Digital Commons Network^™

Attribute-Based Encryption With Searchable Encryption, Yang Yang

Research Collection School Of Computing and Information Systems

Attribute-based encryption (ABE) with searchable encryption is a notion that inherits the advantages of both ABE (Goyal et al., 2006) and searchable encryption (SE) (Boneh et al., 2004; Boneh and Waters, 2007) mechanisms to realize secure share and search for the outsourced data. ABE creates fine-grained access control system to prevent sensitive data from being accessed by unauthorized user or external attacker (Bethencourt et al., 2007). SE generates encrypted index for outsourced data such that it can be searched by a keyword trapdoor (or token) (Shi et al., 2007; Naveed et al., 2014), where the outsourced content and search keyword(s) …

Handwritten Digit Recognition Using Machine Learning Classifiers, Md Ahiduzzaman

Data Science and Data Mining

This project explores and compares the performance of various machine learning classifiers for handwritten digit recognition using the MNIST dataset. The classifiers include Logistic Regression, k-Nearest Neighbors, and Convolutional Neural Networks. Each classifier is evaluated based on accuracy, precision, recall, F1-score, and confusion matrix analysis.

Fault And Cyberattack Diagnosis And Handling Via Large Language Models And State Prediction For Manufacturing And Quantum Systems, Jihan Abou Halloun

Wayne State University Dissertations

In the digitalization era and Smart Manufacturing, companies are harnessing the power of artificial intelligence (AI) and machine learning (ML) across multiple sectors, including process engineering optimization, process control and fault detection, to enhance efficiency and engineering decision making. Although AI and ML are widely used in anomaly detection and handling, there are still areas where it has been less explored. One of the major areas where AI’s potential in manufacturing needs to be characterized is with respect to the applications of large language models (LLMs) in manufacturing troubleshooting for fault/attack handling. A second major area where the potential of …

Cmc Thesis Chatbot, Luis Gomez

CMC Senior Theses

This GitHub repo is a senior thesis for Claremont McKenna College; it is a thesis about theses. The project is an interactive RAG-based chatbot that helps students, researchers, and faculty explore Claremont McKenna College senior theses. The goal was to create a domain-specific chatbot to show that it is possible to combat the limitations of AI, including hallucinations, outdated data, and lack of domain expertise. The website link is:

CMCThesisChatbot

Analyzing Political Sentiment On Micro-Blogging Data: A Lexicon And Machine Learning Approach To The 2024 U.S. Presidential Election, Ava Grey

CMC Senior Theses

This paper explores the trends in sentiment towards U.S. presidential candidates Kamala Harris and Donald Trump through micro-blogging social media text during the five months leading up to the election. Two datasets of varying sizes and origins were used to contextualize and validate analysis findings. The analyses include both a lexicon-based approach and a machine learning predictive method. Common sentiment analysis techniques like term frequency, term frequency inverse, various lexicons, and n-grams were utilized during the lexicon approach. During the modeling, a random forest was utilized in addition to the methods used during the lexicon approach. Results showed that overall …

Training Set Augmentation And Harmonization Enables Radiomic Models To Detect Early Onset Of Lung Cancer, Claire Huchthausen, Menglin Shi, Gabriel L.A. Sousa, James Larner, Einsley Janowski, Jonathan Colen, Krishni Wijesooriya

Data Science Faculty Publications

Radiomics-based machine learning models have the potential to detect lung cancer at inception from CT scans and transform patient outcomes. Low malignancy rates in early-development pulmonary nodules (PNs) and variable image acquisition hinder development of clinically applicable radiomics-based early detection models. To address these challenges, we augmented training using later-development PNs and harmonized for acquisition effects. We first trained machine learning models to predict PN malignancy using radiomic features from scans of early-development benign and malignant PNs (n = 187) harmonized using ComBat. Observing near-chance performance, we augmented training with later-development benign and malignant PNs (n = 225). We evaluated …

An Analytical Model Of Motion Artifacts In A Measured Arterial Pulse Signal—Part Ii: Tactile Sensors, Md Mahfuzur Rahman, Subodh Toraskar, Mamun Hasan, Zhili Hao

Mechanical & Aerospace Engineering Faculty Publications

This paper, the second of two parts, presents an analytical model of motion artifacts (MA) in measured pulse signals by a tactile sensor, which contains a deformable microstructure sitting on a substrate. While the tissue-contact-sensor (TCS) stack and the sensor are both treated as a 1DOF (degree-of-freedom) system, tissue–sensor contact joins their mass together to form a 1DOF system with springs and dampers on both sides. MA on the sensor substrate causes baseline drift and time-varying system parameters (TVSP) of the TCS stack simultaneously. An analytical model is developed to mathematically relate baseline drift and TVSP to a measured pulse …

