Computer Engineering Commons^™

Exploring Machine Learning Techniques For Embedded Hardware, Neel R. Vora

Computer Science and Engineering Theses - Archive

This thesis delves into the intricate symbiosis between machine learning (ML) methodologies and embedded hardware systems, with a primary focus on augmenting efficiency and real-time processing capabilities across diverse application domains. It confronts the formidable challenge of deploying sophisticated ML algorithms on resource-constrained embedded hardware, aiming not only to optimize performance but also to minimize energy consumption. Innovative strategies are explored to tailor ML models for streamlined execution on embedded platforms, with validation conducted across various real-world application domains. Notable contributions include the development of a deep-learning framework leveraging a variational autoencoder (VAE) for compressing physiological signals from wearables while …

Stock Price Trend Prediction Using Emotion Analysis Of Financial Headlines With Distilled Llm Model, Rithesh H. Bhat

Computer Science and Engineering Theses - Archive

Capturing the volatility of stock prices helps individual traders, stock analysts, and institutions alike increase their returns in the stock market. Financial news headlines have been shown to have a significant effect on stock price mobility. Lately, many financial portals have restricted web scraping of stock prices and other related financial data of companies from their websites. In this study we demonstrate that emotion analysis of financial news headlines alone can be sufficient in predicting stock price movement, even in the absence of any financial data. We propose an approach that eliminates the need for web scraping of financial data. …

Development Of A Collaborative Research Platform For Efficient Data Management And Visualization Of Qubit Control, Devanshu Brahmbhatt

Computer Science and Engineering Theses - Archive

This thesis introduces QubiCSV, a pioneering open-source platform for quantum computing field. With an emphasis on collaborative research, QubiCSV addresses the critical need for specialized data management and visualization tools in qubit control. The platform is crafted to overcome the challenges posed by the high costs and complexities associated with quantum experimental setups. It emphasizes efficient utilization of resources through shared ideas, data, and implementation strategies. One of the primary obstacles in quantum computing research has been the ineffective management of extensive calibration data and the inability to visualize complex quantum experiment outcomes effectively. QubiCSV fills this gap by offering …

Adaptive Load-Aware Elastic Data Reduction And Re-Computation For Adaptive Mesh Refinement, Mengxiao Wang

Computer Science and Engineering Theses - Archive

The increasing performance gap between computation and I/O creates huge data management challenges for simulation-based scientific discovery. Data reduction, among others, is deemed to be a promising technique to bridge the gap through reducing the amount of data migrated to persistent storage. However, the reduction performance is still far from what is being demanded from production applications. To this end, we propose a new methodology that aggressively reduces data despite the substantial loss of information, and re-computes the original accuracy on-demand. As a result, our scheme creates an illusion of a fast and large storage medium with the availability of …

A Learning-Based Framework For Autonomous Robotic Operations In Resource-Denied Environments, Joseph M. Cloud

Computer Science and Engineering Dissertations - Archive

Establishing a sustained human presence beyond Earth necessitates the development of autonomous systems capable of extracting and utilizing local resources. On the Moon, in-situ resource utilization (ISRU) is essential to reduce the dependency on Earth-based supplies. Leveraging lunar resources such as water ice for life support and fuel production or regolith for surface construction will enable long-term lunar missions and future deep space exploration. NASA's Artemis program is targeting the lunar south pole (LSP), an area with raw, yet abundant resources available. The harsh environmental conditions present significant challenges for humans operating and installing surface infrastructure. Autonomous robotic systems are …

Web-Based Visualization Of Spatial And Spatio-Temporal Data Using Integrated Datasets, Mohammad Shaito

Computer Science and Engineering Dissertations - Archive

Currently, spatial geographic data can be collected for many applications that involve data on the planet earth. These collected data typically have coordinates (x,y), or longitude and latitude in map space, and thus can be located and displayed on maps. Data alone represents facts and has no meaning on its own but becomes meaningful when it is associated with application knowledge, such as elections, crimes, disease, etc. For example, there is no meaning behind those numbers (1, 23, 125, 355, . . .), yet they are data that can gain meaning when correlated with the total number of cases of …

Bringing "Virtual" To "Reality": Enhancing Security And Usability On Vr System And Applications, Huadi Zhu

