. The proposed circuits of the meminductor have a simple structure in contrast to many of …

Optimizing Neural Network Architecture Using Kernel Principal Component Analysis, Saige Simcox

Theses and Dissertations

Neural networks have proven to be powerful tools for modeling a wide range of problems across applications. However, one of the challenges in implementing a neural network model lies in determining the neural network architecture, i.e. the appropriate number of hidden layers and the number of neurons per hidden layer. It has been suggested that one way to determine the number of hidden layers is by using information on the variability captured by each principal component. In this research, we expand on this idea and propose a new approach to determine the neural network architecture for a multilayer perceptron used …

A Generalized Machine Learning Model For Long-Term Coral Reef Monitoring In The Red Sea, Justin J. Gapper, Surendra Maharjan, Wenzhao Li, Erik Linstead, Surya Prakash Tiwari, Mohamed A. Qurban, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Coral reefs, despite covering less than 0.2 % of the ocean floor, harbor approximately 35 % of all known marine species, making their conservation critical. However, coral bleaching, exacerbated by climate change and phenomena such as El Niño, poses a significant threat to these ecosystems. This study focuses on the Red Sea, proposing a generalized machine learning approach to detect and monitor changes in coral reef cover over an 18-year period (2000–2018). Using Landsat 7 and 8 data, a Support Vector Machine (SVM) classifier was trained on depth-invariant indices (DII) derived from the Gulf of Aqaba and validated against ground …

A Limited-Preemption Scheduling Model Inspired By Security Considerations, Benjamin Standaert, Fatima Raadia, Marion Sudvarg, Sanjoy Baruah, Thidapat Chantem, Nathan Fisher, Christopher Gill

Computer Science and Engineering Publications and Presentations

Safety-critical embedded systems such as autonomous vehicles typically have only very limited computational capabilities on board that must be carefully managed to provide required enhanced functionalities. As these systems become more complex and inter-connected, some parts may need to be secured to prevent unauthorized access, or isolated to ensure correctness.

We propose the multi-phase secure (MPS) task model as a natural extension of the widely used sporadic task model for modeling both the timing and the security (and isolation) requirements for such systems. Under MPS, task phases reflect execution using different security mechanisms which each have associated execution time costs …

Time-Series Feature Selection For Solar Flare Forecasting, Yagnashree Velanki, Pouya Hosseinzadeh, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi

Computer Science Student Research

Solar flares are significant occurrences in solar physics, impacting space weather and terrestrial technologies. Accurate classification of solar flares is essential for predicting space weather and minimizing potential disruptions to communication, navigation, and power systems. This study addresses the challenge of selecting the most relevant features from multivariate time-series data, specifically focusing on solar flares. We employ methods such as Mutual Information (MI), Minimum Redundancy Maximum Relevance (mRMR), and Euclidean Distance to identify key features for classification. Recognizing the performance variability of different feature selection techniques, we introduce an ensemble approach to compute feature weights. By combining outputs from multiple …

Synthesis Of Zno: Zro2 Nanocomposites Using Green Method For Medical Applications, Mohammed J. Tuama, Maysoon F. Alias

Karbala International Journal of Modern Science

These days, nanocomposites are very popular, especially in medical applications. The spread of diseases in general, and those caused by microbes and cancerous diseases in particular, and the increased resistance of these diseases to antibiotics, have led to the need for the rapid, low-cost, and environmentally friendly production of nanocomposites. To create the chemical G-ZnO: ZrO2 and S-ZnO: ZrO2 (green technique), two different plant extracts were utilized: Z. officinal and S. aromaticum. The effective synthesis and acceptable properties features of the nanoparticles were confirmed using characterization techniques such as X-ray diffraction (XRD), Fourier transform infrared (FTIR) , diffuse reflectance spectroscopy …

Systematic Review On Isolation, Purification, Characterization, And Industrial Applications Of Thermophilic Microbial Α- Amylases, Rugaiyah A. Arfah, Sarlan Sarlan, Abdul Karim, Anita Anita, Ahyar Ahmad, Paulina Taba, Harningsih Karim, Siti Halimah Larekeng, Dorothea Agnes Rampisela, Rusdina Bte Ladju