Fine-Tuned Large Language Models (Llms): Improved Prompt Injection Attacks Detection, Md Abdur Rahman, Hossain Shahriar, Guillermo Francia, Fan Wu, Alfredo Cuzzocrea, Sheikh Iqbal Ahamed

Computer Science Faculty Research and Publications

Large language models (LLMs) are becoming a popular tool as they have significantly advanced in their capability to tackle a wide range of language-based tasks. However, LLMs applications are highly vulnerable to prompt injection attacks, which poses a critical problem. These attacks target LLMs applications through using carefully designed input prompts to divert the model from adhering to original instruction, thereby it could execute unintended actions. These manipulations pose serious security threats which potentially results in data leaks, biased outputs, or harmful responses. This project explores the security vulnerabilities in relation to prompt injection attacks. To detect whether a prompt …

Causal Discovery On The Effect Of Antipsychotic Drugs On Delirium Patients In The Icu Using Large Observational Ehr Dataset, Riddhiman Adib, Md. Osman Gani, Sheikh Iqbal Ahamed, Mohammad Adibuzzaman

Computer Science Faculty Research and Publications

Delirium occurs in about 80% of cases in the Intensive Care Unit (ICU) and is associated with an extended hospital stay, increased mortality, and other complications. Delirium lacks biomarker-based diagnosis and is frequently treated with antipsychotic drugs (APD), despite numerous studies debating its efficacy. Since randomized controlled trials (RCT) are expensive and time-consuming, we approach the research question of estimating the efficacy and safety outcomes of APD in treating delirium through retrospective cohort analysis. We employed the Causal inference framework to explore the underlying causal model for Delirium patient cohort. We focus on building a structural causal model for delirium …

A Survey On Non-Invasive Computing: Neurological-Hematological Framework For Early Infection And Stroke Detection With Future Directions, Masud Rabbani, Nafi Us Sabbir Sabith, Sheikh Iqbal Ahamed

Computer Science Faculty Research and Publications

This paper introduces a novel, non-invasive tool for building a holistic neurological-hematological diagnostic framework that can be used for early detection of infection and stroke. We proposed and developed a multimodal sensing approach, where an ear canal–based acoustic system can be used to identify brain activities and a smartphone-based facial video analysis platform to estimate white blood cell (WBC) and hemoglobin (Hb) levels. Our ear-based EEG methodology achieved a remarkable 96% classification accuracy from the low-frequency level (>30hz), with regression models with mean R2 scores above 0.96 across all EEG bands. For hematological diagnostics, the system predicted WBC counts …

Embedding With Large Language Models For Classification Of Hipaa Safeguard Compliance Rules, Md Abdur Rahman, Md Abdul Barek, Abm Kamrul Islam Riad, Md Mostafizur Rahman, Bajlur Rashid, Md Raihan Mia, Hossain Shahriar, Guillermo Francia, Fan Wu, Alfredo Cuzzocrea, Sheikh Iqbal Ahamed

Computer Science Faculty Research and Publications

Although software developers of mHealth apps are responsible for protecting patient data and adhering to strict privacy and security requirements, many of them lack awareness of HIPAA regulations and struggle to distinguish between HIPAA rules categories. Therefore, providing guidance of HIPAA rules patterns classification is essential for developing secured applications for Google Play Store. In this work, we identified the limitations of traditional Word2Vec embeddings in processing code patterns. To address this, we adopt multilingual BERT (Bidirectional Encoder Representations from Transformers) which offers contextualized embeddings to the attributes of dataset to overcome the issues. Therefore, we applied this BERT 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 …

Towards Dynamic Learner State: Orchestrating Ai Agents And Workplace Performance Via The Model Context Protocol, Mohan Yang, Nolan Lovett, Belle Li, Zhen Hou

Educational Leadership & Workforce Development Faculty Publications

Current learning and development approaches often struggle to capture dynamic individual capabilities, particularly the skills they acquire informally every day on the job. This dynamic creates a significant gap between what traditional models think people know and their actual performance, leading to an incomplete and often outdated understanding of how ready the workforce truly is, which can hinder organizational adaptability in rapidly evolving environments. This paper proposes a novel dynamic learner-state ecosystem—an AI-driven solution designed to bridge this gap. Our approach leverages specialized AI agents, orchestrated via the Model Context Protocol (MCP), to continuously track and evolve an individual’s multi-dimensional …

The Reliability Response To Patent Law’S Ai Challenges, Arti K. Rai

Faculty Scholarship

Pervasive AI use adds newfound importance to longstanding debates over patent timing and reliability. Patent claims on speculative ideas generated by AI, or even the infusion of speculative AI-generated ideas into the public domain, may defeat patent incentives for more careful research. Although challenges that AI use poses for patent validity requirements like human inventorship and nonobviousness have received more attention, reliability is equally important.

