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Articles 1981 - 2010 of 25637

Full-Text Articles in Engineering

Vector Estimation For Continuous Tracking Of Observed Radio Signals (V.E.C.T.O.R.) Lunar Navigation System, Dimitry Melnikov, Evan Bartel, Andrew Burrier, Goran Gjorgievski Jan 2025

Vector Estimation For Continuous Tracking Of Observed Radio Signals (V.E.C.T.O.R.) Lunar Navigation System, Dimitry Melnikov, Evan Bartel, Andrew Burrier, Goran Gjorgievski

Williams Honors College, Honors Research Projects

NASA's Artemis program requires precise navigation capabilities to establish the first sustained presence on the lunar surface. However, as launches bring necessary orbital infrastructure, the Artemis program will face a critical period during which reliable lunar navigation is not possible. To address this challenge, the V.E.C.T.O.R. system tracks assets, such as rovers and astronauts, as User Terminals relative to a pre-existing cell tower, or Base Station. To do so, the system leverages existing Base Station hardware to calculate the location of User Terminals in conjunction with existing communications infrastructure.

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Improving The Robustness Of Compressed Deep Learning Models Against Class Imbalance, Baraa Saeed Ali Jan 2025

Improving The Robustness Of Compressed Deep Learning Models Against Class Imbalance, Baraa Saeed Ali

Wayne State University Dissertations

Deep Learning (DL) models are deployed ubiquitously, as they power a wide range of critical applications, including image classification, fraud detection, autonomous vehicles, robots, and NLP. However, their massive size and huge memory footprint (overparameterization) represent a serious challenge to the efficient deployment of such models, especially in resource-scarce environments such as wearable devices, smartphones, edge devices, and embedded systems. Therefore, model compression techniques are typically used to shrink the model size to the currently available computational and memory budget and to accelerate training and inference without sacrificing model accuracy and performance. Therefore, the DL research community considers model compression …

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Development And Evaluation Of Machine Learning Models For Early Pediatric Sepsis Prediction, Ancita M. Andrade Jan 2025

Development And Evaluation Of Machine Learning Models For Early Pediatric Sepsis Prediction, Ancita M. Andrade

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Sepsis is a leading cause of pediatric mortality, claiming more lives in the United States annually than all childhood cancers combined. Early identification in Emergency Departments (EDs) remains challenging, as the current Phoenix criteria establishes an updated international consensus definition for sepsis, however is not designed for use as a screening tool. This study aimed to develop predictive models identifying pediatric patients at risk of sepsis within 24 hours of admission. Multiple tree-based and deep learning models were trained utilizing clinical and laboratory data from the initial four hours of presentation. Both the LightGBM and LSTM architectures demonstrated superior performance, …

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Wearable Sensor Data Analysis For Machine Learning-Based Detection Of Posture And Autonomic Responses, Chaitanya Vardhini Anumula Jan 2025

Wearable Sensor Data Analysis For Machine Learning-Based Detection Of Posture And Autonomic Responses, Chaitanya Vardhini Anumula

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This study investigates how Iyengar yoga postures influence autonomic nervous system (ANS) activity by analyzing multimodal physiological signals collected via wearable sensors. The physiological mechanisms underlying Iyengar yoga’s therapeutic effects remain under-explored at the granular, pose-level. Using data collected from 16 participants, this research evaluates whether machine learning models can distinguish between baseline, parasympathetic-dominant, and sympathetic-dominant states based on wrist-worn sensor data. The goals were to explore whether subtle postural variations elicit measurable autonomic responses and to identify which sensor features most effectively capture these changes. Participants performed a sequence of yoga poses while wearing synchronized sensors measuring electrodermal activity …

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Impact Of Graph Structures For Rag Outcomes In Llms, Chris Davis Jaldi Jan 2025

Impact Of Graph Structures For Rag Outcomes In Llms, Chris Davis Jaldi

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Explainability, interpretability and adaptability (EIA) remain three central motivations for next-generation Artificial Intelligence (AI), especially as Large Language Models (LLMs) continue to engage with ever-increasing knowledge bodies. As the landscape pushes toward controllable agentic Retrieval-Augmented Generation (RAG) systems where AI agents engage in iterative, guided reasoning, a critical question arises as to the extent to which the knowledge design itself shapes these models' reasoning behavior. This work conducts a systematic evaluation of how different conceptualizations and representation of the identical knowledge affect an LLM's path-based reasoning capabilities. Through the introduction of controlled variations along graph structural complexity, linguistic and semantic …

