Computer Sciences Commons^™

Fuzzy Inference Full Implication Method Based On Single Valued Neutrosophic T-Representable T-Norm: Purposes, Strategies, And A Proof-Of-Principle Study, Minxia Luo, Ziyang Sun, Donghui Xu, Lixian Wu

Neutrosophic Systems with Applications

As a generalization of intuitionistic fuzzy sets, single-valued neutrosophic sets have certain advantages in solving indeterminate and inconsistent information. In this paper, we study the fuzzy inference full implication method based on single-valued neutrosophic t-representable t-norm. Firstly, single-valued neutrosophic fuzzy inference triple I principles for fuzzy modus ponens and fuzzy modus tollens are given. Then, single-valued neutrosophic R-type triple I solutions for FMP and FMT are given. Finally, the robustness of the full implication triple I method based on the left-continuous single-valued neutrosophic t-representable t-norm is investigated. As a special case of the main results, the sensitivity of full implication …

Pairing New Approach Of Tree Soft With Mcdm Techniques: Toward Advisory An Outstanding Web Service Provider Based On Qos Levels, Sara Fawaz Al-Baker, Ibrahim El-Henawy, Mona Mohamed

Neutrosophic Systems with Applications

Web services (WSs) have become dynamic because of technological advancements and internet usage. Hence, selecting a WS provider among a variety of WS providers that perform the same function is a critical process. However, the crucial point is that various consumers may have varied needs when it comes to the quality attributes of services, such as cost, response time, throughput, security, availability, etc. These aspects of Web services are known as quality of service (QoS), or non-functional characteristics. Hence, this issue is the robust motivator for conducting this study. The objective of this study is to evaluate a set of …

The Energy Of Interval-Valued Complex Neutrosophic Graph Structures: Framework, Application And Future Research Directions, S.N. Suber Bathusha, Sowndharya Jayakumar, S. Angelin Kavitha Raj

Neutrosophic Systems with Applications

Graph structure is a developing field with many real-world applications and advancements, particularly effective frameworks for integrative problem-solving in computer networks and artificial intelligence systems. To define the idea of an Interval-Valued Complex Neutrosophic Graph Structure (IVCNGS), the concept of an Interval-Valued Complex Neutrosophic Set (IVCNS) is applied to the graph structure. Using the adjacency matrix to calculate the degree of vertex, we have defined some findings about the IVCNGS. Further, we compute the energy and Laplacian energy of IVCNGS. Moreover, we derive the lower and upper bounds for the energy and Laplacian energy of IVCNGS, and we have discussed …

The Energy Of Interval-Valued Complex Neutrosophic Graph Structures: Framework, Application And Future Research Directions, S.N. Suber Bathusha, Sowndharya Jayakumar, S. Angelin Kavitha Raj

Neutrosophic Systems with Applications

Graph structure is a developing field with many real-world applications and advancements, particularly effective frameworks for integrative problem-solving in computer networks and artificial intelligence systems. To define the idea of an Interval-Valued Complex Neutrosophic Graph Structure (IVCNGS), the concept of an Interval-Valued Complex Neutrosophic Set (IVCNS) is applied to the graph structure. Using the adjacency matrix to calculate the degree of vertex, we have defined some findings about the IVCNGS. Further, we compute the energy and Laplacian energy of IVCNGS. Moreover, we derive the lower and upper bounds for the energy and Laplacian energy of IVCNGS, and we have discussed …

Smartgrid-Ng: Blockchain Protocol For Secure Transaction Processing In Next Generation Smart Grid, Lokendra Vishwakarma, Debasis Das, Sajal K. Das, Christian Becker

Computer Science Faculty Research & Creative Works

With the advent of Blockchain and the Internet of Things (IoT), the Smart Grid is a rapidly growing technology in decentralized energy distribution and trading. However, this advancement came with some serious cyber security challenges and attacks, such as single-point failure due to a centralized architecture of smart grids, slow transaction processing, emerging cybersecurity threats, double-spending, fork, and fault tolerance. We propose a comprehensive framework for the smart grid called SmartGrid-NG to solve all these issues. Instead of using blockchain as a blackbox plugin tool, we also propose a reputation-based blockchain protocol called GridChain to increase the performance of blockchain-based …

Splitfed-Based Patient Severity Prediction And Utility Maximization In Industrial Healthcare 4.0, Himanshu Singh, Biken Moirangthem, Ajay Pratap, Shilpi Kumari, Abhishek Kumar, Sajal K. Das

