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A Convolutional Neural Network Based Approach For Visual Question Answering, Lavanya Abhinaya Koduri Apr 2018

A Convolutional Neural Network Based Approach For Visual Question Answering, Lavanya Abhinaya Koduri

Master's Projects

Computer Vision is a scientific discipline which involves the development of an algorithmic basis for the construction of intelligent systems that aim at analysis, understanding and extraction of useful information from visual data. This visual data can be plain images, video sequences, views from multiple cameras, etc. Natural Language Processing (NLP), is the ability of machines to read and understand human languages. Visual Question Answering (VQA), is a multi-discipline Artificial Intelligence (AI) research problem, which is a combination of Natural Language Processing (NLP), Computer Vision (CV), and Knowledge Reasoning (KR). Given an image and a question related to the image …

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Optimal Constrained Wireless Emergency Network Antennae Placement, Swapnil Mohan Gaikwad Apr 2018

Optimal Constrained Wireless Emergency Network Antennae Placement, Swapnil Mohan Gaikwad

Master's Projects

With increasing number of mobile devices, newly introduced smart devices, and the Internet of things (IoT) sensors, the current microwave frequency spectrum is getting rapidly congested. The obvious solution to this frequency spectrum congestion is to use millimeter wave spectrum ranging from 6 GHz to 300 GHz. With the use of millimeter waves, we can enjoy very high communication speeds and very low latency. But, this technology also introduces some challenges that we hardly faced before. The most important one among these challenges is the Line of Sight (LOS) requirement. In the emergent concept of smart cities, the wireless emergency …

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Analysis Of Encrypted Malicious Traffic, Anish Singh Shekhawat Apr 2018

Analysis Of Encrypted Malicious Traffic, Anish Singh Shekhawat

Master's Projects

In recent years there has been a dramatic increase in the number of malware attacks that use encrypted HTTP traffic for self-propagation and communication. Due to the volume of legitimate encrypted data, encrypted malicious traffic resembles benign traffic. As the malicious traffic is similar to benign traffic, it poses a challenge for antivirus software and firewalls. Since antivirus software and firewalls will not typically have access to encryption keys, detection techniques are needed that do not require decrypting the traffic. In this research, we apply a variety of machine learning techniques to the problem of distinguishing malicious encrypted HTTP traffic …

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A Medical Price Prediction System, Anuja Tike Apr 2018

A Medical Price Prediction System, Anuja Tike

Master's Projects

The health care costs constitute a significant fraction of the U.S. economy. Nearly 20% of the Gross Domestic Product (GDP) is spent on health care. The health spending in the US is the highest among all developed nations in absolute numbers as well as a percentage of the economy. The U.S. government bears a large portion of seniors’ health expenditure through its Medicare program. The growing health related expenses combined with the fact that the baby-boomer generation is retiring, and hence they will be eligible for Medicare, puts a great burden on the U.S. exchequer. Therefore, it is essential to …

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Modeling Human Migration Dynamics In Netlogo, Vikram Deshmukh Apr 2018

Modeling Human Migration Dynamics In Netlogo, Vikram Deshmukh

Master's Projects

Human Migration has often been the catalyst for the rise and fall of civilizations. It is imperative to study human migration dynamics if one is to gain insights into migratory behavior among human beings and how migration affects societies. There has been considerable research to study migration. This has given rise to some popular migration theories like the neoclassical approach, network migration, pull-push migration, etc. These theories shed light on some peculiar behaviors that influence the migration decision of an individual or a group, while also trying to predict the outcome of such actions. The goal of this project is …

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Validating Key-Value Based Implementations Of The Raft Consensus Algorithm For Distributed Systems, Deepthi Vishwanath Apr 2018

Validating Key-Value Based Implementations Of The Raft Consensus Algorithm For Distributed Systems, Deepthi Vishwanath

