Digital Commons Network^™

Facile Bio-Synthesis Of Cerium Oxide Nanoparticles, Characterization And Their Photocatalytic Potential In The Degradation Of Real Tannery Wastewater, Ganeshkumar Govindasamy, A. Babu Ponnusami

Journal of Metals, Materials and Minerals

The sustainable metal oxide photocatalysts using plant extract presents a viable alternative to conventional chemical synthesis techniques. In this study, spherical shaped cerium oxide nanoparticles were synthesized using ammonium ceric nitrate and Peepal Leaves (PL) extracted and used as a photocatalyst to remove hexavalent chromium and chemical oxygen demand from actual tannery wastewater. The characterization of cerium oxide nanoparticles was carried out using UV-Visible spectroscopy, X-ray diffraction, Fourier-transform infrared spectroscopy, Brunauer-Emmett-Teller surface area analysis, Field Emission-Scanning Electron Microscopy with energy-dispersive X-ray spectroscopy, Raman analysis, High Resolution-Transmission Electron Microscopy and Zeta potential analysis. The band gap energy of 3.25 eV was …

Dogs Emotion System, Muhammad Anas Baig

ICT

For our capstone project, we built a machine learning model that can look at pictures of dogs and figure out how they’re feeling, like if they’re happy, sad, or just chill. The idea came from how important pets are in people’s lives these days and how cool it would be to actually understand their emotions better using tech. This system will allow users to upload images of dogs, which are then analysed by a trained model to classify the dog's emotional states such as happy, sad, or neutral. We followed the CRISP-DM process to build it, which basically means we …

Cancer Registry Newsletter Vol. 06, Cancer Registry

Cancer Registry, AKUH Archives

• Messages
• Cancer Registry Team
• Cancer Committee Members
• Cancer Registry AKUH
• Our Mission
• Cancer Report
• Age-Group Distribution of Top Five Cancers
• Total Count with Gender Breakdown
• Top Ten Males 2025
• Top Ten Females 2025
• Data Release
• Top Ten Malignancies in Children (2025)
• Distribution of Malignancies According to the Geographical Area of Patients
• Poster Presentation
• Ongoing Projects
• Karachi Cancer Registry
• Implementing a Real-Time Cancer Registry Model
• Cancer Registry Website
• Quarterly Reporting of Cancer Registry Data
• National Media Highlights Scoping Review on Cancer Registries in Pakistan
• Achievements
• Activities
• Head and Neck Cancer Awareness Session 2025
• HIMS Appreciation Lunch 2025
• Independence Day Celebration
• Department …

Customer Service Support. Utilizing Machine Learning To Classify, Prioritize And Summarize Issues., Hoai Nhan Nguyen

ICT

This capstone project investigates the application of machine learning and natural language processing (NLP) to enhance customer support operations through automated ticket classification, prioritization, and summarization. Using the multilingual Customer Support Emails dataset from Kaggle, the project follows the CRISP-DM methodology, performing extensive data cleaning, preprocessing, feature engineering, and class balancing. Five machine learning models—Decision Tree, KNN, LinearSVC, Naive Bayes, and Random Forest—were evaluated using hyperparameter tuning, cross-validation, confusion matrix analysis, and learning curves. LinearSVC demonstrated the strongest performance for both queue and priority classification, achieving accuracies of 89.8% and 81.2% respectively, with consistent generalization across folds. For summarization, extractive …

Dogs Emotion System- Poster, Muhammad Anas Baig

ICT

This project is all about a deep learning-based “Dog Emotion System” that can figure out how dogs are feeling just by looking at their faces. We used a balanced set of 4,000 dog images with four different emotion categories and followed the CRISP-DM process to build it. The model was trained from scratch using a Convolutional Neural Network (CNN) without any pre-existing models. It is deployed using Steamlit, where people can upload pictures of their dogs and get their emotional state predicted in real time. The goal of this tech is to make it easier for pet owners to understand …

Comparison Of Statistical, Machine Learning, And Deep Learning Models For Time-Series Forecasting Using Weather Data From Dublin, Ireland., Erick Eduardo Rios Treviño