Computer Science and Engineering Dissertations - Archive

With the rapid advancements in computer science, electronics, optics, and related fields, virtual reality (VR) gradually penetrates into our daily lives, and is predicted to become a core technology in the near future. Despite its potentials, however, existing designs and solutions for VR applications remain at the infant stage, introducing limited usability and efficiency for real-world users. Besides, the increasing prevalence of VR presents new security and privacy threats due to the vast amount of information stored in or accessible through VR devices. To bridge this gap, we exploit and combine techniques from computer science and human biology, as well …

The Graduation Walk: Pareto Optimization Of Degree Paths, Arianna Gail Sy Chaves

Masters Theses

"A student’s academic history, course availability at their institution, and the overall degree of difficulty of the schedule for each semester are all critical factors in their academic success and experience. This thesis proposes an advanced recommendation algorithm that considers these real and conflicting factors that are involved in identifying a prudent degree path - a semester-by-semester course schedule to graduation - for each student. The original contribution of this work is the use of weighted-sum multi-objective constraint programming to minimize an increased number of optimization criteria and identify Pareto-optimal degree paths to be recommended to the student. The conflicting …

Sdebuddy - Code Documentation Using Large Language Models, Nischay Nagendra

Master's Projects

In this fast developing world of software development, it is crucial to maintain the quality of code and the developers’ productivity. This can be done effectively with good code documentation. SDEBuddy uses the latest generation of Large Language Models (LLMs) and finetuning procedures to create code documentation. In this project, state-of-the-art models such as Llama2 and Llama3 are employed to mimic the behavior of the given code and produce documentation. Such models are tuned for various programming languages and documentation formats using LoRA and QLoRA fine-tuning approaches. These models are evaluated in terms of the BLEU score, ROUGE score and …

Strategies To Mitigate Efflorescence In Walls Constructed Using Clays Bricks; A Review Article, Marwa Abdulkareem. Anber, Soran Abdulrahman Ahmad, Naros Rahman Mahmoud, Kawan Fuoad. Kayani, Mohammed Ali Abdulrehman

Al-Esraa University College Journal for Engineering Sciences

_{The construction of buildings, especially residential housing for communities, is a significant industry on a global scale. Masonry units such as bricks, blocks, and stones are often utilized materials. Bricks are highly regarded because to their widespread availability, cost-effectiveness, and excellent insulating capabilities. Nevertheless, the presence of efflorescence presents a difficulty in the construction of brick walls. Efflorescence refers to the process in which salts move to the outer layer of the brick, resulting in the formation of white deposits and negatively impacting its visual appeal. Efflorescence is caused by various circumstances, such as the existence of salts in bricks, …}

The Importance Of Cryptography In Cloud Computing, Saja Alaam Talib

Al-Esraa University College Journal for Engineering Sciences

Cloud computing has grown as a widely accepted concept simultaneously, as the alarms for the security of data and information. This paper seeks to establish the comprehensive roles of cryptography in tackling these issues in the Cloud environment. The study begins with the historical background of cryptography and then moves on to analyze trends in information technology before discussing on the challenges to security in cloud computing. The goal of its publication is to highlight the importance of using cryptographic technologies for the protection of information confidentiality, its integrity and compliance. The combination of literature review and the case study …

Considering A Unified Model Of Artificial Intelligence Enhanced Social Work: A Systematic Review, Michael Garkish, Lauri Goldkind

Social Service Faculty Publications

Social work, as a human rights–based profession, is globally recognized as a profession committed to enhancing human well-being and helping meet the basic needs of all people, with a particular focus on those who are marginalized vulner- able, oppressed, or living in poverty. Artificial intelligence (AI), a sub-discipline of computer science, focuses on develop- ing computers with decision-making capacity. The impacts of these two disciplines on each other and the ecosystems that social work is most concerned with have considerable unrealized potential. This systematic review aims to map the research landscape of social work AI scholarship. The authors analyzed the …

Privacy And Security Of The Windows Registry, Edward L. Amoruso

Graduate Thesis and Dissertation 2023-2024

The Windows registry serves as a valuable resource for both digital forensics experts and security researchers. This information is invaluable for reconstructing a user's activity timeline, aiding forensic investigations, and revealing other sensitive information. Furthermore, this data abundance in the Windows registry can be effortlessly tapped into and compiled to form a comprehensive digital profile of the user. Within this dissertation, we've developed specialized applications to streamline the retrieval and presentation of user activities, culminating in the creation of their digital profile. The first application, named "SeeShells," using the Windows registry shellbags, offers investigators an accessible tool for scrutinizing and …

