Physical Sciences and Mathematics Commons^™

Influence Of Pavement Conditions On Commercial Motor Vehicle Crashes, Stephen Arhin, Babin Manandhar, Adam Gatiba

Mineta Transportation Institute

Commercial motor vehicle (CMV) safety is a major concern in the United States, including the District of Columbia (DC), where CMVs make up 15% of traffic. This research uses a comprehensive approach, combining statistical analysis and machine learning techniques, to investigate the impact of road pavement conditions on CMV accidents. The study integrates traffic crash data from the Traffic Accident Reporting and Analysis Systems Version 2.0 (TARAS2) database with pavement condition data provided by the District Department of Transportation (DDOT). Data spanning from 2016 to 2020 was collected and analyzed, focusing on CMV routes in DC. The analysis employs binary …

Examining The Externalities Of Highway Capacity Expansions In California: An Analysis Of Land Use And Land Cover (Lulc) Using Remote Sensing Technology, Serena E. Alexander, Bo Yang, Owen Hussey, Derek Hicks

Mineta Transportation Institute

There are over 590,000 bridges dispersed across the roadway network that stretches across the United States alone. Each bridge with a length of 20 feet or greater must be inspected at least once every 24 months, according to the Federal Highway Act (FHWA) of 1968. This research developed an artificial intelligence (AI)-based framework for bridge and road inspection using drones with multiple sensors collecting capabilities. It is not sufficient to conduct inspections of bridges and roads using cameras alone, so the research team utilized an infrared (IR) camera along with a high-resolution optical camera. In many instances, the IR camera …

Gender Classification Via Human Joints Using Convolutional Neural Network, Cheng-En Sung

Master's Projects

With the growing demand for gender-related data on diverse applications, including security systems for ascertaining an individual’s identity for border crossing, as well as marketing purposes of digging the potential customer and tailoring special discounts for them, gender classification has become an essential task within the field of computer vision and deep learning. There has been extensive research conducted on classifying human gender using facial expression, exterior appearance (e.g., hair, clothes), or gait movement. However, within the scope of our research, none have specifically focused gender classification on two-dimensional body joints. Knowing this, we believe that a new prediction pipeline …

Dynamic Predictions Of Thermal Heating And Cooling Of Silicon Wafer, Hitesh Kumar

Master's Projects

Neural Networks are now emerging in every industry. All the industries are trying their best to exploit the benefits of neural networks and deep learning to make predictions or simulate their ongoing process with the use of their generated data. The purpose of this report is to study the heating pattern of a silicon wafer and make predictions using various machine learning techniques. The heating of the silicon wafer involves various factors ranging from number of lamps, wafer properties and points taken in consideration to capture the heating temperature. This process involves dynamic inputs which facilitates the heating of the …

Image Captioning Using Reinforcement Learning, Venkat Teja Golamaru

Master's Projects

Image captioning is a crucial technology with numerous applications, including enhancing accessibility for the visually impaired, developing automated image indexing and retrieval systems, and enriching social media experiences. However, accurately describing the content of an image in natural language remains a challenge, particularly in low-resource settings where data and computational power are limited. The most advanced image captioning architectures currently use encoder-decoder structures that incorporate a sequential recurrent prediction model. This study adopts a typical Convolutional Neural Network (CNN) encoder Recurrent Neural Network (RNN) decoder structure for image captioning, but it has framed the problem as a sequential decision-making task. …

Influence Maximization Based On Community Detection And Dominating Sets, Ameya Marathe

Master's Projects

An online platform where various people come together to share information and communicate is called a social network. These platforms are set apart from other means of communication mostly because you can follow and interact also with different people even some you never met, comment on their posts, and re-sharing their posts. Companies such as Amazon and Walmart use these platforms daily for marketing purposes, like spreading information regarding new products and services they offer. They carefully select a subset of users, called influencers, who are usually the ones with high influence over the rest of the users. Influencers receive …

A Novel Efficient Deep Learning Framework For Facial Inpainting - Face Reconstruction From Masked Images, Akshay Ravi

Master's Projects

The use of face masks due to the covid-19 pandemic has made surveillance of people very difficult. Since a mask covers most of the facial components, security cameras are rendered of little to no use in the identification of criminals. In order to realize what a face looks like behind a mask, we have to construct the facial features in the masked region. On a higher level, this falls under the field of image inpainting, i.e. filling missing regions of images or correcting irregularities in images. Current research on image inpainting shows promising results on images that have missing/incorrect patches …

Wikipedia Web Table Interpretation, Keyword-Based Search, And Ranking, Kartikee Dabir

