Computer Engineering Commons^™

Preserving Yucatán’S Agricultural Heritage: A Mobile App For Agricultural Sustainability, Fernando Rojas, Jason Serrano, Kavya Sharma

Computer Science and Engineering Senior Theses

We developed a mobile application that preserves Yucatán’s agricultural heritage and supports local farmers, while addressing the urgent need for the technology-driven integration of Mayan agricultural knowledge, specifically the Milpa system, into an accessible mobile platform. Our objective was to create an intuitive, culturally appropriate, and low-technology resource to support farmers in making informed, sustainable agricultural decisions, regardless of their internet connection. We saw this as critical for enhancing agricultural practices in Yucatán and beyond. We accomplished this by creating a trilingual (Spanish, English, and Yucatec Maya) mobile app with features such as agrarian cycle information, mapping and location services, …

Bilingual Buddy, Adian Alvarado, Farhaan Pishori, Luis Villalta, Andrea Yao, Ekam Singh

Computer Science and Engineering Senior Theses

Bilingual students often face challenges in mathematics not due to a lack of ability, but because of linguistic barriers that hinder their understanding of math-specific terminology and consequently their problem-solving language ability. This project addresses that issue through the development of a mobile application designed to support English language acquisition in the context of mathematics for Spanish-speaking students. Rather than teaching mathematical concepts directly, the app focuses on vocabulary, syntax, and contextual comprehension through scaffolded lessons, gamified elements, and an integrated AI chatbot.

Built using Flutter for cross-platform compatibility, the app prioritizes accessibility, simplicity, and engagement for young users. Ethical …

New Attack Surfaces Against Emerging Cloud And Web Based Infrastructures And Defenses, Junjie Xiong

USF Tampa Graduate Theses and Dissertations

Emerging network security threats, ranging from cloud-based infrastructure attacks to web-based content subversion, pose significant challenges to modern computing environments. In this dissertation, we explore two novel attack vectors that disrupt both cloud-based infrastructures and web-based content systems.

In this dissertation, we first introduce the Warmonger attack, a novel attack vector that can cause denial-of-service between a serverless computing platform and an external content server. The Warmonger attack exploits the fact that a serverless computing platform shares the same set of egress IPs among all serverless functions, which belong to different users, to access an external content server. As a …

How’S It Growing? Tools For Observing Snow And Sea Ice In A Changing Arctic Ocean, Ian Alexander Raphael

Dartmouth College Ph.D Dissertations

September Arctic sea ice extent has diminished by roughly 50% in the 45 years since satellite observations began. The Arctic Ocean may experience ice-free summers within the next decade, with implications for habitat, resource extraction, geopolitics, and local and global climate change. To predict how Arctic sea ice will change in the future, we need to understand its behavior in the present. In situ sea ice mass balance measurements (snow accumulation, ice growth, snow and ice surface melt, and bottom melt) are essential for studying the processes driving rapid changes in the ice pack, and for validating remote sensing measurements …

Cutting-Edge Methods For Analyzing Student Behavior In Educational Settings: A Review, Shatha Talib Rashid, Hasanen S. Abdullah

Journal of Soft Computing and Computer Applications

The ability to predict students' performance in educational settings like schools and universities is crucial. A key objective of this effort is to increase academic outcomes and prevent dropout rates, among other benefits. Automating student activities, encouraged by information collected from any technology-based learning tool, has an important role in the process here. Those big quantities of information ought to be completely studied theoretically and processed for gaining worthy insights concerning a student's background as well as interacting with scientific missions, facilitating the development of advanced ways and algorithms to predict students' performance. The current study reviews several contemporary mechanisms …

Blockchain-Based Physical Election Votes Digitally Secure Transfer, Mohanad A. Mohammed, Hala B. Abdul Wahab

Journal of Soft Computing and Computer Applications

Responsibility for maintaining election transparency over time and ensuring democratic values intact is held by the Iraqi Independent High Electoral Commission (IHEC). However, transferring physical election votes from election centers is a critical duty, where many challenges appear regarding accountability and security measures. This study proposes a system that utilizes blockchain technology to solve any challenges or difficulties and ensure an effective and improved election process by providing its highest trustworthiness and legitimacy and ensuring a decentralized security process. This system offers unique blockchain characteristics such as immutability, decentralization, and transparency, providing an extra level of security to the data …