Karbala International Journal of Modern Science

The α-amylase enzyme, sourced from diverse organisms, including plants, animals, and bacteria, plays a crucial role in multiple industries, notably food processing sectors like cakes, fruit juices, and starch syrup. Research identifies thermophilic organisms as prime sources of this enzyme thriving at temperatures ranging from 41°C to 122°C. The enzyme purification was carried out using liquid-liquid extraction, which involved the exchange of substances between two liquid phases that were immiscible or partially soluble. The optimal temperature for α-amylase was 45 to 90°C. The best pH for bacterial and fungal α-amylases ranged from 5.0 to 10.5 and 5.0 to 9.0. Based …

The Aimag Project: Using Machine Learning To Predict Crustal Magnetic Anomaly Values, Xavier Gobble, Marlie Mollett, Dr. Dawn King, Dr. Cory Reed, Erin Knese

Undergraduate Research Symposium

A detailed model of the Earth’s total magnetic field is important for acquiring the means for GPS-alternative, magnetic anomaly-based navigation. The Earth’s total magnetic field is an amalgam of 5 mechanisms: the geodynamo generated by the rotation of the Earth’s molten iron core, the fields induced by the flows of electric current in the atmosphere and oceans, the disturbance of the ionosphere by solar wind, and local anomalies attributable to ferromagnetic minerals present in the crust; the lattermost compose the crustal magnetic field. The EMAG2v3 dataset comprises a compilation of satellite, shipborne, and airborne magnetic measurements differenced from the Comprehensive …

Leveraging Propagation Delay For Wormhole Detection In Wireless Networks, Harry May, Travis Atkison

Journal of Cybersecurity Education, Research and Practice

Detecting and mitigating wormhole attacks in wireless networks remains a critical challenge due to their deceptive nature and potential to compromise network integrity. This paper proposes a novel approach to wormhole detection by leveraging propagation delay analysis between network nodes. Unlike traditional methods that rely on signature-based detection or specialized hardware, our method focuses on analyzing propagation delay timings to identify anomalous behavior indicative of wormhole attacks. The proposed methodology involves collecting propagation delay data in both normal network scenarios and scenarios with inserted malicious wormhole nodes. By comparing these delay timings, our approach aims to differentiate between legitimate network …

Understanding The Use Of Artificial Intelligence In Cybercrime, Sinyong Choi, Thomas Dearden, Katalin Parti

International Journal of Cybersecurity Intelligence & Cybercrime

Artificial intelligence is one of the newest innovations that offenders also exploit to satisfy their criminal desires. Although understanding cybercrimes associated with this relatively new technology is essential in developing proper preventive measures, little has been done to examine this area. Therefore, this paper provides an overview of the articles featured in the special issue of the International Journal of Cybersecurity Intelligence and Cybercrime, ranging from deepfake in the metaverse to social engineering attacks. This issue includes articles that were presented by the winners of the student paper competition at the 2024 International White Hat Conference.

Cyber Victimization In The Healthcare Industry: Analyzing Offender Motivations And Target Characteristics Through Routine Activities Theory (Rat) And Cyber-Routine Activities Theory (Cyber-Rat), Yashna Praveen, Mijin Kim, Kyung-Shick Choi

International Journal of Cybersecurity Intelligence & Cybercrime

The integration of computer technology in healthcare has revolutionized patient care but has also introduced significant cyber risks. Despite the healthcare sector being a primary target for cyber-attacks, research on the dynamics of these threats and practical solutions remains limited. Understanding the complexities of cyberattacks in this sector is critical, as the impact extends beyond financial losses to directly affect patient care and the protection of sensitive information. This paper applies Routine Activities Theory (RAT) and Cyber Routine Activities Theory (C-RAT) to analyze high-tech cyber victimization case studies in healthcare. The analysis explores the motivations behind these attacks and identifies …

Investigating The Intersection Of Ai And Cybercrime: Risks, Trends, And Countermeasures, Sanaika Shetty, Kyung-Shick Choi, Insun Park

International Journal of Cybersecurity Intelligence & Cybercrime

No abstract provided.