ICT

This study compares three time-series forecasting paradigms—statistical, machine learning, and deep learning—using ten-years of historical weather data from Dublin. The objective is to evaluate the performance of Prophet, XGBoost, and LSTM when forecasting daily solar radiation under multiple preprocessing strategies. An extensive data analysis was conducted, including descriptive statistics, inferential testing, stationarity assessment, outlier detection, feature selection, and exploratory visualisation. These steps revealed strong annual seasonality, nonlinear feature relationships, and variability across meteorological variables, informing the modelling framework and feature-engineering decisions. Four experiments were conducted using cleaned data, differenced data, log-transformed data, and cross-validation. Results show that XGBoost achieved the …

Enhancing Insider Threat Detection Through A Hybrid Approach Using Different Artificial Intelligence Techniques., Jose Roberto Da Silva Dure

ICT

Insider threats pose significant challenges in cybersecurity due to their origin from individuals with legitimate access. Traditional defenses often fail to detect malicious behavior embedded within normal activities. This study proposes a hybrid artificial intelligence framework that integrates unsupervised anomaly detection, supervised and ensemble learning, and deep learning to enhance insider threat detection. Using the CERT 4.1 dataset, features encompassing temporal, behavioral, network, and psychometric aspects were engineered. Anomaly detection models informed supervised and ensemble classifiers, while a multi-input deep learning architecture captured sequential and contextual patterns. Performance evaluation using ROC-AUC, precision, recall, F1-score, and cost-sensitive analysis demonstrates that the …

Strategic Analysis Of Employment Permit Statistics And Predictive Analytics For Workforce Planning In Ireland, Amy Souza, Thaynna Vieira

ICT

By analysing historical employment permit data from Enterprise.gov.ie (Enterprise.gov.ie, 2024), this project has the aim to use Data Analytics and Machine Learning to make predictions of employment permits trends across sectors and companies, providing insights to optimize workforce planning for Recruitment Agencies and guide international job seekers requiring work visas. The insights gained are intended to enhance strategic recruitment practices and empower job seekers to make informed career decisions in Ireland’s competitive labour market.

Predicting Repeat Purchases In E-Commerce Using Interpretable Machine Learning, Zahid Bhatti

ICT

This project investigates the prediction of repeat purchase behaviour in e-commerce using machine learning, with a focus on balancing predictive accuracy and interpretability. Large volumes of transactional and behavioural data are analysed to identify customer-level features that drive loyalty and repeat purchases. Various supervised learning models, including Random Forests and Logistic Regression, are evaluated for predictive performance, while SHAP (SHapley Additive Explanations) is employed to provide both global and local interpretability. The study aims to generate actionable insights for customer relationship management and marketing strategy, demonstrating how advanced predictive models can support informed business decisions without sacrificing transparency.

Predictive Analytics For Customer Churns In Financial Services., Thant Thiha

ICT

This project presents a customer churn prediction analysis in the telecommunications sector, achieving an ROC-AUC of approximately 0.86 using statistically validated features and interpretable AI models. Key churn drivers identified include the number of products held, customer age, and geographic location. Ensemble models, such as Random Forest and Gradient Boosting, provided the highest predictive performance. Ethical AI principles were applied to ensure fairness, transparency, privacy, and accountability. Business insights derived from the analysis inform targeted retention strategies, prioritising multi-product users, specific age groups, and geographic segments. Deployment recommendations include the tuned Random Forest model with ongoing monitoring, governance, and future …

Predicting Customer Churn And Enhancing Retention Strategies Through Machine Learning., Swan Saung Lwin

ICT

Customer Churn is a critical challenge faced by businesses across industries, especially in the digital market. Many companies struggle to predict customer churn accurately and have difficulties in carrying out effective retention strategies. Key challenges include ineffective traditional methods, lack of insights into impact of different services, generalized retention strategies, and the need to have cost-effective retention strategies. This project aims to predict customer churn using machine learning and identify the impact of key services offered by a telecommunication company.