Undergraduate Research On 5g Propagation Analysis In Naval Ship Environment, Otilia Popescu, Dimitrie Popescu, Murat Kuzlu

Engineering Management & Systems Engineering Faculty Publications

Wireless communication systems have experienced rapid advancements over the last decade, with 5G systems becoming the regular standards and 6G systems being under development. However, the naval ship environment still requires more study due to the special indoor characteristics of the spaces below the deck, where the metal structures strongly impact the propagation characteristics, being prone to signal degradation, dead spots, and unreliable connectivity. This paper presents the research work conducted by a team of undergraduate students from Electrical and Computer Engineering (ECE) and Electrical Engineering Technology (EET) majors. The work was a partnership between Old Dominion University engineering programs …

Streamlining Public Engagement In Transportation Projects Using Text Analytics, Alireza Shamshiri

Civil Engineering Dissertations - Archive

Infrastructure projects impact a broad range of stakeholders, particularly local communities, whose engagement is critical for successful outcomes. Despite the importance of public engagement in these projects, traditional methods of capturing and analyzing public opinion often fail to fully represent the diverse, genuine perspectives involved. This has led to conflicts between community members and project sponsors. On the other hand, despite advancements in text analytics, including natural language processing (NLP) and its subfields such as topic modeling, sentiment analysis, and neural networks, their functionalities and effectiveness in analyzing public opinion in the domain of infrastructure projects have not been fully …

Applications Of Predictive And Generative Ai Algorithms: Regression Modeling, Customized Large Language Models, And Text-To-Image Generative Diffusion Models, Suhaima Jamal

College of Graduate Studies: Theses & Dissertations

The integration of Machine Learning (ML) and Artificial Intelligence (AI) algorithms has radically changed predictive modeling and classification tasks, enhancing a multitude of domains with unprecedented analytical capabilities. Predictive modeling leverages ML and AI to forecast future trends or behaviors based on historical data, while classification tasks categorize data into distinct classes, from email filtering to medical diagnosis. Concurrently, text-to-image generation has emerged as a transformative potential, allowing visual content creation directly from textual descriptions. These advancements are pivotal in design, art, entertainment, and visual communication, as well as enhancing creativity and productivity. This work explores three significant studies in …

The Effects Of Ai Image Synthesis On Graphic Design, Ji Ren

MA Theses

From 1763 when Thomas Bayes developed a framework to infer event probabilities, to the end of 2022 when the world-renowned AI research laboratory Open AI launched Chat GPT, a language model based on AI technology, statistical computing-based AI has revolutionized human life. AI image synthesis can simulate the processes and methods of human painting through machine learning, deep learning, and other methods, thereby generating high-fidelity images. There is growing concern about how AI image synthesis will affect the art world as it advances. The art market could be reimagined, authorship and creativity concepts challenged, and traditional artistic practices disrupted by …

A Metaverse Of Chinese Traditional Folk Villages And Houses With Great Aesthetic Pleasure: A Virtual World Dedicated To The Enthusiasts Of Life With Art, Culture, History And Architectures, Xiaobin Lin

MA Projects

Rapid growth of 3D visualisation technology has led to the maturation of metaverse, bringing innovative and immersive experiences to travellers and representing tremendous potential for revolutionising the tourism industry. Advantages of metaverse, plummeting cost of technology and emerging supportive policies are jointly building up a very promising future of metaverse in tourism. Big companies worldwide are heavily investing in metaverse. In China, Tencent SSV Digital Culture Lab has initiated the annual project “Explore Metaverse Plan”, providing a communication platform and exhibition opportunity for the small and midsize players working on the digitalisation of cultural and artistic heritage. Google Art & …

Expanding Australia's Defence Capabilities For Technological Asymmetric Advantage In Information, Cyber And Space In The Context Of Accelerating Regional Military Modernisation: A Systemic Design Approach, Pi-Shen Seet, Anton Klarin, Janice Jones, Michael N. Johnstone, Violetta Wilk, Stephanie Meek, Summer O'Brien

Research outputs 2022 to 2026

Introduction. The aim of the project was to conduct a systemic design study to evaluate Australia'sopportunities and barriers for achieving a technological advantage in light of regional military technological advancement. It focussed on the three domains of (1) cybersecurity technology, (2) information technology, and (3) space technology.

