PROJECTS & PAPERS
NLP-20260313
Quantified Explainability and Robustness Analysis of Transformer-Based Bug Detection Models
by Debargha Ghosh, Eve Thullen, and Emmanuel Udoh Ph.D.; THULLEN RESEARCHLAB, 02/02/2026
Unplanned equipment failures in manufacturing systems lead to production downtime, increased operational costs, and safety risks. While predictive maintenance techniques have advanced significantly, much of the existing work focuses on binary failure detection and provides limited insight into specific failure mechanisms. This paper presents a multi-class predictive modeling approach for manufacturing equipment maintenance systems that aims to identify distinct failure types using operational sensor data.
The study formulates failure type prediction as an imbalanced multi-class classification problem representative of real-world industrial environments, where failure events are rare compared to normal operation. Model performance is evaluated using imbalance-aware metrics to ensure reliable assessment across both dominant and minority failure classes. The results demonstrate that the proposed approach can effectively distinguish major mechanical and thermal failure types despite severe class imbalance. These findings highlight the importance of multi-class failure prediction for enabling more targeted maintenance decisions and improving the reliability of manufacturing equipment.
Cite: [1] Debargha Ghosh, Eve Thullen Ph.D, Emmanuel Udoh Ph.D, "Quantified Explainability and Robustness Analysis of Transformer-Based Bug Detection Models," International Multidisciplinary Research Journal Reviews (IMRJR), 2026, DOI 10.17148/IMRJR.2026.030304
DS-20260312
A Multi-Class Predictive Model for Manufacturing Equipment Maintenance Systems
by Nikitha Gandra, Chukwuasia Madike, Naaram Srichandana, and Eve Thullen; THULLEN RESEARCHLAB, 02/01/2026
Unplanned equipment failures in manufacturing systems lead to production downtime, increased operational costs, and safety risks. While predictive maintenance techniques have advanced significantly, much of the existing work focuses on binary failure detection and provides limited insight into specific failure mechanisms. This paper presents a multi-class predictive modeling approach for manufacturing equipment maintenance systems that aims to identify distinct failure types using operational sensor data.
The study formulates failure type prediction as an imbalanced multi-class classification problem representative of real-world industrial environments, where failure events are rare compared to normal operation. Model performance is evaluated using imbalance-aware metrics to ensure reliable assessment across both dominant and minority failure classes. The results demonstrate that the proposed approach can effectively distinguish major mechanical and thermal failure types despite severe class imbalance. These findings highlight the importance of multi-class failure prediction for enabling more targeted maintenance decisions and improving the reliability of manufacturing equipment.
Cite: [1] Nikitha Gandra, Chukwuasia Madike, Naaram Srichandana, Eve Thullen, "A Multi-Class Predictive Model for Manufacturing Equipment Maintenance Systems," International Multidisciplinary Research Journal Reviews (IMRJR), 2026, DOI 10.17148/IMRJR.2026.030305
DS-20250801
Forecasting Household Electricity Consumption
by Priyanka Rana, and Krishan Kumar Sidh, University of the Cumberlands, 08/01/2025
Accurate forecasting of household electricity use is important for saving energy, running smart grids efficiently, and supporting sustainability. This study tested three machine learning models—Random Forest, XGBoost, and Long Short-Term Memory (LSTM)—to predict daily household electricity use in New York, United States. The dataset included contextual details such as household size and electricity price. Time-based features (month, day, weekday) were added, and minute-level data were aggregated into daily averages for stability.
Results showed that Random Forest and XGBoost performed better than LSTM in both accuracy and computational efficiency. Removing a highly correlated feature, XGBoost was the best-performing model, with a root mean square error (RMSE) of 0.453, mean absolute error (MAE) of 0.331, and R² of 0.519. The most important predictors were kitchen appliance usage, laundry/cold storage usage, and water heating/cooling usage. These findings suggest that machine learning can be a valuable tool for household energy management. Future research should consider using higher-frequency data, integrating weather and occupancy information, and exploring hybrid modeling approaches to further improve prediction accuracy.