Customer Response To Marketing Campaigns., Alexandru Enoiu

ICT

This project applies machine learning to predict customer responses to marketing campaigns, aiming to enhance customer engagement and optimise resource allocation. Using a dataset containing demographic, lifestyle, and purchase behaviour data, a Random Forest classifier was developed to identify potential responders to marketing initiatives. The project follows a structured methodology including data exploration, preprocessing, model building, and evaluation. By accurately predicting customer behaviour, businesses can improve campaign targeting, design personalised marketing strategies, and allocate resources more efficiently. The results provide actionable insights to support customer segmentation and strategic decision-making, promoting business growth and customer satisfaction.

The Use Of Deep Learning Solutions To Develop A Practice Tool To Support Lámh Language For Communication Partners, Gabriel Bueno Pimentel Borges

ICT

This study has proposed an alternative to promote the learning and enhancement of Lámh language for communication partners that support current users by creating a real time detection tool to recognise 20 chosen Lámh signs based on existing studies in the field. This implementation was carried out by generating primary data composed by MediaPipe landmark numpy arrays of 40 frames and 45 repetitions per sign. The Neural Networks were built using the Python library Keras and the applied SVM models were built with the library sklearn. The real time detection was carried out by integrating the mentioned elements with the …

Predicting Customer Churn Using Machine Learning: A Data-Driven Approach, Alessandro Mendes Martins

ICT

This project focuses on predicting customer churn in the telecommunications sector using machine learning. A public dataset was analysed through exploratory data analysis, data cleaning, feature encoding, and scaling to prepare it for modelling. A Logistic Regression model was trained and optimised to identify customers likely to leave the service, achieving a ROC-AUC score of 0.861, with 79% accuracy, 82.3% recall, 51.9% precision, and an F1-score of 0.637. The analysis highlighted key factors influencing churn, including fibre-optic internet, month-to-month contracts, and electronic cheque payments, while longer tenure, two-year contracts, and usage of support services correlated with retention. These insights can …

Detecting Fake News Using Ai, Gustavo Lambert, Lucas Schultz

ICT

This is a document that presents a strategic analysis of the project “Detecting Fake News using AI”. Developed as part of the BSc (Hons) in Computing in IT at CCT College Dublin. The goal is to analyse the potential advantages, exploiting the viability and the impact of applying Artificial intelligence to check, verify and alert about misinformation found and to answer the question “How can Artificial Intelligence be leveraged to accurately detect and combat fake news while ensuring data privacy and compliance with regulations?” and “To what extent can AI-driven misinformation detection help reduce the spread of fake news on …

Development Of A Deep Learning Model For Synthetic Vs. Real Image Classification Synthetic Vs. Real Image Classification, Bernardo Gandara, Ignacio Varela

ICT

The advancement of generative AI technologies has made it increasingly difficult to distinguish synthetic images from authentic ones. This capstone project addresses the challenge by developing a binary image classification model using deep learning techniques to differentiate AI-generated images from real photographs. Guided by the CRISP-DM methodology, we employed the DeepGuardDB dataset, consisting of 13,000 balanced image samples, evenly split between real and synthetic sources. We implemented and compared three Convolutional Neural Network (CNN) architectures through transfer learning, standardising input pipelines and integrating custom classification heads. Following a performance evaluation across multiple metrics, the best-performing model was selected for further …

Today And Yesterday, Vol. 49, 2025, Illinois State University, Department Of History

Today and Yesterday

Newsletter of the Illinois State University Department of History

Understanding Brady Violations, Jennifer Mason Mcaward

Journal Articles

This largest-ever study of adjudicated violations of Brady v. Maryland provides a detailed and nuanced understanding of who suppresses material evidence in criminal cases, as well as why, how, where, and how often. Its findings complicate the conventional wisdom that Brady violations are the work of nefarious prosecutors who intentionally withhold material evidence from criminal defendants. While it is true that “bad faith” permeates this area of constitutional noncompliance, a substantial minority of Brady cases stem from “good faith” errors by prosecutors and suppression by law enforcement officers. Most Brady violations occur in a small number of states, and most …