Research process. Employing a systemic design approach, the study first leveraged scientometric analysis, utilising informetric mapping software (VOSviewer) to evaluate emerging trends and their implications on defence capabilities. This approach facilitated a broader understanding of the interdisciplinary nature of defence technologies, identifying key areas for further exploration. The subsequent survey study, engaging 828 …

Multimodal Fusion For Audio-Image And Video Action Recognition, Muhammad B. Shaikh, Douglas Chai, Syed M. S. Islam, Naveed Akhtar

Research outputs 2022 to 2026

Multimodal Human Action Recognition (MHAR) is an important research topic in computer vision and event recognition fields. In this work, we address the problem of MHAR by developing a novel audio-image and video fusion-based deep learning framework that we call Multimodal Audio-Image and Video Action Recognizer (MAiVAR). We extract temporal information using image representations of audio signals and spatial information from video modality with the help of Convolutional Neutral Networks (CNN)-based feature extractors and fuse these features to recognize respective action classes. We apply a high-level weights assignment algorithm for improving audio-visual interaction and convergence. This proposed fusion-based framework utilizes …

Intelligent Millimeter-Wave System For Human Activity Monitoring For Telemedicine, Abdullah K. Alhazmi, Mubarak A. Alanazi, Awwad H. Alshehry, Saleh M. Alshahry, Jennifer Jaszek, Cameron Djukic, Anna Brown, Kurt Jackson, Vamsy P. Chodavarapu

Electrical and Computer Engineering Faculty Publications

Telemedicine has the potential to improve access and delivery of healthcare to diverse and aging populations. Recent advances in technology allow for remote monitoring of physiological measures such as heart rate, oxygen saturation, blood glucose, and blood pressure. However, the ability to accurately detect falls and monitor physical activity remotely without invading privacy or remembering to wear a costly device remains an ongoing concern. Our proposed system utilizes a millimeter-wave (mmwave) radar sensor (IWR6843ISK-ODS) connected to an NVIDIA Jetson Nano board for continuous monitoring of human activity. We developed a PointNet neural network for real-time human activity monitoring that can …

Exponential Fusion Of Interpolated Frames Network (Efif-Net): Advancing Multi-Frame Image Super-Resolution With Convolutional Neural Networks, Hamed Elwarfalli, Dylan Flaute, Russell C. Hardie

Electrical and Computer Engineering Faculty Publications

Convolutional neural networks (CNNs) have become instrumental in advancing multi-frame image super-resolution (SR), a technique that merges multiple low-resolution images of the same scene into a high-resolution image. In this paper, a novel deep learning multi-frame SR algorithm is introduced. The proposed CNN model, named Exponential Fusion of Interpolated Frames Network (EFIF-Net), seamlessly integrates fusion and restoration within an end-to-end network. Key features of the new EFIF-Net include a custom exponentially weighted fusion (EWF) layer for image fusion and a modification of the Residual Channel Attention Network for restoration to deblur the fused image. Input frames are registered with subpixel …

แบบจำลองการจัดลำดับความสำคัญของความเสี่ยงของยูสเซอร์สตอรีสำหรับการจัดลำดับความสำคัญโดยใช้ตรรกะคลุมเครือ, เปรม สุนทรภาส

Chulalongkorn University Theses and Dissertations (Chula ETD)

