Mapping Of Product Sales Potential Based On Brands In The East Kalimantan Region Using Hybrid Analytical Framework,
2025
Politeknik Negeri Samarinda
Mapping Of Product Sales Potential Based On Brands In The East Kalimantan Region Using Hybrid Analytical Framework, Achmad F O Gaffar Mr, Mulyanto Mulyanto Mr, Arief Bw Putra Mr, Muhammad Taufiq Sumadi Mr, Emmilya Umma Aziza Gaffar
Knowledge Engineering and Data Science
In geographically dispersed markets, operational costs should be reflected in sales planning to support accurate performance evaluation. However, such considerations are often neglected in practice. This study proposes a hybrid analytical framework to map brand-based product sales potential, with and without operational cost consideration, using historical sales data from PT Karya Inti Total Anugerah (PT KITA) in East Kalimantan. The framework integrates spatial, statistical, and machine learning techniques. Principal Component Analysis (PCA) is used to reduce the dimensionality of variables related to travel distance, total sales, and units sold, where travel distance represents the primary contributor to operational costs. K-Means …
Homomorphically Encrypted Faceted Values,
2025
San Jose State University
Homomorphically Encrypted Faceted Values, Tanmay Singal
Master's Projects
Faceted values prevent the implicit flow of sensitive information by controlling the visibility of program data. They achieve this by maintaining two facets for each variable: a public facet, which is observable, and a private facet, which remains hidden. Although this method secures the flow of sensitive data, it can be leaked if the server storing the faceted values is compromised. While faceted values may be encrypted on the server, doing so would necessitate that the private facets be briefly decrypted during execution to allow arithmetic operations to be performed on them, creating an attack vector for information to be …
Visionmate: Ai-Powered Image Captioning Web Application,
2025
San Jose State University
Visionmate: Ai-Powered Image Captioning Web Application, Sai Anoushka Kokku
Master's Projects
VisionMate is a web application that generates captions for camera-captured images. It is designed
to assist users with visual impairments by converting visual input into spoken and written text. The
application uses the GIT-base model from Hugging Face, which processes the image and returns a
descriptive caption. Users can take a picture using the device camera—either via webcam on
desktop or the native camera interface on mobile. The app provides audio output using the
SpeechSynthesis API and uses full-screen tap interaction to simplify accessibility.
The frontend is implemented in React.js, and the backend is built with FastAPI. The backend calls …
Augmenting Missing Sensor Data For Robust Human Activity Recognition,
2025
San Jose State University
Augmenting Missing Sensor Data For Robust Human Activity Recognition, Suryakangeyan Kandasamy Gowdaman
Master's Projects
Applications of ubiquitous computing, including health monitoring, sports analytics, and ambient-assisted living, rely on Human Activity Recognition (HAR) using wearable sensors. However, model robustness is challenged by missing sensor values, class imbalance, inter-subject variability, and temporal noise. This work proposes a complete HAR pipeline that addresses these challenges through sampling, time-series augmentation, dynamic feature handling, and GAN-PCA-based imputation. Built on the DeepSense architecture, the model integrates convolutional feature extraction with bi-GRUs for temporal modeling. The system is evaluated using 5-fold cross-validation, subject-aware holdout, and LOSEO strategies on the Opportunity dataset. Results demonstrate consistent accuracy across folds and strong generalization to …
Unconditional-To-Conditional Transfer And Optimization For Web-Based Skybox Gan,
2025
San Jose State University
Unconditional-To-Conditional Transfer And Optimization For Web-Based Skybox Gan, Crystal Kwong
Master's Projects
Generative adversarial networks (GANs) are known for their ability to generate high quality
images mimicking real life or even particular art styles. Yet for all their capability, casually
training a GAN on an average machine can be infeasible as GANs require an enormous amount
of time and data to train. Even with a trained GAN, model inference demands heavy
computations, making GANs difficult to deploy on applications. To address these limitations,
techniques such as transfer learning and quantization have been leveraged to speed up training of
GANs and lighten computational cost of GAN inference. This project aims to use such …
Proxy Cap - La Lua Protector,
2025
San Jose State University
Proxy Cap - La Lua Protector, Swift Sheng
Master's Projects
Inspired by the object capability model and sandbox, this project, Proxy Cap, introduces a new Lua access control model that improves the language’s security without sacrificing usability. Object capability is an unconventional but powerful security model. The security model closely observes the principle of least authority. Ambient authority, the omnipresent global environment, does not exist in the object capability computation world, and no resource is accessible unless explicitly assigned. Only connectivity begets connectivity. Lua is an extensible and high-performing scripting language based on ANSI C. The language is popular in many fields but faces security challenges. Lua has a non-traditional …
Identifying Red Sponges On Arms Plates By Preprocessing Images Using Histogram Equalization,
2025
San Jose State University
Identifying Red Sponges On Arms Plates By Preprocessing Images Using Histogram Equalization, Barry Ng
Master's Projects
