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Optimizing Analytics Storage Strategies For Search Engines And Wiki Platforms, Sujith Kakarlapudi Jan 2025

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 …

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Multimodal Deception Detection Via Audio-Text Fusion With Deep Learning And Asr, Xiangyi Li Jan 2025

Multimodal Deception Detection Via Audio-Text Fusion With Deep Learning And Asr, Xiangyi Li

Master's Projects

Emotion detection plays a crucial role in human-computer interaction, enabling machines to recognize and respond appropriately to human emotional states. This project explores a two-stage approach to emotion detection using multimodal data, first predicting dimensional values (Arousal, Valence, Dominance) from textual and audio inputs, then mapping these representations to discrete emotion categories. We compare this approach with direct categorical classification using transformer-based language models like BERT, RoBERTa, and DeBERTa for text processing, alongside various audio feature extraction methods, including MFCCs and spectrograms. Using the IEMOCAP dataset, we evaluate both approaches across text-only, audio-only, and multimodal configurations. Our findings reveal that …

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Large Language Model Powered Etl Pipeline Development A Project Report, Aditya Chandras Jan 2025

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 …

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Dynamic Pricing For Revenue Maximization In 5g Networks With Elastic Network Slicing And Reinforcement Learning, Naisheel Shah Jan 2025

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 …

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Link Failure Localization In Hierarchical, Multidomain Optical Networks, Martin Mihailov Bojinov Jan 2025

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 …

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Suicidal Ideation Detection On Reddit Using Llm-Annotated Data And Graph Neural Networks, Ikbal Singh Gurdev Singh Dhanjal Jan 2025

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 …

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Transforming Chatbot Text: A Sequence-To-Sequence Approach To Human-Like Text Generation, Natesh Reddy Jan 2025

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 …

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Framework For Identity Privacy Through Gender Based Skeletonization, Harrison Hwang Jan 2025

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 …

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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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Multilingual Sentiment Analysis Using Ensemble Learning, Farhan Ansari Jan 2025

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 …

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Faux Capabilities: A Novel Approach For Code Analysis, Tanay Godse Jan 2025

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 …

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Traceai: Intelligent Distributed Tracing Using Large Language Models, Mihir Dhirajlal Satra Jan 2025

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 …

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Combining Esm Models With Experimentally Derived Structural Stability To Identify Functional Missense Mutations, Rucha Deo Jan 2025

Combining Esm Models With Experimentally Derived Structural Stability To Identify Functional Missense Mutations, Rucha Deo

Master's Projects

Missense mutations can impact protein function and structure, yet their effects on protein function are difficult to predict. In this study, I compared two deep learning models, ESM1v and ESM1b, by evaluating their mutation predictions against experimental structural stability data. ESM1v showed a stronger correlation with experimental structural stability scores compared to ESM1b. A sigmoid curve was fitted to explore this relationship further. Over 100,000 mutations were identified where experimental stability differed significantly from model predictions. Many mutations that remained structurally stable experimentally but were predicted as harmful by the ESM models were frequently found at known functional sites. Structural …

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Ribomoe: An Application Of Mixture Of Experts On Artificial Riboswitch Classification, Hainian Audrey Long Jan 2025

Ribomoe: An Application Of Mixture Of Experts On Artificial Riboswitch Classification, Hainian Audrey Long

Master's Projects

Urban water sewage is a potential health concern due to its possibility to spread contagious RNA viruses such as Coxsackievirus B3. However, detection of viral particles remains challenging because of low viral concentrations in wastewater and high mutation rates of the RNA virus. To address this, this study proposes a novel viral detection method using synthetic riboswitches that bind to the target virus and trigger a reporter gene, amplifying the detection signals. To support the design of effective riboswitches, we present a machine learning model for classifying riboswitch performance, integrating RNA sequence data with secondary structural features. This model used …

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Phishing Detection Using Continual Learning And Large Language Models, Gopi Prajeev Battula Jan 2025

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 …

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Detecting Ai-Generated News Articles Using Unsupervised Machine Learning Algorithms, Lilou Sicard-Noel Jan 2025

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 …

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Disease Diagnosis Using Rag Llm With Smart Prompt Engineering, Qadeerullah Syed Jan 2025

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 …

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Hierarchical Bloom Filter Tree (Hbft): Scalable Geospatial Metadata Indexing For Bigdata Systems, Mrudula Patteparapu Jan 2025

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 …

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An Evidence-Based Approach To Predicting Pancreatic Ductal Adenocarcinoma, Surya Teja Nalluri Jan 2025

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 …

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Ai-Based Dynamic Spectrum Allocation Model For Wireless Network Management, Sai Sashank Peddibhotla Jan 2025

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 …

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Mycelia: Cross-Chain Data Oracle Using Frost Signatures, Bala Komatireddy Jan 2025

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. …

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Transformers In Time-Series Forecasting: Enhancing Robustness Via Dynamic Attention Mechanisms, Kush Patel Jan 2025

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 …

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Retrieval-Augmented Generation For Survival Analysis In Cancers: Methods And Evaluation On The Surveillance, Epidemiology, And End Results Database, Jyothi Vaidyanathan Jan 2025

Retrieval-Augmented Generation For Survival Analysis In Cancers: Methods And Evaluation On The Surveillance, Epidemiology, And End Results Database, Jyothi Vaidyanathan

