Computer and Systems Architecture Commons^™

Personalized Point Of Interest Recommendations With Privacy-Preserving Techniques, Longyin Cui

Theses and Dissertations--Computer Science

Location-based services (LBS) have become increasingly popular, with millions of people using mobile devices to access information about nearby points of interest (POIs). Personalized POI recommender systems have been developed to assist users in discovering and navigating these POIs. However, these systems typically require large amounts of user data, including location history and preferences, to provide personalized recommendations.

The collection and use of such data can pose significant privacy concerns. This dissertation proposes a privacy-preserving approach to POI recommendations that address these privacy concerns. The proposed approach uses clustering, tabular generative adversarial networks, and differential privacy to generate synthetic user …

Quantum Computing And Its Applications In Healthcare, Vu Giang

OUR Journal: ODU Undergraduate Research Journal

This paper serves as a review of the state of quantum computing and its application in healthcare. The various avenues for how quantum computing can be applied to healthcare is discussed here along with the conversation about the limitations of the technology. With more and more efforts put into the development of these computers, its future is promising with the endeavors of furthering healthcare and various other industries.

The Need For International Ai Activities Monitoring, Parviz Partow-Navid, Ludwig Slusky

Journal of International Technology and Information Management

This paper focuses primarily on the need to monitor the risks arising from the dual-use of Artificial Intelligence (AI). Dual-use AI technology capability makes it applicable for defense systems and consequently may pose significant security risks, both intentional and unintentional, with the national and international scope of effects. While domestic use of AI remains the prerogative of individual countries, the unregulated and nonmonitored use of AI with international implications presents a specific concern. An international organization tasked with monitoring potential threats of AI activities could help defuse AI-associated risks and promote global cooperation in developing and deploying AI technology. The …

Determinants Of Continuance Intention To Use Mobile Wallets Technology In The Post Pandemic Era: Moderating Role Of Perceived Trust, Shailja Tripathi

Journal of International Technology and Information Management

The Covid-19 pandemic amplified the volume and importance of mobile payments using digital wallets and placed a basis for their continued adoption. The objective of the study is to formulate and test a comprehensive model by integration of the technology acceptance model (TAM) and expectation confirmation model (ECM) with the addition of three constructs, namely perceived trust, perceived risk, and subjective norm, to identify the determinants of continuance intention to use mobile wallets. Questionnaire-based survey method was used to gather the data from 550 users having experience using mobile wallets for more than six months. The data were analyzed using …

Evaluation Of Different Machine Learning, Deep Learning And Text Processing Techniques For Hate Speech Detection, Nabil Shawkat

Graduate Theses/Dissertations

Social media has become a domain that involves a lot of hate speech. Some users feel entitled to engage in abusive conversations by sending abusive messages, tweets, or photos to other users. It is critical to detect hate speech and prevent innocent users from becoming victims. In this study, I explore the effectiveness and performance of various machine learning methods employing text processing techniques to create a robust system for hate speech identification. I assess the performance of Naïve Bayes, Support Vector Machines, Decision Trees, Random Forests, Logistic Regression, and K Nearest Neighbors using three distinct datasets sourced from social …

Machine-Learning-Powered Cyber-Physical Systems, Enrico Casella

Theses and Dissertations--Computer Science

In the last few years, we witnessed the revolution of the Internet of Things (IoT) paradigm and the consequent growth of Cyber-Physical Systems (CPSs). IoT devices, which include a plethora of smart interconnected sensors, actuators, and microcontrollers, have the ability to sense physical phenomena occurring in an environment and provide copious amounts of heterogeneous data about the functioning of a system. As a consequence, the large amounts of generated data represent an opportunity to adopt artificial intelligence and machine learning techniques that can be used to make informed decisions aimed at the optimization of such systems, thus enabling a variety …

A Flexible Photonic Reduction Network Architecture For Spatial Gemm Accelerators For Deep Learning, Bobby Bose

Theses and Dissertations--Electrical and Computer Engineering

As deep neural network (DNN) models increase significantly in complexity and size, it has become important to increase the computing capability of specialized hardware architectures typically used for DNN processing. The major linear operations of DNNs, which comprise the fully connected and convolution layers, are commonly converted into general matrix-matrix multiplication (GEMM) operations for acceleration. Specialized GEMM accelerators are typically employed to implement these GEMM operations, where a GEMM operation is decomposed into multiple vector-dot-product operations that run in parallel. A common challenge that arises in modern DNNs is the mismatch between the matrices used for GEMM operations and the …

Acceptance Of Interoperable Electronic Health Record (Ehrs) Systems: A Tanzanian E-Health Perspective, Emmanuel Mbwambo, Herman Mandari

