Computer Engineering Commons^™

Mediating The Exquisite: Aesthetic Affordances Of The Network Arts, Seth Adams

Journal of Network Music and Arts

This paper argues that the Network Arts afford uniquely nuanced aesthetic interactions among humans and between humans and machines. After defining these moments under a new concept I call exquisite musicking, I investigate how distributed networks of human and nonhuman actors create experiences that challenge or complicate traditional notions of agency, ensemble, aesthetics, and synchrony. Using Actor-Network Theory as a lens of inquiry, I explore the exquisite affordances of network-based musicking, discussing how pitch, timbre, and texture are co-created in networked performances. I also examine how exquisite musicking could serve as a fruitful concept in music education, particularly in pedagogical …

Editorial, Sarah Rose Weaver

Journal of Network Music and Arts

No abstract provided.

Exact Solutions Of M-Fractional Complex Ginzburg-Landau Equation Via Two Analytical Methods, Nematollah Kadkhoda, Mojtaba Baymani, Maad M. Mijwil, Mostafa Abotaleb

Iraqi Journal for Computer Science and Mathematics

This article investigates the M-fractional complex Ginzburg-Landau (CGL) equationby utilizing the modified simplest equation method and the modified G'G-expansion method. The CGL holds significant importance in the realm of oscillatory phenomena and optical fibers. By implementing a fractional complex transformation, the M-fractional CGL equation is transformed into an ordinary differential equation (ODE).By employing these methodologies, bright periodic solutions, periodic solutions, and dark wave solutions relevant to phenomena witnessed in nonlinear optics or plasma environments are ascertained.It is demonstrated that these methods are very powerful and efficient to solve many fractional differential equations.

Reason for Expression of Concern: …

Enhancing Image Denoising Performance Through Cosine Similarity-Based Block Matching And Adaptive Thresholding - Ca-Ebm3d, R. Padmapriya, A. Jeyasekar

Iraqi Journal for Computer Science and Mathematics

Image denoising plays a vital role in enhancing visual quality by effectively suppressing noise while retaining critical image structures and textures. Traditional Block-Matching and 3D (BM3D) Filtering techniques, although widely adopted, often encounter challenges in achieving an optimal trade-off between noise reduction and feature preservation due to limitations in fixed-thresholding strategies and suboptimal block matching. To address these shortcomings, this study introduces a novel Cosine Adaptive BM3D (CA-BM3D) approach, which integrates cosine similarity for more accurate block matching and incorporates adaptive thresholding to enhance denoising efficiency. The proposed method was evaluated on six standard 8-bit grayscale images such as Leena …

From Industrial Automation To Intelligent Automation: The Impact Of Iiot On Process Control - A Review?, Ali S. Allahloh, Mohammad Sarfraz, Duraid Y. Mohammed, Nadeen Khaleel Ibrahim, Hayder Hussein Thary

Iraqi Journal for Computer Science and Mathematics

The Industrial Internet of Things (IIoT) is reshaping process control, turning torrents of plant data into near-instant insight. Field evidence already shows unplanned-downtime cuts of 55%, maintenance-budget savings of 40%, and gains in overall-equipment-effectiveness of roughly 15%; nevertheless, fewer than 30% of facilities have pushed beyond pilot scale to full, always-on predictive maintenance. To clarify why, this review screens 312 Scopus records published between 2010 and 2023, then subjects 62 peer-reviewed studies and a broad set of industry reports to deep content analysis, bibliometric mapping, and thematic clustering.The results reveal four tightly connected research fronts—IIoT/Industry 4.0 core technologies, classical process-control …

Reliability-Based Design Optimization Using Differential-Algebraic Equations, Saad Abbas Abed, Mona Ghassan, Shaimaa Qais Latef, Hind S. Hassan

Iraqi Journal for Computer Science and Mathematics

Reliability-based design optimization (RBDO) determines optimal design parameters by incorporating reliability constraints. This paper presents a RBDO approach using differential-algebraic equations (DAEs) for modeling and constraints. DAEs provide an accurate representation of dynamic engineering systems with coupled differential and algebraic equations. However, the nonlinearity and implicit nature of DAEs pose challenges for uncertainty propagation and optimization. This study proposes an efficient RBDO methodology based on stochastic collocation to quantify uncertainty in DAEs. The DAEs are transformed into an explicit ODE system to enable direct uncertainty analysis via sampling. Optimization under reliability constraints is achieved using a sequential approximate programming strategy. …

Challenges And Constraints In Trajectory Planning For Autonomous Robots, Jawad Abdouni, Tarik Jarou, Toufik Mzili, Abderrahim Waga, Karima Bensassi

