Artificial Intelligence and Robotics Commons^™

Document Classification Using Machine Learning, Ankit Basarkar

Master's Projects

To perform document classification algorithmically, documents need to be represented such that it is understandable to the machine learning classifier. The report discusses the different types of feature vectors through which document can be represented and later classified. The project aims at comparing the Binary, Count and TfIdf feature vectors and their impact on document classification. To test how well each of the three mentioned feature vectors perform, we used the 20-newsgroup dataset and converted the documents to all the three feature vectors. For each feature vector representation, we trained the Naïve Bayes classifier and then tested the generated classifier …

Cascaded Facial Detection Algorithms To Improve Recognition, Edmund Yee

Master's Projects

The desire to be able to use computer programs to recognize certain biometric qualities of people have been desired by several different types of organizations. One of these qualities worked on and has achieved moderate success is facial detection and recognition. Being able to use computers to determine where and who a face is has generated several different algorithms to solve this problem with different benefits and drawbacks. At the backbone of each algorithm is the desire for it to be quick and accurate. By cascading face detection algorithms, accuracy can be improved but runtime will subsequently be increased. Neural …

Computational Analysis Of Cryptic Splice Sites, Remya Mohanan

Master's Projects

DNA in the nucleus of all eukaryotes is transcribed into mRNA where it is then translated into proteins. The DNA which is transcribed into mRNA is composed of coding and non-coding regions called exons and introns, respectively. It undergoes a post-trancriptional process called splicing where the introns or the non-coding regions are removed from the pre-mRNA to give the mature mRNA. Splicing of pre-mRNAs at 5 ́ and 3ˊ ends is a crucial step in the gene expression pathway. The mis-splicing by the spliceosome at different sites known as cryptic splice sites is caused by mutations which will affect the …

Comparing Authentic And Cryptic 5’ Splice Sites Using Hidden Markov Models And Decision Trees, Pratikshya Mishra

Master's Projects

Splicing is the editing of the precursor mRNA produced during transcription. The mRNA contains a large number of nucleotides in the introns and exons which are spliced to remove the introns and bind the exons to produce the mature mRNA which is translated to generate proteins. Hence accurate splicing at 5’ and 3’ splice sites (authentic splice sites (AuthSS)) is of foremost importance. The 5’ and 3’ splice sites are characterized by consensus sequences. Eukaryotic genome also contains splice sites known as Cryptic Splice Sites (CSS) that match the consensus. But the CSS are activated only when there is a …

A Chatbot Framework For Yioop, Harika Nukala

Master's Projects

Over the past few years, messaging applications have become more popular than Social networking sites. Instead of using a specific application or website to access some service, chatbots are created on messaging platforms to allow users to interact with companies’ products and also give assistance as needed. In this project, we designed and implemented a chatbot Framework for Yioop. The goal of the Chatbot Framework for Yioop project is to provide a platform for developers in Yioop to build and deploy chatbot applications. A chatbot is a web service that can converse with users using artificial intelligence in messaging platforms. …

Shopbot: An Image Based Search Application For E-Commerce Domain, Nishant Goel

Master's Projects

For the past few years, e-commerce has changed the way people buy and sell products. People use this business model to do business over the Internet. In this domain, Human-Computer Interaction has been gaining momentum. Lately, there has been an upsurge in agent based applications in the form of intelligent personal assistants (also known as Chatbots) which make it easier for users to interact with digital services via a conversation, in the same way we talk to humans. In e- commerce, these assistants offer mainly text-based or speech based search capabilities. They can handle search for most products, but cannot …

Mining Frequency Of Drug Side Effects Over A Large Twitter Dataset Using Apache Spark, Dennis Hsu

Master's Projects

Despite clinical trials by pharmaceutical companies as well as current FDA reporting systems, there are still drug side effects that have not been caught. To find a larger sample of reports, a possible way is to mine online social media. With its current widespread use, social media such as Twitter has given rise to massive amounts of data, which can be used as reports for drug side effects. To process these large datasets, Apache Spark has become popular for fast, distributed batch processing. In this work, we have improved on previous pipelines in sentimental analysis-based mining, processing, and extracting tweets …

