Computer Sciences Commons^™

A Robust Decision-Making Model For Medical Supplies Via Selecting Appropriate Unmanned Aerial Vehicle, Amira Salam, Mai Mohamed, Rui Yong, Jun Ye

Neutrosophic Systems with Applications

Recently, Unmanned Aerial Vehicles (UAVs) have been used in many fields, including the field of health care, especially in delivering the necessary medical equipment and supplies, due to the many advantages they have compared to other traditional methods and the presence of different types of UAVs, to improve healthcare and provide it with the medical supplies and equipment necessary to save the lives of patients. Choosing the appropriate UAV for a specific situation represents a problem facing decision-makers, which is considered a multi-criteria decision-making problem. Since the decision-making process is cumbersome and complex, and deals with uncertainty and ambiguity. In …

Prediction Of Converter Gas Generation Based On Intermission Production Improved Elman, Jiajie Fei, Dinghui Wu, Junyan Fan, Jing Wang

Journal of System Simulation

Abstract: Aiming at large fluctuations of intermission and low prediction accuracy in iron and steel industry, based on the classification of intermission characteristics, a converter gas generation predicting model(CPSO-Elman) based on Elman neural network(ENN) optimized by chaotic PSO(CPSO) algorithm is proposed. The intermittent characteristics of converter gas generation time series are extracted and raw data is classified according to intermittent duration. The PSO algorithm improved by chaotic disturbance is introduced to optimize the initial weight and threshold of ENN and inertia weight of nonlinear updating is designed to balance global search ability and local search ability. Construct the combined prediction …

Toward Intuitive 3d Interactions In Virtual Reality: A Deep Learning- Based Dual-Hand Gesture Recognition Approach, Trudi Di Qi, Franceli L. Cibrian, Meghna Raswan, Tyler Kay, Hector M. Camarillo-Abad, Yuxin Wen

Engineering Faculty Articles and Research

Dual-hand gesture recognition is crucial for intuitive 3D interactions in virtual reality (VR), allowing the user to interact with virtual objects naturally through gestures using both handheld controllers. While deep learning and sensor-based technology have proven effective in recognizing single-hand gestures for 3D interactions, research on dual-hand gesture recognition for VR interactions is still underexplored. In this work, we introduce CWT-CNN-TCN, a novel deep learning model that combines a 2D Convolution Neural Network (CNN) with Continuous Wavelet Transformation (CWT) and a Temporal Convolution Network (TCN). This model can simultaneously extract features from the time-frequency domain and capture long-term dependencies using …

Generative Machine Learning For Cyber Security, James Halvorsen, Dr. Assefaw Gebremedhin

Military Cyber Affairs

Automated approaches to cyber security based on machine learning will be necessary to combat the next generation of cyber-attacks. Current machine learning tools, however, are difficult to develop and deploy due to issues such as data availability and high false positive rates. Generative models can help solve data-related issues by creating high quality synthetic data for training and testing. Furthermore, some generative architectures are multipurpose, and when used for tasks such as intrusion detection, can outperform existing classifier models. This paper demonstrates how the future of cyber security stands to benefit from continued research on generative models.

Domination On Bipolar Fuzzy Graph Operations: Principles, Proofs, And Examples, Haifa Ahmed, Mohammed Alsharafi

Neutrosophic Systems with Applications

Bipolar fuzzy graphs, capable of capturing situations with both positive and negative memberships, have found diverse applications in various disciplines, including decision-making, computer science, and social network analysis. This study investigates the domain of domination and global domination numbers within bipolar fuzzy graphs, owing to their relevance in these aforementioned practical fields. In this study, we introduce certain operations on bipolar fuzzy graphs, such as intersection, join, and union of two graphs. Furthermore, we analyze the domination number and the global domination number for various operations on bipolar fuzzy graphs, including intersection, join, and union of fuzzy graphs and their …

Fixed Point Results In Complex Valued Neutrosophic B-Metric Spaces With Application, M. Pandiselvi, M. Jeyaraman

Neutrosophic Systems with Applications

In this manuscript, we introduce the idea of complex-valued Neutrosophic b-metric spaces along with numerous significant illustrations. We provide fixed-point results for contraction maps. To support the main result, we establish the existence and uniqueness of solutions for nonlinear integral equations after the work.

