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 …
Automatic Cat Caretaker,
2025
The University of Akron
Automatic Cat Caretaker, Connor Mcclenathan, Ryan Anderson, Andrew Tate
Williams Honors College, Honors Research Projects
This project will involve developing and constructing a self-cleaning litter box with feeding and watering functions attached. The machine will have a user interface for setting both the feeding and watering times, as well as how much to fill the food and water bowls at the same time. The project will involve the use of motors for the cleaning, feeding, and watering functions, sensors to detect when to perform those functions, and a microcontroller to process all the data and to tell the motors when to perform their respective functions. This project's goal is to make the task of caring …
Personalized Physics Learning Through Ai: Insights From Problem Generation, Chatbot Dialogues, And Intelligent Tutoring Systems,
2025
University of Texas at Arlington
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 …
Enhancing Traceability And Sustainability In Smallholder Oil Palm Plantations Through Gap Analysis And Machine Learning,
2025
Faculty Information Technology, Universitas Nusa Mandiri, Jakarta, Indonesia
Enhancing Traceability And Sustainability In Smallholder Oil Palm Plantations Through Gap Analysis And Machine Learning, Kursehi Falgenti, Yandra Arkeman, Khaswar Syamsu, Erliza Hambali
ASEAN Journal on Science and Technology for Development
The European Union Deforestation Regulation (EUDR) requires all products entering the EU market to comply with deforestation-free requirements by using a traceability system that tracks products back to their plantation of origin. However, ensuring traceability for fresh fruit bunches (FFB) supplied by independent smallholders (ISHs) presents challenges. These challenges arise from the segregation of internal and external FFB trucks at weighbridges, where only the FFB weight is recorded, without identifying its source. To address this, a new segregation system was introduced, based on the level of sustainability implementation in plantations. This study aimed to develop an FFB traceability system using …
Rift - Reddit Information Falsity Tagger,
2025
San Jose State University
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 …
Application Of Advanced Convolutional Neural Network With Robust Hashing On Obfuscated Image Based Malware Dataset,
2025
San Jose State University
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 …
Reinforcement Learning-Based End-To-End Monitoring Path Selection In Multi-Domain Optical Networks,
2025
San Jose State University
Reinforcement Learning-Based End-To-End Monitoring Path Selection In Multi-Domain Optical Networks, Soham Choudhury
Master's Projects
This paper presents a novel approach for optimizing network monitoring in optical communication systems using Reinforcement Learning (RL). Assuming a multi-domain architecture with limited domain visibility, we simulate multiple optical connections using an optical communications simulation software, GNPy, obtaining key network metrics to model the system. We developed two RL agents: the first agent selects near-optimal monitoring paths based on network states, and the second agent dynamically adapts its selected paths in response to state changes, such as fiber failures or issues with ROADMs. This adaptive approach allows for continuous improvement of network monitoring, ensuring resilience and efficient fault detection. …
Multilingual Sentiment Analysis Using Ensemble Learning,
2025
San Jose State University
Multilingual Sentiment Analysis Using Ensemble Learning, Farhan Ansari
Master's Projects
The widespread use of multiple social media platforms has amplified the expression of public opinions over the Internet in languages such as English, Hindi and Spanish. With the aid of technological advancements in machine learning, we can analyze opinions posted on the Internet and gauge public sentiments. There are organizations and businesses that are interested in the evaluation of these sentiments as these type of data can generally be used to obtain the opinion of a product, restaurant, a candidate, etc. In this study, we perform a comparative analysis of three popular ensemble learning methodologies (Boosting, Bagging and Stacking) based …
Afapbp: Aggregate Function Accelerated Parallel Bit-Pattern Computing,
2025
University of Kentucky
Afapbp: Aggregate Function Accelerated Parallel Bit-Pattern Computing, Charles Z. Armstrong
Theses and Dissertations--Electrical and Computer Engineering
Classical computing models have proven sufficient for problems of the complexity class ``P", but problems of a higher complexity class like ``NP", ``NP-Hard", etc. have been shown to be more resistant to efficient computation. Quantum Computing is an alternative computing model that specifically targets performing computations within the ``NP" complexity class in close to linear time. However, Quantum Computing has its own set of problems. Methods for dealing with quantum decoherence, error correction, and difficulty in scaling have all inhibited Quantum Computing from becoming a commonly used computational model. This thesis introduces AFAPBP (Aggregate Function Accelerated Parallel Bit Pattern), a …