Generic Online Learning For Partial Visible & Dynamic Environment With Delayed Feedback, Behrooz Shahriari

Master's Projects

Reinforcement learning (RL) has been applied to robotics and many other domains which a system must learn in real-time and interact with a dynamic environment. In most studies the state- action space that is the key part of RL is predefined. Integration of RL with deep learning method has however taken a tremendous leap forward to solve novel challenging problems such as mastering a board game of Go. The surrounding environment to the agent may not be fully visible, the environment can change over time, and the feedbacks that agent receives for its actions can have a fluctuating delay. In …

Masquerade Detection On Mobile Devices, Swathi Nambiar Kadala Manikoth

Master's Projects

A masquerade is an attack where the attacker avoids detection by impersonating an authorized user of a system. In this research we consider the problem of masquerade detection on mobile devices. Our goal is to improve on previous work by considering more features and a wide variety of machine learning techniques. Our approach consists of verifying the authenticity of users based on individual features and combinations of features for all users to determine which features contribute the most to masquerade detection. Also, we determine which of the two approaches - the combination of features or using individual features has performed …

Headline Generation Using Deep Neural Networks, Dhruven Vora

Master's Projects

News headline generation is one of the important text summarization tasks. Human generated news headlines are generally intended to catch the eye rather than provide useful information. There have been many approaches to generate meaningful headlines by either using neural networks or using linguistic features. In this report, we are proposing a novel approach based on integrating Hedge Trimmer, which is a grammar based extractive summarization system with a deep neural network abstractive summarization system to generate meaningful headlines. We analyze the results against current recurrent neural network based headline generation system.

Application Of Computational Methods To Study The Selection Of Authentic And Cryptic Splice Sites, Tapomay Dey

Master's Projects

Proteins are building blocks of the bodies of eukaryotes, and the process of synthesizing proteins from DNA is crucial for the good health of an organism [13]. However, some mutations in the DNA may disrupt the selection of 5’ or 3’ splice sites by a spliceosome. An important research question is whether the disruptions have a stochastic relation to the position of nucleotides in the vicinity of the known authentic and cryptic splice sites. This can be achieved by proving that the authentic and cryptic splice sites are intrinsically different. However, the behavior of the spliceosome is not accurately known. …

Named Entity Recognition And Classification For Natural Language Inputs At Scale, Shreeraj Dabholkar

Master's Projects

Natural language processing (NLP) is a technique by which computers can analyze, understand, and derive meaning from human language. Phrases in a body of natural text that represent names, such as those of persons, organizations or locations are referred to as named entities. Identifying and categorizing these named entities is still a challenging task, research on which, has been carried out for many years. In this project, we build a supervised learning based classifier which can perform named entity recognition and classification (NERC) on input text and implement it as part of a chatbot application. The implementation is then scaled …

Image Spam Detection, Aneri Chavda

Master's Projects

Email is one of the most common forms of digital communication. Spam can be de ned as unsolicited bulk email, while image spam includes spam text embedded inside images. Image spam is used by spammers so as to evade text-based spam lters and hence it poses a threat to email based communication. In this research, we analyze image spam detection methods based on various combinations of image processing and machine learning techniques.

Malware Detection Using The Index Of Coincidence, Bhavna Gurnani

Master's Projects

In this research, we apply the Index of Coincidence (IC) to problems in malware analysis. The IC, which is often used in cryptanalysis of classic ciphers, is a technique for measuring the repeat rate in a string of symbols. A score based on the IC is applied to a variety of challenging malware families. We nd that this relatively simple IC score performs surprisingly well, with superior results in comparison to various machine learning based scores, at least in some cases.

Real-Time Online Chinese Character Recognition, Wenlong Zhang

Master's Projects

In this project, I built a web application for handwritten Chinese characters recognition in real time. This system determines a Chinese character while a user is drawing/writing it. The techniques and steps I use to build the recognition system include data preparation, preprocessing, features extraction, and classification. To increase the accuracy, two different types of neural networks ared used in the system: a multi-layer neural network and a convolutional neural network.