Indeed, as this Article argues, the issues are linked. If requirements for inventorship and nonobviousness were adjusted to emphasize reliability, a human role could be preserved, and AI use would not necessarily threaten patents. Currently, …

A Spoofing Speech Detection Method Combining Multi-Scale Features And Cross-Layer Identification, Hongyan Yuan, Linjuan Zhang, Baoning Niu, Xianrong Zheng

Information Technology & Decision Sciences Faculty Publications

Pre-trained self-supervised speech models can extract general acoustic features, providing feature inputs for various speech downstream tasks. Spoofing speech detection, which is a pressing issue in the age of generative AI, requires both global information and local features of speech. The multi-layer transformer structure in pre-trained speech models can effectively capture temporal information and global context in speech, but there is still room for improvement in handling local features. To address this issue, a speech spoofing detection method that integrates multi-scale features and cross-layer information is proposed. The method introduces a multi-scale feature adapter (MSFA), which enhances the model’s ability …

Toward Embodied Navigation Through Vision And Language, Muraleekrishna Gopinathan

Theses: Doctorates and Masters

Embodied AI is a challenging but exciting field in which a robot learns to interact with human-living spaces to perform various tasks. This thesis studies the embodied navigation problem in which a robotic agent navigates in a previously unseen indoor environment based on a challenging task. In particular, the Vision-and-Language Navigation (VLN) task requires a robot to navigate based on a descriptive human-language instruction. This thesis aims to improve VLN agents on four key aspects - their understanding of the environment, training via additional data, correcting navigational errors, and predicting the layout of the environment for better planning.

First, we …

Machine Learning For Computer-Aided Diagnostics From Complex Medical Images, Afsah Saleem

Theses: Doctorates and Masters

Machine learning has significantly transformed medical image analysis in the current age of artificial intelligence offering vast potential in improving disease diagnosis and management. Cardiovascular diseases (CVDs) are among the leading cause of global mortality, emphasizing the need for early detection for effective intervention and prevention. Abdominal Aortic Calcification (AAC) is an early indicator and contributor to Atherosclerotic Cardiovascular Diseases (ASCVDs) and is commonly assessed through imaging modalities such as computed tomography (CT), X-rays, and Dual-energy X-ray Absorptiometry (DXA). Among these, lateral spine DXA scans, commonly used for osteoporosis screening, offer a cost-effective and low-radiation opportunity for opportunistic CVD risk …

Boden Lecture: Taxation Of Autonomous Artificial Intelligence, Reuven S. Avi-Yonah, Lucas Brasil Salama, Herbert Snitz, W. Robert Thomas

Articles

This Article proposes that tax can be a useful supplement to other measures to regulate Autonomous Artificial Intelligence (AAI) and limit its potential harmful effects. This proposal differs from command-and-control regulation of AAI along the lines of European Union legislation that may unduly limit the development of AAI. It also differs from existing proposals to tax AAI to generate revenue to help workers displaced by AAI programs, or to tax the data used by AAI The proposal is based on granting AAI programs like ChatGPT separate legal personhood, like corporate personhood, while incentivizing or requiring their corporate owner to place …

A Study Of User Experiences Of Pediatric Physicians With Electronic Health Record Systems: Encounters With Task Complexity And Efficiency Of User Task Flows, Roseanne Alhindi

CCAC Theses and Dissertations

way patient information is stored, managed, and accessed. This transition to Electronic Health Record (EHR) systems has enhanced the efficiency and accuracy of healthcare delivery by enabling quick access to patient records, reduction of errors, and facilitation of coordination among healthcare providers. In the EHR system, diverse tasks are performed for clinical processes and patient care. These tasks can be considered simple or complex, ranging from documenting patient visits and updating medical histories to ordering tests and managing prescriptions. Although EHR systems have become more prevalent in their use, there are noted challenges associated with the design of the system …