Sponges play a vital role in marine ecosystems, being the only organisms capable of converting dissolved organic matter (DOM) into particulate organic matter (POM). They provide nutrients for coral reefs to thrive in oligotrophic waters. Autonomous reef monitoring structures (ARMS) are used to measure the biodiversity of coral reefs by simulating the complex cavities inside reef structures. Organisms settle on them and scientists can retrieve them after a period of time for analysis. Images are taken of ARMS plates after they are retrieved. Human analysis is unsuitable for the analysis of ARMS plates due to the huge number of images. …
On The Adversarial Robustness Of Quantized Neural Networks Against Common Adversaries In Time-Series Forecasting,
2025
San Jose State University
On The Adversarial Robustness Of Quantized Neural Networks Against Common Adversaries In Time-Series Forecasting, Maanak Arora
Master's Projects
Real-world edge applications now use modern machine learning models which require both resource efficiency and robustness against adversarial threats. Deep neural networks which include time series forecasting models still face risks from adversarial perturbations while quantization techniques used for memory and compute efficiency create unpredictable robustness challenges. This project investigates the adversarial resistance of Long Short-Term Memory (LSTM) models after applying post-training quantization at three different precision levels: 16-bit floating point (FP16), 8-bit integer (INT8) and custom 4-bit quantization. The Jena Climate dataset serves as our main benchmark for training a fullprecision LSTM model followed by multiple quantization strategies which …
Moving Target Defense With Quantized Morphence: Defense Quantification Against Common Adversaries In Image And Time Series Problems,
2025
San Jose State University
Moving Target Defense With Quantized Morphence: Defense Quantification Against Common Adversaries In Image And Time Series Problems, Rithika Dhamala
Master's Projects
In recent years, the vulnerability of deep learning models to adversarial attacks has emerged as a serious threat, particularly in domains where reliability and robustness are critical. This project builds upon the Morphence framework, a Moving Target Defense (MTD) strategy designed to counter adversarial threats by maintaining a dynamic pool of models and introducing randomness at inference time. While Morphence was originally developed for image classification tasks, this work not only reproduces the original architecture using MNIST and CIFAR-10 datasets but also extends the core principles to an entirely new domain: time series forecasting. The project proposes a unified defense …
Energy Considerations For Large Pre-Trained Neural Networks,
2025
San Jose State University
Energy Considerations For Large Pre-Trained Neural Networks, Leo Mei
Master's Projects
In recent years, neural network models have achieved phenomenal performance due to the increasing parameters and complexity of model architectures. However, these advancements come with high environmental costs as they require massive computational resources and consume substantial amounts of electricity, leading to high carbon emissions. Previous studies have demonstrated that substantial redundancies exist in large pre-trained models, and reducing these redundancies through compression would not compromise model performance. While these studies focused on retaining comparable model performance, the direct impact of compression on energy consumption when training models appears to have received little attention. By quantifying the energy usage associated …
Photoproof: A Mobile Application For Verifying The Authenticity Of Images,
2025
San Jose State University
Photoproof: A Mobile Application For Verifying The Authenticity Of Images, Pruthviraj Urankar
Master's Projects
The easy access to artificial intelligence (AI) technologies, such as deepfakes and generative adversarial networks (GANs), has facilitated the creation of highly realistic artificial images, thereby undermining the authenticity of photos in today’s digital age. Misinformation and manipulation are key dangers to digital content due to this advancement. Therefore, the need for reliable methods of photo verification and authentication has become increasingly important. This report presents a decentralized iOS app that uses blockchain to ensure photo authenticity. The app leverages Ethereum smart contracts and cryptographic hashing to securely log image metadata. When a user takes a photo, the app hashes …
Optimizing Analytics Storage Strategies For Search Engines And Wiki Platforms,
2025
San Jose State University
Optimizing Analytics Storage Strategies For Search Engines And Wiki Platforms, Sujith Kakarlapudi
Master's Projects
Modern search engines and wiki platforms generate vast quantities of user inter-
/="/">action data such as page views, edits, clicks, and session events. This data must be stored and aggregated efficiently to enable scalable analytics and responsive querying. Yioop, an open-source search engine framework, serves as our primary case study, processing millions of such events to power its indexing and recommendation features. This report explores a shift from the conventional database storage based model to a log based model, in order to improve scalability and write efficiency. A size-limited, append-only logging facility was provided to log analytics events: the …
Large Language Model Powered Etl Pipeline Development A Project Report,
2025
San Jose State University
Large Language Model Powered Etl Pipeline Development A Project Report, Aditya Chandras
Master's Projects
This projects discusses the use of Large language model to simplify and automate Extract, Transform, Loading (ETL) development. Data engineering tasks oUen need technical experDse, thereby challenging non-experts and even industry professionals for such Dme intensive operaDons. The project tackles these challenges by employing quanDzaDon of a base Lllama-2-7b-Chat model using QLora technique. The lowered precision of the base model allow further execuDon on a limited hardware resource. The project further performs Supervised fine-tuning trainer (SFT) to perform fine-tuning, specializing the model for generaDng script for ETL tasks. The fine-tuned model is evaluated through a series of micro and comprehensive …