Master's Projects

Distributed systems are a group of systems connected via a network, all working towards achieving a common goal. To achieve fault tolerance and reliability, all the systems should work towards achieving consensus. Paxos is the most widely used consensus algorithm since 2 or 3 decades, but the shift is now happening towards a new algorithm known as Raft. Raft is a consensus algorithm (paper published in the year 2014) which is easier to understand and works like Paxos in terms of fault tolerance and performance. Since Raft is new, there is a need for a tool that verifies systems built …

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Speech Emotion Detection Using Machine Learning Techniques, Neethu Sundarprasad Apr 2018

Speech Emotion Detection Using Machine Learning Techniques, Neethu Sundarprasad

Master's Projects

Communication is the key to express one’s thoughts and ideas clearly. Amongst all forms of communication, speech is the most preferred and powerful form of communications in human. The era of the Internet of Things (IoT) is rapidly advancing in bringing more intelligent systems available for everyday use. These applications range from simple wearables and widgets to complex self-driving vehicles and automated systems employed in various fields. Intelligent applications are interactive and require minimum user effort to function, and mostly function on voice-based input. This creates the necessity for these computer applications to completely comprehend human speech. A speech percept …

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A Study On Effects Of Data Poisoning On Hmms, Rachel Gonsalves Apr 2018

A Study On Effects Of Data Poisoning On Hmms, Rachel Gonsalves

Master's Projects

With the ever increasing use of burgeoning volumes of data, machine learning systems involving minimal human oversight are crucial for classification and analysis tasks. Machine learning algorithms used for such purposes have revolutionized the way we sort, classify, and analyze data. The accuracy of any machine learning algorithm depends heavily on the data it is trained on. In some circumstances, an attacker can attempt to poison the training data to subvert a machine learning system. In this research, we analyze the effects of training data poisoning attacks on hidden Markov models (HMMs), in the context of malware classification. With the …

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Image Robust Hashing For Malware Detection, Wei-Chung Huang Apr 2018

Image Robust Hashing For Malware Detection, Wei-Chung Huang

Master's Projects

This research is focused on a novel approach to detect malware based on static analysis of executable files. Specifically, we treat each executable file as a twodimensional image and use robust hashing techniques to identify whether a given executable belongs to a particular family or not. The hashing stage comprises two steps, namely, feature extraction, and compression. We compare our robust hashing approach to other machine learning-based techniques.

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Anomaly Detection For Application Log Data, Aarish Grover Apr 2018

Anomaly Detection For Application Log Data, Aarish Grover

Master's Projects

In software development, there is an absolute requirement to ensure that a system once developed, functions at its best throughout its lifetime. Application log data is critical to maintaining application performance and thus techniques to parse, understand and detect anomalies in application log data are critical to ensuring efficiency in software development. While initially hampered by limited hardware and lack of quality datasets, anomaly detection techniques have recently received a surge of interest with advancements in machine learning technology and especially neural networks. In this paper, we explore anomaly detection, historical techniques to detect anomalies and recent advancements in neural …

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Intrusion Detection In Containerized Environments, Shyam Sundar Durairaju Apr 2018

Intrusion Detection In Containerized Environments, Shyam Sundar Durairaju

Master's Projects

In this paper, we present the results of using Hidden Markov Models for learning the behavior of Docker containers. This is for use in anomaly-detection based intrusion detection system. Containers provide isolation between the host system and the containerized environment by efficiently packaging applications along with their dependencies. This way, containers become a portable software environment for applications to run and scale. Unlike virtual machines, containers share the same kernel as the host operating system. This is leveraged to monitor the system calls of the container from the host system for anomaly detection. Thus, the monitoring system is not required …

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Subtopics In Yelp Reviews, Riya Suchdev Apr 2018

Subtopics In Yelp Reviews, Riya Suchdev

Master's Projects

Yelp is a review platform that connects people to local businesses. It is a very popular platform that helps customers decide which business to choose. It relies on crowd sourced plain text reviews. From the business’s description some facts can be determined, such as category and location. However, more detailed description can be extracted from the reviews. Discovering latent topics and subtopics in Yelp reviews, can help summarize the reviews to gain knowledge. For example, we can deduce that reviews related to the Restaurant category tend to emphasize on service, food, order etc. Additionally, one can deduce positive or negative …

