Artificial Intelligence and Robotics Commons^™

Robust Lightweight Object Detection, Siddharth Kumar

Master's Projects

Object detection is a very challenging problem in computer vision and has been a prominent subject of research for nearly three decades. There has been a promising in- crease in the accuracy and performance of object detectors ever since deep convolutional networks (CNN) were introduced. CNNs can be trained on large datasets made of high resolution images without flattening them, thereby using the spatial information. Their superior learning ability also makes them ideal for image classification and object de- tection tasks. Unfortunately, this power comes at the big cost of compute and memory. For instance, the Faster R-CNN detector required …

Deep Learning Based Real Time Devanagari Character Recognition, Aseem Chhabra

Master's Projects

The revolutionization of the technology behind optical character recognition (OCR) has helped it to become one of those technologies that have found plenty of uses in the entire industrial space. Today, the OCR is available for several languages and have the capability to recognize the characters in real time, but there are some languages for which this technology has not developed much. All these advancements have been possible because of the introduction of concepts like artificial intelligence and deep learning. Deep Neural Networks have proven to be the best choice when it comes to a task involving recognition. There are …

Predicting Off-Target Potential Of Crispr-Cas9 Single Guide Rna, Ishita Mathur

Master's Projects

With advancements in the field of genome engineering, researchers have come up with potential ways for site-specific gene editing. One of the methods uses the Clustered Regularly Interspaced Short Palindromic Repeats - CRISPR-Cas technology. It consists of a Cas9 nuclease and a single guide RNA (sgRNA) that cleaves the DNA at the intended target site. However, the target genome could contain multiple potential off-target sites and cleaving an off-target site can have deleterious effects in case of gene editing in humans.

Lab based assays have been developed to test the off-target effects of guide RNAs. However, it is not feasible …

Deep Learning On Graphs Using Graph Convolutional Networks, Saurabh Mithe

Master's Projects

Graphs are a powerful way to model network data with the objects as nodes and the relationship between the various objects as links. Such graphs contain a plethora of valuable information about the underlying data which can be extracted, analyzed, and visualized using Machine Learning (ML). The challenge to this task is that graphs are non-Euclidean structures which means that they cannot be directly used with ML techniques because ML techniques only work with Euclidean structures like grids or sequences. In order to overcome this challenge, the graph structure first needs to be encoded into an equivalent Euclidean representation in …

Glovenor - Global Vectors For Node Representations, Shishir Kulkarni

Master's Projects

A graph is a very powerful abstract data type that can be used to model entities (nodes) and relationships (edges). Many real world networks like biological, computer and friendship networks can be represented as graphs. Graphs can be mined to extract interesting patterns and interactions between the participating entities. Recently, various Artificial Intelligence (AI) and Machine Learning (ML) techniques are used for this purpose. In order to do that, the nodes of a graph have to be represented as low dimensional feature vectors. Node embedding is the process of generating a �-dimensional feature vector corresponding to each node of a …

Learning To Play The Trading Game, Neeraj Kulkarni

Master's Projects

Can we train a stock trading bot that can take decisions in high-entropy envi- ronments like stock markets to generate profits based on some optimal policy? Can we further extend this learning for any general trading problem? Quantitative Al- gorithms are responsible for more than 75% of the stock trading around the world. Creating a stock market prediction model is comparatively easy. But creating a prof- itable prediction model is still considered as a challenging task in the field of machine learning and deep learning due to the unpredictability of the financial markets. Us- ing biologically inspired computing techniques of …

Graph Classification Using Machine Learning Algorithms, Monica Golahalli Seenappa

Master's Projects

In the Graph classification problem, given is a family of graphs and a group of different categories, and we aim to classify all the graphs (of the family) into the given categories. Earlier approaches, such as graph kernels and graph embedding techniques have focused on extracting certain features by processing the entire graph. However, real world graphs are complex and noisy and these traditional approaches are computationally intensive. With the introduction of the deep learning framework, there have been numerous attempts to create more efficient classification approaches.

