Computer Engineering Commons^™

Retraction Notice: Data Envelopment Analysis Using Stochastic Frontier Analysis And Bootstrap Confidence Intervals, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF RETRACTION FOR: Alqahtani, Fahad F. (2025) ``Data Envelopment Analysis using Stochastic Frontier Analysis and Bootstrap Confidence Intervals,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 2, Article 11. DOI: https://doi.org/10.52866/2788-7421.1254.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss2/11.

Retraction Notice: Comparative Study Based On Continuous Analysis Of Autism Spectrum Disorder Using Advanced Deep Learning With Model Interpretability Insights, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF RETRACTION FOR: Sar, Ayan; Mahdi, Hussain Falih; Aich, Sumit; Singh, Pranav; and Choudhury, Tanupriya (2025) ``Comparative Study based on Continuous Analysis of Autism Spectrum Disorder Using Advanced Deep Learning with Model Interpretability Insights,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 3, Article 16. DOI: https://doi.org/10.52866/2788-7421.1291.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss3/16.

Retraction Notice: Cipher Text To Secure Li-Fi System Using Hybrid Encryption Algorithm, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF RETRACTION FOR: Ahmed, Mohammed M. and Alnajjar, Satea H. (2025) ``Cipher Text to Secure Li-Fi System Using Hybrid Encryption Algorithm,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 2, Article 16. DOI: https://doi.org/10.52866/2788-7421.1257.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss2/16.

Retraction Notice: Capsule Network Model For Detecting Spoofing Attack In The Internet Of Medical Things (Iomt), Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF RETRACTION FOR: Alsharaiah, Mohammad A.; Almaiah, Mohammed Amin; Obeidat, Mansour; and Shehab, Rami (2025) ``Capsule Network Model for Detecting Spoofing Attack in the Internet of Medical Things (IoMT),'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 3, Article 35. DOI: https://doi.org/10.52866/2788-7421.1306.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss3/35.

Retraction Notice: Automated Diagnosis Of Orthopedic Patients With Vertebral Column Disorders Using Advanced Mathematical Modeling, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF RETRACTION FOR: Feng, Chen; Sun, Zhenhua; Dai, Xinheng; and Wen, Hongli (2025) ``Automated Diagnosis of Orthopedic Patients with Vertebral Column Disorders Using Advanced Mathematical Modeling,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 3, Article 34. DOI: https://doi.org/10.52866/2788-7421.1304.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss3/34.

Retraction Notice: Analysis Of Energy Sector Co2 Emanations Using Wavelet-Based Numerical Technique, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF RETRACTION FOR: R., Yeshwanth and S., Kumbinarasaiah (2025) ``Analysis of Energy Sector CO2 Emanations Using Wavelet-Based Numerical Technique,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 4, Article 2. DOI: https://doi.org/10.52866/2788-7421.1313.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss4/2.

Retraction Notice: Ai-Driven Flood Prediction, Monitoring, And Warning Systems: Design, Evaluation, And Simulation, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF RETRACTION FOR: Alkharabsheh, Abdel Rahman A. and Momani, Lina M. (2025) ``AI-Driven Flood Prediction, Monitoring, and Warning Systems: Design, Evaluation, and Simulation,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 4, Article 8. DOI: https://doi.org/10.52866/2788-7421.1337.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss4/8.

Retraction Notice: Adaptive Crossover And Mutation Mechanisms For Enhanced Lpb Algorithm Performance, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF RETRACTION FOR: Ahmed, Abbas M. and Rashid, Tarik A. (2025) ``Adaptive Crossover and Mutation Mechanisms for Enhanced LPB Algorithm Performance,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 3, Article 48. DOI: https://doi.org/10.52866/2788-7421.1323.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss3/48.

Retraction Notice: Accurate Electrocardiogram Classification Of Heart Disease Using Deep Learning Network, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF RETRACTION FOR: Saleh, Hadeel M.; Ahmed, Sahar Hamad; and Mahmoud, Akeel Sh. (2025) ``Accurate Electrocardiogram Classification of Heart Disease Using Deep Learning Network,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 2, Article 18. DOI: https://doi.org/10.52866/2788-7421.1259.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss2/18.

Retraction Notice: A Review Of Breast Cancer Histological Image Classification: Challenges And Limitations, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF RETRACTION FOR: Jassam, Israa Faisal; Mukhlif, Abdulrahman Abbas; Nafea, Ahmed Adil; Tharthar, Mustafa Adnan; and Khudhair, Ahmed Isam (2025) ``A Review of Breast Cancer Histological Image Classification: Challenges and Limitations,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 1, Article 1. DOI: https://doi.org/10.52866/2788-7421.1232.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss1/1.