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Sentiment Analysis Using An Ensemble Of Feature Selection Algorithms, Manankumar Bhagat Apr 2018

Sentiment Analysis Using An Ensemble Of Feature Selection Algorithms, Manankumar Bhagat

Master's Projects

To determine the opinion of any person experiencing any services or buying any product, the usage of Sentiment Analysis, a continuous research in the field of text mining, is a common practice. It is a process of using computation to identify and categorize opinions expressed in a piece of text. Individuals post their opinion via reviews, tweets, comments or discussions which is our unstructured information. Sentiment analysis gives a general conclusion of audits which benefit clients, individuals or organizations for decision making. The primary point of this paper is to perform an ensemble approach on feature reduction methods identified with …

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Bitcoin Transaction Fee Estimation Using Mempool State And Linear Perceptron Machine Learning Algorithm, Abdullah Al-Shehabi Apr 2018

Bitcoin Transaction Fee Estimation Using Mempool State And Linear Perceptron Machine Learning Algorithm, Abdullah Al-Shehabi

Master's Projects

Bitcoin, the world’s most valued cryptocurrency, uses a network of computers across the globe to create an immutable transaction record on a public ledger known as the blockchain. The blockchain consists of a series of timestamped blocks, where each block contains a series of transactions selected for inclusion in the block, generally based on how high of a fee the transaction allocates to the party responsible for confirming the transaction. Estimating an appropriate fee for Bitcoin transactions is a challenge for many transacting parties using Bitcoin as a digital currency. This work aims to help Bitcoin users save funds in …

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Visual Question Answering, Pankti Kansara Apr 2018

Visual Question Answering, Pankti Kansara

Master's Projects

There has been immense progress in the fields of computer vision, object detection and natural language processing (NLP) in recent years. Artificial Intelligence (AI) systems, such as question answering models, use NLP to provide a "comprehensive" capability to the machine. Such a machine can answer natural language queries about any portion of an unstructured text. An extension of this system is to combine NLP with computer vision to accomplish the task of Visual Question Answering (VQA), which is to build a system that can answer natural language queries about images. A number of systems have been proposed for VQA that …

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Sql Injection Detection Using Machine Learning Techniques And Multiple Data Sources, Kevin Ross Apr 2018

Sql Injection Detection Using Machine Learning Techniques And Multiple Data Sources, Kevin Ross

Master's Projects

SQL Injection continues to be one of the most damaging security exploits in terms of personal information exposure as well as monetary loss. Injection attacks are the number one vulnerability in the most recent OWASP Top 10 report, and the number of these attacks continues to increase. Traditional defense strategies often involve static, signature-based IDS (Intrusion Detection System) rules which are mostly effective only against previously observed attacks but not unknown, or zero-day, attacks. Much current research involves the use of machine learning techniques, which are able to detect unknown attacks, but depending on the algorithm can be costly in …

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Resolving Cold Start Problem Using User Demographics And Machine Learning Techniques For Movie Recommender Systems, Sahil Motadoo Apr 2018

Resolving Cold Start Problem Using User Demographics And Machine Learning Techniques For Movie Recommender Systems, Sahil Motadoo

Master's Projects

There is a substantial increase in demand for recommender systems which have applications in a variety of domains. The goal of recommendations is to provide relevant choices to users. In practice, there are multiple methodologies in which recommendations take place like Collaborative Filtering (CF), Content-based filtering and Hybrid approach. For this paper, we will consider these approaches to be traditional approaches. The advantages of these approaches are in their design, functionality and efficiency. However, they do suffer from some major problems such as data sparsity, scalability and cold start to name a few. Among these problems, cold start is an …

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The Evolution Of Nintendo Company, Yaochen Wei Apr 2018

The Evolution Of Nintendo Company, Yaochen Wei

ART 108: Introduction to Games Studies

Through consistent innovation, investment in quality, research, and diversification, Nintendo managed to evolve from a playing card manufacturer to a world-leading video game company.