DS-20250511
Advanced Time Series Forecasting Model for Gross Domestic Product (GDP) Prediction
by Akshatha Atmaram, and Stone Barnard, University of the Cumberlands, 05/11/2025
Forecasting the Gross Domestic Product (GDP) has long been a problem in economic analysis, particularly for governments, analysts, and organizations that want to predict macroeconomic changes. For the creation of policies, investment planning, evaluations of economic stability, and long-term strategic decision-making, precise GDP forecasts are essential. By creating a strong, AI-driven time series forecasting framework that uses historical data and macroeconomic variables to anticipate GDP at the national level, this study tackles these issues. This report presents a comprehensive analysis of advanced forecasting techniques aimed at improving the accuracy and efficiency of GDP predictions across diverse global economies. The study leverages a curated collection of open-source datasets, including annual GDP data, inflation rates, trade balances, and employment statistics, spanning multiple decades and regions. The primary objective of this project is to design an automated data pipeline capable of processing varied and often inconsistent economic data formats while building a scalable, deep learning model tailored for time-series forecasting.
The methodology centers on the use of Long Short-Term Memory (LSTM) neural networks, chosen for their ability to model long-term dependencies in sequential data. The model was trained using a 10-year historical input sequence, optimized through extensive hyperparameter tuning, and evaluated using established regression metrics: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R²). Feature engineering techniques were applied to enhance performance, including the creation of lag variables, log transformations, and scaling operations. Baseline models such as ARIMA, Linear Regression, and Random Forest were implemented for comparison, providing a comprehensive performance benchmark. Through iterative experimentation and validation—including time-series cross-validation—the LSTM model demonstrated superior performance in forecasting GDP trends. The model outperformed traditional approaches in predictive accuracy, particularly in countries with stable historical data, and showed strong generalizability across varied economic contexts. Visual and quantitative evaluations revealed that the model effectively captured both linear growth and cyclical economic patterns, offering valuable insights into macroeconomic behavior.
DS-20250509
Hospital Readmissions Predictive Model
by Yvonna Donaldson, and Prasun Pokharel, University of the Cumberlands, 05/09/2025
This project focuses on predicting 30-day hospital readmissions using machine learning models trained on both clinical and public health data. By combining CMS hospital readmission data with social determinants of health from the County Health Rankings and CDC PLACES datasets, the model captures a broader range of factors influencing patient outcomes. After preprocessing and feature engineering, logistic regression, decision tree, and random forest classifiers were trained and evaluated. The final logistic regression model, calibrated using isotonic regression and tuned for a threshold of 0.4, achieved 81% accuracy, 83% precision, 95% recall, and an F1-score of 89%. Key predictors included readmission rate, flu vaccination rate, obesity, stroke, and short sleep duration. These findings support the value of integrating behavioral health indicators into predictive healthcare models. The results highlight the model’s potential to support early intervention and reduce preventable readmissions, with future work focusing on external validation and deployment in clinical settings.
DS-20250512
Website and Domain Phishing Detection
by Akshada Bauskar, Enoch Johnson, University of the Cumberlands, 05/12/2025
Phishing continues to be one of the most prevalent forms of cybercrime, exploiting unsuspecting users by mimicking legitimate websites. This paper presents an end-to-end, machine learning-based phishing detection system that uses domain and structure-level URL features to classify websites as either phishing or legitimate. The UCI Phishing Websites Dataset is used for training and evaluation. A variety of models were assessed, with Gradient Boosting emerging as the top performer, achieving 93.5% accuracy and a 94.2% recall rate. The system is built with scalability in mind and incorporates MLOps tools like MLflow, Docker, and AWS for real-world deployment.
DS-20241013
Advanced Time Series Forecasting for Optimizing Retail Sales
by Kunle Dare, University of the Cumberlands, 10/13/2024
Retail sales forecasting is a critical challenge in business management, especially for large organizations like Corporación Favorita, a leading grocery chain. Accurate demand predictions are vital for optimizing inventory management, reducing wastage, ensuring product availability, and enhancing customer satisfaction. This study addresses these challenges by developing a sophisticated, data-driven time series forecasting model tailored to predict storelevel sales across thousands of items in multiple locations.