Integrated Model Of Cybercrime Dynamics: A Comprehensive Framework For Understanding Offending And Victimization In The Digital Realm, Troy Smith Phd

International Journal of Cybersecurity Intelligence & Cybercrime

This article introduces the Integrated Model of Cybercrime Dynamics (IMCD), a novel theoretical framework for examining the complex interplay between individual characteristics, online behavior, environmental factors, and outcomes related to cybercrime offending and victimization. The model incorporates key concepts from existing theories, empirical evidence, and interdisciplinary perspectives to provide a comprehensive framework. In contrast to traditional criminological theories, the proposed model integrates concepts from multiple disciplines to offer a holistic framework that captures the complexity of cybercrime and specifically caters for the uniqueness of cyberspace. The article will provide a detailed overview of the conceptual model, its theoretical underpinnings drawing …

On Signifiable Computability: Part I: Signification Of Real Numbers, Sequences, And Types, Vladimir A. Kulyukin

Computer Science Faculty and Staff Publications

Signifiable computability aims to separate what is theoretically computable from what is computable through performable processes on computers with finite amounts of memory. Real numbers and sequences thereof, data types, and instances are treated as finite texts, and memory limitations are made explicit through a requirement that the texts be stored in the available memory on the devices that manipulate them. In Part I of our investigation, we define the concepts of signification and reference of real numbers. We extend signification to number tuples, data types, and data instances and show that data structures representable as tuples of discretely finite …

Institutional Data Repositories Are Vital, Jen Darragh, Mikala R. Narlock, Halle Burns, Peter A. Cerda, Wind Cowles, Leslie M. Delserone, Seth Erickson, Joel Herndon, Heidi Imker, Lisa R. Johnston, Sherry Lake, Michael Lenard, Alicia Hofelich Mohr, Jennifer Moore, Jonathan Petters, Brandie Pullen, Shawna Taylor, Briana Wham

UNL Libraries: Faculty Publications

As funding agencies and publishers reiterate research data sharing expectations (1), many higher-education institutions have demonstrated their commitment to the long-term stewardship of research data by connecting researchers to local infrastructure, with dedicated staffing, that eases the burden of data sharing. Institutional repositories are an example of this investment (2). They provide support for researchers in sharing data that might otherwise be lost: data without a disciplinary repository, data from projects with limited funding, or data that are too large to sustainably store elsewhere. The staffing and technical infrastructure provided by institutional repositories ensures responsible access to information while considering …

Review Of Data Bias In Healthcare Applications, Atharva Prakash Parate, Aditya Ajay Iyer, Kanav Gupta, Harsh Porwal, P. C. Kishoreraja, R. Sivakumar, Rahul Soangra

Physical Therapy Faculty Articles and Research

In the area of medical artificial intelligence (AI), data bias is a major difficulty that affects several phases of data collection, processing, and model building. The many forms of data bias that are common in AI in healthcare are thoroughly examined in this review study, encompassing biases related to socioeconomic status, race, and ethnicity as well as biases in machine learning models and datasets. We examine how data bias affects the provision of healthcare, emphasizing how it might worsen health inequalities and jeopardize the accuracy of AI-driven clinical tools. We address methods for reducing data bias in AI and focus …

Exploring Artificial Intelligence: A Collaborative Small Group Analysis And Application, Ellamarie Powell

AI Assignment Library

In this small group project, students will collaborate to explore the principles and applications of Artificial Intelligence (AI). Each group will research, analyze, and present on a specific AI topic, highlighting its real-world implications and ethical considerations. The project involves team members contributing to various roles, including research, technical analysis, and presentation. The final deliverable will be a video presentation integrating individual contributions, showcasing a comprehensive understanding of AI and its impact on society. This assignment fosters teamwork, critical thinking, and effective communication skills.