Gerard V. Bradley And The American Law Of Church And State, Richard W. Garnett

Journal Articles

This paper provides an overview and appreciation of Prof. Gerard V. Bradley’s scholarly contributions to the American constitutional law of church and state. The Supreme Court of the United States has, in recent years, handed down a number of closely watched and much-remarked-on rulings in cases involving the First Amendment’s Religion Clause and various religion-related statutes. The Court has moved the law of religious freedom and church–state relations toward coherence and clarity, and better aligned it with American history, tradition, and practice, and with an appropriate understanding of judges’ capacities and of the judicial role in a democracy. It appears …

Litigating Climate Vulnerability: Climate Change Reparations In Global Jurisprudence And Empirical Results From The Notre Dame Climate Change Reparations Dataset, Diane A. Desierto, Anibal Perez-Linan, Faisal Yamil Meneses, Yuta Inada, Rachael Degaugh, Andrew Marciano

Journal Articles

From the article

The global surge in climate change litigation provokes inquiry into the nature, frequency, and scope of climate change reparations that international, regional, and national courts, as well as arbitral tribunals and other adjudicative bodies such as UN treaty body mechanisms, determine to be adequate, both on legal merits as well as intrinsic claims of justice, for the resolution of disputes brought by climate vulnerable persons, groups, and constituencies against States, multinational corporations, and other actors. Our findings from the Notre Dame Climate Change Reparations Dataset reveal a broad spectrum of pecuniary and non-pecuniary reparations being adjudicated in …

Meta-Interpretive Questions And The Aims Of Theories Of Interpretation: Beyond The Remedial Answer, Francisco J. Urbina

Journal Articles

This invited Response to Bill Watson’s “What Are We Debating When We Debate Legal Interpretation?” examines the point and purpose of interpretive theories. Watson argues that theories of interpretation are about how legal actors should exercise discretion when the law runs out. The Response builds on Watson’s sophisticated conceptual framework to explain that interpretive theories can be about at least two other topics. They can be about the most normatively appealing way of operating with sources—whether this tracks legal content or not—and they can be about which criteria for identifying something as law the legal community should adopt.

Shortcomings Of Law School And Big Law, Julian Velasco

Journal Articles

This paper, presented as part of a colloquium on  Lawyers and Their Institutions, considers two important institutions — law schools and "big law," especially transaction practice at elite law firms — and argues that they exert a problematic influence on developing attorneys by inculcating opposing types of disrespect for the law. First, the process of legal education often suggests that the law is fundamentally indeterminate. Second, big law tends to suggest to junior associates that law is largely irrelevant. Such misunderstandings can result in a misconception of the purpose of law and give a false impression of the lawyer's …

Originalism's Difference?, Sherif Girgis

Journal Articles

From the Aritcle

My topic is whether originalism makes any practical difference to constitutional reasoning and outcomes, relative to other methods of interpretation.

INTRODUCTION ............................................. 1945

I. JUSTICE SCALIA'S VISION OF CONTRASTS .................. 1949

II. THE BREAKDOWN OF CONTRASTS ........................... 1952

III. REMAINING CONTRASTS: REAL BUT ELUSIVE TO THEORY....................................................... 1961

IV. THE CHAMBERS ORIGINALISM DIFFERENCE .................. 1966

CONCLUSION: LOOKING AHEAD ................................ 1973

Voice-Activated Self-Monitoring Application (Vois): User Acceptance And Satisfaction In The Field, Hyunkyoung Oh, Li Yang, Tala Abu Zahra, Shiyu Tian, Min Sook Park, Jake Luo, Sheikh Iqbal Ahamed, Evelyn Chan, Jeffrey Whittle

Computer Science Faculty Research and Publications

This paper illustrates the processes and results of user acceptance and satisfaction tests for the voice-activated self-monitoring (VoiS) application conducted in the field. VoiS was designed and developed for individuals with diabetes (DM) and hypertension (HTN) to support their routine and convenient self-management using a smart speaker platform. VoiS is also accessible on users’ mobile devices to visualize user-generated data. A total of nine adults with DM and HTN participated and were asked to use the VoiS system at home for a week to identify its acceptability in real-world conditions. Participants completed phone interviews to report operational errors and a …

