Digital Commons Network^™

Parallel Algorithms For Large Scale Dynamic Graph Analysis, Arindam Khanda

Doctoral Dissertations

A complex system of interacting entities in contemporary scenarios, be it biological, technological, or social, can be represented using graphs. Dynamic graphs, unlike their static counterparts, are ones in which the underlying topology changes over time. These networks act as a model for numerous systems, from transportation to social interactions, capturing the ever-evolving nature of real-world phenomena. However, the inherent temporality of these networks presents a unique set of challenges and the traditional static graph algorithms often fall short in efficiency and applicability. In our research, we delve into the complexities presented by large dynamic networks and suggest various methodologies …

Thermal Hydraulics Investigation Of A Horizontal Dual Channel Setup Representing Micro Nuclear Reactors Using Integrated Advanced Measurement Techniques, Zeyad Zeitoun

Doctoral Dissertations

The urgency to reduce greenhouse gas emissions has spotlighted nuclear energy as a carbon-free solution. Despite its promise, nuclear safety concerns, highlighted by the 2011 Fukushima disaster, necessitate advancements in reactor technology. The United States has prioritized the development of safer, prismatic micronuclear reactors for the coming decade, focusing on the safe operation of these facilities. These reactors' design addresses potential hazards, particularly in loss of flow accident scenarios, making the understanding of natural circulation gas flow dynamics critical. Experiments on a horizontal dual-channel plenum-to-plenum facility (P2PF) were conducted to closely mimic the conditions within prismatic micronuclear reactors. This study …

Investigation Of The Transport And Plugging Behavior Of Preformed Particle Gels (Ppgs) In Fractures For Conformance Control In Carbonate Reservoirs, Abdulaziz A. Almakimi

Doctoral Dissertations

This study aims to systematically evaluate the performance of preformed particle gels (PPGs) in fractured carbonates mainly through a series of core flooding experiments. Carbonate reservoirs exhibit unique features of heterogeneity and pore geometry, while (PPGs) are known for their plugging efficiency, deformability, and dehydratable nature. The main goal of gel injection in fractured reservoirs is to place a gel pack appropriately into the fracture, diverting injected fluid to the oil-bearing matrix and reducing excessive water production. However, considerable dehydration of (PPGs) is possible at the adjacent matrix and within the fracture, which could lead to a filter-cake formation and/or …

Development Of Catalytic Membranes And Composites For Energy Storage Devices And Nonenzymatic Biosensors, Harish Singh

Doctoral Dissertations

The excessive use of fossil fuels has led to their rapid depletion, causing an energy crisis and environmental issues. Consequently, there's a growing focus on sustainable energy conversion. Electrocatalysts are pivotal in this development, particularly for fuel cells and solar fuel generators, necessitating high-efficiency, cost-effective catalysts for large-scale adoption. Transition metal chalcogenides have emerged as promising electrocatalysts due to their high efficiency, electrochemical tunability, and abundant active sites. This work is divided in two parts. In Part A, we have investigated transition metal chalcogenide and few atom clusters FACs as electrocatalysts for oxygen evolution and reduction, highlighting their structure-property relationships …

Preserving The Future: Recognizing Intergenerational Equity In United States Constitutional Jurisprudence In Light Of Evolving Climate Rights Litigation, Molly Morgan

Cardozo Journal of Equal Rights & Social Justice

Climate rights litigation is an essential part of holding states accountable for their climate change obligations. This type of litigation has increased across the globe, and domestic and international courts have issued landmark rulings that serve as precedent for reinforcing state obligations and protecting constitutional and human rights in the process. One focus of these cases is intergenerational equity, which implicates the theory that inadequate state action on climate change violates the rights of future generations. This Article explores the evolution of this theory in domestic and international law, illustrating its increasing importance in climate rights litigation and the necessity …

Equitable Incorporation: How History And Tradition Can Progressively Redefine The Fourteenth Amendment, Robert D'Alessandro

Cardozo Journal of Equal Rights & Social Justice

The Fourteenth Amendment, designed to ensure equality before the law, has been misinterpreted by the Supreme Court through its incorporation doctrine, leading to rulings that harm marginalized communities. The article advocates for "Equitable Incorporation," a doctrine requiring courts to consider the impact of their decisions on historically discriminated groups, ensuring the Amendment's purpose of equity and justice is upheld. This approach would necessitate the incorporation of unincorporated rights and reinterpret existing ones to reflect the Amendment's equitable intent.