Retraction Notice: A Quantum Convolutional Neural Network Approach For Early And Accurate Diagnosis Of Parkinson's Disease, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF RETRACTION FOR: Ibrahim, Aiesha Mahmoud; Mohammed, Mazin Abed; and Al-Boridi, Omar (2025) ``A Quantum Convolutional Neural Network Approach for Early and Accurate Diagnosis of Parkinson's Disease,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 3, Article 13. DOI: https://doi.org/10.52866/2788-7421.1288.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss3/13.

Retraction Notice: A Novel Scheme To Optimize Lsb Steganography Based On A Logistic Chaotic Map And Genetic Algorithm, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF RETRACTION FOR: Laila, Dena Abu; obeidt, Ibrahim Moh'd; Aljaidi, Mohammad; Almaiah, Mohammed Amin; AlBourini, Muneer; Al-Na'amneh, Qais; Samara, Ghassan; Shehab, Rami; and Momani, Khaled (2025) ``A Novel Scheme to Optimize LSB Steganography Based on a Logistic Chaotic Map and Genetic Algorithm,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 2, Article 24. DOI: https://doi.org/10.52866/2788-7421.1265.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss2/24.

Retraction Notice: A Novel Benchmarking Framework For Selecting The Best Deep Learning Model Diagnosing Covid-19 Based On New Development For Dual Mcdm Methods, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF RETRACTION FOR: Salih, Mahmood M.; Muhsen, Yousif Raad; Ahmed, M.A.; Ismael, Reem D.; Shuwandy, Moceheb Lazam; and Al-qaysi, Z.T. (2025) ``A Novel Benchmarking Framework for Selecting the Best Deep Learning Model Diagnosing COVID-19 Based on New Development for Dual MCDM Methods,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 3, Article 2. DOI: https://doi.org/10.52866/2788-7421.1276.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss3/2.

Retraction Notice: A Group Decision-Making For Selecting Multi-Deep Face Recognition Models, Iraqi Journal For Computer Science And Mathematics

Iraqi Journal for Computer Science and Mathematics

NOTICE OF RETRACTION FOR: Alazzawi, Abdulbasit; Yas, Qahtan M.; and Albayati, Burhan (2025) ``A Group Decision-Making for Selecting Multi-Deep Face Recognition Models,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 2, Article 21. DOI: https://doi.org/10.52866/2788-7421.1262. Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss2/21.

Green Technology: A Systematic Review Of Ai And Iot Solutions For A Sustainable Future, Nesma Abd El-Mawla, Mohamed A. Berbar, Nawal A. El-Fishawy, Mohamed A. El-Rashidy, Mahmoud Badawy

Mansoura Engineering Journal

Green technology offers a solution to the pressing environmental crisis. It can change the structure and generation of waste so as not to harm the earth, and people can become environmentally friendly. To address complex environmental challenges like climate change and pollution, innovative Artificial Intelligence (A.I.) and Internet of Things (IoT) solutions are essential. These technologies can help optimize resource use, reduce waste, and promote sustainable development. However, it's crucial to balance economic growth, social equity, and environmental protection when implementing green technologies. This survey paper systematically examines the landscape of Green Technology, focusing on its pivotal components: Measures of …

Biologically-Inspired Multiscale Neuromorphic Architecture, Christian O'Reilly, Ramtin Zand

Publications

This white paper proposes a biologically-inspired multiscale neuromorphic architecture that bridges key gaps between artificial neural networks (ANNs), spiking neural networks (SNNs), and biological neural networks (BNNs). While SNNs offer promising energy efficiency, their broader adoption remains limited by suboptimal performance and the need for novel learning paradigms. To address these challenges, the proposed framework integrates structural and functional principles observed in the brain, including hierarchical organization, sparse and modular connectivity, predictive coding, and diverse neuronal dynamics.

The architecture operates across micro-, meso-, and macro-scales, incorporating neuron-level diversity (e.g., excitatory/inhibitory and principal/support cells), canonical microcircuits (CMCs), and large-scale hierarchical organization. …

Artificial Intelligence, Society 5.0 And Smart City Adaptation Initiatives For Businesses: An Integrated Approach, Ines A. M. Gila, Fernando A. F. Ferreira, Neuza C. M. Q. F. Ferreira, Florentin Smarandache, Momtaj Khanam, Tugrul Unsal Daim

Engineering and Technology Management Faculty Publications and Presentations

The mass migration of human populations to urban areas has resulted in unprecedented challenges for city services. To address and find solutions for these emerging issues, decision-makers must embrace the smart city and Society 5.0 paradigms, which comprehensively tackle various dimensions of the problem and ensure adaptability to evolving citizen needs. Central to the success of these paradigms is technology, particularly artificial intelligence (AI). AI’s transformative capabilities enable the expansion of services, automation of tasks, efficient operationalization and processing vast amounts of data to address urban challenges, aligning with several sustainable development goals (SDGs) such as sustainable cities and communities …