Digital Health Using Data Science, Padmapriya Velupillai Mekandan, Paramita Basak Upama, Masud Rabbani, Amity Ali, Sheikh Iqbal Ahamed

Computer Science Faculty Research and Publications

The digitization of healthcare has led to an unprecedented growth in health-related data, offering new opportunities to transform clinical decision-making, disease prediction, and patient engagement. However, extracting actionable insights from diverse data sources such as electronic health records, wearable devices, and mobile health apps requires a fusion of domain knowledge in healthcare and technical expertise in data science. This paper presents a structured, interdisciplinary framework for digital health that emphasizes practical strategies for data acquisition, preprocessing, feature engineering, machine learning, and ethical data use. The model promotes a holistic understanding of digital health challenges and opportunities, preparing future professionals to …

Secure Ios Mhealth Apps Development: An Ide-Embedded Framework For Hipaa-Aware Coding, Bajlur Rashid, Md Abdul Barek, Mostafizur Rahman, Sharmin Yeasmin, Hossain Shahriar, Sheikh Iqbal Ahamed

Computer Science Faculty Research and Publications

With the rapid growth of technology, accessing digital health records has become increasingly easier. Especially mobile health technology like mHealth apps help users to manage their health information, as well as store, share and access medical records and treatment information. Along with this huge advancement, mHealth apps are increasingly at risk of exposing protected health information (PHI) when security measures are not adequately implemented. The Health Insurance Portability and Accountability Act (HIPAA) ensures the secure handling of PHI, and mHealth applications are required to comply with its standards. But it is unfortunate to note that many mobile and mHealth app …

Public Writing As Open Pedagogy, Rachael Nevins

Open Educational Resources

This guide to inviting students to share their writing publicly was developed for faculty participating in seminars on open pedagogy at BMCC. It discusses public writing as a way to engage in open pedagogy, outlines the types of writing that students may engage in publicly, and presents the relevant pedagogical, technical, and ethical considerations.

Safety Of Healthcare Workers In Acute Care During Patient Mobilization: A Systematized Review, Lina Al Azzawi, Peyton Collier, Delaney Ehlers, Ruby Kulovitz, Danielle M. Westmark, Stacy Smallfield, Molly Whitlow

Student Systematized Reviews: Occupational Therapy

Abstract synopsis

Evidence shows that low-tech aids and lifting equipment promote safe patient handling in acute care, with the most consistent support found for lifting equipment in reducing worker injuries. There was mixed evidence to support ergonomic training.

Abstract

The aim of this study was to identify interventions within the scope of occupational therapy that enhance safety during patient mobilization for healthcare workers in acute care settings.

This systematized review of safe patient handling interventions included peer-reviewed studies that target healthcare worker safety during patient mobilization in acute care settings.

A medical librarian performed searches in Medline, CINAHL, and Scopus. …

Metaphoric Sense Developments In The Names Of Places Of Worship In Contemporary American English, John G. Newman, Sylwester Łodej

Writing and Language Studies Faculty Publications

This paper offers an account of the metaphorical extension of the names of religious houses of worship such as temple, church, mosque, and synagogue in contemporary American English, reflecting processes of linguistic secularization. Using data from the Corpus of Contemporary American English (COCA), the study investigates how the meanings of religious terms enter into secular domains, signalling shifts in societal perceptions of religious experience. The findings show that temple and church frequently undergo metaphorization, appearing in phrases like temple of learning and church of football, where non-religious modifiers drive semantic extension. These terms increasingly denote secular places characterized by human …

Including Conscientiously: How Charter School Educators Experience Inclusion Education Implementation, Joe A. Petty

Dissertations

This narrative inquiry examined how charter school educators experience inclusive education implementation while confronting ableist systems that maintain exclusion through seemingly neutral practices. This research emerged from my lived experience of how educational structures systematically marginalize disabled students while appearing progressive. The study investigated two research questions: how support structures and leadership practices either perpetuate or dismantle ableist assumptions embedded in educational practice, and how educators negotiate tensions between implementing evidence-based inclusive strategies and addressing environmental factors that create the need for those strategies.

Data collection employed semistructured interviews with eight credentialed educators across two Southern California charter school sites, …