Analysis On Alergia Algorithm: Pattern Recognition By Automata Theory, Xuanyi Qi

Master's Projects

Based on Kolmogorov Complexity, a finite set x of strings has a pattern if the set x can be output by a Turing machine of length that is less than minimum of all |x|; this Turing machine, that may not be unique, is called a pattern of the finite set of string. In order to find a pattern of a given finite set of strings (assuming such a pattern exists), the ALERGIA algorithm is used to approximate such a pattern (Turing machine) in terms of finite automata. Note that each finite automaton defines a partition on formal language Σ*, ALERGIA …

Analyze Large Multidimensional Datasets Using Algebraic Topology, David Le

Master's Projects

This paper presents an efficient algorithm to extract knowledge from high-dimensionality, high- complexity datasets using algebraic topology, namely simplicial complexes. Based on concept of isomorphism of relations, our method turn a relational table into a geometric object (a simplicial complex is a polyhedron). So, conceptually association rule searching is turned into a geometric traversal problem. By leveraging on the core concepts behind Simplicial Complex, we use a new technique (in computer science) that improves the performance over existing methods and uses far less memory. It was designed and developed with a strong emphasis on scalability, reliability, and extensibility. This paper …

Dna Analysis Using Grammatical Inference, Cory Cook

Master's Projects

An accurate language definition capable of distinguishing between coding and non-coding DNA has important applications and analytical significance to the field of computational biology. The method proposed here uses positive sample grammatical inference and statistical information to infer languages for coding DNA.

An algorithm is proposed for the searching of an optimal subset of input sequences for the inference of regular grammars by optimizing a relevant accuracy metric. The algorithm does not guarantee the finding of the optimal subset; however, testing shows improvement in accuracy and performance over the basis algorithm.

Testing shows that the accuracy of inferred languages for …

Machine Learning On The Cloud For Pattern Recognition, Tien Nguyen

Master's Projects

Pattern recognition is a field of machine learning with applications to areas such as text recognition and computer vision. Machine learning algorithms, such as convolutional neural networks, may be trained to classify images. However, such tasks may be computationally intensive for a commercial computer for larger volumes or larger sizes of images. Cloud computing allows one to overcome the processing and memory constraints of average commercial computers, allowing computations on larger amounts of data. In this project, we developed a system for detection and tracking of moving human and vehicle objects in videos in real time or near real time. …

Supervised Learning For Multi-Domain Text Classification, Siva Charan Reddy Gangireddy

Master's Projects

Digital information available on the Internet is increasing day by day. As a result of this, the demand for tools that help people in finding and analyzing all these resources are also growing in number. Text Classification, in particular, has been very useful in managing the information. Text Classification is the process of assigning natural language text to one or more categories based on the content. It has many important applications in the real world. For example, finding the sentiment of the reviews, posted by people on restaurants, movies and other such things are all applications of Text classification. In …

Multi Faceted Text Classification Using Supervised Machine Learning Models, Abhiteja Gajjala

Master's Projects

In recent year’s document management tasks (known as information retrieval) increased a lot due to availability of digital documents everywhere. The need of automatic methods for extracting document information became a prominent method for organizing information and knowledge discovery. Text Classification is one such solution, where in the natural language text is assigned to one or more predefined categories based on the content. In my research classification of text is mainly focused on sentiment label classification. The idea proposed for sentiment analysis is multi-class classification of online movie reviews. Many research papers discussed the classification of sentiment either positive or …

Movie Script Shot Lister, David Robert Smith

Master's Projects

The making of a motion picture almost always starts with the script, the written version of a story envisioned within the mind of its creator. The script is then broken down into shots. Each individual shot is filmed and then they are edited together to create the motion picture. The goal of the Movie Script Shot Lister thesis project is to be able to read in a script for a movie or television show, and automatically generate a shot list. While a script is text, a shot list is the blue print for how to visualize that script, so the …

Multiple Sequence Alignment With Pro Le Hidden Markov Models, Shubhangi Rakhonde

Master's Projects

The human genome consists of various patterns and sequences that are of biolog- ical signi cance. Capturing these patterns can help us in resolving various mysteries related to the genome, like how genomes evolve, how diseases occur due to genetic mutation, how viruses mutate to cause new disease and what is the cure for these diseases. All these applications are covered in the study of bioinformatics.