Neurosymbolic Cognitive Methods For Enhancing Foundation Model-Based Reasoning, Kaushik Roy, Siyu Wu, Alessandro Oltramari

Faculty Publications

Foundation models have emerged as powerful tools, exhibiting extraordinary performance across various tasks, such as language processing, visual recognition, code generation, and human-centered engagement. However, recent studies have highlighted their limitations when grounded, abstract, and generalized reasoning capabilities are required. Complex tasks often involve multiple hierarchical reasoning steps, which are typical features of human thinking processes. In fact, in this chapter we claim that cognitively-inspired computational models, such as the so-called Common Model of Cognition, are key to enable complex reasoning within foundation model-based artificial intelligence (AI) systems. We investigate neurosymbolic approaches for mapping AI system components to those of …

A Primer On How Al Algorithms Control You, Russell Fulmer

Journal of Technology in Counselor Education and Supervision

Artificial intelligence (AI) algorithms can control you by exerting heavy influence on your worldview. Your worldview is akin to your personal philosophy, which affects how you perceive and label social systems and structures, groups of people, and politics. Algorithms impact your decision-making, beliefs, mood, relationships, and more. My rhetoric is intentionally strong when discussing algorithms, and I invite you to assess its merit by reviewing related literature and thinking critically.

Malware Classification Through Abstract Syntax Trees And L-Moments, Anthony J. Rose [*], Christine M. Schubert Kabban, Scott R. Graham, Wayne C. Henry, Christopher M. Rondeau

Faculty Publications

The ongoing evolution of malware presents a formidable challenge to cybersecurity: identifying unknown threats. Traditional detection methods, such as signatures and various forms of static analysis, inherently lag behind these evolving threats. This research introduces a novel approach to malware detection by leveraging the robust statistical capabilities of L-moments and the structural insights provided by Abstract Syntax Trees (ASTs) and applying them to PowerShell. L-moments, recognized for their resilience to outliers and adaptability to diverse distributional shapes, are extracted from network analysis measures like degree centrality, betweenness centrality, and closeness centrality of ASTs. These measures provide a detailed structural representation …

Interoperability In Deep Learning: A User Survey And Failure Analysis Of Onnx Model Converters, Purvish Jajal, Wenxin Jiang, Arav Tewari, Erik Kocinare, Joseph Woo, Anusha Sarraf, Yung-Hsiang Lu, George K. Thiruvathukal, James C. Davis

Computer Science: Faculty Publications and Other Works

Software engineers develop, fine-tune, and deploy deep learning (DL) models using a variety of development frameworks and runtime environments. DL model converters move models between frameworks and to runtime environments. Conversion errors compromise model quality and disrupt deployment. However, the failure characteristics of DL model converters are unknown, adding risk when using DL interoperability technologies.
This paper analyzes failures in DL model converters. We survey software engineers about DL interoperability tools, use cases, and pain points (N=92). Then, we characterize failures in model converters associated with the main interoperability tool, ONNX (N=200 issues in PyTorch and TensorFlow). Finally, we formulate …

Interoperability In Deep Learning: A User Survey And Failure Analysis Of Onnx Model Converters, Purvish Jajal, Wenxin Jiang, Arav Tewari, Erik Kocinare, Joseph Woo, Anusha Sarraf, Yung-Hsiang Lu, George Thiruvathukal, James C. Davis

Computer Science: Faculty Publications and Other Works

Software engineers develop, fine-tune, and deploy deep learning (DL) models using a variety of development frameworks and runtime environments. DL model converters move models between frameworks and to runtime environments. Conversion errors compromise model quality and disrupt deployment. However, the failure characteristics of DL model converters are unknown, adding risk when using DL interoperability technologies. This paper analyzes failures in DL model converters. We survey software engineers about DL interoperability tools, use cases, and pain points (N=92). Then, we characterize failures in model converters associated with the main interoperability tool, ONNX (N=200 issues in PyTorch and TensorFlow). Finally, we formulate …

Evaluating The Cost Of Classifier Discrimination Choices For Iot Sensor Attack Detection, Mathew Nicho, Brian Cusack, Shini Girija, Nalin Arachchilage

All Works

The intrusion detection of IoT devices through the classification of malicious traffic packets have become more complex and resource intensive as algorithm design and the scope of the problems have changed. In this research, we compare the cost of a traditional supervised pattern recognition algorithm (k-Nearest Neighbor (KNN)), with the cost of a current deep learning (DL) unsupervised algorithm (Convolutional Neural Network (CNN)) in their simplest forms. The classifier costs are calculated based on the attributes of design, computation, scope, training, use, and retirement. We find that the DL algorithm is applicable to a wider range of problem-solving tasks, but …