Implementation Of Zero Trust Architecture On Local Server Management In Educational Institutions, Joko Purwanto, Safar Dwi Kurniawan

Journal of Strategic and Global Studies

Educational institutions are facing increasingly complex cyber threats, particularly as they continue to rely on on-premises servers with limited cybersecurity resources. Zero Trust Architecture (ZTA) offers a security model that rejects implicit trust and requires strict verification of each access request. This study aims to examine the applicability of ZTA in managing local servers within educational institutions through a qualitative literature review approach. Relevant literature from 2014 to 2025 was analyzed using thematic synthesis to identify recurring concepts, strategies, and gaps. The results show that ZTA, when integrated with Identity and Access Management (IAM), Security Information and Event Management (SIEM), …

Digital Replica For Trustworthy Cooperative Autonomous Vehicles, Hady Farahat

Theses and Dissertations

Trust in an automated system can be defined as confidence in a vehicle's reliability, safety, and predictability, which is essential for the acceptance and widespread adoption of fully autonomous vehicles (FAVs); without it, users might disengage from using autonomous vehicles or reject the technology altogether. Most of the previous research has focused on trust from an ego vehicle perspective.

However, next-generation vehicles are becoming more autonomous and connected, relying on vehicle-to-vehicle technology and vehicle-to-infrastructure technology with no human intervention. Hence, trust becomes more complex and fragile as multiple agents interact with each other, and it might become harder to establish …

Cognivault: Enabling Privacy-Aware Cognitive Distortion Detection In Intelligent Mental Health Applications, Mariam Dawoud

Theses and Dissertations

Mental health applications are increasingly leveraging intelligent systems to sup- port psychological well-being, yet preserving user privacy remains a major concern. This thesis presents CogniVault, a secure ecosystem for cognitive distortion data. The framework includes Cognify, a mobile journaling application that detects cog- nitive distortions in user-written journal entries using a locally deployed machine learning model. Cognitive distortions are maladaptive thought patterns such as catastrophizing or personalization, which the app identifies to provide therapeu- tic insights. To ensure privacy-preserving data analytics, CogniVault includes the design and implementation of a hybrid security architecture, PRISM-HDI, that combines Paillier Homomorphic Encryption (HE), Differential …

Human-Machine Communication: Complete Volume. Volume 12

Human-Machine Communication

This is the complete volume of HMC Volume 12.

Whole Earth Machines: Human-Machine Communication For A Green Transition, Klaus Bruhn Jensen

Human-Machine Communication

The climate crisis of the 21st century represents an existential risk to humanity and biodiversity, posing essential questions of how communication may serve to coordinate mitigation of and adaptation to climate change. One recent response has been massive investments by governments and corporations in systems providing feedback on the state of Earth— Whole Earth Machines (WEMs). For human-machine communication (HMC) studies, WEMs invite sustained engagement with communication infrastructures as a key constituent of research agendas, beyond the interface encounters at the center of many HMC studies to date. The article presents a conceptualization and operationalization of WEMs as critical infrastructures …

A Modular Llm Approach To Argument Extraction In Philosophical Texts, Nate Miller

Masters Theses

Engaging with philosophical works is a rewarding but demanding task that challenges both human readers and computational systems designed to extract arguments from dense philosophical reasoning, and although large language models (LLMs) have made substantial progress in argument extraction, the most advanced models are often costly to run. As a result, there is growing interest in determining if multi-agent pipelines that divide a task into smaller stages can reduce cost while maintaining or improving performance.

This study investigates a modular multi-agent approach for extracting arguments from philosophical texts using LLMs, and compares its performance, cost, and runtime to both single-agent …

The Risc-V Fpga (Rvfpga) Teaching Package, Daniel Chaver, Sarah Harris, Luis Pinuel, Olof Kindgren, Zubair Kakakhel, Chris Owen, Jose I. Gomez-Perez, Fernando Castro, Katzalin Olcoz, Julio Villalba-Moreno, Alexander Grinshpun, Freddy Gabbay, Luke Seed, Rui Duarte, Manuel Lopez, Oscar Alonso, Robert Owen

Electrical & Computer Engineering Faculty Research

RISC-V is a free and open-standard ISA based on RISC principles, allowing anyone to design, manufacture, and sell RISC-V chips and software. Its flexibility and growing ecosystem have made it popular in research, education, and industry, increasing the need for educational materials. This paper provides an in-depth description of the RVfpga course, which offers a solid introduction to computer architecture using the RISC-V instruction set and FPGA technology. It focuses on providing hands-on experience with real-world RISC-V cores, the VeeR EH1 and EL2 cores, developed by Western Digital and hosted by ChipsAlliance. The course targets students and educators in computing-related …