This report presents a comprehensive analysis of advanced time series forecasting techniques aimed at improving sales forecasting accuracy for Corporación Favorita stores. The study leverages a rich dataset from the Kaggle "Store Sales - Time Series Forecasting" competition, containing daily sales, promotions, holidays, oil prices, and other external factors.
The key objective of this project is to develop a robust forecasting solution that can handle the intricacies of retail sales, including seasonal patterns, economic factors, promotions, and storelevel variations.
NLP-20250701
Social Media Sentiment Analysis Model for Stock Market Trends
by Samuel Adebayo, and Guru Batchu, University of the Cumberlands, 07/01/2025
This study explores the predictive power of social media sentiment analysis on short-term financial market movements. Social media platforms, particularly Reddit, significantly influence market dynamics through the rapid dissemination of retail investor sentiment. Leveraging sentiment analysis, technical indicators, and advanced machine learning techniques such as Random Forest and LSTM networks, we assessed Reddit-derived sentiment's predictive capabilities.
Our comprehensive analysis revealed weak correlations between social sentiment scores and stock returns, primarily due to high volatility and sentiment noise. Enhanced modeling approaches incorporating technical indicators showed minor improvements, emphasizing the market predictions' multifaceted nature. Practical implications suggest caution in solely relying on sentiment analysis, recommending a combined approach with traditional market indicators. The study underscores the necessity of integrating broader macroeconomic indicators and detailed sentiment metadata for more accurate predictive results.
NLP-20250702
Social Media Text Analysis Model for Mental Health Prediction
by Shivakshi Chauhan, and Hissey Lama, University of the Cumberlands, 07/01/2025
The problem of depression, anxiety and stress is growing alarmingly everyday in the world. Social media is another platform that people regularly use to express their emotions, and it is possible to research them to reveal any probable mental issues.
The project proposes a machine learning solution in natural language processing (NLP) to extract mental health signals in social media articles, particularly that of the Reddit and Twitter data sets.
The project utilizes the classical and advanced models such as TF-IDF with logistic regression, Random Forests, and a BERT-based classifier with the addition of sentiment features. High degree of accuracy has been shown in the use of experimental findings, where the BERT model achieved the level of 99 percent as far as accuracy is concerned, a fact that has in turn affirmed the prospect of digital tools in the facilitation of early intervention and detection in mental health.
IT-20250427
Exploring Resources for Elderly Living in the Community and Use of Electronic Health Record
by Sansila Sunwar Texas Woman’s University, 04/27/2025
The purpose of this project is to recognize the potential resources and solution to elderly living in the community and use of electronic health record (EHR) to improve services. All the articles mainly focus on how to enhance the adoption of health information technology, and the electronic methods or simply a digitalization of the health field for upgrading the health facilities.
The proposed approach for the topic of elderly living in the community, and Diabetes is to recognize the potential resources and solution to elderly living in the community and use of electronic health record (EHR) to improve services. Health care facilities utilizing this Health Information technology approach can be expected to increase in the quality of health facilities provided to the patient along with decreasing the cruciality among the old patient.
IT-20250426
A Digital Business Plan for Paw’s Boutique
by Gudiguntla Suryateja, Chukwuasia Madike, Dhaval Kalsariya, Md Ismail Siddiqui, and Vijay Nagallapati, Westcliff University, 04/26/2025
This project presents the design and strategic development of Paw’s Boutique, an e-commerce platform specializing in fashionable and ethically sourced pet apparel. The study explores the key components required to build and operate a sustainable online retail business, including product selection, pricing strategies, revenue models, and ethical supplier partnerships. The platform’s revenue model consists of three streams: one-time product purchases, subscription-based fashion boxes, and affiliate product sales through a content-driven blog.
The project also examines operational and technological considerations necessary for a modern e-commerce business. This includes the implementation of a technology stack supporting secure payment gateways, inventory management systems, and customer relationship management (CRM) tools. Additionally, the research highlights the importance of cybersecurity, data privacy, and ethical practices in maintaining customer trust and protecting brand reputation.