Master's Projects

Information retrieval and data interpretation on the web, for the purpose of gaining knowledgeable insights, has been a widely researched topic from the onset of the world wide web or what is today popularly known as the internet. Web tables are structured tabular data present amidst unstructured, heterogenous data on the web. This makes web tables a rich source of information for a variety of tasks like data analysis, data interpretation, and information retrieval pertaining to extracting knowledge from information present on the web. Wikipedia tables which are a subset of web tables hold a huge amount of useful data, …

Rideshare Using Degrees Of Separation: A Social Network-Based Approach, Gokul Garikipati

Master's Projects

Conventional ride-sharing services, such as Lyft and Uber, routinely match drivers with riders based on their proximity to each other, using GPS coordinates and mapping technology. The application then calculates the cost of the ride based on factors such as distance traveled and time spent in the car. The concept of six degrees of separation suggests that a maximum of 6 steps or relationships can connect any two individuals in the world. This idea could be applied to a ride-share service to provide a more personalized and efficient experience for users. Instead of just matching riders with drivers based on …

Comparative Analysis Of Transformer-Based Models For Text-To-Speech Normalization, Pankti Dholakia

Master's Projects

Text-to-Speech (TTS) normalization is an essential component of natural language processing (NLP) that plays a crucial role in the production of natural-sounding synthesized speech. However, there are limitations to the TTS normalization procedure. Lengthy input sequences and variations in spoken language can present difficulties. The motivation behind this research is to address the challenges associated with TTS normalization by evaluating and comparing the performance of various models. The aim is to determine their effectiveness in handling language variations. The models include LSTM-GRU, Transformer, GCN-Transformer, GCNN-Transformer, Reformer, and a BERT language model that has been pre-trained. The research evaluates the performance …

Sign Language Recognition Using A Hybrid Machine Learning Model, Peeyusha Shivayogi

Master's Projects

Sign Language is a visual language used by millions of people around the world. American Sign Language (ASL) is one of the most popular sign languages and the third most popular language in the United States. Automatic recognition of ASL signs can help bridge the communication gap between deaf and hearing individuals. In this project, we explore the use of deep learning models for ASL sign recognition, using the MNIST dataset as a benchmark. We preprocessed the data by reshaping the images to the input layer size of the models and normalized the pixel values. We evaluated five popular deep-learning …

Graph Deep Learning Based Hashtag Recommender For Reels On Social Media, Sriya Balineni

Master's Projects

Many businesses, including Facebook, Netflix, and YouTube, rely heavily on a recommendation system. Recommendation systems are algorithms that attempt to provide consumers with relevant suggestions for items such as movies, videos, or reels (microvideos) to watch, hashtags for their posts, songs to listen to, and products to purchase. In many businesses, recommender systems are essential because they can generate enormous amounts of revenue and make the platform stand out when compared to others. Reels are a feature of the social media platforms that enable users to create and share videos of up to sixty seconds in length. Individuals, businesses, and …

Application Of Knowledge Graph Techniques On Textbooks, Yutong Yao

Master's Projects

Textbooks are written and organized in a way that facilitates learning and understanding. Sections like glossary terms at the end of a textbook provide guidance on the topic of interest. However, it takes manual effort to create the index terms in the glossary that highlight the key referenced terminologies and related terms. Knowledge graphs, which have been used to represent and even reason over data and knowledge, can potentially capture textbook’s important terms, concepts, and their relations. Popular since the initial introduction by Google Knowledge Graphs (KGs), they combine graph and data to capture and model enormous amounts of relational …

Analyzing Improvement Of Mask R-Cnn On Arms Plates (And Sponges And Coral), James Lee

Master's Projects

Coral Reefs and their diverse array of life forms play a vital role in maintaining the health of our planet's environment. However, due to their fragility, it can be challenging to study the reefs without damaging their delicate ecosystem. To address this issue, researchers have employed non-invasive methods such as using Autonomous Reef Monitoring Structures (ARMS) plates to monitor biodiversity. Data was collected as genetic samples from the plates, and high-resolution photographs were taken. To make the best use of this image data, scientists have turned to machine learning and computer vision. Prior to this study, MASKR-CNN was utilized as …

Malware Classification Using Api Call Information And Word Embeddings, Sahil Aggarwal

Master's Projects

Malware classification is the process of classifying malware into recognizable categories and is an integral part of implementing computer security. In recent times, machine learning has emerged as one of the most suitable techniques to perform this task. Models can be trained on various malware features such as opcodes, and API calls among many others to deduce information that would be helpful in the classification.