Iraqi Journal for Computer Science and Mathematics

Autonomous mobile robots have revolutionized navigation by operating without human intervention, leveraging advanced data acquisition systems such as cameras, radar, and LIDAR, along with sophisticated planning, localization, and control algorithms. A critical challenge in this domain is path planning: determining optimal trajectories to ensure safe and efficient travel in diverse environments. This paper addresses the need to systematically evaluate trajectory-planning algorithms, whose selection directly impacts navigation performance. We present a comprehensive analysis of various classes of path-planning methods, detailing their advantages, limitations, and application contexts. By comparing these algorithms, we identify key criteria for selecting the most suitable approach for …

Diagnosis Of Covid-19 And Viral Pneumonia With Chest X-Ray Images Using Resnet-34, Sudhir Anakal, Krishna Prasad K, Chandrashekhar Uppin, Dileep Kumar M

Iraqi Journal for Computer Science and Mathematics

COVID-19 is a highly contagious viral infection that primarily affects the respiratory system, causing symptoms such as high fever, cough, and severe respiratory distress. Early detection of the disease is of utmost importance to control the spread and severity. Common diagnostic methods include Reverse Transcription Polymerase Chain Reaction (RT-PCR), antigen tests, chest X-rays, and computed tomography (CT) scans. Similarly, viral pneumonia, another severe lung infection, leads to fluid or pus accumulation in the lungs, causing symptoms such as chest pain, fatigue, excessive sweating, and nausea. The elderly and young children are particularly vulnerable to severe complications. Similarly, viral pneumonia, another …

Algorithms For Combined Regular Synthesis Of Controller Parameters In Control Systems For Dynamic Objects, Khusan Zakirovich Igamberdiev, Latafat Abbas Gizi Gardashova, Yulduz Mukhtarkhodjayevna Abdurakhmanova, Uktam Farkhodovich Mamirov

Technical science and innovation

Nonlinear control system that includes m-dimensional input control signal and extended (n+s) - dimensional state vector, the last s components of which form a vector of unknown parameters θ satisfying a general difference equation is being considered. The quality criterion is determined by the loss function. The optimal control must satisfy the Bellman equation with respect to the optimal loss function. To be defined an approximate solution that preserves an active use of information. For this purpose, the system is linearized in accordance to the nominal trajectory. This problem is seen as incorrectly stated. The values of the preliminary data …

Intelligent Control Of Reducing Energy Consumption And Water Loss In Smart Home Systems, Hakimjon Nasriddinovich Zaynidinov, Damira Farxodovna Hodjaeva, Dhananjay Singh

Technical science and innovation

The article presents an innovative approach to developing an intelligent control system for managing temperature and water level in smart home systems. The proposed method integrates an adaptive PID controller with fuzzy logic algorithms, enabling dynamic adjustment of the PID controller coefficients in real time. The mathematical model of the system incorporates heat balance equations, differential heat transfer relations, and nonlinear models of fluid loss. The adaptive control algorithms developed within the study allow the system to effectively respond to changes in input data.

A comprehensive analysis of the membership functions is conducted, along with adaptive tuning of the PID …

A Comprehensive Review Of Software Requirements Dependencies Analysis Techniques, Nahla Mohamed, Sherif Mazen, Waleed Helmy

Iraqi Journal for Computer Science and Mathematics

The software Requirements Prioritization (RP) process is essential for producing a successful software project. Requirements are interdependent in software projects, so handling their dependency during the RP process is mandatory. Many researchers have shown that requirements dependency is challenging for large-scale systems. Extracting requirements dependency is difficult since requirements are documented in natural language. Improper handling of dependencies among requirements while prioritization can cause inaccurate prioritization results and deadlocks, which cause project delays, rework, and redesign. Many techniques have been introduced to automate the dependency analysis process among software requirements, including artificial intelligence (AI) and other logic-based methods such as …

Fixed Points Of Multi-Valued Graph Maps In Strong B-Metric, Shaimia Qais Latif, Salwa Salman Abed, Haider Ahmed Shihab

Iraqi Journal for Computer Science and Mathematics

This paper involves adopting a well-known generalization method in the branches that dealing with fixed points via weakening the suppositions. As appearing in previous sources, letting (Ω,Sb,K) be a strong b-MS, and F,H be two multi-valued maps on Ω equipped with a graph σ s.t the set of vertices of σ,Λ(σ) = Ω and the set of edges of σ, Ξ(σ) ⊆ Ω × Ω. The acceptable assupmtions have been adopted to finding a common fixed point for in Ω. …

Enhanced Detection Of Intracranial Hemorrhage: A New Hybrid Model Design Based On The U-Net Segmentation Method, Hassan F. Hassan, Hadeel K. Aljobouri, Oktay Algin

