Computer Engineering Commons^™

Ensemble Transformer Architecture For Efficient And Real Time Sign Language Translation, Sumeet Ghegade

Master's Projects

Sign language is a form of visual language that uses face expression and hand gestures to communicate thoughts and concepts. The term refers to multiple visual languages that share some common visual cues but differ in their grammar and syntax. Sign language translation (SLT) is a crucial step in closing the communication gap between hearing and hearingimpaired people. The study of SLT using machine learning has gotten a lot of interest during the last three years but despite progress, SLT research is still in its early phases. Most of the previous approaches first convert the signs to glosses and then …

Interest-Based Recommendation System Using Gmail Topic Modelling, Pranav Ghaskadbi

Master's Projects

Emails are a fundamental part of modern communication. Much of communicative discourse in modern society occurs over email, resulting in personal collections for each mail user which are rich in latent user’s interests. Conventional recommendation systems require historical data of user activity and interactions to derive user interests. The absence of activity and interaction data poses an interesting challenge for generating relevant recommendations for users. We were motivated to investigate approaches to identify user interests in the absence of historical data to generate personalized content recommendations. There is opportunity to derive user interests from email data, which can be used …

Identifying Potential Alzheimer’S Disease Biomarkers Beyond Amyloid-Beta And Tau, Frank Cai

Master's Projects

Alzheimer's Disease (AD) and other forms of Mild Cognitive Impairment (MCI) affect millions of people around the world. The buildup of Amyloid-Beta (Aβ) and Tau proteins in the brain produced by amyloid precursor protein (APP) has been identified as an important cofactor in the onset and progression of AD. However, although patients diagnosed with AD exhibit Aβ and Tau buildup, about 40% of the subjects with Aβ and Tau buildup are not diagnosed with AD. In this project, we hypothesize the involvement of other epigenetic interactions between APP and related genes in addition to the buildup of Aβ and Tau …

Prediction Of 2024 Indian Pm Election Results Using Sentiment Analysis On Twitter Data, Surabhi Gupta

Master's Projects

This sentiments analysis study presents a methodical approach to predict the 2024 Indian Prime Minister Election. Data collected spanning from 2020 to 2023 from Twitter using hashtags such as IndianPMElection2024 and on topics such as the revocation of the special status of Jammu and Kashmir, the Farm Bill, and the Digital India initiative, form the core of this research. We utilized a combination of sentiment extraction tools-namely, the NLP Town's Bidirectional Encoder Representations from Transformers (BERT)-based multilingual uncased sentiment model, Valance Aware Dictionary for Sentiment Reasoning (VADER), and TextBlob. Additionally, we used a well-established machine learning model Naive Bayes, deep …

Image-Based Malware Classification On Noise Extraction, Venkata Sai Sathwik Nadella

Master's Projects

Any malicious software designed to cause harm or damage to a computer system can be termed as malware. One common form of malware is as executable files. Such files are often used as a delivery mechanism for malware since they can be easily disguised as legitimate software and can be executed without raising suspicion. They are often used to exploit vulnerabilities in software, allowing malware to bypass security measures and gain access to sensitive information.

There are several methods used to detect malware in executable files, including Signaturebased detection, Behavioral-based detection, Heuristic-based detection, Sandboxing, Machine Learning and Artificial Intelligence (AI). …

Image-Based Malware Detection Using Convolutional Neural Network Techniques, Brandon Palomino

Master's Projects

In this study, we delve into the realm of malware detection and classification, leveraging the capabilities of different Convolutional Neural Networks (CNNs). Our approach involves transforming executable files into image formats and subsequently applying advanced CNN techniques for image recognition. The study emphasizes the use of two distinct CNN architectures: a traditional CNN model and a modified CNN variant known as a convolutional recurrent neural network (CRNN), each with unique structural and functional attributes. To effectively train these models, we adopt a transfer learning strategy, utilizing pre-existing CNN models that have been extensively trained on large-scale image datasets. This methodology …

Advances In Robustness Of Image-Based Malware Detection, Rishika Pamanji

Master's Projects

In recent years, deep learning has emerged as a powerful tool for image classification tasks. However, the performance of individual deep learning models can be limited by their architecture and training data. In this project, various Convolutional Neural Network (CNN) architectures are proposed to train the malware data for feature extraction for various color coordinates such as L, CMYK, RGB, RGBA, and YCbCr. Different optimization techniques like Stochastic Gradient Descent, Root Mean Square Propagation, Ada Delta, Adam, and Adaptive Gradient are used to minimize errors in the trained data, leading to enhanced accuracy. The proposed ensemble deep learning model for …

Identification Of Copy Number Variations (Cnvs) Of Epigenetic Factors Related To The Progression Of Pancreatic Ductal Adenocarcinoma (Pdac), Pavithra Raju

Master's Projects

Pancreatic ductal adenocarcinoma (PDAC) is a formidable challenge in oncology due to its aggressive form and late-stage detection. PDAC is known to be influenced by various epigenetic factors like DNA methylation and histone modifications. This study focuses on copy number variations (CNVs) within epigenetic factors which for their role in early diagnosis. Thus, paving the way for identification of potential biomarkers. The epigenetic pipeline was extended based on CNVs and the CNV modified sequences extracted were compared with the wild type sequences of epigenetic PDAC genes. The epigenetic gene KCNJ11 with copy number gain of CNV id 46771406 was used …

Mild Cognitive Impairment And Alzheimer’S Disease Detection And Testing Interface (Mci-Addti) Modeller10.4 Integrating Structure-Function Prediction Modules, Grant Galileo Jacobson

Master's Projects

In the population of adult human patients who over express Beta and Tau Amyloids, it is unclear why 40% of them do not have Alzheimer’s Disease (AD), when all patients with AD have an overexpression of Beta and Tau Amyloids. The MCI-AD-DTI project’s epigenetic pipeline is an evolving computation tool that seeks epigenetic-related information related to the observed disparity. The MCI-AD-DTI’s epigenetic pipeline’s ability to identify mutations currently relies solely on PyPDB for verification of its protein functionality evaluation. The assessment process of the industry standard application, Modeller10.4, is independent from the current epigenetic pipeline’s protein evaluation algorithm. Thus, this …

Estimating Air Pollution Levels Using Machine Learning, Srujay Rao Devaraneni

Master's Projects

Air pollution has emerged as a substantial concern, especially in developing countries worldwide. An important aspect of this issue is the presence of PM2.5. Air pollutants with a diameter of 2.5 or less micrometers are known as PM2.5. Due to their size, these particles are a serious health risk and can quickly infiltrate the lungs, leading to a variety of health problems. Due to growing concerns about air pollution, technology like automatic air quality measurement can offer beneficial assistance for both personal and business decisions. This research suggests an ensemble machine learning model that can efficiently replace the standard air …