Annotated Legal Bibliography

Cardozo Journal of Equal Rights & Social Justice

No abstract provided.

Table Of Contents - Cardozo Journal Of Equal Rights & Social Justice, Vol. 31, Iss. 2

Cardozo Journal of Equal Rights & Social Justice

No abstract provided.

Shining Light On Policy: The Case For Solar Panel Mandates On New Construction Projects, Emily Glazier

Cardozo Journal of Equal Rights & Social Justice

The note argues that solar panel mandates on new construction projects are a necessary and sensible approach to reducing greenhouse gas emissions and achieving climate goals, but their implementation must include provisions to protect vulnerable communities and address environmental justice concerns. While such mandates face legal and political challenges, the benefits of decreased emissions and energy independence outweigh the costs, particularly when paired with measures like community solar systems, incentives, and exemptions.

An Interpretative Phenomenological Study Of Sexual Assault Investigations In Sexual Assault Response Teams (Sarts), Caitlyn Marie Clark

Theses and Dissertations

This applied dissertation was designed to provide further insight into sexual assault investigations and response from the lived experiences of Sexual Assault Response Team and Task Force criminal justice professionals. There has been limited research on Sexual Assault Response Teams (SARTs), Multidisciplinary Teams (MDTs), and Sexual Assault Task Force (SATF) units, specifically encompassing the lived experiences of their members. This interpretative phenomenological study sought to explore these collaborative multidisciplinary teams from the lived experiences of the criminal justice professionals who represent them and to analyze the factors of sexual assault investigations and response that these members find significant.

In this …

Tap, Swipe, And Connect: Exploring Perceptions Of Inclusivity Through Smartphone Design And Accessibility, Carmen Van Ommen

Doctoral Dissertations and Master's Theses

There has been an increasing importance placed on customization of products and desire for the product to allow the consumer to express themselves or fit into desired social groups with their product choices. Designing products to be inclusive can encompass these desires, as well as ensure that a wide range of user groups can use the products well. In general, there is little research in the field of perceptions of inclusivity with consumer technology products. Previous research has validated a scale to measure Perceptions of Technology Product Inclusivity (PTPI) with different user groups and technology product groups, however no research …

Mini-Maps, Crucial Or Controversial: Assessing The Influence Of Perceived Spatial Orientation And Cognitive Mapping Performance On Video Game User Experience, Angela Le

Doctoral Dissertations and Master's Theses

Decades of game studies, also known as video games research, have revealed frequent video gameplay is positively correlated with enhanced cognition. An area of study that is limited, however, is the relationship between spatial cognition, specifically the domains of spatial orientation and cognitive mapping and their effects on video game user experience for games employing visual navigational aids. Video game user experience is influenced by many factors such as graphics and audio, gameplay mechanics, user interface design, etc. The present study aims to explore whether spatial abilities influence and are significantly correlated with user experience for games using heads-up displays …

The Effect Of Need For Cognition & Need For Affect On Human Reliance And Artificial Intelligence Interactions, Aliyah Mcgowan

Doctoral Dissertations and Master's Theses

Abstract

With the increased use of Artificial Intelligence (AI) automations in fields like medical diagnoses and mental health queries, there are concerns regarding an individual’s trust and reliance on the technology. Reliance on AI output may lead an individual to accept inaccurate or incorrect information without further analysis. Trust may influence reliance and trust formation may be a product of affective processing. This study investigated the relationship between Need for Affect (NFA), Need for Cognition (NFC), and trust and reliance on AI interactions. Participants were assessed on the NFA scale for willingness to approach or avoid emotional stimuli, the NFC …

Models Of Machine Learning To Diagnose Chronic Kidney Disease Using A  Weka-Based Classifier, Shaymaa Adnan Abdulrahman, Sameerah Faris Khlebis

Mesopotamian Journal of Artificial Intelligence in Healthcare

In the present day, humans are confronted with a variety of diseases as a result of their lifestyle and the current environmental conditions. Therefore, it is crucial to identify and predict these diseases in their early phases in order to prevent their severe manifestations. Manually identifying maladies is a challenging task for physicians on a regular basis. Predicting chronic illnesses is the aim of this article. This goal is applicable through a state-of-the-art approach to classification correctly identifies people with chronic illnesses. Predicting maladies is also a difficult endeavor. Therefore, disease prediction is significantly influenced by data mining. To get …