Journal of International Technology and Information Management

The study assessed factors that influence the acceptance of interoperable electronic Health Records (EHRs) Systems in Tanzania Public Hospitals. The study applied a hybrid model that combined the Technology Acceptance Model (TAM) and Technology-Organization-Environment (TOE). Snowball sampling technique was applied and a total of 340 questionnaires were distributed to selected clinics, polyclinics and hospitals, of which 261 (77%) received questionnaires were considered to be valid and reliable for subsequent data analysis. IBM SPSS software version 27.0 was employed for data analysis. Findings indicated that relative advantage, compatibility, management support, organizational competency, training and education, perceived ease of use, perceived usefulness, …

Analysis Of The Impact Of Vaccinations On Pandemic Metrics In The New York Metropolitan Area, Oredola A. Soluade, Heechang Shin, Robert Richardson

Journal of International Technology and Information Management

This study evaluates the relationship between pandemic cases and vaccination usage, ICU bed utilization, hospitalizations, and deaths in the New York City metropolitan area. The study includes variables for the lockdown period and confirmed infections. The evaluation addresses three periods: (1) before vaccinations, (2) after vaccinations, and (3) the lockdown period. In addition, the number of vaccines per day for the manufacturers (Pfizer, Moderna, and Johnson & Johnson) are included in the study. Comparisons with New Jersey and Connecticut are used to validate that New York statistics are consistent with other states. The results provide a general model of the …

Comparative Analytics On Chilli Plant Disease Using Machine Learning Techniques, Sai Abhishta Roy Seelam

College of Graduate Studies: Theses & Dissertations

This thesis concerns the detection of diseases in chilli plants using machine learning techniques. Three algorithms, viz., Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Multi-Layer Perceptron (MLP), and their variants have been employed. Chilli-producing countries, India, Mexico, China, Indonesia, Spain, the United States, and Turkey. India has the world’s largest chilli production of about 49% (according to 2020). Andhra Pradesh (Guntur) is the largest market in India, where their varieties are more popular for pungency and color. This study classifies five kinds of diseases that affect the chilli, namely, leaf spot, whitefly, yellowish, healthy, and leaf curl. A …

Wifi Sensing At The Edge Towards Scalable On-Device Wireless Sensing Systems, Steven M. Hernandez

Theses and Dissertations

WiFi sensing offers a powerful method for tracking physical activities using the radio-frequency signals already found throughout our homes and offices. This novel sensing modality offers continuous and non-intrusive activity tracking since sensing can be performed (i) without requiring wearable sensors, (ii) outside the line-of-sight, and even (iii) through the wall. Furthermore, WiFi has become a ubiquitous technology in our computers, our smartphones, and even in low-cost Internet of Things devices. In this work, we consider how the ubiquity of these low-cost WiFi devices offer an unparalleled opportunity for improving the scalability of wireless sensing systems. Thus far, WiFi sensing …

Anomaly Based Intrusion Detection System Through Remote Virtual Machine Introspection, Huseyn Huseynov

Dissertations and Theses

Research on identifying malicious applications is an important direction in information security, especially when it comes to detection of evasive malware such as keyloggers, trojans, rootkits and their derivatives. Inspired by a biological immune system and based on negative selection algorithm approach to detect various types of malwares is proposed in this paper.

By deeply studying Linux kernel, understanding links behind different internal system processes, examining, and experimenting with hundreds of various keyloggers we propose a single Artificial Intelligence based solution as a comprehensive protection against wide range of malwares. Developed Intrusion Detection System (IDS) can be deployed in the …

Improving The Performance, Energy Efficiency And Security Of Gpus, Xin Wang

Theses and Dissertations

The work in this dissertation achieves to enhance the performance, energy-efficiency, and security of the GPUs. We noticed that, as the demand of hardware resources keeps rising in GPUs, the energy consumption becomes unaffordable and places barriers for further performance boost. To resolve this issue, we have proposed several novel GPU micro-architectures that are able to assist the GPUs to execute in an energy-efficient manner. They also provide the potential for further performance enhancement in GPUs. Firstly, we proposed a GPU register packing scheme that stores multiple narrow-width operands to a single register to save register file resources. The unoccupied …

Governance-Aware Cloud Architectures For Enterprise Information Systems, Manikantha Varaprasad Inakollu

Computer Science and Engineering Faculty Publications

Enterprise cloud adoption has accelerated dramatically, yet governance frameworks struggle to keep pace with evolving architectural complexity. This research examines how organizations can embed governance principles directly into cloud architecture designs rather than treating compliance as an afterthought. We investigate the integration of regulatory requirements, risk management protocols, and organizational policies into cloud infrastructure patterns that enforce governance automatically. The study addresses critical gaps where traditional governance approaches fail in dynamic cloud environments, particularly around data sovereignty, access control, audit requirements, and regulatory compliance. Through analysis of existing cloud governance challenges and architectural patterns, we propose a comprehensive framework that …