Modern Face Recgognition Systems: A Review Of Methods And Empirical Findings, Zahraa Naji Razoqi, Raheem Ogla, Abdul Monem S. Rahma

Journal of Soft Computing and Computer Applications

The face recognition system is a biometric technique that replaces traditional passwords and personal identification. This research is dedicated to presenting a study of some facial recognition systems. Since it is unlikely to replicate and is more stable over time, the domain of facial feature extraction has proven to be more effective in attaining exact facial recognition, which is important, especially in intelligent security surveillance systems. Face recognition systems encounter several challenges, primarily related to pose variations, illumination conditions, and occlusions such as hair, glasses, and so on. To address these challenges, enhance performance, and boost the accuracy and speed …

Arson Event Detection Using Yolov9, Ali Abbas Abbod, Matheel E. Abdulmunimb, Ismail A. Mageed

Journal of Soft Computing and Computer Applications

Detecting event anomalies is crucial for surveillance systems, as it enables the identification of occurrences in videos, both temporally and spatially. It can identify deviations from patterns without requiring human oversight by learning from past information to distinguish normal behavior and pinpoint irregularities. Early detection of arson fires is critical to mitigating damage, public safety, property, and the environment, as well as saving lives and aiding in law enforcement investigations. The objective of this study is to evaluate a system for detecting events using the You Only Look Once version 9 (YOLOv9) model in surveillance videos with a focus on …

Enhancing Cybersecurity Based On Blockchain Technology: A Systematic Review, Sarah Mohammed Shareef, Rehab Flaih Hassan

Journal of Soft Computing and Computer Applications

Cybersecurity is a crucial component of the security system that guards against unauthorized access to digital transactions. Blockchain is a decentralized ledger used to securely exchange digital currencies and conduct trades and transactions. Blockchain technology has led to significant changes in electronic transactions. The enormous potential is being exploited in many areas such as financial services, real estate, supply chain, and the Internet of Things. Despite being a security system, it has suffered from security threats to sensitive data. Phishing and 51% attacks can circumvent blockchain security, highlighting the need for thorough user education and awareness. Additionally, blockchains based on …

Predicting Earthquake Location Using Convolutional Neural Network-Attention Mechanism Approach, Mohammed A. Jaleel Shaneen, Suhad M. Kadhem

Journal of Soft Computing and Computer Applications

In seismically active areas, earthquake prediction is essential for minimizing potential damages and preserving lives. However, precise forecasts are complicated to achieve because of seismic events’ complex and unpredictable nature. The current study presents an advanced prediction approach to address such issues, combining Convolutional Neural Networks (CNNs) and Attention Mechanism (AM). The primary goal is to improve the accuracy of the earthquake predictions and the generalizability across various mainland Chinese regions. AM layer emphasizes significant features for improving the prediction performance, whereas CNNs are utilized to extract spatial features of seismic data. The efficiency and effectiveness of the proposed approach …

A Machine Learning-Driven Framework For Real Time Detection And Prevention Of Replica Node Attacks In Wireless Sensor Networks, Maram Pavani, Tanguturi Sharani, Amutha Jeevakumari S A

Northeast Journal of Complex Systems (NEJCS)

Mobile devices and wireless sensor networks (WSNs) are increasingly vulnerable to security threats such as unauthorized access and replica node attacks. Mobile devices face risks from replication and anomalous behavior, while attackers compromise WSNs by cloning legitimate nodes, thus threatening network integrity. Traditional security mechanisms often fall short in detecting such sophisticated threats, especially in resource-constrained environments. This research proposes a dual-component security system. A Machine Learning-Based Intrusion Detection System (IDS) for WSNs leverages Graph Neural Networks (GNNs) to detect replica nodes through structural network analysis and applies Federated Learning to preserve data privacy. The Sequential Probability Ratio Test (SPRT) …

Gbotuner: Autotuning Of Openmp Parallel Codes With Bayesian Optimization And Code Representation Transfer Learning, Kimsong Lor

Computer Science and Engineering Master's Theses

Empirical autotuning methods such as Bayesian optimization (BO) are a powerful approach that allows us to optimize tuning parameters of parallel codes as black-boxes. However, BO is an expensive approach because it relies on empirical samples from true evaluations for varying parameter configurations. In this thesis, we present GBOTuner, an autotuning framework for optimizing the performance of OpenMP parallel codes, where OpenMP is a widely used API that enables shared-memory parallelism in C, C++, and Fortran using simple compiler directives. GBOTuner improves sample efficiency of BO by combining code representation learning from a Graph Neural Network (GNN) into a BO …