Biogpt: A Generative Transformer-Based Framework For Personalized Genomic Medicine And Rare Disease Diagnosis, Ghada Al-Kateb, Emine Cengiz, Murat Gök

Mesopotamian Journal of Artificial Intelligence in Healthcare

This paper introduces BioGPT, a generative transformer-based framework designed to advance personalized genomic medicine and rare disease diagnosis. Unlike conventional models that process either genomic sequences or clinical narratives in isolation, BioGPT employs a cross-modal architecture that effectively fuses both data streams, enabling precise classification and interpretable natural language generation. The model is pre-trained on large-scale genomic and electronic health record datasets and fine-tuned for rare disease tasks. Comprehensive experiments demonstrate BioGPT’s superiority over state-of-the-art biomedical models, including RarePT, BioBERT, and DNABERT, with improvements of up to 10% in F1-score and over 20 BLEU points in justification fluency. Ablation studies …

Prediction Of Cardiac Arrest Using A Hybrid Voting Classifier Combining Random Forest And Support Vector Machine, Ahmed Hamid Elias, Amr Badr, Raad S. Alhumaima

Mesopotamian Journal of Artificial Intelligence in Healthcare

Early and adequate identification of patients at risk of cardiac arrest remains a fundamental challenge in medical environments. Within this piece of work, we design and evaluate a hybrid machine-learning pipeline integrating Random Forest (RF) and Support Vector Machine (SVM) classifiers by soft voting to detect cardiac arrest occurrences from a publicly available dataset of 303 patient records with 13 clinical features. After median imputation and data cleaning, continuous features were normalized and the cohort was divided into 70 % training and 30 % test sets. RF scored 97 % with an AUC of 1.00 on its own, and SVM …

Advanced Machine Learning Models For Accurate Kidney Cancer Classification Using Ct Images, Dhuha Abdalredha Kadhim, Mazin Abed Mohammed

Mesopotamian Journal of Big Data

Kidney cancer, particularly renal cell carcinoma (RCC), poses significant challenges in early and accurate diagnosis due to the complexity of tumor characteristics in computerized tomography (CT) images. Traditional diagnostic approaches often struggle with variability in data and lack the precision required for effective clinical decision-making. This study aims to develop and evaluate machine learning (ML) models for the accurate classification of kidney cancer using CT images, focusing on improving diagnostic precision and addressing potential challenges of overfitting and dataset heterogeneity. Two ML models, Support Vector Machines (SVM) and Multi-Layer Perceptrons (MLP), were employed for classification. Key attribute extraction techniques, including …

Bridging Law And Machine Learning: A Cybersecure Model For Classifying Digital Real Estate Contracts In The Metaverse, Faris Kamil Mihna, Hazim Akram Sallal, Lobna Abdalhusen Al-Seedi, Hasan Ali Al- Tameemi, Mustafa Abdulfattah Habeeb, Yahya Layth Khaleel, Dheyaa A. Mohammed

Mesopotamian Journal of Big Data

The metaverse indicates an ever-evolving digital ecosystem where virtual real estate has now become an asset class. These properties, subject to smart contracts on the blockchain and represent as non-fungible tokens (NFTs), gives rise to new legal and cyber issues due to the decentralized and dematerialized nature of these digital assets .This paper proposes a machine learning approach to classify the digital real estate contracts into Ownership and Lease contracts. The study utilizes a dataset of one thousand digital real estate contracts collected from platforms such as Decentraland and The Sandbox. The dataset also included attributes such as plot size, …

Overview Of The Ciciot2023 Dataset For Internet Of Things Intrusion Detection Systems, Wisam Ali Hussein Salman, Chan Huah Yong

Mesopotamian Journal of Big Data

The rapid expansion of the use of the Internet of Things (IoT) has encouraged many attackers to exploit the vulnerabilities in these networks to violate data privacy or disrupt service; they are easy targets due to the diversity of devices within the network, which has led to the loss of unified security standards. intrusion detection system (IDS) play a pivotal role in securing IoT networks by monitoring inbound and outbound traffic to these networks and issuing a security alarm when there is an attack; moreover, they respond directly to these security threats to prevent them from harming the network and …

Enhancing Throughput In A Network Function Virtualization Environment Via The Manta Ray Foraging Optimization Algorithm, Sanaa S. Alwan, Asia Ali Salman