Faux Capabilities: A Novel Approach For Code Analysis,
2025
San Jose State University
Faux Capabilities: A Novel Approach For Code Analysis, Tanay Godse
Master's Projects
When using third-party packages or libraries, it is crucial to understand their behavior. Typically, this requires developers to either conduct code reviews or set up sandbox environments for testing or write unit tests with mocked values for every function used in their code. However, these approaches are often inefficient and time-consuming. A more effective solution would provide developers with a broad understanding of the functionality required by the code they plan to import. This can be done using object capabilities, where a particular functionality is the capability that an object must possess, in order to be able to perform the …
Traceai: Intelligent Distributed Tracing Using Large Language Models,
2025
San Jose State University
Traceai: Intelligent Distributed Tracing Using Large Language Models, Mihir Dhirajlal Satra
Master's Projects
Distributed systems are difficult to trace using traditional methods due to the scale of data volume and complexity, and they usually require a lot of manual analysis. TraceAI tries to solve these problems by integrating Large Language Models with the tracing tools to automatically enhance the trace data evaluation. The project aims to provide an AI-driven solution for monitoring and understanding the flow of requests across services, anomaly detection, root cause analysis and performance optimization. It can thus automate finding out systems problems using LLMs thereby carrying out large scale trace data analysis. Anticipated results from the effort will be …
Effects Of Data Augmentation On Sponge Identification Using Computer Vision Models,
2025
San Jose State University
Effects Of Data Augmentation On Sponge Identification Using Computer Vision Models, George Ku
Master's Projects
Coral reefs can be primarily found in tropical and sub-tropical regions of our oceans, providing a thriving habitat for millions of species. Marine sponges, which can be frequently found in coral reefs, play a critical role that contributes to the maintenance of these ecosystems, including the recycling of nutrients through water filtration. However, rising ocean temperatures and acidification due to climate change have resulted in the bleaching and death of coral reefs worldwide. In order to preserve these reefs and the sponges that depend on them, scientists have been performing studies on their biodiversity. This includes collecting numerous images of …
Phishing Detection Using Continual Learning And Large Language Models,
2025
San Jose State University
Phishing Detection Using Continual Learning And Large Language Models, Gopi Prajeev Battula
Master's Projects
Adaptive phishing detection remains crucial as the nature of cyber-attacks changes over time, which renders static models obsolete. This project extends phishing detection through the implementation of continual learning approaches, namely Elastic Weight Consolidation (EWC) and Learning Without Forgetting (LWF) with RoBERTa, a Large Language Model (LLM) and compares the results of these approaches against GPT-4o-mini, another LLM. Our approach begins with fine-tuning RoBERTa on multiple phishing datasets to establish an effective baseline. EWC is then implemented to preserve vital model parameters based on their importance measured by the Fisher Information Matrix, while LWF uses knowledge distillation to retain prior …
Detecting Ai-Generated News Articles Using Unsupervised Machine Learning Algorithms,
2025
San Jose State University
Detecting Ai-Generated News Articles Using Unsupervised Machine Learning Algorithms, Lilou Sicard-Noel
Master's Projects
The widespread adoption of Large Language Models (LLMs) has revolutionized text generation and heightened concerns over misinformation and the erosion of journalistic integrity. Detecting AI-generated text is critical to addressing these challenges, yet current detection methods face adaptability, scalability, and accuracy limitations. This research paper uses machine-learning techniques to explore the classification of human and AI-generated articles, including a mix of human and AI-written content. The primary focus is on evaluating the effectiveness of clustering algorithms (K-Means and Agglomerative Clustering), auto-encoders, and Part-Of- Speech Tag Transition Matrix Log-Likelihood for distinguishing between AI-generated and human-written texts. Our findings reveal that while …
Disease Diagnosis Using Rag Llm With Smart Prompt Engineering,
2025
San Jose State University
Disease Diagnosis Using Rag Llm With Smart Prompt Engineering, Qadeerullah Syed
Master's Projects
Although recent trends indicate that LLMs outperform traditional methods in solving complex problems with enhanced reasoning, there has been barely any progress in replicating the quality of diagnoses like those of actual human doctors. The identification of an accurate diagnosis with thorough reasoning is still a significant challenge, even with advanced AI models. The process of performing accurate diagnosis remains challenging due to a lack of transparency in state-of-the-art models existing today, a lack of explanation in the diagnosis process, an emphasis on results rather than reasoning, and a lack of foundational knowledge in models, along with limited exploration of …