Development Of An Embedded Iot Board For Real-Time Floor Estimation Of Autonomous Robots, Carter J. Sorensen

Electronic Theses and Dissertations

As service robots become more prevalent in multi-story environments such as hospitals, hotels, and laboratories, accurate floor-level detection is critical to ensuring operational reliability. Consider a robot tasked with delivering medical samples in a multi-story laboratory. Without accurate feedback, a robot exiting on the wrong floor could introduce delays, disrupt workflows, or compromise sample integrity. Internet of Things (IoT) technologies offer a way to address these risks by providing real-time error detection and corrective capability. However, current IoT-based floor estimation systems often require invasive modifications to building infrastructure—particularly elevator control panels. These approaches introduce challenges related to cost, liability, backward …

An Advanced Hexacopter For Autonomous Exploration Of Mars: Attitude Control And Navigation Strategies, Laura Sopegno

Electronic Theses and Dissertations

Mars exploration has recently witnessed major interest within the scientific community. Unmanned robotic platforms offer reliable solutions to acquire and collect data and information from the Red Planet. Particularly, rovers, landers, and orbiters have significantly shaped planetary exploration on the Moon and Mars, contributing significantly to past missions while also highlighting limitations in their capacity to cover diverse terrains over wide ranges. Given current advances in Unmanned Aircraft Systems (UASs), Unmanned Aerial Vehicles (UAVs) offer promising alternatives for future scientific missions.

It is argued that hexacopters, with their relatively compact design and redundancy, present a promising …

Reducing Attention Complexity In Graph Transformers Through Subgraph Partitioning, Ranjan Kumar Choubey

Master’s Dissertations

This dissertation addresses the challenge of scaling Graph Transformers by proposing a subgraph-based strategy to reduce attention complexity. The proposed framework preserves representational power while making attention computation tractable for largescale graphs. The method begins by partitioning the input graph into K subgraphs using the METIS algorithm. Each subgraph is encoded using a combination of local structural features from a Graph Convolutional Network (GCN) and global positional cues from Laplacian Positional Embeddings (LPEs). These embeddings are fused via a trainable projection function to form subgraph tokens. A supergraph is constructed to model interactions among subgraphs, allowing attention to be applied …

Online Hyperparameter Tuning For Llm Optimization, Ethan Lin, Nathan Yu, Jeromy Chang

Computer Science and Engineering Senior Theses

Large Language Models (LLMs) are becoming increasingly popular in modern society. However, despite their popularity, the deployment of LLMs in real-world scenarios is extremely challenging due to substantial computational costs and memory constraints. Edge devices, like smartphones and IoT devices, lack resources needed to run these models locally, instead offloading computations for cloud computing. Cloud computing requires users to send their data over the internet leading to numerous privacy and security concerns. In some domains, such as health and finances, sending such sensitive information is not an option. Existing solutions to compress or increase inference speed include Small Language Models …

Intelligent Intrusion Detection In Clustered Wireless Sensor Networks: A Dynamic Clustering And Machine Learning-Based Approach, Abdullah R. Abdulwahhab, Mohd Fadzli Mohd Salleh, Muhammad Firdaus Akb, Mohammed Najm Abdullah

Iraqi Journal for Computer Science and Mathematics

Traditional Intrusion Detection Systems (IDS) designed for more conventional network infrastructures are often ill-equipped to handle the unique challenges WSNs pose, leading to significant gaps in security and resilience. This paper introduces an Intelligent Intrusion Detection System (IIDS) explicitly tailored for clustered WSNs to address these critical challenges. The proposed IIDS integrates dynamic clustering with advanced machine learning algorithms to create a robust and adaptive security solution capable of real-time threat detection and mitigation. The dynamic clustering mechanism is designed to continuously monitor and respond to changes in sensor node network topology and energy levels, ensuring that energy consumption is …

Non-Overlapping Patch-Based Pre-Trained Cnn For Breast Cancer Classification, Lamyaa Sabeeh Ashour, Ahmed Abed Mohammed, Mustafa M. Abd Zaid, Putra Sumari, Ahmed Kateb Jumaah Al-Nussairi, Sura Abdulateef Al-Shammari, Sarah Thabit Abdulmunem