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Re-Parameterizing Adversarial Reprogramming In Low-Dimensional Subspace For Efficient Software Vulnerability Detection, Hootan Alavizadeh Jan 2025

Re-Parameterizing Adversarial Reprogramming In Low-Dimensional Subspace For Efficient Software Vulnerability Detection, Hootan Alavizadeh

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Software vulnerabilities are a major cause of security breaches, making effective detection critical. Traditional learning-based methods require large datasets and significant computational resources, which are often impractical due to high annotation costs and data scarcity. To address this, we propose an innovative system, RearVul, which Re-parameterizes adversarial reprogramming in a low-dimensional subspace for software vulnerability detection. Unlike conventional approaches, RearVul repurposes a pre-trained classification model using adversarial reprogramming, enabling detection with minimal modifications. It learns a universal perturbation applied to program representations, preserving the original model’s feature extraction capabilities while adapting it to a new domain. Furthermore, we introduce a …

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Subjective Readiness Forecasting Using Supervised Machine Learning And Wearable Device Data, Nathaniel Michael Weiland Jan 2025

Subjective Readiness Forecasting Using Supervised Machine Learning And Wearable Device Data, Nathaniel Michael Weiland

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Recent advances in wearable technology allow continuous monitoring of physiological and behavioral data, opening new opportunities for real-time assessments of readiness and well-being. However, creating predictive models that generalize across diverse users remains challenging, especially in high-stakes settings like the military, where preventable injuries, illnesses, and stress-related performance declines are frequent. This research assesses the feasibility of using supervised machine learning models trained on wearable device data to predict subjective readiness indicators—recovery, stress, injury, and illness. Data from over 10,000 users in the OHWS (Optimizing the Human Weapons System) program combined daily check ins with physiological metrics from Garmin, Polar, …

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Dataset Generation For Routing Policy Study In Ad Hoc Wireless Networks, Vishnu Vishnu Priya Jan 2025

Dataset Generation For Routing Policy Study In Ad Hoc Wireless Networks, Vishnu Vishnu Priya

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Ad Hoc wireless networks, with their decentralized architecture and dynamic topology, present challenges in reliable and energy-efficient routing. While machine learning (ML) and reinforcement learning (RL) offer promising solutions, progress is limited by the lack of realistic, high-fidelity datasets. This research introduces a simulation-based framework for generating four diverse datasets representing combinations of node mobility (mobile vs. static) and spatial distribution (random vs. clustered). Each dataset captures critical metrics such as Signal-to-Interference-plus-Noise Ratio (SINR), bottleneck rate, and power consumption across multi-hop paths. A lookahead-based greedy routing algorithm with scenario-aware power control is implemented to emulate practical behavior. Supervised ML models, …

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Isometric Centroid Encoder (Ice) And Synthetic Data Generation Approaches For Biological Datasets, Prathyusha Kanakamalla Jan 2025

Isometric Centroid Encoder (Ice) And Synthetic Data Generation Approaches For Biological Datasets, Prathyusha Kanakamalla

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This thesis addressed two main challenges in biological data analysis: structure-preserving dimensionality reduction and synthetic data generation for small sample datasets. I proposed the Isometric Centroid Encoder (ICE), a supervised dimensionality reduction method that preserves pairwise distances between class centroids during dimension reduction. Unlike existing methods like Centroid Encoder and Super Encoder, ICE explicitly maintains geometric relationships between biological classes, achieving nearly perfect structure preservation at C dimensions (where C equals the number of classes) with strong performance even in 2D and 3D spaces. Additionally, I compared three generative models (VAE, LSH-GAN, and scDiffusion) for synthetic data generation on small …

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Detecting Wireless Security Threats Through Ieee 802.11 Frame Field Anomalies, Aria Young Jan 2025

Detecting Wireless Security Threats Through Ieee 802.11 Frame Field Anomalies, Aria Young