Panoscu: A Simulation-Based Dataset For Panoramic Indoor Scene Understanding, Mariia Khan, Yue Qiu, Yuren Cong, Jumana Abu-Khalaf, David Suter, Bodo Rosenhahn

Research outputs 2022 to 2026

Panoramic images offer a comprehensive spatial view that is crucial for indoor robotics tasks such as visual room rearrangement, where an agent must restore objects to their original positions or states. Unlike existing 2D scene change understanding datasets, which rely on single-view images, panoramic views capture richer spatial context, object relationships, and occlusions—making them better suited for embodied artificial intelligence (AI) applications. To address this, we introduce Panoramic Scene Change Understanding (PanoSCU), a dataset specifically designed to enhance the visual object rearrangement task. Our dataset comprises 5,300 panoramas generated in an embodied simulator, encompassing 48 common indoor object classes. PanoSCU …

Zero Day Ransomware Detection With Pulse: Function Classification With Transformer Models And Assembly Language, Matthew Gaber, Mohiuddin Ahmed, Helge Janicke

Research outputs 2022 to 2026

Finding automated AI techniques to proactively defend against malware has become increasingly critical. The ability of an AI model to correctly classify novel malware is dependent on the quality of the features it is trained with and the authenticity of the features is dependent on the analysis tool. Peekaboo, a Dynamic Binary Instrumentation tool defeats evasive malware to capture its genuine behaviour. The ransomware Assembly instructions captured by Peekaboo, follow Zipf's law, a principle also observed in natural languages, indicating Transformer models are particularly well-suited to binary classification. We propose Pulse, a novel framework for zero day ransomware detection with …

Genai’S Impact On Global It Management: A Multi-Expert Perspective And Research Agenda, Yogesh K. Dwivedi, Laurie Hughes, Mohammad S. Al-Ahmadi, Vincent Dutot, Syed Q. Ahmed, Shahriar Akter, Rahul De’, Keyao Li, Nitish Singh, Paul Walton

Research outputs 2022 to 2026

Generative AI (GenAI) is disrupting global IT management and challenging established practice. The increasing use of GenAI technology is redefining localization, transforming existing workforce roles, outsourcing strategy, and team dynamics. Simultaneously, GenAI’s security complexities have prompted the rethinking of existing risk frameworks to meet a new set of challenges from GenAI enhanced cyber threats. This article explores these complex and converging factors, providing a roadmap to address GenAI’s significant impact on global IT management. We advocate the responsible adoption of GenAI and importance of building resilient, value-driven, globally consistent IT ecosystems able to adapt to the significant challenges and opportunities …

Development Of An Ecg-Based Deep Learning Model For Pediatric Congenital Heart Disease (Chd) Diagnosis, Annbar Mekouar

Selected Full-Text Master Theses 2021-

Congenital heart disease (CHD) stands as the leading congenital anomaly which affects pediatric populations throughout the world. The effectiveness of treatment depends on both early diagnosis and accurate identification but echocardiography requires manual interpretation which proves time-consuming and inconsistent especially when examining pediatric patients with their distinct cardiac systems. The research aims to create a deep learning-based diagnostic framework which uses ECG data to identify coronary artery disease subtypes in pediatric patients. The model uses high-quality datasets from Dr. Ignacio Lugones to extract R-R intervals and QRS durations through convolutional neural networks (CNNs). The system addresses pediatric-specific challenges while enhancing …

A Machine Learning Based Framework For Predicting Drug Cardiotoxicity Using A Combination Of Ecg Biomarkers And Drug Dosage Data, Jamie Wong

Selected Full-Text Master Theses 2021-

Drug-induced cardiotoxicity presents a significant challenge in clinical practice and drug clinical development, particularly with medications that modulate calcium, potassium, and sodium channels that influence cardiac electrophysiology. Clinical practice often relies on QTc prolongation alone as a predictor, which lacks specificity and may lead to excluding other safe therapeutic options. To address this limitation, this study integrates electrocardiogram (ECG) biomarkers with normalized drug dosage data to improve the accuracy of cardiotoxicity risk prediction using machine learning techniques. ECG features, including QT, QRS, RR, and PR intervals, were analyzed alongside normalized dosage data to account for dose-dependent cardiac effects. A physiologically …

Multi-Modal Graph Learning For Vision Language Model In General And Medical Domains, Xinyue Hu