Mesopotamian Journal of Big Data

Network function virtualization (NFV) has emerged as a transformative paradigm in which traditional hardware appliances are replaced with virtual network functions (VNFs) running on commodity hardware. While NFV offers scalability and flexibility, it faces major challenges in sustaining high throughput and minimizing resource overhead under dynamic traffic conditions. In particular, flooding algorithm-based request propagation often leads to excessive redundancy, congestion, and resource waste. To address this limitation, this study applies Manta ray foraging optimization (MRFO), a swarm intelligence algorithm inspired by natural foraging behaviors, to optimize packet routing and resource allocation in NFV environments. The research employs a Barabási–Albert (BA) …

Recognition Of Alzheimer’S Disease Stages Via Inceptionv3 And Resnet50, Iman Aljubouri, Mostafa Ragheb, Mohamad Hamady

Mesopotamian Journal of Big Data

Early and precise detection of Alzheimer’s disease (AD) is essential for successful treatment. This research presents a system that autonomously detects and categorizes the phases of Alzheimer’s disease via brain scans and sophisticated deep learning techniques, including InceptionV3 and ResNet50. These models started with pretrained weights and were augmented by including bespoke classification layers, which consisted of dropout, batch normalization, and dense layers to increase performance and mitigate overfitting. The preprocessing processes included scaling the picture to 224 by 224 pixels, using average filtering for denoising, and converting the color space to guarantee compatibility with the models. Evaluations of the …

Concise Comparison Of Cnn Models On A Specified Dataset, Humam K. Yaseen, Saif S. Kareem, Bashar I. Hameed, Salam K. Abdullah

Mesopotamian Journal of Big Data

Recently, interest in Deep Learning (DL), which is a subset of Machine Learning (ML), has emerged. The most famous and used from the DL is the Convolutional Neural Network (CNN). CNN is particularly effective in image processing. There are many duties in image processing that CNN can do, i.e., segmentation, classification, object detection, facial recognition, etc. Image classification is one of the most important applications due to its relevance to various fields, including the healthcare industry and others. One of the challenges researchers face is selecting the appropriate algorithm for the classification task, particularly when dealing with binary or multi-class …

Optimized Wavelet Scatter Method For Accurate Classification And Segmentation Of Lung Nodules, Enas Hamood Al-Saadi, Ahmed Nidhal Khdiar, Nidhal K. El Abbadi

Mesopotamian Journal of Big Data

Lung cancer is a significant global health concern due to its high mortality rate. This is primarily due to the difficulty of identifying malignant growths in early-stage computed tomography (CT) images. The thing is, it's often hard to tell the difference between a benign nodule and a malignant one. This makes it tricky to figure out what's going on with the patient. That's where these computer-aided diagnosis (CAD) approaches come in. This study introduces a lightweight and interpretable framework for comprehensively analyzing lung nodules, including their detection, classification, and segmentation. This approach uses the wavelet scattering transform (WST) to extract …

Hybrid Cooperative Spectrum Structured (Hcss) Approach For Adaptive Routing In Cognitive Radio Ad Hoc Networks, Sadeem Dheyaa Shamsi, Ameer Sameer Hamood Mohammed Ali, Wameed Deyah Shamsi

Mesopotamian Journal of CyberSecurity

Cognitive radio networks provide an important function in the efficient use of the radio spectrum. Therefore, dedicated cognitive radio ad hoc networks (CRAHNs) are expected to improve communication performance in a multihop network that requires a dedicated routing protocol that considers the dynamic mobility of secondary user nodes on the basis of the random availability of primary node channels. To enhance routing in the CRAHNs environment, in this paper, a novel Hybrid Cooperative Spectrum Structured (HCSS) approach that combines cooperative spectrum sensing and spectrum-aware routing protocols can be effective in appropriate decision making for routing packets in such a network. …

Exploring The Impact Of Blockchain Revolution On The Healthcare Ecosystem: A Critical Review, Maad M. Mijwil, Mohammad Aljanabi, Mostafa Abotaleb, Ban Salman Shukur, Ban Salman Shukur, Indu Bala, Kamal Kant Hiran, Ruchi Doshi, Klodian Dhoska