For this project, we will be focusing on modifying an existing kernel graph …

Tsar : A System For Defending Hate Speech Detection Models Against Adversaries, Brian Tuan Khieu

Master's Projects

Although current state-of-the-art hate speech detection models achieve praiseworthy results, these models have shown themselves to be vulnerable to attack. Easy to execute lexical manipulations such as the removal of whitespace from a given text create significant issues for word-based hate speech detection models. In this paper, we reproduce the results of five cutting edge models as well as four significant evasion schemes from prior work. Only a limited amount of evasion schemes that also maintain readability exists, and this works to our advantage in the recreation of the original data. Furthermore, we demonstrate that each lexical attack or evasion …

On Adversarial Attacks On Deep Learning Models, Nag Mani

Master's Projects

With recent advancements in the field of artificial intelligence, deep learning has created a niche in the technology space and is being actively used in autonomous and IoT systems globally. Unfortunately, these deep learning models have become susceptible to adversarial attacks which can severely impact their integrity. Research has shown that many state-of-the-art models are vulnerable to attacks by well-crafted adversarial examples. These adversarial examples are perturbed versions of clean data which have small amount of noise added to them. These adversarial samples are imperceptible to the human eye but can easily fool the targeted model. The exposed vulnerabilities of …

Benchmarking Optimization Algorithms For Capacitated Vehicle Routing Problems, Pratik Surana

Master's Projects

The Vehicle Routing Problem (VRP) originated in the 1950s when algorithms and mathematical approaches were applied to find solutions for routing vehicles. Since then, there has been extensive research in the field of VRPs to solve real-life problems. The process of generating an optimal routing schedule for a VRP is complex due to two reasons. First, VRP is considered to be an NP-Hard problem. Second, there are several constraints involved, such as the number of available vehicles, the vehicle capacities, time-windows for pickup or delivery etc.

The main goal for this project was to compare different optimization algorithms for solving …

Intelligent Log Analysis For Anomaly Detection, Steven Yen

Master's Projects

Computer logs are a rich source of information that can be analyzed to detect various issues. The large volumes of logs limit the effectiveness of manual approaches to log analysis. The earliest automated log analysis tools take a rule-based approach, which can only detect known issues with existing rules. On the other hand, anomaly detection approaches can detect new or unknown issues. This is achieved by looking for unusual behavior different from the norm, often utilizing machine learning (ML) or deep learning (DL) models. In this project, we evaluated various ML and DL techniques used for log anomaly detection. We …

Breaking Audio Captcha Using Machine Learning/Deep Learning And Related Defense Mechanism, Heemany Shekhar

Master's Projects

CAPTCHA is a web-based authentication method used by websites to distinguish between humans (valid users) and bots(attackers). Audio captcha is an accessible captcha meant for the visually disabled section of users such as color-blind, blind, near-sighted users. In this project, I analyzed the security of audio captchas from attacks that employ machine learning and deep learning models. Audio captchas of varying lengths (5, 7 and 10) and varying background noise (no noise, medium noise or high noise) were analyzed. I found that audio captchas with no background noise or medium background noise were easily attacked with 99% - 100% accuracy. …

Influence Analysis Based On Political Twitter Data, Jace Rose

Master's Projects

Studies of online behavior often consider how users interact online, their posting behaviors, what they are tweeting about, and how likely they are to follow other people. The problem is there is that no deeper study on the people that a user has interacted with and how these other users affect them. This study examines if it is possible to draw similar sentiment from users with whom the target user has interacted with. The data collection process gathers data from Twitter users posting to popular political hashtags, which the highest at the time published were #MAGA and #TRUMP, as well …

Classification Of Humans Into Ayurvedic Prakruti Types Using Computer Vision, Gayatri Gadre

Master's Projects

Ayurveda, a 5000 years old Indian medical science, believes that the universe and hence humans are made up of five elements namely ether, fire, water, earth, and air. The three Doshas (Tridosha) Vata, Pitta, and Kapha originated from the combinations of these elements. Every person has a unique combination of Tridosha elements contributing to a person’s ‘Prakruti’. Prakruti governs the physiological and psychological tendencies in all living beings as well as the way they interact with the environment. This balance influences their physiological features like the texture and colour of skin, hair, eyes, length of fingers, the shape of the …

Improving Steering Ability Of An Autopilot In A Fully Autonomous Car, Shivanku Mahna

Master's Projects

The world we live in is developing at a really rapid pace and along with it is developing the technology that we use. We have clearly come a long way from calling a car modern because it had a touch screen infotainment system to calling it modern because it drives on its own. The progress has been so rapid that it demands for us to analyze this and try to improvise a small part of this journey. With the same thought in mind, this project focuses on improvising the steering ability of an autonomous car. In order to make more …