Iraqi Journal for Computer Science and Mathematics

Intracranial hemorrhage (ICH) denotes bleeding inside the skull, which can occur in or around the brain. Computed tomography (CT) has been used to detect ICH due to its high efficiency and accuracy. Nowadays, deep learning model design is introduced to allow an accurate and efficient classification of ICH in CT images. This work focused on developing U-Net-based models for the segmenting of ICH. Furthermore, the proposed model employed two transfer learning models, MobileNet and Xception, as the backbones of the U-Net topology. This approach aims to establish metrics that improve ICH treatment through precise segmentation techniques. A free dataset from …

Retracted: A Novel Benchmarking Framework For Selecting The Best Deep Learning Model Diagnosing Covid-19 Based On New Development For Dual Mcdm Methods, Mahmood M. Salih, Yousif Raad Muhsen, M.A. Ahmed, Reem D. Ismael, Moceheb Lazam Shuwandy, Z.T. Al-Qaysi

Iraqi Journal for Computer Science and Mathematics

COVID-19 was diagnosed using deep learning models by a group of studies. Evaluating and benchmarking these models are essential to achieving the most suitable model for diagnosing coronavirus. Objective: In this investigation, we offer an inclusive valuation of several deep learning models to detect the maximum appropriate and active model which gratifies doctors' requirements and assessment criteria. Method: This study combines Fuzzy decision by the opinion score method (FDOSM) and Fuzzy-Weighted Zero-Inconsistency (FWZIC). According to the advantage of Trapezoidal Intuitionistic fuzzy, we developed FWZIC into Trapezoidal Intuitionistic fuzzy named (TrIF-FWZIC) for weighting criteria and FDOSM into Trapezoidal Intuitionistic fuzzy FDOSM …

Leveraging Machine Learning For Accurate Prediction Of Nba Player Salaries, Ye Cheng, Yan Song, Mingqi Wang

Iraqi Journal for Computer Science and Mathematics

Basketball players in the NBA are renowned for their talent, athleticism, and commitment to the game. NBA players may make enormous sums of money; however, they vary greatly. Rookie agreements begin at a lower price and go up following performance. NBA players’ pays are influenced by several factors. Because they influence games and the success of the club, exceptional players fetch larger compensation. This study employs Machine Learning (ML) techniques, including Lasso Regression and Random Forest Regression (RFR) models to analyze wage trends, enhanced by the Slime Mould Algorithm (SMA) and Artificial Rabbit Optimization (ARO) for accuracy. The goal is …

Tkbe : Two Key Broadcast Encryption For The Iot, Rachit Parikh

Master’s Dissertations

The growing usage of the Internet of Things (IoT) has made it necessary to ensure

the security of these interconnected devices. Key management becomes particularly

challenging when devices are not always online due to resource constraints or business

decisions. Moreover, the IoT infrastructure typically relies on the publish-subscribe

model for communication, which raises additional security considerations since the

message broker becomes a central point of attack. Existing solutions with end-toend

encryption from publisher to subscriber are either computationally expensive for

resource constrained devices or compromise on the decoupling in publish/subscribe

systems. This thesis tackles the problem of efficient key management …

Uncertainty-Driven Fusion For Conflictive Multiview Data: Beyond View Alignment Assumptions, Puspamalya Sahoo

Master’s Dissertations

Multiview learning aims to integrate diverse feature representations to achieve a comprehen- sive understanding of data. Traditional approaches often assume strict alignment across views, making them ill-suited for real-world scenarios where low-quality conflictive instances, i.e. in- stances with conflicting information across views are prevalent. Existing methods largely focus on eliminating conflicting instances by discarding them or substituting conflicting views, over- looking the need for practical decision making in such cases. Furthermore, while the recently proposed Reliable Conflictive Multiview Learning (RCML) framework introduces the idea of attaching reliabilities to decision outcomes, it leaves certain theoretical gaps unaddressed, es-pecially prioritization of conflictive …

Syntax-Enhanced Boundary-Aware Named Entity Recognition Model, Chuanming Yu, Bin Deng, Zhengang Zhang

Journal of Scientific Information Research

[Purpose/significance] This study addresses the issue of inadequate perception of entity boundaries in traditional character-level modeling-based named entity recognition models by integrating syntax information containing entity boundary features into the task using a multi-head graph attention network with dense connections. This integration enhances the effectiveness of named entity recognition.

[Method/process] This study proposes a Syntax-enhanced Boundary-aware Named Entity Recognition Model (SynBNER), which utilizes BERT for text semantic representation and integrates syntax information using a dense-connected graph attention network. This integration incorporates implicit entity boundary information from syntax information into word representations, thereby enhancing the model's entity boundary perception capability.