Iraqi Journal for Computer Science and Mathematics

Breast cancer (BC) significantly impacts women's mortality rates and requires early detection to improve survival chances and enable appropriate treatment. Thus, a computer-aided system with high performance can speed up this process. A convolutional neural network (CNN) is considered sensitive to insufficient, noisy data. It cannot achieve high performance, however, restricted access to high-quality medical data, stemming from stringent confidentiality and privacy issues, is a considerable obstacle to the successful training of deep learning models. The current study aims to develop a remarkable, influential model for BC classification whilst considering modern pre-trained models ResNet50, AlexNet, InceptionV3 and VGG16 for extracting …

Retracted: Navigating The Complexities And Artificial Intelligence Of Internet Of Things Security Claims, Tamara Saad Mohamed, Saad Mohammed Khalifah

Iraqi Journal for Computer Science and Mathematics

The term ``Internet of Things'' (IoT) describes a system that allows everyday objects to communicate with one another and with controlled systems, servers, and other linked devices through a variety of connectivity constructions by means of embedded software, sensor technology, electronics, and connections. Data from the Internet of Things (IoT) will be sent to the servers over the internet from a variety of sensors, nods, and collectors. Governments, medical facilities, consumers, and corporations all make use of IoT devices. approximately, More than 65 billion Internet of Things devices are expected to be in operation by 2024. The proliferation of IoT …

Topological Indices For The Resize Graph Of (G2(3)), Manar Musab Ftekhan, Ali Abd Aubad

Iraqi Journal for Computer Science and Mathematics

Indexes of topological play a crucial role in mathematical chemistry and network theory, providing valuable insights into the structural properties of graphs. In this study, we investigate the Resize graph of G₂(3), a significant algebraic structure arising from the exceptional Lie group (G₂) over the finite field F₃. We compute several well-known topological indices, including the Zagreb indices, Wiener index, and Randić index, to analyze the graph's connectivity and complexity. Our results reveal intricate relationships between the algebraic structure of G₂(3) and its graphical properties, offering a deeper understanding of its combinatorial …

Machine Learning Algorithms To Detect Cyber-Attack In The Internet Of Things Platform, Maad Kamal Al-Anni, Rafah M. Almuttairi, Ammar A. Al-Hamadani, Khamis A. Zidan, Husam Ibrahiem Husain Alsaadi, Ghaidaa A. Al-Sultany

Iraqi Journal for Computer Science and Mathematics

In response to escalating cyber threats in Internet of Things (IoT) networks, this research investigates several traffic classification techniques by using benchmark datasets, such as CICIoT2023. This dataset was exclusively used to test the effectiveness of the proposed system, which aims to enhance the data processing for machine learning algorithms amid increasingly complex cyber threats. To prepare the naturally unstructured and raw dataset for analysis, Linear Discriminant Analysis (LDA) is applied for dimensionality reduction. The processed data and attributes are then fed into the proposed Fuzzy-Integrated Relevance Vector Machine Classifier (FIRVM), which is implemented in Python 3.10⁺ using the …

Multi Trajectory Guided Model Inversion Attacks, Indrajit Nandi

Master’s Dissertations

Recent advancements in deep learning have brought significant concerns regarding the privacy of training data due to overfitting and memorization, as well as a lack of defense mechanisms. In model inversion attacks, where Attackers aim to reconstruct original private training samples (i.e images) just by using the DNN model’s last layer output. Traditional black-box MI attacks face three key challenges: (1) Non-convex optimization landscapes often trap reconstructions in poor local minima, degrading output quality. (2) Black-box scenarios require excessive queries to approximate gradients, raising detection risks. (3) Unconstrained pixel-space optimization generates unrealistic artifacts since the inverse mapping lacks natural image …

Monochromatic Empty Triangles In Two-Colored Point Sets, Adrish Paik

Master’s Dissertations

A key problem in combinatorial geometry is the identification of properties of a subset of a point set which has properties like convexity, monochromaticity, and emptiness. This thesis actual works on this sides of combinatorial geometry: efficiently finding empty monochromatic triangles in two-colored point sets on the plane. While earlier research focused on counting empty triangles in point set without any color, our work takes an optimal algorithm to explicitly detect them. By using geometric insights and visibility-based techniques. We then address a relaxed but equally compelling variant: monochromatic triangles containing at most one point of the opposite color. For …