Yoda – Your Only Design Assistant, Siddharth Kulkarni

Master's Projects

Converting user interface designs created by graphic designers into computer code is a typical job of a front end engineer in order to develop functional web and mobile applications. This conversion process can often be extremely tedious, slow and prone to human error. In this project, deep learning based object detection along with optical character recognition is used to generate platform ready prototypes directly from design sketches. Also, a new design language is introduced to facilitate expressive prototyping and allowing the creation of more expressive and functional designs. It is observed that the AI powered application along with modern web …

Measuring Malware Evolution Using Support Vector Machines, Mayuri Wadkar

Master's Projects

Malware is software that is designed to do harm to computer systems. Malware often evolves over a period of time as malware developers add new features and fix bugs. Thus, malware samples from the same family from different time periods can exhibit significantly different behavior. Differences between malware samples within a single family can originate from various code modifications designed to evade signature-based detection or changes that are made to alter the functionality of the malware itself. In this research, we apply feature ranking based on linear support vector machine (SVM) weights to identify, quantify, and track changes within malware …

Sensor - Based Human Activity Recognition Using Smartphones, Mustafa Badshah

Master's Projects

It is a significant technical and computational task to provide precise information regarding the activity performed by a human and find patterns of their behavior. Countless applications can be molded and various problems in domains of virtual reality, health and medical, entertainment and security can be solved with advancements in human activity recognition (HAR) systems. HAR is an active field for research for more than a decade, but certain aspects need to be addressed to improve the system and revolutionize the way humans interact with smartphones. This research provides a holistic view of human activity recognition system architecture and discusses …

Chatbots With Personality Using Deep Learning, Susmit Gaikwad

Master's Projects

Natural Language Processing (NLP) requires the computational modelling of the complex relationships of the syntax and semantics of a language. While traditional machine learning methods are used to solve NLP problems, they cannot imitate the human ability for language comprehension. With the growth in deep learning, these complexities within NLP are easier to model, and be used to build many computer applications. A particular example of this is a chatbot, where a human user has a conversation with a computer program, that generates responses based on the user’s input. In this project, we study the methods used in building chatbots, …

Optimizing E-Commerce Product Classification Using Transfer Learning, Rashmeet Kaur Khanuja

Master's Projects

The global e-commerce market is snowballing at a rate of 23% per year. In 2017, retail e-commerce users were 1.66 billion and sales worldwide amounted to 2.3 trillion US dollars, and e-retail revenues are projected to grow to 4.88 trillion USD in 2021. With the immense popularity that e-commerce has gained over past few years comes the responsibility to deliver relevant results to provide rich user experience. In order to do this, it is essential that the products on the ecommerce website be organized correctly into their respective categories. Misclassification of products leads to irrelevant results for users which not …

Human Activity Recognition Based On Multimodal Body Sensing, Anish Hemant Narkhede

Master's Projects

In the recent years, human activity recognition has been widely popularized by a lot of smartphone manufacturers and fitness tracking companies. It has allowed us to gain a deeper insight into our physical health on a daily basis. However, with the evolution of fitness tracking devices and smartphones, the amount of data that is being captured by these devices is growing exponentially. This paper aims at understanding the process of dimensionality reduction such as PCA so that the data can be used to make meaningful predictions along with novel techniques using autoencoders with different activation functions. The paper also looks …

Next Level: A Course Recommender System Based On Career Interests, Shehba Shahab

Master's Projects

Skills-based hiring is a talent management approach that empowers employers to align recruitment around business results, rather than around credentials and title. It starts with employers identifying the particular skills required for a role, and then screening and evaluating candidates’ competencies against those requirements. With the recent rise in employers adopting skills-based hiring practices, it has become integral for students to take courses that improve their marketability and support their long-term career success. A 2017 survey of over 32,000 students at 43 randomly selected institutions found that only 34% of students believe they will graduate with the skills and knowledge …

Speaker Recognition Using Machine Learning Techniques, Abhishek Manoj Sharma

Master's Projects

Speaker recognition is a technique of identifying the person talking to a machine using the voice features and acoustics. It has multiple applications ranging in the fields of Human Computer Interaction (HCI), biometrics, security, and Internet of Things (IoT). With the advancements in technology, hardware is getting powerful and software is becoming smarter. Subsequently, the utilization of devices to interact effectively with humans and performing complex calculations is also increasing. This is where speaker recognition is important as it facilitates a seamless communication between humans and computers. Additionally, the field of security has seen a rise in biometrics. At present, …