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Vgm-Rnn: Recurrent Neural Networks For Video Game Music Generation, Nicolas Mauthes Apr 2018

Vgm-Rnn: Recurrent Neural Networks For Video Game Music Generation, Nicolas Mauthes

Master's Projects

The recent explosion of interest in deep neural networks has affected and in some cases reinvigorated work in fields as diverse as natural language processing, image recognition, speech recognition and many more. For sequence learning tasks, recurrent neural networks and in particular LSTM-based networks have shown promising results. Recently there has been interest – for example in the research by Google’s Magenta team – in applying so-called “language modeling” recurrent neural networks to musical tasks, including for the automatic generation of original music. In this work we demonstrate our own LSTM-based music language modeling recurrent network. We show that it …

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Image To Latex Via Neural Networks, Avinash More Apr 2018

Image To Latex Via Neural Networks, Avinash More

Master's Projects

Many research papers in mathematics, computer science, and physics are written in LaTeX. Technical papers and articles in these areas often involve mathematical equations. Writing such equations in LaTeX takes longer than handwriting the same equations on paper. In this report, we want to show that the time-consuming process of typesetting LaTeX equations from images of these equations can be automated and optimized. Neural networks are good at solving related problems such as handwritten digit recognition, so we adapted these well-studied approaches to the LaTeX problem. Neural network model training involves large amounts of good quality data. So, for our …

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Genetic Barcode Identification With Profile Hidden Markov Models, Vishrut Sharma Apr 2018

Genetic Barcode Identification With Profile Hidden Markov Models, Vishrut Sharma

Master's Projects

DNA barcoding is a method that uses an organism’s DNA to identify its species. The gene cytochrome c oxidase I (COI) has been used effectively as a DNA barcode to identify organisms and elucidate relationships among species [1]. There also exists a database BOLD (Barcode Of Life Database) that contains COI sequences used for DNA barcoding for more than 1 million different species. Using BOLD to identify samples that have a match in the database is an uncomplicated process. However, this method fails to determine samples that are absent from the database. Given a sample that is not represented in …

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Fine-Grained Object Detection, Rahul Dalal Apr 2018

Fine-Grained Object Detection, Rahul Dalal

Master's Projects

Object detection plays a vital role in many real-world computer vision applications such as selfdriving cars, human-less stores and general purpose robotic systems. Convolutional Neural Network(CNN) based Deep Learning has evolved to become the backbone of most computer vision algorithms, including object detection. Most of the research has focused on detecting objects that differ significantly e.g. a car, a person, and a bird. Achieving fine-grained object detection to detect different types within one class of objects from general object detection can be the next step. Fine-grained object detection is crucial to tasks like automated retail checkout. This research has developed …

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Outfit Recommender System, Nikita Ramesh Apr 2018

Outfit Recommender System, Nikita Ramesh

Master's Projects

The online apparel retail market size in the United States is worth about seventy-two billion US dollars. Recommendation systems on retail websites generate a lot of this revenue. Thus, improving recommendation systems can increase their revenue. Traditional recommendations for clothes consisted of lexical methods. However, visual-based recommendations have gained popularity over the past few years. This involves processing a multitude of images using different image processing techniques. In order to handle such a vast quantity of images, deep neural networks have been used extensively. With the help of fast Graphics Processing Units, these networks provide results which are extremely accurate, …

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Compression Of Wearable Body Sensor Network Data Using Improved Two-Threshold-Two-Divisor Data Chunking Algorithm, Robinson Raju Apr 2018

Compression Of Wearable Body Sensor Network Data Using Improved Two-Threshold-Two-Divisor Data Chunking Algorithm, Robinson Raju

Master's Projects

Compression plays a significant role in Body Sensor Networks (BSN) data since the sensors in BSNs have limited battery power and memory. Also, data needs to be transmitted fast and in a lossless manner to provide near real-time feedback. The paper evaluates lossless data compression algorithms like Run Length Encoding (RLE), Lempel Zev Welch (LZW) and Huffman on data from wearable devices and compares them in terms of Compression Ratio, Compression Factor, Savings Percentage and Compression Time. It also evaluates a data deduplication technique used for Low Bandwidth File Systems (LBFS) named Two Thresholds Two Divisors (TTTD) algorithm to determine …