Iterative Data Augmentation For Enhancing Deep Learning Performance With Limited Training Data, Avinash Singh

ETDs from 2020-2029

Deep learning models have demonstrated impressive performance across different domains; however, their effectiveness heavily depends on large, well annotated datasets. In practice, data are often limited in size, leading to overfitting, poor generalization, and degraded model robustness and performance. Moreover, conventional augmentation techniques are typically static in nature, lack adaptability during training, and can produce geometrically inconsistent or unrealistic mixed images. This dissertation addresses three major challenges in data augmentation and model generalization: (1) the scarcity of labeled data and limited dataset size, (2) the absence of adaptive mechanisms for dynamically adjusting learning parameters during training, and (3) the creation …

In-Situ Eval: A Modular Framework For Custom And Real-Time Rag Benchmarking, Ritvik Garimella, Kaushik Roy, Chathurangi Shyalika, Amit Sheth

Publications

Retrieval-Augmented Generation (RAG) has become the standard approach for integrating domain knowledge into Large Language Models (LLMs). However, fair comparison of RAG pipelines remains difficult: data preparation is often ad hoc, subsampling methods are opaque, parameters vary across implementations, and evaluation is fragmented. We present In-Situ Eval, a unified and reproducible framework that operationalizes the full RAG pipeline with configurable subsampling strategies and both RAG-specific and generic evaluation metrics. The platform supports two execution modes: an offline Dataset mode for evaluating precomputed outputs, and a live Retrieval mode for benchmarking RAG variants with state-of-the-art LLMs. Users can flexibly select datasets, …

When Disasters Trigger Cyber Vulnerabilities: Mapping Physical-Digital Interdependencies In Critical Infrastructure Systems, Dikshya Panta, Sicheng Wang, Aditya Sapkota, Prakash Ranganathan

Faculty Publications

Critical infrastructure (CI) systems such as power, water, communications, and emergency services are increasingly exposed to compound risks in which natural disasters and cyber incidents interact and amplify one another. Traditional risk assessments often isolate physical and digital threats, overlooking the cascading dependencies that emerge when operational stress, emergency reconfiguration, and adversarial exploitation coincide. This study conducts a 2019–2025 scoping review and introduces a Geographic Information System (GIS) driven six-stage disaster cyber compounding framework that characterizes, maps, and operationalizes compound risk across interdependent CI sectors. The framework integrates a common operating picture, analytic situational understanding, exposure mapping, threat-fingerprint encoding, detection …

3d Object Tracking Registration Based On Improved Rbot Method, Jiarui Zhou, Haihua Cui, Pengcheng Li, Shihao Gu, Huipu Hao, Xifu Zhao, Anan Zhao, Tao Jiang

Journal of System Simulation

Abstract: To address the limitations of region-based object tracking (RBOT) in handling isotropic objects and scenarios with similar foreground-background colors, an improved method integrating edge features is proposed. The approach employs edge detection to extract object contours and designs a region segmentation strategy incorporated into an energy function framework to optimize internal line and edge consistency, thereby enhancing adaptability in dynamic environments and improving pose estimation accuracy. Validation through augmented reality assembly experiments on an aero-engine demonstrates that the proposed method effectively reduces rotational and translational errors, achieving initialization deviations of less than 1.5° and 0.5%, respectively. For static …

3d Reconstruction For Stadium Cad Drawings Based On Graphic Element Arrangement Pattern Analysis, Shang Ma, Mengyu Zhang, Lan Zhang, Gang Yang

Journal of System Simulation

Abstract: To address the issue of the time-consuming and labor-intensive manual conversion of two-dimensional CAD design drawings of buildings into three-dimensional models, and leveraging the characteristic that stadiums contain a large number of repetitively and regularly arranged objects, this study proposes a similar graphical element detection algorithm. This algorithm detects similarities between graphical elements by constructing their bounding boxes and calculating the L2-Norm distance, identifying all graphical elements of the same type within the CAD drawing. Furthermore, a transformation sequence detection algorithm is proposed. Based on the geometric transformation relationships between graphical elements, a geometric transformation space is defined. By …

Visual Relocalization Method Combining Region Classification And Local Feature Enhancement, Yining Wang, Yanli Liu, Guanyu Xing

Journal of System Simulation

Abstract: Visual relocalization tasks have important application value in fields such as digital twin and augmented reality. The current mainstream methods still face challenges such as mismatch between coordinate regression scale and receptive field and insufficient attention to local information. A visual relocalization method that combines region classification and local feature enhancement is proposed. The coordinate regression problem in large space is transformed into a multi-region classification problem and a coordinate regression problem inside a small scene, which significantly reduces the uncertainty of coordinate regression and makes the network globally have a large receptive field. A conditioning layer using deep …