Image Retrieval Using Image Captioning, Nivetha Vijayaraju

Master's Projects

The rapid growth in the availability of the Internet and smartphones have resulted in the increase in usage of social media in recent years. This increased usage has thereby resulted in the exponential growth of digital images which are available. Therefore, image retrieval systems play a major role in fetching images relevant to the query provided by the users. These systems should also be able to handle the massive growth of data and take advantage of the emerging technologies, like deep learning and image captioning. This report aims at understanding the purpose of image retrieval and various research held in …

Javascript Metamorphic Malware Detection Using Machine Learning Techniques, Aakash Wadhwani

Master's Projects

Various factors like defects in the operating system, email attachments from unknown sources, downloading and installing a software from non-trusted sites make computers vulnerable to malware attacks. Current antivirus techniques lack the ability to detect metamorphic viruses, which vary the internal structure of the original malware code across various versions, but still have the exact same behavior throughout. Antivirus software typically relies on signature detection for identifying a virus, but code morphing evades signature detection quite effectively.

JavaScript is used to generate metamorphic malware by changing the code’s Abstract Syntax Tree without changing the actual functionality, making it very difficult …

Topic Classification Using Hybrid Of Unsupervised And Supervised Learning, Jayant Shelke

Master's Projects

There has been research around the idea of representing words in text as vectors and many models proposed that vary in performance as well as applications. Text processing is used for content recommendation, sentiment analysis, plagiarism detection, content creation, language translation, etc. to name a few. Specifically, we want to look at the problem of topic detection in text content of articles/blogs/summaries. With the humungous amount of text content published each and every minute on the internet, it is imperative that we have very good algorithms and approaches to analyze all the content and be able to classify most of …

Pose Estimation And Action Recognition In Sports And Fitness, Parth Vyas

Master's Projects

The emergence of large datasets and major improvements in Deep Learning has lead to many real-world applications. These applications have been focused on automotive markets, mobile markets, stock markets, and the healthcare market. Although Deep Learning has strong foundations across many areas, the few applications in Sports, Fitness, or even Injury Rehabilitation could benefit greatly from it. For example, if you are performing a workout and you need to evaluate your form, but do not have access or resources for an instructor to evaluate your form, it would be great to have an Artificial Intelligent agent provide real time feedback …

Detecting Cars In A Parking Lot Using Deep Learning, Samuel Ordonia

Master's Projects

Detection of cars in a parking lot with deep learning involves locating all objects of interest in a parking lot image and classifying the contents of all bounding boxes as cars. Because of the variety of shape, color, contrast, pose, and occlusion, a deep neural net was chosen to encompass all the significant features required by the detector to differentiate cars from not cars. In this project, car detection was accomplished with a convolutional neural net (CNN) based on the You Only Look Once (YOLO) model architectures. An application was built to train and validate a car detection CNN as …

An Ensemble Model For Click Through Rate Prediction, Muthaiah Ramanathan

Master's Projects

Internet has become the most prominent and accessible way to spread the news about an event or to pitch, advertise and sell a product, globally. The success of any advertisement campaign lies in reaching the right class of target audience and eventually convert them as potential customers in the future. Search engines like the Google, Yahoo, Bing are a few of the most used ones by the businesses to market their product. Apart from this, certain websites like the www.alibaba.com that has more traffic also offer services for B2B customers to set their advertisement campaign. The look of the advertisement, …

Classifying Classic Ciphers Using Machine Learning, Nivedhitha Ramarathnam Krishna

Master's Projects

We consider the problem of identifying the classic cipher that was used to generate a given ciphertext message. We assume that the plaintext is English and we restrict our attention to ciphertext consisting only of alphabetic characters. Among the classic ciphers considered are the simple substitution, Vigenère cipher, playfair cipher, and column transposition cipher. The problem of classification is approached in two ways. The first method uses support vector machines (SVM) trained directly on ciphertext to classify the ciphers. In the second approach, we train hidden Markov models (HMM) on each ciphertext message, then use these trained HMMs as features …