Master's Projects

Healthcare is one of the most important fields that benefits from advancements in Artificial Intelligence (AI). From classic models like linear regression to cuttingedge transformers, AI is applied across various healthcare subdomains, such as drug discovery, predictive analytics, and personalized medicine, to name a few. These techniques enable medical practitioners to make more informed decisions, significantly improving both the speed and accuracy of diagnoses and treatments. Machine learning has played a transformative role in oncology, especially in areas like early detection, diagnosis, treatment planning, and patient monitoring, by analyzing medical images, clinical information, genomic data, sensor information. Our research aims …

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Rul Estimation Of N-Cmapss Turbofan Engines Using Deep Learning With Customized Penalty And Expanded Sensors, Prathmesh Pethkar Jan 2025

Rul Estimation Of N-Cmapss Turbofan Engines Using Deep Learning With Customized Penalty And Expanded Sensors, Prathmesh Pethkar

Master's Projects

Accurate prediction of Remaining Useful Life (RUL) for aircraft engines is important to enhance maintenance efficiency and flight safety. For this project, a solution to RUL prediction on NASA's N-CMAPSS data set, mimicking realistic engine degradation under simulated full-flight scenarios, is being proposed. For addressing the high-dimensional noisy sensor data challenge, a new feature engineering pipeline was utilized. Models trained on healthy data predict normal sensor behavior, and the discrepancy between these predictions—referred to as residual features—is a measure of degradation. To handle the size and computational demands of the dataset, training was conducted on Google Cloud Platform using GPU-supported …

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Medilightrag: A System For Medical Query Response Using Fine-Tuned Llms And Graph Based Retrieval, Rajiv Karthik Reddy Kodimala Jan 2025

Medilightrag: A System For Medical Query Response Using Fine-Tuned Llms And Graph Based Retrieval, Rajiv Karthik Reddy Kodimala

Master's Projects

The exponential increase in medical data has created a greater demand for precise and efficient information retrieval systems. Existing Large Language Models (LLMs) face domain-specific difficulties such as sophisticated medical jargon, situational comprehension, and the continual advancement of healthcare knowledge. To tackle these challenges, we present MediLightRAG, an innovative two-stage system which integrates parameter-efficient fine-tuning of Large Language models with LightRAG’s graph-based retrieval. The first stage focuses on enabling accurate resource-efficient model adaptation for the medical domain through QLoRA fine-tuning. In the second stage, LightRAG’s two-tiered retrieval architecture that combines graph-based indexing with dynamic knowledge retrieval is employed to enhance …

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Physiotrack: A Gamified Physiotherapy System, Pranavi Chaturvedula Jan 2025

Physiotrack: A Gamified Physiotherapy System, Pranavi Chaturvedula

Master's Projects

Traditional physiotherapy methods tend to be non-interactive and provide little to no personalized instruction, even though physiotherapy is critical to stroke recovery. This thesis explores a fully adaptive, sensor-based, feedback architecture intended for stroke patients which remotely supervises movement and personalizes exercises enabled by multimodal sensors. The system uses filtering and windowed segmentation of accelerometer and skeletal data to compute features like jerk, speed, and joint movement angular range. A game engine applies accelerometer and skeletal features together with optimized, lightweight ML models to drive adaptive feedback, scoring, and difficulty adjustment. The architecture supports responsive continuous sensor streaming within the …

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Transformer Integration, Fine-Tuning And Zero-Shot Learning For State Of Health Estimation In Li-Ion Batteries Using Large Language Models, Chinmay Nilesh Mahagaonkar Jan 2025

Transformer Integration, Fine-Tuning And Zero-Shot Learning For State Of Health Estimation In Li-Ion Batteries Using Large Language Models, Chinmay Nilesh Mahagaonkar

Master's Projects

In this thesis, we present a comparative analysis of the use of transformerbased and Large Language Model (LLM) models for State of Health (SoH) and Remaining Useful Life (RUL) prediction of lithium-ion batteries. With electric cars and renewable energy systems based on batteries at the forefront, the need to predict degradation accurately in order to enhance the performance and reduce maintenance costs has become imperative. Most traditional prediction methods lag the complex and non-linear characteristics of degradation in batteries, and hence the usage of sophisticated methods becomes a necessity. The research employs the CALCE dataset, which includes long-horizon cycling data …

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Edurag: Improving Ai Teaching Assistants With Retrieval-Augmented Generation, Geethika Vadlamudi Jan 2025

Edurag: Improving Ai Teaching Assistants With Retrieval-Augmented Generation, Geethika Vadlamudi

Master's Projects

This paper is based on the emerging need for AI-driven teaching assistants to deliver personalized, effective, and responsive educational assistance. Earlier proposals for educational technology such as rule-based systems and adaptive learning platforms have been limited by the lack of flexibility, domain accuracy, and slow, non-interactive support. With the launch of large language models (LLMs) like GPT-3, GPT-4, they have demonstrated that they can generate the kind of human-responsive text. When it comes to more substantive education matters, though, such models suffer from domain accuracy, in addition to whether accurate information can be imparted. And that is where the aspect …

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Image-To-Text Transcription: Analyzing And Describing Visual Content, Zixiao Fan Jan 2025

Image-To-Text Transcription: Analyzing And Describing Visual Content, Zixiao Fan

Master's Projects

Image captioning, which provides a textual understanding of visual content, is the fundamental support for the advancement of Human-A.I. Interaction technology. In the hope of exploring the application of such technology, this project focuses on two specific goals. One is to directly explore the application of the image informationretrieving abilities, and the other is to dive into the specifics of the pipeline and components of image captioning models. As a result, this project presents a working app that exploits the text retrieval functionalities to enable image storage with functions like tagging and transcription. It also supports search functionality with a …

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