On Heptagonal Neutrosophic Semi-Open Sets In Heptagonal Neutrosophic Topological Spaces: Testing Proofs By Examples, Subasree R, Basarikodi K

Neutrosophic Systems with Applications

In terms of heptagonal neutrosophic topological spaces, the purpose of this paper is to present the idea of heptagonal neutrosophic semi-open sets. Additionally, we examine a few of its characterizations and heptagonal neutrosophic semi-interior and heptagonal neutrosophic semi-closure operators.

Software Reliability Model Estimation For An Indeterministic Crime Cluster Through Reinforcement Learning, Dileep Kumar Kadali, R.N.V. Jagan Mohan, M. Chandra Naik

Neutrosophic Systems with Applications

The software reliability model estimates the probability of data failure in a specific environment, significantly impacting reliability and trustworthiness. The paper study focuses on cluster crime data, i.e., indeterministic in Neutrosophic Logic, using a software reliability model. The study utilizes reinforcement learning, Neutrosophic logic, and non-homogeneous Poisson process crime data to estimate indeterministic cluster data in crime. The "Non-homogeneous Poisson Process with Neutrosophic Logic" technique performs well in evaluating and deterring crime based on crime data analysis. The crime cluster involving offenders correctly classified as failure to accomplish does better than uncertain cluster reliability estimation with least squares and logistic …

Engineering Education In The Age Of Ai: Analysis Of The Impact Of Chatbots On Learning In Engineering, Flor A. Bravo, Juan M. Cruz Bohorquez

Henry M. Rowan College of Engineering Departmental Research

The purpose of this paper is to explore the influence of using AI chatbots on learning within the context of engineering education. We framed this study on the principles of how learning works in order to describe the contributions and challenges of AI chatbots in five categories: (1) facilitating the acquisition, completion, or activation of prior knowledge and helping organize knowledge and making connections; (2) enhancing student motivation to learn; (3) fostering self-directed learning and the acquisition, practice, and application of the skills and knowledge they acquire; (4) supporting goal-directed practice and feedback; and (5) addressing student diversity and creating …

Star-Based Reachability Analysis Of Binary Neural Networks On Continuous Input, Mykhailo Ivashchenko

School of Computing: Dissertations, Theses, and Student Research

Deep Neural Networks (DNNs) have become a popular instrument for solving various real-world problems. DNNs’ sophisticated structure allows them to learn complex representations and features. However, architecture specifics and floating-point number usage result in increased computational operations complexity. For this reason, a more lightweight type of neural networks is widely used when it comes to edge devices, such as microcomputers or microcontrollers – Binary Neural Networks (BNNs). Like other DNNs, BNNs are vulnerable to adversarial attacks; even a small perturbation to the input set may lead to an errant output. Unfortunately, only a few approaches have been proposed for verifying …

Domination On Bipolar Fuzzy Graph Operations: Principles, Proofs, And Examples, Haifa Ahmed, Mohammed Alsharafi

Neutrosophic Systems with Applications

Bipolar fuzzy graphs, capable of capturing situations with both positive and negative memberships, have found diverse applications in various disciplines, including decision-making, computer science, and social network analysis. This study investigates the domain of domination and global domination numbers within bipolar fuzzy graphs, owing to their relevance in these aforementioned practical fields. In this study, we introduce certain operations on bipolar fuzzy graphs, such as intersection, join, and union of two graphs. Furthermore, we analyze the domination number and the global domination number for various operations on bipolar fuzzy graphs, including intersection, join, and union of fuzzy graphs and their …

Fixed Point Results In Complex Valued Neutrosophic B-Metric Spaces With Application, M. Pandiselvi, M. Jeyaraman

Neutrosophic Systems with Applications

In this manuscript, we introduce the idea of complex-valued Neutrosophic b-metric spaces along with numerous significant illustrations. We provide fixed-point results for contraction maps. To support the main result, we establish the existence and uniqueness of solutions for nonlinear integral equations after the work.