Williams Honors College, Honors Research Projects

It is not uncommon for most public spaces to offer Wi-Fi, while it is convenient and affordable, there are significant security risks due to its open nature. Rogue access points pose a security threat to many networks because they have the potential to bypass security measures and intercept traffic containing sensitive information. The attempt to formulate a method that one hundred percent guarantees the detection of a rogue access point has proven to be an intricate and complex problem for many to tackle, as there are numerous ways a rogue access point can be configured. This project aims to demonstrate …

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Ultrasonic Sensor-Based Sound Synthesis Using Raspberry Pi Pico W, Niraj Jaishwal Jan 2025

Ultrasonic Sensor-Based Sound Synthesis Using Raspberry Pi Pico W, Niraj Jaishwal

Williams Honors College, Honors Research Projects

At the intersection of Human Computer Interaction and digital art, this project transforms simple motion into musical expression. It explores an interactive real-time sound synthesis system using ultrasonic sensors to generate continuous audio. The objective is to design a system that maps physical distances into musical parameters such as pitch and amplitude, which will create a responsive audio environment. Two ultrasonic sensors are used in combination with the Raspberry Pi Pico W microcontroller running CircuitPython and Adafruit Audio Hat for real-time sound output. One sensor controls the pitch of the generated tone, while the other controls volume. This enables expressive …

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Personalized Physics Learning Through Ai: Insights From Problem Generation, Chatbot Dialogues, And Intelligent Tutoring Systems, Atharva Dange Jan 2025

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 …

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Llm-Driven Fmea For Safe Human-Robot Collaboration In Disassembly, Morteza Jalali Alenjareghi, Samira Keivanpour, Yuvin Adnarain Chinniah, Sabrina Jocelyn Jan 2025

Llm-Driven Fmea For Safe Human-Robot Collaboration In Disassembly, Morteza Jalali Alenjareghi, Samira Keivanpour, Yuvin Adnarain Chinniah, Sabrina Jocelyn

Articles dans des actes de congrès

Disassembly operations often present unstructured and unpredictable scenarios, such as handling hazardous materials, addressing ergonomic strain, and managing dynamic robot interactions that pose safety risks. To tackle these challenges, we propose an innovative use of large language models (LLMs) to enhance failure mode and effect analysis (FMEA) in the context of human-robot collaboration (HRC) for disassembly tasks. We developed an LLM system leveraging retrieval-augmented generation (RAG) for real-time risk analysis and recommendation generation. RAG retrieves domain-specific information from the FMEA knowledge database, enabling accurate risk analysis, contextual understanding, and relevant recommendations based on user input and operational data. Evaluation of …

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Leveraging Counterfactuals For Enhanced Natural Language Inference: A Multitask Knowledge Distillation Approach, Brian J. Davis Jan 2025

Leveraging Counterfactuals For Enhanced Natural Language Inference: A Multitask Knowledge Distillation Approach, Brian J. Davis

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Natural-language inference (NLI) asks whether a hypothesis is entailed by, contradicts, or is neutral with respect to a premise. Modern transformers reach high raw accuracy on benchmarks such as SNLI, MNLI, and ANLI, yet they often rely on brittle lexical shortcuts and provide little insight into their decision process. This thesis shows that counterfactual-augmented knowledge distillation can simultaneously boost robustness and supply faithful, token-level explanations—without scaling model size. Four T5-v1_1 students (60M, 220M, 770M, 3B parameters) are trained under four curricula: (1) standard fine-tuning, (2) fine-tuning with free-text rationales, (3) multi-task distillation with naive counterfactuals, and (4) multi-task distillation with …

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Optimizing Cloud Computing Resources For Operational Cost And Application Performance Using Machine Learning, Isaac K. Matthew Jan 2025

Optimizing Cloud Computing Resources For Operational Cost And Application Performance Using Machine Learning, Isaac K. Matthew

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As AI-driven workloads accelerate the growth of cloud initiatives and spending, resource waste also increases due to persistent inefficiencies in cloud compute and infrastructure management. Overprovisioned resources and suboptimal configurations often lead to operational inefficiencies and unnecessary financial overhead. These challenges arise from the difficulty of anticipating resource demands in dynamic workloads and selecting suitable virtual machines to ensure optimal performance. Our research proposes a holistic, data-driven framework for managing cloud compute resources that reduces costs without compromising application performance. We integrate a predictive, model-driven, threshold-based autoscaling solution for cloud-native applications with an optimized instance right-sizing approach to select cost-effective …

