Data Science Commons^™

Neural Networks Activation Functions And Hybrid Activations Functions Accuracy And Loss Comparison On Small Dataset Against Large Datasets For Classification Problems, Antonio Felipe Cora Martins

ICT

Even on the era of Big Data, small datasets are the reality of many companies and sectors. Many datasets in rare disease diagnosis, custom manufacturing, military sciences, bioengineering, and disaster events are commonly limited in size, making machine learning predictive modelling difficult. Being the Activation Function choice crucial for Neural Networks learning, it raises the question of their effectiveness in such scenarios. This study compares five standard (single) activation functions (Sigmoid, Tanh, ReLU, Leaky ReLU, ELU) and two hybrid variants (one a mix of ReLU plus Tanh and a Learnable Activation Function with a trainable weight (alpha) that balances ReLU …

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 …

Big Data Vs Big Law: The Impact Of Big Data And Machine Learning In Anonymising Or Synthesizing Data For Use Across Borders., Kenneth Darker

ICT

This research investigates the viability of anonymization and synthetic data generation in the area of big data so that the data could be shared across borders and exist outside the constraints of privacy laws. These privacy laws are growing around the world to help protect individual identity and prevent open sharing of private data. These privacy laws all provide guidance on how data may be shared and the strict conditions upon how that may occur. Two methods which are growing in popularity are anonymization of data, specifically k-Anonymity, l-Diversity and t-Closeness, and generating synthetic data from a real dataset leveraging …

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 …

Personalized Physics Learning Through Ai: Insights From Problem Generation, Chatbot Dialogues, And Intelligent Tutoring Systems, Atharva Dange

Physics Dissertations - Archive

Artificial intelligence (AI) is poised to transform science education, yet questions remain on how best to integrate these technologies into teaching and learning. This dissertation investigates the use of AI-driven tools in university physics courses through three complementary studies. In the first study, a generative language model (ChatGPT) was used to create novel physics homework problems aligned with course objectives. Analysis showed that, after expert vetting, AI-generated questions can foster higher-order problem-solving and reduce student reliance on solution memorization, though careful instructor oversight is required to ensure accuracy. The second study embedded an AI chatbot as a learning aid in …

Multitask Learning For Named Entity Recognition And Relationship Extraction, Adrienne D. Hembrick

Theses and Dissertations

Information Extraction (IE) is a fundamental task in Natural Language Processing (NLP), involving the identification of structured information from unstructured text. Two core components of IE—Named Entity Recognition (NER) and Relation Extraction (RE)—are widely used to extract key concepts and the relationships between them across various domains. However, the sequential dependency of RE on the output of NER makes it vulnerable to error propagation: inaccuracies in entity recognition can negatively affect downstream relation extraction.

To mitigate this issue, Multitask Learning (MTL) has been proposed as an approach that jointly models NER and RE, aiming to improve overall performance and reduce …

Theoretical Analysis Of Cnns For Automatic Seizure Detection In Eeg Signals, Jackson T. Small

Honors Undergraduate Theses

Epilepsy is a common brain disorder where neurons in the brain rapidly fire, causing recurring seizures. The brain activity during a seizure can be detected by electroencephalogram (EEG) signals; however, this process is not only labor-intensive and time-consuming but is also subject to inter-rater variability, with a study showing only moderate agreement when diagnosing patients, even among experts. Convolutional Neural Networks (CNNs) are often proposed to detect seizures automatically, achieving high performance. The focus on performance comes at a cost of losing interpretability, leaving the model as effective but seen as a ’black box’. This thesis confronts the interpretability knowledge …

Centralized Deep Reinforcement Learning For Homogeneous Multi-Component Maintenance Optimization, Joseph W. Wittrock

Theses and Dissertations

This thesis explores an application of reinforcement learning (RL) in maintenance optimization. Recent advances in hardware-accelerated computation and deep learning have made RL a powerful tool for solving optimization problems which are too complex for traditional methods. Maintenance optimization involves improving the efficiency and effectiveness of maintenance activities through data-driven approaches, ultimately reducing costs and increasing asset availability. Making informed maintenance decisions is crucial to long-term sustainability.