Software Reliability Model Estimation For An Indeterministic Crime Cluster Through Reinforcement Learning, Dileep Kumar Kadali, R.N.V. Jagan Mohan, M. Chandra Naik

Neutrosophic Systems with Applications

The software reliability model estimates the probability of data failure in a specific environment, significantly impacting reliability and trustworthiness. The paper study focuses on cluster crime data, i.e., indeterministic in Neutrosophic Logic, using a software reliability model. The study utilizes reinforcement learning, Neutrosophic logic, and non-homogeneous Poisson process crime data to estimate indeterministic cluster data in crime. The "Non-homogeneous Poisson Process with Neutrosophic Logic" technique performs well in evaluating and deterring crime based on crime data analysis. The crime cluster involving offenders correctly classified as failure to accomplish does better than uncertain cluster reliability estimation with least squares and logistic …

On Heptagonal Neutrosophic Semi-Open Sets In Heptagonal Neutrosophic Topological Spaces: Testing Proofs By Examples, Subasree R, Basarikodi K

Neutrosophic Systems with Applications

In terms of heptagonal neutrosophic topological spaces, the purpose of this paper is to present the idea of heptagonal neutrosophic semi-open sets. Additionally, we examine a few of its characterizations and heptagonal neutrosophic semi-interior and heptagonal neutrosophic semi-closure operators.

Side Channel Detection Of Pc Rootkits Using Nonlinear Phase Space, Rebecca Clark

Poster Presentations

Cyberattacks are increasing in size and scope yearly, and the most effective and common means of attack is through malicious software executed on target devices of interest. Malware threats vary widely in terms of behavior and impact and, thus, effective methods of detection are constantly being sought from the academic research community to offset both volume and complexity. Rootkits are malware that represent a highly feared threat because they can change operating system integrity and alter otherwise normally functioning software. Although normal methods of detection that are based on signatures of known malware code are the standard line of defense, …

Magdm Model Using Single-Valued Neutrosophic Credibility Matrix Energy And Its Decision-Making Application, Jun Ye, Rui Yong, Wanlu Du

Neutrosophic Systems with Applications

This paper aims to develop a MAGDM model using single-valued neutrosophic credibility matrix (SVNCM) energy in a SVNCM scenario. To do it, first, SVNCM energy and its score function are presented as a conceptual extension of existing single-valued neutrosophic matrix (SVNM) energy. Then, a MAGDM model is developed in terms of SVNCM energy and its score function in a SVNCM scenario and also its decision algorithm is provided to solve MAGDM problems with SVNCMs. Finally, the developed MAGDM model is applied in the school site selection problem as an actual example, then the comparative investigation of the decision results in …

Navigate The World Of Rfid: Diversity, Capabilities, And Constraints Of Readers And Tags, Rachana Pandey

2024 Spring Honors Capstone Projects - Archive

Radio-Frequency Identification (RFID) technology, a method for storing and retrieving data through electromagnetic transmission to an RFID tag, is revolutionizing inventory and asset management in various sectors, including healthcare. This research explores the applications of RFID in a medical setting. It assesses various RFID readers and tags, focusing on their functional capabilities, ranges, and limitations within a medical environment. Employing a comprehensive approach, the study integrates an extensive literature review, comparative analysis, and empirical data from both experimental simulations and real-world healthcare scenarios. The aim is to identify RFID solutions that optimize surgical equipment management, thereby enhancing both operational efficiency …

Robust And Trustworthy Deep Learning: Attacks, Defenses And Designs, Bingyin Zhao

All Dissertations

Deep neural networks (DNNs) have achieved unprecedented success in many fields. However, robustness and trustworthiness have become emerging concerns since DNNs are vulnerable to various attacks and susceptible to data distributional shifts. Attacks such as data poisoning and out-of-distribution scenarios such as natural corruption significantly undermine the performance and robustness of DNNs in model training and inference and impose uncertainty and insecurity on the deployment in real-world applications. Thus, it is crucial to investigate threats and challenges against deep neural networks, develop corresponding countermeasures, and dig into design tactics to secure their safety and reliability. The works investigated in this …

Data Recovery Beyond The Obvious Using Digital Forensic Techniques, Smit Chandrakant Nayak