Detection Of Fake News In Short Videos: A Multimodal Approach, Mona Kumari

Master’s Dissertations

The rise of generative models and affordable video editing tools has fueled the spread of fake and manipulated videos, undermining information reliabilityespecially on social media. Traditional detection methods, focused on single modalities like visual artifacts or text cues, often struggle with diverse, user-generated content. This dissertation presents a unified framework for fake video detection that integrates multimodal semantics, narrative structure, and propagation behavior. Visual, audio, text, and OCR features are extracted using pretrained models (CLIP, Wav2Vec2), and segment-level graphs are built to model narrative flow using Graph Attention Networks (GATv2Conv). User engagement dynamics are modeled via a bidirectional LSTM. A …

Santa Clara Radio Astronomy Project (Scrap) Iv - Data Processing, Noah Abe, Wesley Durbano

Computer Science and Engineering Senior Theses

The Santa Clara Radio Astronomy Project (SCRAP) IV: Data Processing is the fourth year of a multi-year project that aims to provide SCU with a fully functioning radio telescope in a costefficient manner. The project aims to provide this radio telescope as a hands-on tool that can be used not just for scientific discovery, but also for SCU academic use in a wide variety of different subjects, such as electrical engineering and physics. This year, our SCRAP team aimed to bolster the long-term operation of the telescope, which is a key feature it must have for it to be considered …

Xmr: Extensible Model Representation, Jason Cisneros, Lucas Van Der Heijden

Computer Science and Engineering Senior Theses

In safety critical industries, such as aerospace, verification and validation are crucial steps in system development. Validation begins with decomposing a system into its requirements. These requirements are used to create the high level model of the system. The low level system that is created from this must then be verified. Manually ensuring that the system being verified exactly matches the validated design is tedious, time consuming, and error prone. To solve this, we have created an open source tool that can automatically generate the code from UML models. This enables going from the high level model to the low …

Deepfake Detection, Timothy Tong, Abem Lucas

Computer Science and Engineering Senior Theses

From elections, to wars, to people’s personal lives, deepfakes have become ever more present, blurring the distinction between reality and fiction. Many deep learning-based deepfake detection methods lack generalizability. They often overfit to deepfake generation techniques in their training distribution, and fail to detect deepfake generation techniques outside of their training distribution. One approach to solve the generalization problem has been to use Vision Language Models, such as the pretrained CLIP model, to extract features that generalize across different deepfake generation techniques, including diffusion and GAN images. However, most CLIP approaches only consider image level features for detection, such as …

Exploring Novel Methods For Real-Time Multi-Camera People Tracking In Machine Learning, Johan Kou, Connor Vallero

Computer Science and Engineering Senior Theses

Multi-camera people tracking is the process of tracking persons and their paths, continuously across different camera fields of view. It can help track suspects across large areas, and assist individuals in emergency situations. The current state-of-the-art (SOTA) method involves using geometric-consistent constraints, information on the appearance of subjects, and pose estimation for dealing with occlusion issues. This current SOTA works well, but is still lacking in its ability to handle occlusion and perform well in real-time applications on a network. With occlusion, the IDs assigned to persons can be accidentally swapped in high density areas, or places where there are …

Vintage Game Emulator, Andrew Katchour, Ashwin Raman

Computer Science and Engineering Senior Theses

This project explores the integration of artificial intelligence into retro-style arcade titles to enhance the gameplay, while still preserving the nostalgic aesthetic and overall mechanics of the titles. By using Pygame to explore clones of the existing video game titles, we modified the existing games to develop AI into the games using various AI algorithms. The system introduces adaptive enemy behavior that responds to the players actions and also introduces an additional player to some games that will play alongside the player as well. This helps to create a more engaging and unpredictable gaming experience. The AI logic is implemented …

An Ai-Driven Microfinance Platform To Enhance The Growth Of Small Businesses, Abdullah Naveed, Yunzhou Wang

Computer Science and Engineering Senior Theses

Microfinance provides financial services to businesses which do not have access to regular banking institutions. There have been many digital applications developed to support microfinance schemes globally. However, the applications currently on the market do not yet fully leverage technology features, which are already being widely used in traditional banking. In conjunction with the Miller Center of Social Entrepreneurship at Santa Clara University, we have developed an application utilizing the latest technologies to help small businesses and lenders in all stages of a microfinance project.