A desirable maintenance policy maximizes a utility signal while minimizing the cost of maintenance. Techniques in sequential decision making such as dynamic programming (DP) and RL have found success in optimizing these maintenance …

The Effect Of Facial Phenotypes On Differential Performance Of Facial Recognition, Evan R. Garrett

Graduate Theses, Dissertations, and Problem Reports (ETD)

Facial recognition technology is utilized in many facets of life. As the use has become more widespread these systems have improved in reliability and performance approaching the level of human accuracy. With these improvements the problem of bias still remains as a persistent problem. Efforts have been made to minimize the bias prevalent in the systems via studies into various demographic factors, creating training datasets that have a more uniform distribution of subjects, and other methods. As facial recognition is one of the most utilized forms of biometric recognition it is vital to analyze potential causes of bias to help …

Optimal Data Splitting Methods, Sujay Mudalgi

Theses and Dissertations

In predictive modeling, effective data splitting is crucial for creating statistically representative training and validation sets. The state-of-the-art data splitting methods are based on minimizing the energy distance between the split subsets. However, there are a number of limitations in the existing methods, which this dissertation aims to address. First, the existing methods were computationally inefficient. Thus, Chapter 2 proposes a method to scale up these approaches for big data. Here, we introduce scalable Twinning (s-Twinning), which significantly improves the execution speed of data splitting without sacrificing accuracy. Second, the existing methods did not consider the predictive relationship in the …

Application Of Machine Learning And Large Language Models In Healthcare For Data Prediction And Summarization, Chiazam Chisom Izuchukwu

College of Graduate Studies: Theses & Dissertations

This study aims to examine the use of machine learning (ML) and large language models (LLMs) in healthcare to enhance disease prediction, clinical decision-making, and information management. Five supervised ML models—Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Decision Trees (DT), and Naïve Bayes (NB)—on three different computing platforms—Google Colab, Databricks, and Snowflake—were employed for disease classification. Data preprocessing included treating missing values, encoding categorical variables utilizing one-hot-encoding, feature scaling when needed, and tackling class imbalance with Synthetic Minority Over-sampling Technique (SMOTE) before an 80-20 train-test separation. Models were created with Scikit-learn (Google Collab), Spark MLlib (Databricks), and …

Machine Learning Methods For Intrusion Detection And Response In Network Security, Ayomide Oyemaja

College of Graduate Studies: Theses & Dissertations

Intrusion Detection Systems (IDS) play a crucial role in computer network security by identifying malicious activities and potential cyberattacks. This thesis combines machine learning and cybersecurity by applying Reinforcement Learning (RL) in intrusion detection and response using the NSL-KDD dataset.

We designed and implemented a Q-learning framework where an agent learns to classify network traffic over time by interacting with the environment and receiving rewards based on detection accuracy. We also look at the importance of feature selection and classification techniques and how effective they are in improving model performance, reducing the complexity of computation, and producing more desirable results. …

Majority Decision Using Top-Performing Neural Networks Models For Improved Credit Risk Prediction, Vincent Dey

College of Graduate Studies: Theses & Dissertations

Credit risk prediction remains both a challenging and high-interest problem due to the inherently unbalanced nature of financial datasets and the continuous drive for higher pre- dictive precision. In this work, I build upon previous advancements in credit risk modeling and introduce an ensemble-based Artificial Neural Network (ANN) architecture designed to enhance classification performance. By leveraging a selective ensemble of decision net- works, this approach not only improves prediction accuracy but also mitigates the chal- lenges posed by imbalanced data distributions. While the primary focus is on credit risk prediction, my analysis demonstrates that the proposed model can be effectively …

Leveraging Distributed Semantics From Deep Learning Architectures For Literature-Based Discovery, Clint A. Cuffy

Theses and Dissertations

Literature-based discovery (LBD) is a scientific process that introduces methods to automatically identify novel insights between non-interacting sets of literature. To date, numerous statistical and machine learning-based methods have been applied in the biomedical domain to find treatments for diseases such as Raynaud's disease, Parkinson's disease, and Multiple Sclerosis. However, the lack of standardized practices and creation of bespoke methodologies produces a scenario where the adoption of LBD remains challenging in real-world systems. Our work addresses these concerns through the improvement of five critical areas: 1) error propagation within LBD's a priori dependent tasks, 2) exploring the integration of modern …