Theses, Dissertations and Culminating Projects

Advancement in drone technology, particularly for smaller drones, are creating new research fields and potential applications, particularly with regard to smaller drones. On the other hand, these enhancements bring forth additional hurdles in terms of adaptability, homogeneity, and safety. The purpose of this study is to investigate the science and technology behind drones, as well as their applications, the many ways in which citizens implement them, and the risks, precautions, and privacy problems that are associated with their utilization. This article discusses the existing literature, as well as the available solutions for drone cybersecurity, the security challenges related with drones …

Magdm Model Using Single-Valued Neutrosophic Credibility Matrix Energy And Its Decision-Making Application, Jun Ye, Rui Yong, Wanlu Du

Neutrosophic Systems with Applications

This paper aims to develop a MAGDM model using single-valued neutrosophic credibility matrix (SVNCM) energy in a SVNCM scenario. To do it, first, SVNCM energy and its score function are presented as a conceptual extension of existing single-valued neutrosophic matrix (SVNM) energy. Then, a MAGDM model is developed in terms of SVNCM energy and its score function in a SVNCM scenario and also its decision algorithm is provided to solve MAGDM problems with SVNCMs. Finally, the developed MAGDM model is applied in the school site selection problem as an actual example, then the comparative investigation of the decision results in …

Side Channel Detection Of Pc Rootkits Using Nonlinear Phase Space, Rebecca Clark

Honors Theses

Cyberattacks are increasing in size and scope yearly, and the most effective and common means of attack is through malicious software executed on target devices of interest. Malware threats vary widely in terms of behavior and impact and, thus, effective methods of detection are constantly being sought from the academic research community to offset both volume and complexity. Rootkits are malware that represent a highly feared threat because they can change operating system integrity and alter otherwise normally functioning software. Although normal methods of detection that are based on signatures of known malware code are the standard line of defense, …

Identifying Temporomandibular Disorder Morphological Risk Factors Via Explainable Deep Learning And Multiscale Biomechanical Modeling, Shuchun Sun

All Dissertations

Clarifying multifactorial musculoskeletal disorder etiologies supports risk analysis and development of targeted prevention and treatment modalities. Deep learning enables comprehensive risk factor identification through systematic analysis of disease datasets but does not provide sufficient context for mechanistic understanding, limiting clinical applicability for etiological investigations. Conversely, multiscale biomechanical modeling can evaluate mechanistic etiology within the relevant biomechanical and physiological context. We propose a hybrid approach combining 3D explainable deep learning and multiscale biomechanical modeling; we applied this approach to investigate temporomandibular joint (TMJ) disorder etiology by systematically identifying risk factors and elucidating mechanistic relationships between risk factors and TMJ biomechanics and …

Vr Circuit Simulation With Advanced Visualization For Enhancing Comprehension In Electrical Engineering, Elliott Wolbach

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

As technology advances, the field of electrical and computer engineering continuously demands innovative tools and methodologies to facilitate effective learning and comprehension of fundamental concepts. Through a comprehensive literature review, it was discovered that there was a gap in the current research on using VR technology to effectively visualize and comprehend non-observable electrical characteristics of electronic circuits. This thesis explores the integration of Virtual Reality (VR) technology and real-time electronic circuit simulation with enhanced visualization of non-observable concepts such as voltage distribution and current flow within these circuits. The primary objective is to develop an immersive educational platform that makes …

From Bits To Beds: Design And Implementation Of A Hotel Booking System A Coding Project, Alexa Gilman

University Honors College

This thesis presents a comprehensive hotel booking system designed and implemented to showcase the transition "From Bits to Beds". There are two main motivations behind this project: firstly, to provide users with a user-friendly and efficient platform for booking accommodations. Secondly, it serves as a great learning opportunity, providing hands-on practice with coding, collaboration with teammates, and exposure to real-world challenges. The system utilizes technologies such as JavaScript, Express.js, and HTML/CSS to offer features like browsing hotels, filtering options, user authentication, and booking management. The implementation of this project demonstrates a successful integration of backend and frontend components, ensuring reliability …

Next-Generation Crop Monitoring Technologies: Case Studies About Edge Image Processing For Crop Monitoring And Soil Water Property Modeling Via Above-Ground Sensors, Nipuna Chamara

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Artificial Intelligence (AI) has advanced rapidly in the past two decades. Internet of Things (IoT) technology has advanced rapidly during the last decade. Merging these two technologies has immense potential in several industries, including agriculture.