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Analyzing Android Adware, Supraja Suresh Apr 2018

Analyzing Android Adware, Supraja Suresh

Master's Projects

Most Android smartphone apps are free; in order to generate revenue, the app developers embed ad libraries so that advertisements are displayed when the app is being used. Billions of dollars are lost annually due to ad fraud. In this research, we propose a machine learning based scheme to detect Android adware based on static and dynamic features. We collect static features from the manifest file, while dynamic features are obtained from network traffic. Using these features, we initially classify Android applications into broad categories (e.g., adware and benign) and then further classify each application into a more specific family. …

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Boosted Hidden Markov Models For Malware Detection, Aditya Raghavan Apr 2018

Boosted Hidden Markov Models For Malware Detection, Aditya Raghavan

Master's Projects

Digital security is an important issue today, and efficient malware detection is at the forefront of research into building secure digital systems. As with many other fields, malware detection research has seen a dramatic increase in the application of machine learning algorithms. One machine learning technique that has found widespread application in the field of pattern matching and malware detection is hidden Markov models (HMMs). Since HMM training is a hill climb technique, we can often significantly improve a model by training multiple times with different initial values. In this research, we compare boosted HMMs (using AdaBoost) to HMMs trained …

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Using Sentiment Analysis And Pattern Matching To Signal User Review Abnormalities, Stefan S. Gloutnikov Apr 2018

Using Sentiment Analysis And Pattern Matching To Signal User Review Abnormalities, Stefan S. Gloutnikov

Master's Projects

User opinions on websites like Amazon, Yelp, and TripAdvisor are a key input for consumers when figuring out what to purchase, or where and what to eat. This means that in order for such websites to provide a better service to their customers, they must guard against fake and targeted reviews. Detecting such users and reviews automatically is a very complex multi-step process, and there is no direct mechanism for solving the problem reliably. Multiple AI and Machine Learning algorithms are coupled together when examining user reviews in determining if a review is fake or not. In this project we …

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Facial Emotion Recognition Using Machine Learning, Nitisha Raut Apr 2018

Facial Emotion Recognition Using Machine Learning, Nitisha Raut

Master's Projects

Face detection has been around for ages. Taking a step forward, human emotion displayed by face and felt by brain, captured in either video, electric signal (EEG) or image form can be approximated. Human emotion detection is the need of the hour so that modern artificial intelligent systems can emulate and gauge reactions from face. This can be helpful to make informed decisions be it regarding identification of intent, promotion of offers or security related threats. Recognizing emotions from images or video is a trivial task for human eye, but proves to be very challenging for machines and requires many …

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Load Balancing And Virtual Machine Allocation In Cloud-Based Data Centers, Saily Satish Ghodke Apr 2018

Load Balancing And Virtual Machine Allocation In Cloud-Based Data Centers, Saily Satish Ghodke

Master's Projects

As cloud services see an exponential increase in consumers, the demand for faster processing of data and a reliable delivery of services becomes a pressing concern. This puts a lot of pressure on the cloud-based data centers, where the consumers’ data is stored, processed and serviced. The rising demand for high quality services and the constrained environment, make load balancing within the cloud data centers a vital concern. This project aims to achieve load balancing within the data centers by means of implementing a Virtual Machine allocation policy, based on consensus algorithm technique. The cloud-based data center system, consisting of …

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Stock Price Prediction Using Deep Learning, Abhinav Tipirisetty Apr 2018

Stock Price Prediction Using Deep Learning, Abhinav Tipirisetty

Master's Projects

Stock price prediction is one among the complex machine learning problems. It depends on a large number of factors which contribute to changes in the supply and demand. This paper presents the technical analysis of the various strategies proposed in the past, for predicting the price of a stock, and evaluation of a novel approach for the same. Stock prices are represented as time series data and neural networks are trained to learn the patterns from trends. Along with the numerical analysis of the stock trend, this research also considers the textual analysis of it by analyzing the public sentiment …

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