[Result/conclusion] …

Differential Privacy Enabled Deep Skin Image Classification Model Development, Prasun Kumar Mandal

Master’s Dissertations

Abstract In the era of big data, the explosive growth in data volume has significantly accelerated the development of deep learning. Deep learning is the most promising area of AI, yielding significant advancements in medical image classification. However, healthcare data contains important sensitive information and so privacy and security are crucial to preventing unauthorized access. Note that there are several data protection rules from multiple regulations to penalize any kind of data security violation, for example, the data protection principles (Article 5.1-2) and the data protection by design and by default (Article 25) of the General Data Protection Regulation from …

Towards Efficient Privacy-Preserving Deep Learning: He-Friendly Structures, Flexible Pruning, He-Efficient Architectures, And Secure Transformer Token Drop, Yifei Cai

Electrical & Computer Engineering Theses & Dissertations

Deep learning (DL) has become a powerful tool for solving complex problems, but developing DL models typically requires vast datasets, high computational resources, and expert knowledge—barriers that limit accessibility. Machine Learning as a Service (MLaaS) addresses this challenge by allowing resource-rich providers to deliver pre-trained DL models as services. However, privacy concerns arise: clients hesitate to share sensitive data, while providers protect their proprietary models. To address this, privacy-preserving MLaaS integrates cryptographic techniques into DL computations, as seen in frameworks like Cryptonets, SecureML, GAZELLE, CrypTFlow2, Cheetah, and BOLT. Among them, Homomorphic Encryption (HE) enables computation on encrypted data but remains …

Statistical Monitoring Of Hard Faults In Digital Systems, Dany Akshay Deep Isukapalli

Electrical Engineering Theses and Dissertations

Achieving a high test coverage is crucial for helping to ensure that integrated circuits are working correctly and are non-defective. Although scan-based structural tests are used throughout the industry, high-level functional tests may be needed to detect some defects— especially those that are environmentally sensitive. Unfortunately, the character of functional test makes it difficult to obtain high coverage, and it is even hard to estimate coverage because fault simulation times of large circuits are long. As a result, some method is required for predicting the ability of a functional test that has not been fault simulated to detect defects. In …

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 …

Biocomputing Approach To Modeling And Modulating Calcium Signaling, Sehee Sun

School of Computing: Dissertations, Theses, and Student Research

Biocomputing is an emerging field that seeks to perform computational tasks using biological substrates and processes. Unlike conventional computing systems based on silicon hardware, biocomputing leverages the parallelism, energy efficiency, and complex dynamics of living systems. Among various cellular mechanisms, calcium (Ca2+) signaling stands out as a central regulator of diverse biological functions, offering a promising basis for programmable logic and control in living cells.

This thesis introduces a novel framework for modeling and modulating Ca2+ dynamics using biologically inspired Boolean logic circuits. Specifically, we propose the Ca2+ Boolean Logic (CaBL) model, in which Ca2+ fluxes and interactions are abstracted …

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.

A Uas-Centered Investigation Of Vorticity Characteristics And Cold Pool Structure Across Forward And Left-Flank Boundaries In Supercells, Mark R. De Bruin

Department of Earth and Atmospheric Sciences: Dissertations, Theses, and Student Research

Supercell internal boundaries are the locus of tornadogenesis; thus, understanding the characteristics of these boundaries, particularly in terms of vorticity, is important for identifying the role they play in tornado formation. Insight into the overall characteristics of internal boundaries and their possible role in tornadogenesis have been driven by studies reliant on numerical modeling-based experiments. Observational studies often neglect above-surface conditions or, when these observations are made, lack the spatial resolution to resolve boundary characteristics. During TORUS (Targeted Observation by Radars and UAS of Supercells) 2019 and TORUS-LItE (TORUS Left-flank Intensive Experiment) 2023, uncrewed aircraft systems (UAS) and mobile mesonets …

Secure Query On Encrypted Data By Using Fully Homomorphic Encryption, Mrinmoy Bera

Master’s Dissertations

Cloud service providers typically store user data in an encrypted form (data at rest). However, when a user performs a query, the server first decrypts the data, processes the query on plaintext, and then sends the result back to the user (data in transit). This process exposes a critical vulnerability—if the cloud server is ever compromised, the decrypted data becomes accessible to the attacker. To address this security gap, we design a secure query protocol that eliminates the need to decrypt data on the server side. Fully Homomorphic Encryption (FHE) offers a groundbreaking solution by enabling arbitrary computations directly on …

Federated Learning Using Fully Homomorphic Encryption, Sk Golam Kuddus

Master’s Dissertations

Traditional machine learning approaches require centralizing data for training, which raises significant privacy concerns when dealing with sensitive information. Federated learning (FL) addresses this by keeping data local and enabling multiple users to collaboratively train a shared machine learning model. In spite of this, FL remains vulnerable to inference attacks, as sensitive information can still be extracted from the model’s learned parameters. While traditional privacy-enhancing techniques such as di!erential privacy introduce noise to model updates to obscure individual data points, they often present a fundamental trade-o! between privacy and utility. Furthermore, these approaches still carry risks of data leakage if …