Computer Science Faculty Research & Creative Works

The healthcare industry has transitioned from traditional healthcare 1.0 to AI-powered healthcare 4.0. However, overall cost for patient treatment remains high and challenging to manage due to the absence of a centralized cost evaluation mechanism before hospital visits. Therefore, in this paper, we devise a cloud-based mechanism to calculate hospitals' star rating based on questionnaire with the application of Z-score and K∗clustering algorithm. To evaluate disease severity at cloud, splitfed technique is utilized in coordination with Wireless Body Area Network (WBAN). Finally, the cloud calculates provisional treatment costs and finds a preferable hospital with a low payable treatment cost and …

Piecing Together Performance: Collaborative, Participatory Research-Through-Design For Better Diversity In Games, Daniel L. Gardner, Louanne Boyd, Reginald T. Gardner

Engineering Faculty Articles and Research

Digital games are a multi-billion-dollar industry whose production and consumption extend globally. Representation in games is an increasingly important topic. As those who create and consume the medium grow ever more diverse, it is essential that player or user-experience research, usability, and any consideration of how people interface with their technology is exercised through inclusive and intersectional lenses. Previous research has identified how character configuration interfaces preface white-male defaults [39, 40, 67]. This study relies on 1-on-1 play-interviews where diverse participants attempt to create “themselves” in a series of games and on group design activities to explore how participants may …

Novel Techniques In Imaging Congenital Heart Disease: Jacc Scientific Statement, Ritu Sachdeva, Aimee K Armstrong, Rima Arnaout, Lars Grosse-Wortmann, B Kelly Han, Luc Mertens, Ryan A Moore, Laura J Olivieri, Anitha Parthiban, Andrew J Powell

Faculty, Staff and Students Publications

Recent years have witnessed exponential growth in cardiac imaging technologies, allowing better visualization of complex cardiac anatomy and improved assessment of physiology. These advances have become increasingly important as more complex surgical and catheter-based procedures are evolving to address the needs of a growing congenital heart disease population. This state-of-the-art review presents advances in echocardiography, cardiac magnetic resonance, cardiac computed tomography, invasive angiography, 3-dimensional modeling, and digital twin technology. The paper also highlights the integration of artificial intelligence with imaging technology. While some techniques are in their infancy and need further refinement, others have found their way into clinical workflow …

The Sudden Escalation In Average Fastball Velocity: A Biomechanical And Cost-Benefit Analysis, Jacob Meiners

Capstone Projects

Average fastball velocity throughout Major League Baseball has increased dramatically over the previous 15 years. This research examines the factors leading to this increase and provides a cost-benefit analysis to determine whether throwing consistently at high velocities is worth the injury risks, compared to financial benefits, from the player perspective. Additionally, this analysis examines if paying hard-throwing pitchers is worth the monetary risk from a team executive’s perspective, due to increases in leaguewide throwing injury rates. The production of advanced motion capture technology to obtain real-time biomechanical data has given way to the optimization of pitchers’ biomechanics, with the goals …

Improved Binary Differential Evolution With Dimensionality Reduction Mechanism And Binary Stochastic Search For Feature Selection, Behrouz Ahadzadeh, Moloud Abdar, Fatemeh Safara, Leyla Aghaei, Seyedali Mirjalili, Abbas Khosravi, Salvador García, Fakhri Karray, U. Rajendra Acharya

Machine Learning Faculty Publications

Computer systems store massive amounts of data with numerous features, leading to the need to extract the most important features for better classification in a wide variety of applications. Poor performance of various machine learning algorithms may be caused by unimportant features that increase the time and memory required to build a classifier. Feature selection (FS) is one of the efficient approaches to reducing the unimportant features. This paper, therefore, presents a new FS, named BDE-BSS-DR, that utilizes Binary Differential Evolution (BDE), Binary Stochastic Search (BSS) algorithm, and Dimensionality Reduction (DR) mechanism. The BSS algorithm increases the search capability of …

Approximation Algorithm For Connected Submodular Function Maximization Problems, Wenzheng Xu, He Xue, Jing Li, Weifa Liang, Zichuan Xu, Pan Zhou, Xiaohua Jia, Sajal K. Das

Computer Science Faculty Research & Creative Works

In this paper, we study a connected submodular function maximization problem, which arises from many applications including deploying UAV networks to serve users and placing sensors to cover Points of Interest (PoIs). Specifically, given a budget K, the problem is to find a subset S with K nodes from a graph G so that a given submodular function f (S) on S is maximized while the induced subgraph G[S] by the nodes in S is connected, where the submodular function f can be used to model many practical application problems, such as the number of users within different service areas …

Building Societal Resilience Against Child Grooming Using Digital Technologies: The Solution Design, Christina Thorpe, Armin Shams, Matt Bowden