ในกระบวนการพัฒนาซอฟต์แวร์แบบแอไจล์ ยูสเซอร์สตอรีเป็นองค์ประกอบสำคัญที่สะท้อนความต้องการของผู้ใช้ แต่หากยูสเซอร์สตอรีมีคุณภาพไม่เพียงพอ อาจก่อให้เกิดความเสี่ยงที่ส่งผลกระทบต่อความสำเร็จของโครงการอย่างมีนัยสำคัญ ดังนั้นงานวิจัยนี้จึงนำเสนอแบบจำลองการจัดลำดับความสำคัญของความเสี่ยงของยูสเซอร์สตอรีโดยใช้ตรรกะคลุมเครือ เพื่อช่วยให้สามารถจัดการกับความไม่แน่นอนในการตัดสินใจได้อย่างมีประสิทธิภาพ โดยเริ่มจากการนิยามความเสี่ยงของยูสเซอร์สตอรีจำนวน 12 ประเภท พร้อมทั้งกำหนดเกณฑ์การประเมินใน 2 ด้าน ได้แก่ ด้านความพยายามในการบริหารจัดการ และด้านข้อจำกัดในการบริหารจัดการ จากนั้นจึงประยุกต์ใช้ตรรกะคลุมเครือร่วมกับตัวแปรภาษาและฟังก์ชันความเป็นสมาชิกในการคำนวณค่าคะแนนความเสี่ยง และจัดลำดับความสำคัญผ่านแบบจำลองที่พัฒนาขึ้น ผลการประเมินการใช้งานแบบจำลองกับผู้มีประสบการณ์ในโครงการแอไจล์พบว่า แบบจำลองสามารถช่วยในการจำแนกความเสี่ยงได้อย่างเหมาะสมและมีความสอดคล้องกับความคิดเห็นของผู้ประเมิน ซึ่งผลการประเมินสะท้อนถึงความสมเหตุสมผลของแบบจำลองและความสามารถในการประยุกต์ใช้จริง ดังนั้นงานวิจัยนี้จึงเป็นประโยชน์ต่อทีมพัฒนาในการประเมินและจัดลำดับความเสี่ยงของยูสเซอร์สตอรีได้อย่างเป็นระบบ และเพิ่มประสิทธิภาพในการบริหารความเสี่ยงในโครงการพัฒนาซอฟต์แวร์แบบแอไจล์ได้ดียิ่งขึ้น

Parameter Identifiability Analysis And Calibration For Power System Dynamic Models, Lei Wang

Electronic Theses and Dissertations

Accurate dynamic models are crucial for stable and reliable power system operation, making their validation essential. The accuracy of parameter estimation is influenced by several factors, including available observables, model structure, and estimation methods. However, in dynamic models, estimating parameters can be difficult when some of them are unidentifiable, meaning their values cannot be uniquely determined from the available data. This challenge may stem from limited impact of parameters on the measured outputs, parameter interactions, or poor data quality. Therefore, an identifiability analysis should be conducted prior to parameter estimation or model calibration. This preliminary step helps to determine which …

Improving Question Answering Retrieval System Through Multi-Result Ranking Model, Danupat Khamnuansin

Chulalongkorn University Theses and Dissertations (Chula ETD)

Recent trends in various industries involve integrating artificial intelligence (AI) systems to enhance operational efficiency. Among these advancements, AI-supported question-answering (QA) systems have gained significant attention. These systems typically employ a two-stage process, integrating QA system capabilities with Information Retrieval (IR) methods. The introduction of the Retrieval Question Answering (ReQA) has further refined this process, offering a more practical solution, aiming to improve real-world applicability. Considering the wide availability of diverse QA retrieval models, employing a combination of multiple systems presents as a viable solution. However, the approach to combining QA retrieval systems remains relatively limited. We propose a method …

การออกแบบและพัฒนาระบบการจัดการความรู้สำหรับการพัฒนาซอฟต์แวร์แบบสกรัมตามมาตรฐาน Iso/Iec 12207, กีรติกา ห่อเกียรติ

Chulalongkorn University Theses and Dissertations (Chula ETD)