Hierarchical Bloom Filter Tree (Hbft): Scalable Geospatial Metadata Indexing For Bigdata Systems,
2025
San Jose State University
Hierarchical Bloom Filter Tree (Hbft): Scalable Geospatial Metadata Indexing For Bigdata Systems, Mrudula Patteparapu
Master's Projects
Big Data infrastructure growth has produced overwhelming metadata volumes which create extensive problems for spatial indexing and both system scalability and database queries. Traditional solutions consisting of R-trees and conventional Bloom filters manage to provide either range query support or approximate membership testing, yet they face performance issues when used at large-scale metadata management. This research proposes Hierarchical Bloom Filter Tree (HBFT) as an improved framework that integrates hierarchical spatial partitioning with partitioned, scalable, cuckoo, and striped Bloom filter variants based on existing studies in hierarchical and probabilistic indexing. The complete evaluation process shows that HBFT outperforms PostGIS (an industry-standard …
An Evidence-Based Approach To Predicting Pancreatic Ductal Adenocarcinoma,
2025
San Jose State University
An Evidence-Based Approach To Predicting Pancreatic Ductal Adenocarcinoma, Surya Teja Nalluri
Master's Projects
Pancreatic ductal adenocarcinoma (PDAC) is a complex disease with hidden clinical indicators, so a reliable diagnosis of PDAC requires high precision and sophisticated analysis. Traditional probabilistic methods often rely on making unwarranted assumptions or undesirable approximations about probabilistic estimates, limiting their ability to provide the precision needed for correct diagnosis and treatment planning. In contrast, Dempster–Shafer Theory offers a formal framework for integrating uncertain and potentially conflicting evidence. This makes it well-suited for analyzing incomplete and ambiguous data typically associated with PDAC. By employing an evidential reasoning (ER) model based on Dempster-Shafer Theory, this approach systematically combines and evaluates imperfect …
Ai-Based Dynamic Spectrum Allocation Model For Wireless Network Management,
2025
San Jose State University
Ai-Based Dynamic Spectrum Allocation Model For Wireless Network Management, Sai Sashank Peddibhotla
Master's Projects
The growth of wireless communication has introduced challenges in the dynamic and resource contrived space which is the efficient utilization of bandwidth and spectrum. This research presents a model for dynamic spectrum allocation with the help of Convolutional Neural Network (CNN) for feature extraction and the Deep Q-Network (DQN) model’s reinforcement learning architecture. The CNN captures both spatial and temporal features of the network states and gives them to the DQN for optimal allocation decision making. This CNN-DQN architecture effectively implements spectrum resource allocation in wireless networks and adapts to resource allocation changes within performance bounds. The system’s performance is …
Mycelia: Cross-Chain Data Oracle Using Frost Signatures,
2025
San Jose State University
Mycelia: Cross-Chain Data Oracle Using Frost Signatures, Bala Komatireddy
Master's Projects
The interoperability of heterogeneous blockchain networks is the basis for the widespread application of blockchains in various fields. Cross-chain data oracles play a significant role in enabling distributed applications to exchange data and assets across different blockchains, thereby greatly enriching and expanding the application scenarios and use of blockchains. With the continuous advancement of blockchain technology, more and more researchers and industry participants have begun to focus on developing cross-chain data oracles. Current cross-chain data oracles face issues with trust, as they rely on centralized intermediaries or limited validator networks, increasing the risk of manipulation or single points of failure. …
Transformers In Time-Series Forecasting: Enhancing Robustness Via Dynamic Attention Mechanisms,
2025
San Jose State University
Transformers In Time-Series Forecasting: Enhancing Robustness Via Dynamic Attention Mechanisms, Kush Patel
Master's Projects
Transformer architectures have emerged as powerful tools for time series forecasting, excelling at capturing complex temporal dependencies across multivariate inputs. However, these models are highly susceptible to adversarial attacks such as the Fast Gradient Sign Method (FGSM) and Basic Iterative Method (BIM), which can significantly degrade predictive performance through small, targeted perturbations. This work integrates dynamic attention mechanisms, adaptive masking modules that introduce controlled variability into attention pathways, into a transformer forecasting model to enhance robustness against such attacks. Using two distinct datasets, we compare the performance of a standard transformer and a dynamic attention-enhanced transformer under both clean and …