One of the very common tasks in bioinformatics involves simultaneous alignment of a number of biological sequences. In bioinformatics, this is widely known as Mul- tiple Sequence Alignment. Multiple sequence alignments help in grouping …

Hive - An Agent Based Modeling Framework, Roohi Bharti

Master's Projects

This thesis begins by defining agent based modeling. Agent based models are used to model the emergent behavior of complex systems with many interacting components, known as agents. Several model examples are given using NetLogo, which is a popular agent-based modeling platform. A model of concurrent computation is described that uses message passing as the only form of communication between the model’s components, which are called actors. The model is called an actor model. Actors are primitive objects of concurrency in an actor model. In particular, we describe the actor model implemented by Akka, which is Scala’s new actor library. …

Detection Of Locations Of Key Points On Facial Images, Manoj Gyanani

Master's Projects

In field of computer vision research, One of the most important branch is Face recognition. It targets at finding size and location of human face on digital image, by identifying and separating faces from the surrounding objects like building, plants etc. For the purpose of developing an advanced face recognition algorithm, Detection of facial key points is the basic and very important task, basically it is about finding out the location of specific key points on facial images. This key points can be mouths, noses, left eyes, right eyes and so on.

For implementation of solution, I have used amazon …

Pattern Discovery In Dna Using Stochastic Automata, Shweta Shweta

Master's Projects

We consider the problem of identifying similarities between different species of DNA. To do this we infer a stochastic finite automata from a given training data and compare it with a test data. The training and test data consist of DNA sequence of different species. Our method first identifies sentences in DNA. To identify sentences we read DNA sequence one character at a time, 3 characters form a codon and codons form proteins (also known as amino acid chains).Each amino acid in proteins belongs to a group. In total we have 5 groups’ polar, non-polar, acidic, basic and stop codons. …

Malware Detection Using Dynamic Analysis, Swapna Vemparala

Master's Projects

In this research, we explore the field of dynamic analysis which has shown promis- ing results in the field of malware detection. Here, we extract dynamic software birth- marks during malware execution and apply machine learning based detection tech- niques to the resulting feature set. Specifically, we consider Hidden Markov Models and Profile Hidden Markov Models. To determine the effectiveness of this dynamic analysis approach, we compare our detection results to the results obtained by using static analysis. We show that in some cases, significantly stronger results can be obtained using our dynamic approach.

A Comparison Of Clustering Techniques For Malware Analysis, Swathi Pai

Master's Projects

In this research, we apply clustering techniques to the malware detection problem. Our goal is to classify malware as part of a fully automated detection strategy. We compute clusters using the well-known �-means and EM clustering algorithms, with scores obtained from Hidden Markov Models (HMM). The previous work in this area consists of using HMM and �-means clustering technique to achieve the same. The current effort aims to extend it to use EM clustering technique for detection and also compare this technique with the �-means clustering.

Clustering Versus Svm For Malware Detection, Usha Narra

Master's Projects

Previous work has shown that we can effectively cluster certain classes of mal- ware into their respective families. In this research, we extend this previous work to the problem of developing an automated malware detection system. We first compute clusters for a collection of malware families. Then we analyze the effectiveness of clas- sifying new samples based on these existing clusters. We compare results obtained using �-means and Expectation Maximization (EM) clustering to those obtained us- ing Support Vector Machines (SVM). Using clustering, we are able to detect some malware families with an accuracy comparable to that of SVMs. One …

Optimization Of Scheduling And Dispatching Cars On Demand, Vu Tran

Master's Projects

Taxicab is the most common type of on-demand transportation service in the city because its dispatching system offers better services in terms of shorter wait time. However, the shorter wait time and travel time for multiple passengers and destinations are very considerable. There are recent companies implemented the real-time ridesharing model that expects to reduce the riding cost when passengers are willing to share their rides with the others. This model does not solve the shorter wait time and travel time when there are multiple passengers and destinations. This paper investigates how the ridesharing can be improved by using the …