Mesopotamian Journal of CyberSecurity

Blockchain technology is a type of distributed ledger that provides secure and efficient storage, management, and transmission of data over a decentralized network. With its ability to ensure transparency and immutability, blockchain is increasingly adopted across various sectors ranging from finance, healthcare, and logistics to education. In healthcare, blockchain technology is attracting attention because of its potential to fundamentally transform health ecosystems. The healthcare sector has significantly benefited from blockchain technology by enhancing data security and interoperability and reducing medical errors. In this context, a set of studies highlighted the importance of blockchain in the field of healthcare, enhancing trust …

Encrypting Text Messages Via Iris Recognition And Gaze Tracking Technology, Sura Abed Sarab Hussien, Basim Najim Al-Din Abed, Kamaran Adil Ibrahim

Mesopotamian Journal of CyberSecurity

This study explores the integration of eye-tracking technology, specifically the gaze point (GP3) eye tracker, to develop a novel encryption and decryption model that leverages an individual's unique eye patterns as a cryptographic key. This research addresses the critical problem of balancing user-friendly encryption mechanisms with robust security. This study aims to design a gaze-based encryption system that uses the eye's binary matrix as a dynamic encryption key. The methodology involves capturing an eye image, converting it into a binary matrix, and using the extracted matrix to generate a unique iris key (IK) applied to encrypt and decrypt text messages …

Antdroidnet Cybersecurity Model: A Hybrid Integration Of Ant Colony Optimization And Deep Neural Networks For Android Malware Detection, Riyadh Rahef Nuiaa Al Ogaili, Osamah Adil Raheem, Mohamed H Ghaleb Abdkhaleq, Zaid Abdi Alkareem Alyasseri, Zaid Abdi Alkareem Alyasseri, Ali Hakem Alsaeedi, Yousif Raad Muhsen, Selvakumar Manickam

Mesopotamian Journal of CyberSecurity

Malware detection is a vital problem, and efficient methods that can efficiently detect malware are needed. The increasing use of mobile computers makes malware detection a vital part of security in an era where smartphones have come to play a key role in many of our daily lives. Earlier approaches, however, suffer from high false positive rates; they are not scalable for larger databases, or they are not amenable to adapt well to novel zero-day malware. For these reasons, the demand for more sensitive and flexible detection models is high. In this study, we develop a hybrid mobile malware detection …

Enhancing Crime Detection In Video Surveillance Via A Lightweight Blockchain And Homomorphic Encryption-Based Computer Vision System, Tanya Abdulsattar Jaber

Mesopotamian Journal of CyberSecurity

Blockchain technology consists of distributed ledgers or database systems, regarded as immutable, secure, and innovative, characterized by unsupervised internal maintenance with a special security protocol used to prevent inference from malicious or third parties. The widespread use of this technology has led to deep research into the problems posed by this technology, which can be summarized in terms of computational cost and latency time. The crime detection process in video surveillance has made great progress with the use of technologies such as the Internet of Things and blockchain technologies. However, to reach high levels of security in the physical crime …

Smartphone Authentication Based On 3d Touch Sensor And Finger Locations On Touchscreens Via Decision-Making Techniques, Moceheb Lazam Shuwandy, Rawan Adel Fawzi Alsharida, Maytham M. Hammood

Mesopotamian Journal of CyberSecurity

Smartphone authentication systems must balance security and user convenience, which is a persistent challenge in the digital realm. Traditional biometrics, such as fingerprints and facial recognition, face vulnerabilities to spoofing and environmental conditions, limiting reliability. This study introduces a novel approach by integrating three-dimensional (3D) touch sensors with finger location data for authentication. The goal is to develop a system that improves accuracy while minimizing false positives and negatives, leveraging touch pressure and spatial interaction as unique biometric identifiers. Data from 20 participants, including pressure levels, spatial coordinates, and timestamps, were analysed using Random Forest (RF) and Extreme Gradient Boosting …

Early Entering Research And Writing (Eerw) In College-Level Statistics, Rebecca Fang

2025 Faculty Bibliography

With the high demand for data-driven knowledge, students today need to comprehend skills in problem-solving, critical thinking, communication, and collaboration. However, in traditional statistics lectures, students learn descriptive algorithms, hypothesis, and linear regression at their early stage of college. Unfortunately, they often analyze numbers without considering the context, purpose, audience, and the meaning of the numbers. Therefore, providing students the early opportunity to research and write with data in college will allow them to deepen their understanding of the subjects, to think critically about the data and the results, to communicate effectively with diverse audience, and to prepare for their …