Conference Papers

Extended Abstract

Child grooming is when someone builds a relationship, trust and emotional connection with a child so they can manipulate, exploit and abuse them, therefore building societal resilience against it is of prime importance1. Although progress has been made, it is among the yet unresolved challenges in Ireland (the focus of research). With direct SFI2 training support and using Theory of Change and Design Science as the methods for research and innovation, we have done extensive stakeholder analysis, and we keep doing it further such that a solution is evolved in order to create societal resilience against child grooming, …

Creative Technologies: A Conversation With Roy Magnuson, Roy Magnuson, Maureen Russell

Faculty Publications - Music

[In lieu of an abstract, the introduction is provided.] Today I am speaking with Roy Magnuson, Associate Professor Creative Technologies in the School of Music at Illinois State University (ISU). (see Figure 1) His music has been performed throughout the United States and Europe at venues such as the World Saxophone Congress, WASBE, CBDNA, the RED NOTE New Music Festival, and the Robb Composers’ Symposium. Magnuson is also the creator of the virtual reality composition software solsticeVR and the conducting software RibbonsVR. He is a member of ASCAP, and his music is recorded on Albany Records and NAXOS.

Tasr: A Novel Trust-Aware Stackelberg Routing Algorithm To Mitigate Traffic Congestion, Doris E.M. Brown, Venkata Sriram Siddhardh Nadendla, Sajal K. Das

Computer Science Faculty Research & Creative Works

A Stackelberg routing platform (SRP) reduces congestion in one-shot traffic networks by proposing optimal route recommendations to the selfish travelers. Traditionally, Stackel-berg routing is cast as a partial control problem where a fraction of the traveler flow complies with route recommendations, while the remaining responds as selfish travelers. In this paper, we formulate a novel Stackelberg routing framework where the agents exhibit probabilistic compliance by accepting SRP's route recommendations with a trust probability. Specifically, we propose a greedy Trust-Aware Stackelberg Routing algorithm (in short, TASR) for SRP to compute unique path recommendations to each traveler flow with a unique demand. …

Racism Detection In Tweets, Ndjeuha Gihane

All Graduate Projects

Since the advent of social networks in 1997, businesses and people’s lives have changed in a good way. From promoting companies to reaching out to friends and family, social networking has become a major element in our lives. X is a very popular platform that is used by many people, including celebrities and politicians, to communicate with their audience. Like other platforms, X is not spared by the racism contained in the tweets. We should be able to catch those racist comments on any social media and block the accounts of those responsible for them. To do so, we have …

Shedding Light On Software Engineering-Specific Metaphors And Idioms, Mia Mohammad Imran, Preetha Chatterjee, Kostadin Damevski

Computer Science Faculty Research & Creative Works

Use of figurative language, such as metaphors and idioms, is common in our daily-life communications, and it can also be found in Software Engineering (SE) channels, such as comments on GitHub. Automatically interpreting figurative language is a challenging task, even with modern Large Language Models (LLMs), as it often involves subtle nuances. This is particularly true in the SE domain, where figurative language is frequently used to convey technical concepts, often bearing developer affect (e.g., 'spaghetti code). Surprisingly, there is a lack of studies on how figurative language in SE communications impacts the performance of automatic tools that focus on …

On The K-Weak Coverage Of Random Mobile Sensors, Sajal K. Das, Rafal Kapelko

Computer Science Faculty Research & Creative Works

This paper studies the fundamental problem of energy consumption in the movement of mobile random sensors ensuring k-weak coverage on the domain. In particular, we analyze two notions of k-weak coverage on the unit square, namely (1) (k, x)-weak coverage in which every straight-line path across the width of the unit square passes through the sensing range of at least k sensors; and (2) (k, x, y)-weak coverage in which every straight-line path across the width and the length of the unit square passes through the sensing range of at least k sensors. The number of reliable and p-reliable sensors …

Real-Time Analysis Of Encrypted Dns Traffic For Threat Detection, Marta Moure-Garrido, Sajal K. Das, Celeste Campo, Carlos Garcia-Rubio

Computer Science Faculty Research & Creative Works

Domain Name System (DNS) tunneling is a well-known cyber-attack that allows data exfiltration - the attackers exploit this tunnel to extract sensitive information from the system. Advanced Persistent Threat (APT) attackers encapsulate malicious traffic in a DNS connection to elude security mechanisms such as Intrusion Detection System (IDS). Although different techniques have been implemented to detect these targeted attacks, their rise induces a threat to Cyber-Physical Systems (CPS). The DNS over HTTPS (DoH) tunnel detection is a challenge because the encrypted data prevents an analysis of DNS traffic content. In this paper, we present a novel detection system that identifies …