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Reducing Operator Training Time Through Virtual Reality: A Case Study On The Lpkf Protomat E44 Machine, Joshua C. Patel Jan 2025

Reducing Operator Training Time Through Virtual Reality: A Case Study On The Lpkf Protomat E44 Machine, Joshua C. Patel

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This thesis presents the development of an immersive virtual reality (VR) simulation that replicates the operation of the LPKF ProtoMat E44 PCB milling machine. Aimed at reducing operator training time and improving procedural understanding, the simulation offers an interactive and realistic environment where users can safely engage with machine workflows and start-up sequences. The emphasis is on accurate representation, usability, and maintaining immersion to support intuitive learning. Although formal evaluation is outside the scope of this work, the system is designed to serve as a foundation for cost-effective, scalable training in technical and manufacturing contexts, offering a modern alternative to …

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Scalable Real-Time Stream Clustering For Unbounded Text Streams, Nathaniel C. Crossman Jan 2025

Scalable Real-Time Stream Clustering For Unbounded Text Streams, Nathaniel C. Crossman

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Social media, AI systems, IoT sensors, and other platforms generate vast amounts of streaming data. Given this vast volume of information, techniques that can reduce and aggregate data into meaningful topics are essential. One such technique is the two-phase stream clustering approach. In the first, online micro-clustering phase, the system forms micro-clusters from the incoming data stream, incrementally merges new items into related existing micro-clusters, and prunes or fades micro-clusters as they become inactive, producing a constantly updating yet compact set of micro-clusters representing potential topics and subtopics of the stream. In the second, offline macro-clustering phase, these micro-clusters are …

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Patient Subset Classification Using Encoded Embeddings And Knowledge Graph Retrieval-Augmented Generation, Benjamin A. Holmes Jan 2025

Patient Subset Classification Using Encoded Embeddings And Knowledge Graph Retrieval-Augmented Generation, Benjamin A. Holmes

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The widespread adoption of electronic medical records has created a vast reservoir of clinical data that can be leveraged to better understand how interventions relate to patient outcomes. Much of this information, however, exists as unstructured free-text, posing significant challenges for traditional statistical and machine-learning methods. Solving these challenges would allow the extraction of specific patient subpopulations (clinically relevant cohorts of individuals who share overlapping symptoms, risk factors, or diagnostic criteria), which could be used in precision medicine. Despite this promise, extracting these subpopulations from unstructured medical notes is an ongoing challenge due to the variability of clinical language and …

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Generative Adversarial Networks (Gans) For High-Dimensional Biological Data, Harigovind Harikumar Jan 2025

Generative Adversarial Networks (Gans) For High-Dimensional Biological Data, Harigovind Harikumar

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This thesis investigates the application of Generative AI models, mainly Generative Adversarial Network (GAN) models to high dimensional and low sample size biological datasets like Motion Sickness, Breast Cancer, Crohn, and Melanoma. We utilized and compared three generative AI frameworks: Vanilla GAN, Wasserstein GAN (WGAN), Locality-Sensitive Hashing GAN (LSH-GAN) and Omics GAN. To address the challenges associated with high-dimensionality and low sample size, which was leading to very poor outputs of biological synthetic samples, we came up with an approach to stop the model when it reaches its saturation level. That is, we printed the loss plots to see where …

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Pixmix Attack: Implementation And Evaluation Of A Novel Pixel Injection On Digital Video Port (Dvp) Interface In Embedded Camera Systems With Pcb Hardware Trojan, Sayed Md Tashfi Nowroz Jan 2025

Pixmix Attack: Implementation And Evaluation Of A Novel Pixel Injection On Digital Video Port (Dvp) Interface In Embedded Camera Systems With Pcb Hardware Trojan, Sayed Md Tashfi Nowroz

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Image sensors are at the heart of machine vision systems in robotics, industrial automation, and surveillance systems which ideally operate with minimal human supervision and only occasional maintenance. The image sensors convert visible light into electrical signals which are locally decoded to image on the printed circuit board (PCB) by an ordinary embedded processor System on Chip (SoC). This thesis investigates a critical vulnerability in such systems, targeting the communication protocol at the signal level during runtime. Specifically, it focuses on a novel attack in the Digital Video Port (DVP) protocol, possible to exploit with PCB-based hardware Trojans, to craft …