Word embeddings are a key part of natural language processing and can be seen as a representation of text wherein similar words will have closer representations. These embeddings can be used to discover a quantifiable …

Job Tailored Resume Content Generation, Sumedh Kale

Master's Projects

Generally candidates apply to multiple jobs with a single resume and do not tend to customize their resume to match the job description. This hampers their chances of getting a resume shortlisted for the job. The project aims to help such candidates build job tailored resumes that help them create a customized and targeted resume for a specific job or industry. The tool specifically targets candidates’ employment history, for resume content generation. We then use natural language processing

(NLP) techniques to extract and organize this data into a structured format for the dataset. We experiment with multiple variations of the …

Multi-Label Text Classification With Transfer Learning, Likhitha Yelamanchili

Master's Projects

Multi-label text categorization is a crucial task in Natural Language Processing, where each text instance can be simultaneously assigned to numerous labels. This project's goal is to assess how well several deep learning models perform on a real-world dataset for multi-label text classification. We employed data augmentation techniques like Synonym Substitution and Random Word Substitution to address the problem of data imbalance. We conducted experiments on a toxic comment classification dataset to evaluate the effectiveness of several deep learning models including Bi-LSTM, GRU, and Bi-GRU, as well as fine- tuned pre-trained BERT models. Many metrics, including log loss, recall@k, and …

Malware Classification Using Graph Neural Networks, Manasa Mananjaya

Master's Projects

Word embeddings are widely recognized as important in natural language pro- cessing for capturing semantic relationships between words. In this study, we conduct experiments to explore the effectiveness of word embedding techniques in classifying malware. Specifically, we evaluate the performance of Graph Neural Network (GNN) applied to knowledge graphs constructed from opcode sequences of malware files. In the first set of experiments, Graph Convolution Network (GCN) is applied to knowledge graphs built with different word embedding techniques such as Bag-of-words, TF-IDF, and Word2Vec. Our results indicate that Word2Vec produces the most effective word embeddings, serving as a baseline for comparison …

Personalized Tweet Recommendation Using Users’ Image Preferences, Shashwat Avinash Kadam

Master's Projects

In the era of information explosion, the vast amount of data on social media platforms can overwhelm users. Not only does this information explosion contain irrelevant content, but also intentionally fabricated articles and images. As a result, personalized recommendation systems have become increasingly important to help users navigate and make sense of this data. We propose a novel technique to use users’ image preferences to recommend tweets. We extract vital information by analyzing images liked by users and use it to recommend tweets from Twitter. As many images online have no descriptive metadata associated with them, in this framework, we …

Container Caching Optimization Based On Explainable Deep Reinforcement Learning, Divyashree Jayaram

Master's Projects

Serverless edge computing environments use lightweight containers to run IoT services on a need basis i.e only when a service is requested. These containers experience a cold start up latency when starting up. One probable solution to reduce the startup delay is container caching on the edge nodes. Edge nodes are nodes that are closer in proximity to the IoT devices. Efficient container caching strategies are required since the resource availability on these edge devices is limited. Because of this constraint on resources, the container caching strategies should also take proper resource utilization into account. This project tries to further …

Video Sign Language Recognition Using Pose Extraction And Deep Learning Models, Shayla Luong

Master's Projects

Sign language recognition (SLR) has long been a studied subject and research field within the Computer Vision domain. Appearance-based and pose-based approaches are two ways to tackle SLR tasks. Various models from traditional to current state-of-the-art including HOG-based features, Convolutional Neural Network, Recurrent Neural Network, Transformer, and Graph Convolutional Network have been utilized to tackle the area of SLR. While classifying alphabet letters in sign language has shown high accuracy rates, recognizing words presents its set of difficulties including the large vocabulary size, the subtleties in body motions and hand orientations, and regional dialects and variations. The emergence of deep …

Yelp Restaurant Popularity Score Calculator, Sneh Bindesh Chitalia

Master's Projects

Yelp is a popular social media platform that has gained much traction over the last few years. The critical feature of Yelp is it has information about any small or large-scale business, as well as reviews received from customers. The reviews have both a 1 to 5 star rating, as well as text. For a particular business, any user can view the reviews, but the stars are what most users check because it is an easy and fast way to decide. Therefore, the star rating is a good metric to measure a particular business’s value. However, there are other attributes …