โครงงานมหาบัณฑิตนี้มีวัตถุประสงค์ในการปรับปรุงกระบวนการจัดการความรู้ของหน่วยงานแห่งหนึ่งให้สอดคล้องกับมาตรฐานไอเอสโอ/ไออีซี 12207 กระบวนการจัดการความรู้นี้นำเสนอสำหรับโครงการที่ใช้ระเบียบวิธีแบบเอจายล์ตามกรอบงานสกรัม พร้อมทั้งได้ออกแบบและพัฒนาระบบการจัดการความรู้ เพื่อสนับสนุนการประยุกต์ใช้กระบวนการจัดการความรู้ภายในองค์กร ระบบที่พัฒนาสามารถให้บริการในการจัดเก็บ ค้นหา และแบ่งปันความรู้จากโครงการต่าง ๆ ขององค์กร อีกทั้งสนับสนุนให้มีการแบ่งปันความรู้เพื่อให้มีการพัฒนาซอฟต์แวร์ที่ความสอดคล้องตามความต้องการของผู้ใช้ และช่วยเพิ่มสมรรถนะในการทำงานของทีมพัฒนาซอฟต์แวร์ การดำเนินงานเริ่มจากการศึกษาแนวคิดและมาตรฐานที่เกี่ยวข้องกับการจัดการความรู้ รวมถึงการวิเคราะห์มาตรฐาน ไอเอสโอ/ไออีซี 12207 เพื่อประเมินและปรับปรุงกระบวนการจัดการความรู้ในปัจจุบันของ 2 กิจกรรม ได้แก่ “กิจกรรมที่ 3) การแบ่งปันสินทรัพย์ความรู้ทั่วทั้งองค์กร” และ “กิจกรรมที่ 4) การจัดการความรู้ ทักษะ และสินทรัพย์ความรู้” ในการปรับปรุงกระบวนการนั้นได้นำเสนอ โครงสร้างพื้นฐานกระบวนการ และการนิยามกระบวนการที่ปรับปรุง จากนั้นได้ทำการพัฒนาระบบการจัดการความรู้ ที่สามารถรองรับกระบวนการจัดการความรู้ที่ปรับปรุงแล้ว รวมถึงทวนสอบระบบกับความต้องการเชิงฟังก์ชัน และเพื่อยืนยันว่าระบบสามารถสนับสนุนกระบวนการทำงานของทีมสกรัมตามการนิยามกระบวนการได้อย่างมีประสิทธิภาพ ผลลัพธ์ของโครงงานนี้แสดงว่า ได้ช่วยเพิ่มประสิทธิภาพในการจัดการความรู้ภายในองค์กรตัวอย่าง และสามารถใช้เป็นแนวทางสำหรับองค์กรอื่น ๆ ที่ต้องการปรับปรุงกระบวนการจัดการความรู้ในบริบทของการพัฒนาซอฟต์แวร์แบบสกรัม

Enhancing Multilingual Sentence Representation Learning For The Job Recruitment Domain, Napat Laosaengpha

Chulalongkorn University Theses and Dissertations (Chula ETD)

With the advancement in natural language processing (NLP), there has been significant development in multilingual pretraining sentence encoder. Typically, these pretraining models are trained on large-scale datasets that consist of general text data from various sources such as Wikipedia. However, the general proposed models aren't enough to understand contexts in such a domain-specific, especially in the job recruitment domain. It is due to its niche nature and the lack of readily available related information. To enhance the existing multilingual pretraining sentence encoder and mitigate the aforementioned problems, we first propose multi-task dual-encoder framework to improve the sentence encoder for general-purpose …

Benchmarking And Enhancing Generalization In Multilingual Speech Emotion Recognition, Mohamed Osman Ismael

Theses and Dissertations

Speech Emotion Recognition (SER) is pivotal in advancing human-computer interaction by enabling machines to understand and respond to human emotions. Despite significant progress with self-supervised learning models, SER systems often struggle with generalization across diverse languages and unseen data distributions, limiting their real-world applicability. This thesis addresses these challenges by first introducing a large-scale benchmark to evaluate the robustness and adaptability of state-of-the-art SER models in both in-domain and out-of-domain settings. The benchmark includes a diverse set of multilingual datasets, emphasizing cross-lingual and out-of-domain evaluations to assess model generalization. Surprisingly, we find that the Whisper model, originally designed for automatic …

Social Media Bot Detection Using Dropout-Gan, Anant Shukla

Master's Projects

Bot activity on social media platforms is a pervasive problem, undermining the credibility of online discourse and potentially leading to cybercrime. We propose an approach to bot detection using Generative Adversarial Networks (GAN). We discuss how we overcome the issue of mode collapse by utilizing multiple discriminators to train against one generator, while decoupling the discriminator to perform social media bot detection and utilizing the generator for data augmentation. We demonstrate that our approach outperforms---in terms of accuracy---the state-of-the-art techniques in this field. We also show how the generator in the GAN can be used to evade such a classification …

Comparing Balancing Techniques For Malware Classification, Ranjit John

Master's Projects

There have been many breakthroughs over the years in the field of Machine Learning to detect and classify malware threats. However, training a holistic machine learning model to effectively classify malware has been an ongoing topic of research. Datasets represent some malware types disproportionately, which can affect the performance of machine learning classifiers. Without ample data, less common but highly dangerous malware can go undetected by classifiers, leading to devastating outcomes. Data balancing techniques have proven to be effective in representing minority classes better and lessening the bias towards the majority class. Also, recent research showed that generative modeling effectively …