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A Secure Ml-Assisted Framework For Resilient And Efficient Prediction Of Physiotherapy Sequence In Bilateral Carpal Tunnel Syndrome, Pratik Pandurang Kharat Jan 2025

A Secure Ml-Assisted Framework For Resilient And Efficient Prediction Of Physiotherapy Sequence In Bilateral Carpal Tunnel Syndrome, Pratik Pandurang Kharat

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Bilateral idiopathic carpal tunnel syndrome (CTS) is a neuromuscular disorder characterized by compression of the median nerve at both wrists, leading to symptoms such as pain, numbness, tingling, and muscle weakness. Unlike unilateral cases, bilateral idiopathic CTS presents distinct therapeutic challenges due to the simultaneous involvement of both hands and the lack of an identifiable underlying cause. This study explores the application of machine learning techniques to predict the optimal sequence of physiotherapeutic interventions Stretching followed by Myofascial Mobilization (S/M) or the reverse (M/S) in female patients with bilateral idiopathic CTS and right hand dominance. Data were drawn from a …

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Learning Under Data Scarcity: Reasoning And Negative Distillation For Texts And Graphs, Calvin T. Greenewald Jan 2025

Learning Under Data Scarcity: Reasoning And Negative Distillation For Texts And Graphs, Calvin T. Greenewald

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Modern machine learning (ML) models rely on large amounts of high-quality labeled data to achieve optimal performance. However, in many real-world domains, such as cyber security, acquiring sufficient labeled data is often infeasible due to cost, privacy concerns, and the rapid evolution of underlying phenomena. This challenge underscores the importance of learning under data scarcity. This thesis addresses this challenge by proposing distinct, modality-specific techniques for text and graph domains, which allow models to generalize effectively with minimal data. For text classification task, we incorporate distilled rationales from large language models and adversarial perturbations into the input space to improve …

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Coral Vision – Crustose Coralline Algae Detection With Computer Vision, Ryan Tseng Jan 2025

Coral Vision – Crustose Coralline Algae Detection With Computer Vision, Ryan Tseng

Master's Projects

Crustose coralline algae (CCA) are a group of red algae that are vital contributors to the health of coral reef ecosystems. Monitoring CCA abundance can serve as an indicator for coral reef health and improve reef conservation efforts. Autonomous Reef Monitoring Structures (ARMS) are artificial structures that can be deployed into coral reef ecosystems and retrieved to gather ecological data without harming reef structures. Traditional methods of calculating CCA abundance require manual analysis and are labor-intensive. Recent developments in computer vision and deep learning technology have provided an avenue to fully automate this task. This research aims to train a …

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Traffic Forecasting With Vset-Nets: A Vgae Spatial Embedding For Temporal Networks Approach, Mrunmayee Dhapre Jan 2025

Traffic Forecasting With Vset-Nets: A Vgae Spatial Embedding For Temporal Networks Approach, Mrunmayee Dhapre

Master's Projects

Traffic forecasting is important for improving transportation systems by enabling better traffic management, congestion reduction, and urban planning. However, predicting traffic accurately is challenging due to the strong spatial dependencies between different road segments and the temporal changes in traffic patterns over time. Traditional time-series and graph models often struggle to capture both of these aspects effectively. In response, recent research has focused on temporal graph representation learning methods that jointly consider spatial relationships and temporal features in networks. This project proposes a hybrid model called VSET-Nets (VGAE Spatial Embedding for Temporal Networks) that employs Variational Graph Autoencoders (VGAEs) for …

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Semanticgraphrec: Lightweight Hybrid Recommendations Powered By Semantic Item Representations And Graph Collaborative Filtering, Devi Surya Kumari Akula Jan 2025

Semanticgraphrec: Lightweight Hybrid Recommendations Powered By Semantic Item Representations And Graph Collaborative Filtering, Devi Surya Kumari Akula