Twitter Bot Detection Using Nlp And Graph Classification, Warada Jayant Kulkarni

Master's Projects

Social media platforms are one of the primary resources for information as it is easily accessible, low in cost, and provides a high rate of information spread. Online social media (OSM) have become the main source of news information around the world, but because of the distributed nature of the web, it has increased the risk of fake news spread. Fake news is misleading information that is published as real news. Therefore, identifying fake news and flagging them as such, as well as detecting sources that generate them is an ongoing task for researchers and OSM companies. Bots are artificial …

Leveraging Tweets For Rapid Disaster Response Using Bert-Bilstm-Cnn Model, Satya Pranavi Manthena

Master's Projects

Digital networking sites such as Twitter give a global platform for users to discuss and express their own experiences with others. People frequently use social media to share their daily experiences, local news, and activities with others. Many rescue services and agencies frequently monitor this sort of data to identify crises and limit the danger of loss of life. During a natural catastrophe, many tweets are made in reference to the tragedy, making it a hot topic on Twitter. Tweets containing natural disaster phrases but do not discuss the event itself are not informational and should be labeled as non-disaster …

Federated Learning For Protecting Medical Data Privacy, Abhishek Reddy Punreddy

Master's Projects

Deep learning is one of the most advanced machine learning techniques, and its prominence has increased in recent years. Language processing, predictions in medical research and pattern recognition are few of the numerous fields in which it is widely utilized. Numerous modern medical applications benefit greatly from the implementation of machine learning (ML) models and the disruptive innovations in the entire modern health care system. It is extensively used for constructing accurate and robust statistical models from large volumes of medical data collected from a variety of sources in contemporary healthcare systems [1]. Due to privacy concerns that restrict access …

Machine Learning-Based Anomaly Detection In Cloud Virtual Machine Resource Usage, Tarun Mourya Satveli

Master's Projects

Anomaly detection is an important activity in cloud computing systems because it aids in the identification of odd behaviours or actions that may result in software glitch, security breaches, and performance difficulties. Detecting aberrant resource utilization trends in virtual machines is a typical application of anomaly detection in cloud computing (VMs). Currently, the most serious cyber threat is distributed denial-of-service attacks. The afflicted server's resources and internet traffic resources, such as bandwidth and buffer size, are slowed down by restricting the server's capacity to give resources to legitimate customers.

To recognize attacks and common occurrences, machine learning techniques such as …

Codeval, Aditi Agrawal

Master's Projects

Grading coding assignments call for a lot of work. There are numerous aspects of the code that need to be checked, such as compilation errors, runtime errors, the number of test cases passed or failed, and plagiarism. Automated grading tools for programming assignments can be used to help instructors and graders in evaluating the programming assignments quickly and easily. Creating the assignment on Canvas is again a time taking process and can be automated. We developed CodEval, which instantly grades the student assignment submitted on Canvas and provides feedback to the students. It also uploads, creates, and edits assignments, thereby …

The Bias Report: An Automated News Aggregator For Political Bias Classification And News Summarization, Anant Joshi

Master's Projects

Political polarization is on the rise in the US, driven in large part by divisive news that goes viral on the Internet. Specifically, many media outlets use slanted language and publish misinformation in order to drive user traffic and engagement. Almost 80% of US citizens get their news from online sources, but there is a lack of public safeguards against biased news. A large amount of news is published online every day by media organizations, and it is impossible to manually analyze this amount of data. There is a clear need for automated, public-facing solutions in the current political climate …

Resource Coordination Learning For End-To-End Network Slicing Under Limited State Visibility, Xiang Liu

Master's Projects

This paper discusses a resource coordination problem under limited state visibility to realize end-to-end network slices that are hosted by multiple network domains. We formulate this resource coordination problem as a special type of the multi- armed bandit (MAB) problem called the combinatorial multi-armed bandit (CMAB) problem. Based on this formulation, we convert the problem to a regret minimization problem with a linear objective function and solve it by adapting the Learning with Linear Rewards (LLR) algorithm. In this paper, we present a new hybrid approach that incorporates state reports, which include partial resource information in each domain, into the …

Navigating Classic Atari Games With Deep Learning, Ayan Abhiranya Singh

Master's Projects

Games for the Atari 2600 console provide great environments for testing reinforcement learning algorithms. In reinforcement learning algorithms, an agent typically learns about its environment via the delivery of periodic rewards. Deep Q-Learning, a variant of Q-Learning, utilizes neural networks which train a Q-function to predict the highest future reward given an input state and action. Deep Q-learning has shown great results in training agents to play Atari 2600 games like Space Invaders and Breakout. However, Deep Q-Learning has historically struggled with learning how to play games with greater emphasis on exploration and delayed rewards, like Ms. PacMan. In this …