We have identified several research gaps in utilizing IoT technology in agriculture. One problem was the digital divide between rural, unconnected, or limited connected areas and urban areas for utilizing images for decision-making, which has advanced with the growth of AI. Another area for improvement was the farmers' demotivation to use in-situ soil moisture sensors for irrigation decision-making due to inherited installation difficulties. As Nebraska …

An Edge Computing System With Amd Xilinx Fpga Ai Customer Platform For Advanced Driver Assistance System, Tsun Kuang Chi, Tsung Yi Chen, Yu Chen Lin, Ting Lan Lin, Jun Ting Zhang, Cheng Lin Lu, Shih Lun Chen, Kuo Chen Li, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

The convergence of edge computing systems with Field-Programmable Gate Array (FPGA) technology has shown considerable promise in enhancing real-time applications across various domains. This paper presents an innovative edge computing system design specifically tailored for pavement defect detection within the Advanced Driver-Assistance Systems (ADASs) domain. The system seamlessly integrates the AMD Xilinx AI platform into a customized circuit configuration, capitalizing on its capabilities. Utilizing cameras as input sensors to capture road scenes, the system employs a Deep Learning Processing Unit (DPU) to execute the YOLOv3 model, enabling the identification of three distinct types of pavement defects with high accuracy and …

Database And Machine Learning Model For Classifying Autism Spectrum Disorder From Smartphone Based Electroretinography, Rory Harris

Honors Scholar Theses

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that negatively affects a patient’s cognitive and communication aptitude and, therefore, can severely impact that patient’s quality of life. Because of this, early diagnosis is paramount. In recent studies, electroretinography (ERG), which is a measure of the retina’s electrical response to a brief flash of light into the eye, has shown promise in detecting ASD. Access to these scans can provide early diagnosis, improving well-being. Current ERG devices are very expensive due to their on board processing capabilities. This paper aims to create an ERG device using a smartphone as the main …

Multithreaded Applications On The Heterogeneous Research Computing Environment., Sungbo Jung

Electronic Theses and Dissertations

Bioinformatics is a domain that has experienced rapid research growth in recent years, as evidenced by the increasing number of articles in biomedical databases such as PubMed, which adds over a million publications every year. However, this also poses a challenge for researchers who need to find relevant citations for their work. Therefore, developing efficient indexing and searching methods for text data is crucial for Bioinformatics. One key technique for information retrieval is document inversion, which involves creating an inverted index to enable efficient searching through vast collections of text or documents. This Ph.D. research aims to design the research …

Non-Vacuous Generalization Bounds For Adversarial Risk In Stochastic Neural Networks, Mustafa Waleed, Liznerski Philipp, Antoine Ledent, Wagner Dennis, Wang Puyu, Kloft Marius

Research Collection School Of Computing and Information Systems

Adversarial examples are manipulated samples used to deceive machine learning models, posing a serious threat in safety-critical applications. Existing safety certificates for machine learning models are limited to individual input examples, failing to capture generalization to unseen data. To address this limitation, we propose novel generalization bounds based on the PAC-Bayesian and randomized smoothing frameworks, providing certificates that predict the model’s performance and robustness on unseen test samples based solely on the training data. We present an effective procedure to train and compute the first non-vacuous generalization bounds for neural networks in adversarial settings. Experimental results on the widely recognized …

Cloud Computing Integration Into Mixed-Reality: Physical To Abstraction, Yassine Chahid, Patrick Slattery

Publications and Research

This research evaluates the progression of cloud computing and mixed-reality technologies, and to identify how these technologies influence advancements in the latter. Both cloud computing and mixed reality have significantly impacted the IT field and the services available to the public and various institutions. Cloud computing provides a valuable way to process information or allocate computational resources on otherwise limited hardware. Augmented or virtual reality hardware would greatly benefit from this by offloading resource-intensive tasks to other machines. The research methodology involves analyzing essential components of both innovations, divided into multiple categories. These components range from physical, hardware-based elements to …