L3dml: Facilitating Geo-Distributed Machine Learning In Network Layer, Xindi Hou, Shuai Gao, Ningchun Liu, Fangtao Yao, Bo Lei, Hongke Zhang, Sajal K. Das

Computer Science Faculty Research & Creative Works

Geo-Distributed Machine Learning (GDML) aims to train large-scale machine learning models across geographically dispersed datacenters. However, the performance of GDML systems is constrained by the limited Wide Area Network (WAN) bandwidth and the presence of the straggler problem. Existing GDML designs often show contradictory effects in addressing these challenges, while in-network computing attempts are typically restricted to single datacenter environments rather than the more complex GDML scenarios. To overcome these limitations, this paper proposes L3DML to facilitate GDML using the P4-based Software-defined Network (SDN). Our approach incorporates three key innovations. Firstly, we introduce a novel network addressing scheme that enables …

Interlude: Interactions Between Labeled And Unlabeled Data To Enhance Semi-Supervised Learning, Zhe Huang, Xiaowei Yu, Dajiang Zhu, Michael C. Hughes

Computer Science Faculty Research & Creative Works

Semi-supervised learning (SSL) seeks to enhance task performance by training on both labeled and unlabeled data. Mainstream SSL image classification methods mostly optimize a loss that additively combines a supervised classification objective with a regularization term derived solely from unlabeled data. This formulation often neglects the potential for interaction between labeled and unlabeled images. In this paper, we introduce InterLUDE, a new approach to enhance SSL made of two parts that each benefit from labeled-unlabeled interaction. The first part, embedding fusion, interpolates between labeled and unlabeled embeddings to improve representation learning. The second part is a new loss, grounded in …

Core-Periphery Multi-Modality Feature Alignment For Zero-Shot Medical Image Analysis, Xiaowei Yu, Lu Zhang, Zihao Wu, Dajiang Zhu

Computer Science Faculty Research & Creative Works

Multi-modality learning, exemplified by the language-image pair pre-trained CLIP model, has demonstrated remarkable performance in enhancing zero-shot capabilities and has gained significant attention recently. However, simply applying language-image pre-trained CLIP to medical image analysis encounters substantial domain shifts, resulting in severe performance degradation due to inherent disparities between natural (non-medical) and medical image characteristics. To address this challenge and uphold or even enhance CLIP's zero-shot capability in medical image analysis, we develop a novel approach, Core-Periphery feature alignment for CLIP (CP-CLIP), to model medical images and corresponding clinical text jointly. To achieve this, we design an auxiliary neural network whose …

Enhancing Group-Wise Consistency In 3-Hinge Gyrus Matching Via Anatomical Embedding And Structural Connectivity Optimization, Chao Cao, Xiaowei Yu, Lu Zhang, Tong Chen, Yanjun Lyu, Tianming Liu, Dajiang Zhu

Computer Science Faculty Research & Creative Works

Recently, a novel cortical folding pattern known as the 3-hinge gyrus (3HG) has been identified. 3HGs are defined as the convergence of the gyri coming from three distinct directions on gyral crests. In contrast to cortical regions, 3HGs are defined at a finer scale and they widely exist across different individuals, representing both commonalities and individualities of cortical folding patterns. It is important to note that 3HGs are identified in individual spaces, lacking natural cross-subject correspondences. To address this issue, we have developed a learning-based method to encode anatomical features of 3HGs into a set of embedding vectors that can …

Capturing Biomarkers Associated With Alzheimer Disease Subtypes Using Data Distribution Characteristics, Kenneth Smith, Sharlee Climer

Computer Science Faculty Works

Late-onset Alzheimer disease (AD) is a highly complex disease with multiple subtypes, as demonstrated by its disparate risk factors, pathological manifestations, and clinical traits. Discovery of biomarkers to diagnose specific AD subtypes is a key step towards understanding biological mechanisms underlying this enigmatic disease, generating candidate drug targets, and selecting participants for drug trials. Popular statistical methods for evaluating candidate biomarkers, fold change (FC) and area under the receiver operating characteristic curve (AUC), were designed for homogeneous data and we demonstrate the inherent weaknesses of these approaches when used to evaluate subtypes representing less than half of the diseased cases. …

Resource Aware Clustering For Tackling The Heterogeneity Of Participants In Federated Learning, Rahul Mishra, Hari Prabhat Gupta, Garvit Banga, Sajal K. Das