Post-Implementation Erp Value Realization: A Decision Intelligence Framework, Manikantha Varaprasad Inakollu

Computer Science and Engineering Faculty Publications

Enterprise Resource Planning systems represent substantial organizational investments, yet many organizations struggle to realize expected benefits after implementation. This research develops a decision intelligence framework specifically designed to maximize ERP value realization during the critical post-implementation phase. While extensive literature addresses ERP implementation challenges, significantly less attention focuses on extracting value after systems go live. Our framework integrates data analytics, organizational learning, and strategic decision-making into a cohesive approach that transforms ERP systems from operational tools into strategic assets. Through analysis of post-implementation patterns across multiple organizations, we identify key decision points where intelligent interventions dramatically improve value capture. The …

Erp As A Digital Backbone: Redefining Enterprise Systems For Continuous Value Creation, Manikantha Varaprasad Inakollu

Computer Science and Engineering Faculty Publications

Enterprise Resource Planning systems have evolved from transactional processing tools into strategic digital backbones that orchestrate organizational value creation. This research examines how modern ERP implementations transcend traditional operational efficiency goals to enable continuous innovation, real-time decision-making, and ecosystem integration. Through analysis of contemporary ERP architectures and their impact on organizational capabilities, we demonstrate that successful digital transformation requires reconceptualizing ERP not as a software package but as an adaptive infrastructure supporting diverse business models. Our findings reveal that organizations treating ERP as a digital backbone achieve 35% higher agility scores and 42% faster time-to-market for new capabilities compared to …

A Novel Approach To Detecting And Mitigating Keyloggers, Damilola Osedumbi Elelegwu

College of Graduate Studies: Theses & Dissertations

As the digital world gets increasingly ingrained in our daily lives, cyberattacks—especially those involving malware—are growing more complex and common, which calls for developing innovative safeguards. Keylogger spyware, which combines keylogging and spyware functionalities, is one of the most insidious types of cyberattacks. This malicious software stealthily monitors and records user keystrokes, amassing sensitive data, such as passwords and confidential personal information, which can then be exploited. This research work introduces a novel browser extension designed to thwart keylogger spyware attacks effectively. The extension is underpinned by a cutting-edge algorithm that meticulously analyzes input-related processes, promptly identifying and flagging any …

Bilstm−Bigru: A Fusion Deep Neural Network For Predicting Air Pollutant Concentration, Prasanjit Dey, Soumyabrata Dev, Bianca Schoen-Phelan

Conference papers

Predicting air pollutant concentrations is an efficient way to prevent incidents by providing early warnings of harmful air pollutants. A precise prediction of air pollutant concentrations is an important factor in controlling and preventing air pollution. In this paper, we develop a bidirectional long-short-term memory and a bidirectional gated recurrent unit (BiLSTM−BiGRU) to predict PM 2.5 concentrations in a target city for different lead times. The BiLSTM extracts preliminary features, and the BiGRU further extracts deep features from air pollutant and meteorological data. The fully connected (FC) layer receives the output and makes an accurate prediction of the PM 2.5 …

Combinedeepnet: A Deep Network For Multistep Prediction Of Near-Surface Pm2.5 Concentration, Prasanjit Dey, Soumyabrata Dev, Bianca Schoen-Phelan

Conference papers

PM2.5 is a type of air pollutant that can cause respiratory and cardiovascular problems. Precise PM2.5 ( μg/m3 ) concentration prediction may help reduce health concerns and provide early warnings. To better understand air pollution, a number of approaches have been presented for predicting PM2.5 concentrations. Previous research used deep learning models for hourly predictions of air pollutants due to their success in pattern recognition, however, these models were unsuitable for multisite, long-term predictions, particularly in regard to the correlation between pollutants and meteorological data. This article proposes the combine deep network (CombineDeepNet), which combines multiple deep networks, including a …

An Optimized And Scalable Blockchain-Based Distributed Learning Platform For Consumer Iot, Zhaocheng Wang, Xueying Liu, Xinming Shao, Abdullah Alghamdi, Md. Shirajum Munir, Sujit Biswas

School of Cybersecurity Faculty Publications

Consumer Internet of Things (CIoT) manufacturers seek customer feedback to enhance their products and services, creating a smart ecosystem, like a smart home. Due to security and privacy concerns, blockchain-based federated learning (BCFL) ecosystems can let CIoT manufacturers update their machine learning (ML) models using end-user data. Federated learning (FL) uses privacy-preserving ML techniques to forecast customers' needs and consumption habits, and blockchain replaces the centralized aggregator to safeguard the ecosystem. However, blockchain technology (BCT) struggles with scalability and quick ledger expansion. In BCFL, local model generation and secure aggregation are other issues. This research introduces a novel architecture, emphasizing …