Computer Science and Engineering Dissertations - Archive

Multi-modal learning has gained significant attention in deep learning for its ability to integrate and process information from multiple modalities, such as text, images, and videos. By leveraging complementary information from different modalities, it enables a more comprehensive understanding of complex data in various tasks. Simultaneously, graph learning, a prominent paradigm that models structured data as graphs, captures both local and global dependencies, providing a natural framework to represent intricate interactions and contextual relationships. When combined with multi-modal learning, these graph-based approaches have the potential to enhance feature representation and reasoning by effectively fusing heterogeneous data, leading to more robust …

Age Of Information-Based Optimal Scheduling With Energy Cost Trade-Off For Smart Warehouse: A Deep Reinforcement Learning-Based Approach, Sandip Roy, Abhishek Bisht, Ashok Kumar Das, Sachin Shetty

VMASC Publications

Recent advances in the integration of high-speed mobile networks and real-time IoT devices have facilitated in building of smart warehouses, where a set of beacons and Internet of Things (IoT) devices (or source nodes) can monitor the status of various physical processes in a time-critical way. In real-time status monitoring systems, like smart warehouses, quantifying the freshness of the Internet of Things (IoT) data based on the age of information (AoI) metrics becomes quite crucial. As source nodes are battery-constrained, a balanced trade-off between AoI minimization and preservation of source node battery energy is essential. In this paper, in a …

Faithful Narratives From Complex Conceptual Models: Should Modelers Or Large Language Models Simplify Causal Maps, Tyler J. Gandee, Philippe J. Giabbanelli

VMASC Publications

(1) Background: Comprehensive conceptual models can result in complex artifacts, consisting of many concepts that interact through multiple mechanisms. This complexity can be acceptable and even expected when generating rich models, for instance to support ensuing analyses that find central concepts or decompose models into parts that can be managed by different actors. However, complexity can become a barrier when the conceptual model is used directly by individuals. A ‘transparent’ model can support learning among stakeholders (e.g., in group model building) and it can motivate the adoption of specific interventions (i.e., using a model as evidence base). Although advances in …

5g-Practical Byzantine Fault Tolerance: An Improved Pbft Consensus Algorithm For The 5g Network, Xin Liu, Xing Fan, Baoning Niu, Xianrong Zheng

Information Technology & Decision Sciences Faculty Publications

The consensus algorithm is the core technology of blockchain systems to maintain data consistency, and its performance directly affects the efficiency and security of the whole system. Practical Byzantine Fault Tolerance (PBFT) plays a crucial role in blockchain consensus algorithms by providing a robust mechanism to achieve fault-tolerant and deterministic consensus in distributed networks. With the development of 5G network technology, its features of high bandwidth, low latency, and high reliability provide a new approach for consensus algorithm optimization. To take advantage of the features of the 5G network, this paper proposes 5G-PBFT, which is an improved practical Byzantine fault-tolerant …

Physics-Informed Deep Learning With Kalman Filter Mixture For Traffic State Prediction, Niharika Deshpande, Hyoshin (John) Park

Engineering Management & Systems Engineering Faculty Publications

Accurate traffic forecasting is crucial for understanding and managing congestion for efficient transportation planning. However, conventional approaches often neglect epistemic uncertainty, which arises from incomplete knowledge across different spatiotemporal scales. This study addresses this challenge by introducing a novel methodology to establish dynamic spatiotemporal correlations that captures the unobserved heterogeneity in travel time through distinct peaks in probability density functions, guided by physics-based principles. We propose an innovative approach to modifying both prediction and correction steps of the Kalman Filter (KF) algorithm by leveraging established spatiotemporal correlations. Central to our approach is the development of a novel deep learning model …

Comprehensive Benchmarking Of Several Machine Learning And Bayesian Models For Early-Stage Diabetes Risk Prediction: A Large-Scale Comparative Study, Md. Iqbal Hossain, Najila Alam Porno

Mathematics & Statistics Faculty Publications

Diabetes remains a critical global health challenge, with early detection is crucial for effective management. This study presents a comprehensive benchmarking analysis of 14 diverse machine learning and Bayesian models for early-stage diabetes risk prediction using clinical data [2] from Sylhet, Bangladesh. This research evaluated traditional methods (Logistic Regression, Decision Trees), ensemble techniques (Random Forest, XGBoost, LightGBM), Bayesian approaches (BART, Bayesian Logistic Regression), and advanced neural architectures (Deep Belief Networks) using both 70-30 train-test splits and 10-fold cross-validation. The results demonstrate that ensemble methods consistently outperformed other approaches, with Random Forest(RF) achieving the highest cross-validated AUC (0.9951) and accuracy (0.9699). …