Marketing strategies emphasize emotional brand positioning, social media engagement, influencer partnerships, and data-driven email campaigns to attract and retain customers. The project further integrates innovative technologies such as AI-powered product recommendation systems and chatbot-based customer support to enhance user experience.
IT-20250425
Business Plan for FitQuest Virtual, a Holistic E-Commerce Platform
by Jasmin Patel, Twisha Patel, Md Shahidur, Ankita Sharad, and Yuvam Patel, Westcliff University, 04/25/2025
This project presents the business plan for FitQuest Virtual, a holistic e-commerce platform designed to provide integrated health, fitness, and wellness solutions through a digital marketplace. The platform offers a variety of products and services, including virtual fitness programs, wellness coaching, digital fitness resources, and related health products. The goal of FitQuest Virtual is to create an accessible and engaging environment that promotes healthier lifestyles while leveraging modern e-commerce technologies.
The business model includes diversified revenue streams such as subscription-based fitness programs, one-time product purchases, and premium wellness services. The project also evaluates key operational components including cost estimation, marketing strategies, supply chain management, and customer relationship management to ensure long-term sustainability and growth. Additionally, the plan emphasizes the importance of effective technology management and IT infrastructure, highlighting secure e-commerce platforms, scalable system architecture, and digital service delivery. Ethical considerations, staff training, and responsible data management are addressed to ensure compliance and customer trust.
The project further proposes innovative techniques for e-commerce program management and process improvements to enhance operational efficiency and user experience.
IT-20250421
A Scalable Digital Learning Platform for Creators and Learners
by Md Jobaer Ahmed, Rumana Akther Nipa, Tazul Islam Bappy, and Sicong Wang, Westcliff University, 04/21/2025
This project proposes the development of a scalable digital learning platform designed to connect creators and learners through an innovative online marketplace. The platform operates under a dual-sided business model (C2B and B2C), allowing content creators to offer digital courses, learning materials, and specialized knowledge while enabling learners to access high-quality educational resources in a flexible and accessible environment.
The project outlines the platform’s core products and services, supported by a robust technology management and infrastructure framework to ensure scalability, reliability, and secure operations. Key business components including marketing strategy, supply chain management, and customer relationship management are explored to support sustainable growth and user engagement. The platform also addresses important operational factors such as sourcing digital content, fulfillment strategies, and effective service delivery.
In addition, the study examines implementation challenges, ethical considerations, and cybersecurity measures essential for protecting user data and maintaining platform integrity. The integration of artificial intelligence technologies is proposed to enhance operational efficiency, improve content recommendations, and personalize learning experiences. Overall, the project demonstrates how a technology-driven digital learning ecosystem can create value for both creators and learners while supporting scalable and secure e-commerce education services.
IT-20240627
Ayurveda Healthcare Portal Case Study
by Amar Dave, Dhanush Chanda, Ian Chuang, and Kanad Bhagat, Westcliff University, 06/27/2024
This project proposes the development of an Ayurvedic Healthcare Portal, an online platform designed to support the growing global interest in Ayurveda, a traditional system of holistic medicine originating in India. The portal aims to provide users with access to Ayurvedic knowledge, natural remedies, and personalized wellness guidance based on individual body types (doshas) and health imbalances. By integrating traditional Ayurvedic principles with modern digital technology, the platform seeks to promote preventive healthcare, balanced lifestyles, and natural healing practices.
The portal will offer a range of services including expert guidance on Ayurvedic treatments, access to high-quality herbal supplements, natural cosmetic products, and wellness accessories. It will also feature interactive community tools that encourage user engagement, knowledge sharing, and collaboration among practitioners and users worldwide. These features will help build a global community dedicated to holistic health and sustainable well-being.
Technologically, the platform will be built using a modern web technology stack, including HTML, CSS, and JavaScript, supported by cloud-based infrastructure to ensure scalability, reliability, and accessibility for users across different regions. Advanced cybersecurity measures will be implemented to protect user data and maintain privacy. By combining traditional healthcare knowledge with modern IT infrastructure, the Ayurvedic Healthcare Portal aims to deliver reliable digital healthcare support, enhance user experience, and promote the global adoption of Ayurvedic wellness practices.