Computer Science Faculty Research & Creative Works

Federated Learning Is A Training Framework That Enables Multiple Participants To Collaboratively Train A Shared Model While Preserving Data Privacy. The Heterogeneity Of Devices And Networking Resources Of The Participants Delay The Training And Aggregation. The Paper Introduces A Novel Approach To Federated Learning By Incorporating Resource-Aware Clustering. This Method Addresses The Challenges Posed By The Diverse Devices And Networking Resources Among Participants. Unlike Static Clustering Approaches, This Paper Proposes A Dynamic Method To Determine The Optimal Number Of Clusters Using Dunn Indices. It Enables Adaptability To The Varying Heterogeneity Levels Among Participants, Ensuring A Responsive And Customized Approach To …

Crafting Effective Prompts: Leveraging Generative Ai In Libraries, April Sheppard, Kristin Flachsbart

Staff and Faculty Scholarship

Discover how strategic prompt design can help you harness the power of generative artificial intelligence (AI) in your library. Through a series of examples, the presenters will demonstrate the impact that well-crafted prompts can have on the quality and relevance of AI-generated outputs.

A Comparison Of Machine Learning Surrogate Models Of Street-Scale Flooding In Norfolk, Virginia, Diana Mcspadden, Steven Goldenberg, Binata Roy, Malachi Schram, Jonathan L. Goodall, Heather Richter

Community & Environmental Health Faculty Publications

Low-lying coastal cities, exemplified by Norfolk, Virginia, face the challenge of street flooding caused by rainfall and tides, which strain transportation and sewer systems and can lead to personal and property damage. While high-fidelity, physics-based simulations provide accurate predictions of urban pluvial flooding, their computational complexity renders them unsuitable for real-time applications. Using data from Norfolk rainfall events between 2016 and 2018, this study compares the performance of a previous surrogate model based on a random forest algorithm with two deep learning models: Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The comparison of deep learning to the random …

Usage And Knowledge Of Online Tools And Generative Ai: A Survey Of Students, Rahul R. Divekar Phd, Lisette Gonzalez, Sophia Guerra, Natasha Boos

Department of Experience Design (XD) Faculty Publications

Artificial Intelligence (AI) tools like ChatGPT are poised to transform student and educator workflows in higher education. However, there is less documentation on the range of tools students in higher education use, how they use them and in coordination with other online tools for learning, and their expertise using AI tools. We present a mixed-method analysis of a survey conducted at a doctoral-granting university in the United States investigating the adoption of AI tools in the context of other technologies. The findings include how the students used GenAI tools in light of other on-line technologies, their perception of expertise on …

Understanding Patient Profiles In Sickle Cell Disease Using Unsupervised Machine Learning, Raj Kamal Somavarapu

Browse all Theses and Dissertations

Sickle Cell Disease (SCD) is one of the most prevalent genetic blood disorders affecting millions of people worldwide. It is often accompanied by acute and/or chronic pain leading to increased healthcare costs and adverse outcomes. Effective management of SCD requires an understanding of the diverse physiological profiles. This study employs unsupervised machine learning, specifically K-means clustering to categorize the patients suffering with SCD into different clusters based on their vital signs. The main aim is to identify the groups that reflect similarities in physiological and pain profiles, allowing an in-depth analysis to reveal distinctive features distinguishing patient clusters. The project …

Mime: Mobility-Induced Dynamic Matching For Partial Offloading In Vehicular Edge Computing, Mahmuda Akter, Debjyoti Sengupta, Anurag Satpathy, Sajal K. Das

Computer Science Faculty Research & Creative Works

Autonomous vehicles (AVs) execute compute intensive control operations like adjusting speed and steering, causing significant energy dissipation and latency due to resource limited onboard units (OBUs). Offloading these tasks to Roadside Units (RSUs) is a solution, but it faces challenges. First, the stringent latency requirements are impacted by the vehicle's stochastic velocity. Second, allocating limited RSU resources to numerous vehicles within its coverage area is difficult. This paper proposes the MIME framework to address these issues. We use Discrete Fourier transform (DFT) that computes the average velocity over an aperiodic velocity signal extracted from a real-world dataset. for resource allocation, …

Introduction To Computer Architecture And Operating Systems, Kevin Preston

Open Educational Resources (OER)

Computer architecture is a set of rules and methods that describe the functionality, organization, and implementation of computer systems. The architecture of a system refers to its structure in terms of separately specified components of that system and their interrelationships.

In a similar manner to other uses of the word architecture, computer architecture is focused on determining the needs of the user/system/technology and creating a logical design and standards based on those requirements.

The goals for this course include:

• To learn how to write advanced ARM Assembly Language programs for the Raspberry Pi and the relationship of these instructions to …