Classification Of Variable Stars Using Convolutional Neural Network, Abhina Premachandran Bindu

Dissertations and Theses

This research focuses on developing Convolutional Neural Networks (CNNs), for the process of classifying and identifying variable stars through the analysis of unprocessed light curves from Transiting Exoplanet Survey Satellite (TESS). As astronomical data is becoming increasingly complex, and as advanced missions deploy sophisticated instruments for data collection, both the quality and quantity of the data are improving at a rapid pace. This has created an urgent need to automate the analysis process using efficient and effective methods, such as those based on machine learning. While previous research has explored machine learning approaches, there has been limited focus on implementing …

Using Deep Neural Nets In Writer Identification & Analysis, Aditya Majithia

Dissertations and Theses

This thesis focuses on developing automated deep learning methods for writer identification and writer attribute prediction from handwriting. It introduces two novel architectures: Convolutional Transformer Encoder (CTE) and Convolutional Swin Encoder (CSE). CTE is designed for determining authorship from handwritten text, be it modern or historical handwriting. CTE tracks subtle features and cues from handwriting strokes to distinguish authorship. It is the first of its kind capable to operate on historical handwritten fragments, setting it apart from existing methods that rely on entire document pages. CSE is designed to determine multiple attributes of an author such as authorship, gender, age …

Utilizing Deep Learning Audio Models For Blind And Low Vision Crosswalk Assistance, Wayne Lam

Dissertations and Theses

Navigating urban environments poses significant challenges for blind and low vision (BLV) individuals, particularly at street intersections where determining when it is safe to cross can be life-threatening. In New York City, where pedestrian fatalities are on the rise and only 2% of intersections are equipped with Accessible Pedestrian Signals (APS), alternative solutions are urgently needed. This thesis proposes an audio-based deep learning approach to support BLV individuals at crosswalks by detecting traffic movement direction and idling states using spatial sound. With 4-channel audio capturing capabilities of wearables, such as Meta Project Aria glasses, we explore state-of-the-art sound event localization …

Cognitive Map Generation For Vision And Language Navigation, Alexander Sandoval Mesa

Dissertations and Theses

Visual-Language Navigation (VLN) presents significant challenges for autonomous agents, such as robots and virtual assistants, particularly in complex, dynamic environments where the seamless integration of visual perception and natural language understanding is critical. Traditional VLN systems often struggle with effectively aligning language instructions and visual scene understanding, limiting their adaptability and navigation efficiency.

This thesis proposes a novel Cognitive Map-based framework that addresses these challenges by transforming natural language navigation instructions into structured graph representations. The Cognitive Map consists of nodes representing waypoints, landmarks, decision points, and edges encoding spatial relationships and navigational actions. These maps are generated using Large …

5g-Enabled Iot Gateway For Educational Purposes, Murat Kuzlu

Engineering Technology Faculty Publications

The project aims to develop and implement a 5G-IoT gateway for efficient management and interconnection of IoT devices through advanced sensing and communication technologies. Sensing technologies encompass modern approaches for detecting and measuring physical properties with high accuracy across manufacturing, smart grids, healthcare, smart cities, and other domains. Communication technologies represent the latest developments in high-speed data transmission, offering enhanced reliability and network capacity. This 5G-IoT gateway functions as an educational platform, providing students and educators with hands-on experience in emerging technologies. The system creates opportunities for practical learning and research in telecommunication technologies by enabling direct engagement with 5G …

Large Scale Machine Learning Over Knowledge Graphs, Bedirhan Gergin

Electronic Theses & Dissertations (2024 - present)

Knowledge graphs (KGs) have become popular across various fields, providing convenient access to web-based knowledge while storing and formalizing domain-specific information. By analyzing KGs, patterns, connections, and dependencies can be identified across different data sources, enabling the inference of new knowledge from given facts. As the use of KGs expands, the size of modern KGs has grown significantly, making them impossible to process within the main memory of a single computer. Distributed computing offers a viable solution to this challenge by leveraging the combined capabilities of multiple servers within a cluster. This thesis explores how distributed computing can be effectively …