IT-20240626
Enhancing FedEx Supply Chain Management System
by Twisha Patel, Washington Silva, Chukwuebuka Ozueigbo, and Siva Teja Vejandla, Westcliff University, 06/26/2024
This project explores the enhancement of the FedEx Supply Chain Management System through the integration of partial automation and blockchain-based smart contracts. The goal is to improve operational efficiency, transparency, and reliability in logistics and supply chain operations. The study begins with the concept development phase, which includes system needs analysis, stakeholder identification, and the definition of system boundaries. Functional and non-functional requirements are evaluated to determine the most suitable technological solutions for improving supply chain performance.
The proposed system incorporates automated processes and smart contracts to streamline logistics operations, reduce manual intervention, and increase transaction transparency among supply chain stakeholders. A detailed system architecture and decision analysis framework are presented to guide the implementation of the enhanced system. The project also addresses risk management, cost and profitability analysis, and system design considerations, including functional analysis, component design, and system integration strategies.
During the engineering development phase, testing and evaluation strategies are introduced to ensure system reliability, performance, and scalability. The post-development phase outlines implementation plans, prototype deployment, operational support, and monitoring mechanisms for real-world application. Overall, the project demonstrates how integrating emerging technologies such as automation and blockchain-based smart contracts can strengthen supply chain management systems, improve operational visibility, and support more efficient and secure logistics operations for large-scale organizations like FedEx.
IT-20240625
A Comprehensive Ticketing System for International Events
by Chinmay Joshi, Renu Dighe, Suryateja Guidiguntla, and Sindhu Lakshminarayana, Westcliff University, 06/25/2024
This project focuses on the development of a comprehensive ticketing system for international events, designed to support large-scale event management with improved efficiency, scalability, and user accessibility. The system aims to provide a secure and integrated platform that enables event organizers, vendors, and attendees to manage event registration, ticket sales, payment processing, and customer support through a unified digital environment. The study begins with a detailed analysis of key stakeholders, system boundaries, and operational needs, followed by functional and non-functional requirements to guide system development.
The proposed solution evaluates multiple technology options—including custom-built, off-the-shelf, open-source, and hybrid platforms—before selecting the most suitable architecture based on performance, cost, scalability, and security considerations. The system architecture incorporates web servers, application servers, database management systems, mobile applications, and integrated payment gateways to support reliable ticket distribution and transaction management. Additional components such as reporting tools, analytics dashboards, and customer support systems enhance operational transparency and decision-making.
Risk management, system integration strategies, and testing methodologies are included to ensure system reliability and performance. The project also outlines implementation, deployment, and post-development support plans, including monitoring, maintenance, and user training. Overall, this project demonstrates how a scalable, technology-driven ticketing platform can streamline event management processes, enhance user experience, and support secure global ticket sales for international events.
IT-20240623
Alexa Development with AI Integration
by Henrique Nagassima, Hannan Nadeem, Terry Ng, and Shaker Morshed, Westcliff University, 06/23/2024
This project explores the development of an Alexa-based intelligent system with integrated artificial intelligence (AI) to enhance the capabilities of voice-enabled digital assistants. The system aims to improve user interaction, automation, and smart service delivery through advanced AI integration. The project follows a structured systems engineering lifecycle, including concept development, engineering design, and post-development implementation phases. The initial phase focuses on identifying key stakeholders, defining system boundaries, and conducting a comprehensive needs and requirements analysis to ensure the system aligns with user expectations and operational goals.
The project evaluates alternative system solutions and applies decision analysis techniques to determine the most effective architecture and design framework. Functional and non-functional requirements are developed to support intelligent voice interaction, system scalability, and efficient operations. The engineering development phase emphasizes system architecture design, component development, configuration management, and system integration to ensure seamless communication among system components.
Testing and evaluation processes—including developmental testing, operational testing, and human factors testing—are conducted to verify system performance, usability, and reliability. The final phase addresses production, deployment, maintenance, and system upgrades to ensure long-term operational sustainability. Overall, this project demonstrates how integrating AI technologies with voice assistant platforms like Alexa can improve automation, enhance user experience, and support scalable intelligent systems in modern digital environments.
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