Master's Projects

Graph neural networks (GNNs) have emerged as a powerful paradigm for collaborative filtering. However, they often fall short in fully leveraging side textual content, resulting in suboptimal recommendations. To address this limitation, we explore the synergy between GNNs and deep contextual embeddings of item descriptions, aiming to enhance recommendation quality on the Amazon-Books dataset. We propose SemanticGraphRec, which combines GNNs with Large Language Models (LLMs) to leverage both collaborative filtering and textual item content. Experimental results demonstrate that incorporating semantic item embeddings produced by fine-tuning LLMs consistently improves performance. Our approach enhances recommendation relevance in sparse data scenarios by leveraging …

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Secured Data Storage Management With Deduplication In Cloud Computing And Local Gpt Integration, Pavan Myana Jan 2025

Secured Data Storage Management With Deduplication In Cloud Computing And Local Gpt Integration, Pavan Myana

Master's Projects

Exponential growth in cloud computing has brought enormous changes in data storage and processing, but also raised several questions on the security, privacy, and efficient storage of data. This report provides a dual-focused approach toward solving these challenges. First, we try to build an application securely and efficiently using data deduplication and Proxy Re-Encryption for optimization of storage and enabling secure data sharing. Deduplication ensures that redundant data is removed before encryption for maximum efficiency in storage, while PRE enables the safe sharing of encrypted data by re-encrypting the keys for specified recipients without the leakage of sensitive information. We …

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Rift - Reddit Information Falsity Tagger, Parth Joshi Jan 2025

Rift - Reddit Information Falsity Tagger, Parth Joshi

Master's Projects

Social media platforms such as Reddit are widely used for sharing and consuming information. User-generated content poses a great risk for misinformation creation and dissemination on these platforms. “Fake news”, as it is commonly referred to, has far-reaching social implications, swaying public perception, making political viewpoints more radical, and adversely impacting health decisions. The covariable features that come with fake news make it even harder to detect because it is presented in the form of text, images, videos, and even social interactions. This paper describes a novel method for detecting fake news on Reddit: RIFT, short for Reddit Information Falsity …

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Application Of Advanced Convolutional Neural Network With Robust Hashing On Obfuscated Image Based Malware Dataset, Sanket Shekhar Kulkarni Jan 2025

Application Of Advanced Convolutional Neural Network With Robust Hashing On Obfuscated Image Based Malware Dataset, Sanket Shekhar Kulkarni

Master's Projects

This project report provides in-depth details on the creation of a malware classification system that makes use of Convolutional Neural Networks (CNNs) that have been strengthened by data set obfuscation and strong hashing. We test many CNN architectures, including MobileNet, ResNet, and DenseNet, using rigorous hashing and obfuscation techniques on datasets. The entire pipeline is described in this research, which ranges from the gathering and preprocessing of data sets to the application of novel hashing techniques that boost overall accuracy in classification and increase resilience against malicious attacks. Parallel to this, we show that dataset obfuscation adds an additional level …

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Malware Generation And Classification Using Pixelcnn, Mounika Krishna Teja Karumudi Jan 2025

Malware Generation And Classification Using Pixelcnn, Mounika Krishna Teja Karumudi

Master's Projects

Malware poses a serious threat to both data privacy and system security. With the wide variety of malware families and the surge in cyber-attacks, the accurate classification of malware is crucial for building effective detection and prevention systems. In recent years, deep learning (DL) methods in computer vision have shown promise in classifying malware by converting malware files into visual representations and applying DL algorithms to classify the resulting images. Among the different approaches to malware family classification, image-based methods have gained significant interest. This research focuses on leveraging DL techniques for image-based classification of malware. The success of identifying …

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Enhancing Robustness Of Cnn Model For Malware Detection Using Gan-Based Data Augmentation And Transfer Learning, Milind Anand Pathak Jan 2025

Enhancing Robustness Of Cnn Model For Malware Detection Using Gan-Based Data Augmentation And Transfer Learning, Milind Anand Pathak

Master's Projects

Malware classification is a critical component in the field of cybersecurity. Accurate identification of a malware family can enable timely threat detection and response. In this thesis, we propose a robust image-based malware classification pipeline using Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), with a focus on improving performance for underrepresented malware families. We train a baseline CNN model on the Malimg dataset across 25 malware families, but observe misclassifications in classes with limited data and overlapping visual features. To address this, we apply targeted augmentations and generate class-specific synthetic data using StyleGAN2-ADA. A CNN trained on the …

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