Dynamic Pricing For Revenue Maximization In 5g Networks With Elastic Network Slicing And Reinforcement Learning,
2025
San Jose State University
Dynamic Pricing For Revenue Maximization In 5g Networks With Elastic Network Slicing And Reinforcement Learning, Naisheel Shah
Master's Projects
Network slicing is a concept that enables a diverse array of network applications with lots of unique service requirements. For the scope of this research, we delve into elastic network slicing, which has a potential benefit for both the providers and users through cost-effective resource utilization. Dynamic pricing of these slice resources is a method for operators to realize the balance of different types of network slices by implicitly communicating the current network state to slice users. We have designed a custom pricing scheme using Deep Reinforcement Learning for elastic network slices that maximizes the revenue of slice providers while …
Link Failure Localization In Hierarchical, Multidomain Optical Networks,
2025
San Jose State University
Link Failure Localization In Hierarchical, Multidomain Optical Networks, Martin Mihailov Bojinov
Master's Projects
Optical networks transport data encoded on light signals over optical fiber cables. Individual optical networks are managed by domain administrators, such as service providers, vendors, or regional bodies. Multidomain optical networking explores the possibility of enabling seamless data transmission across domain boundaries. In this project, we explore how we can perform network monitoring in a hierarchical multidomain optical network while preserving domain security and autonomy. This is done by allocating dedicated monitoring trails across various broker abstractions of our network topologies. We propose three heuristic functions that expedite trail selection. Of the three heuristics, our least monitored algorithm performs the …
Suicidal Ideation Detection On Reddit Using Llm-Annotated Data And Graph Neural Networks,
2025
San Jose State University
Suicidal Ideation Detection On Reddit Using Llm-Annotated Data And Graph Neural Networks, Ikbal Singh Gurdev Singh Dhanjal
Master's Projects
Suicide is the fourth leading cause of death among people aged 15-29. More than 720, 000 people commit suicide every year. During the COVID-19 pandemic, we saw an increase in people seeking out mental health support on anonymous forums like Reddit. These anonymous forums allow people to express their suicidal ideation without judgment and give them a support structure that not everyone has. The aim of this project is to detect suicidal ideation using Reddit. In this work, we propose SIRGEL (Suicidal Ideation on Reddit using Graph Embeddings and LLMs), a dual-pipeline approach that combines large language model (LLM)- based …
Transforming Chatbot Text: A Sequence-To-Sequence Approach To Human-Like Text Generation,
2025
San Jose State University
Transforming Chatbot Text: A Sequence-To-Sequence Approach To Human-Like Text Generation, Natesh Reddy
Master's Projects
With the advancements in Large Language Models (LLMs) such as ChatGPT, the boundary between text written by a human and that created by AI has become blurred. This poses a threat to systems designed to detect AI-generated content. In this work, we adopt a novel strategy to adversarially transform GPT-generated text using sequence-to-sequence (Seq2Seq) models, to make the text more human-like. We concentrate on improving GPT-generated sentences by including significant linguistic, structural, and semantic components that are typical of human-authored text. The goal is to then use this transformed data to train a more robust detection model. Our experiments show …
Framework For Identity Privacy Through Gender Based Skeletonization,
2025
San Jose State University
Framework For Identity Privacy Through Gender Based Skeletonization, Harrison Hwang
Master's Projects
The protection of one’s privacy and sensitive information is becoming increasingly difficult in the modern age full of surveillance and data collection. Through the use of image based object detection machine learning models trained for human and facial recognition, people can be identified and tracked to a terrifyingly accurate degree. On the other hand, the information present in surveillance media can play a key role in security and law enforcement. This presents a problem of how to preserve key information without compromising the privacy of any individuals present in the video. In this research project, Computer Vision techniques and a …
Learning From Leads: A 1d Dilated Resnet For Ecg Chagas Disease Screening,
2025
Case Western Reserve University
Learning From Leads: A 1d Dilated Resnet For Ecg Chagas Disease Screening, Somesh Saini, Matheus Lima Diniz Araujo
Student Scholarship
No abstract provided.
Prediction Of Glass Transition Temperature Of Polymers Using Structure-Based Models,
2025
The University of Akron
Prediction Of Glass Transition Temperature Of Polymers Using Structure-Based Models, Nicholas Wolfe
Williams Honors College, Honors Research Projects
The focus for this study is prediction of glass transition temperatures of polymers using machine learning models. The preprocessing of the data included the generation of descriptors, the scaling of the data, the principal component analysis for dimensionality reduction, and the clustering of the data. Three methods of property predictions were utilized including Linear Regression, Random Forest, and a Feedforward Neural Network. The dataset consisted of over 7000 polymers each with their respective glass transition temperatures. This study found that the Random Forest model returned the best results followed by the Feedforward Neural Network then the Linear Regression model. Each …
