Anant Agarwal

Abstract: Opinion Mining or Sentiment Analysis is the technique of determining the attitude of a speaker with respect to a topic or an overall document. Twitter is a popular platform where people express their opinions and sentiments. Sentiment analysis of twitter data can help in the search of products, companies, movies, books etc. based on the overall sentiment associated with that tweet. We present a sentiment analysis classification engine for Twitter data. We apply certain preprocessing approaches to cleanse the data, extract useful features from it and then build a training model on it. We present a comparative model using Naives Bayes, Max Entropy and SVM classifiers. We also collected results using the Random Forest and AdaBoost ensemble classifiers. We achieved an average accuracy of ~75% through different experiments using varied classifiers and preprocessing steps.

Abstract: VoiceTravel is an interactive flight search dialogue system designed for mobile devices running the Android mobile operating system. The app accepts the search criteria using voice based input from user and returns the flights matching the criteria. The search criteria in the current version of the application includes origin-city, destination-city, date of travel and an optional time parameter. The criteria can be specified in regular English and in any order. Multiple criteria can be specified in a single utterance as well. VoiceTravel utilizes advancements in computational linguistics and smartphone space to disrupt how air travel search is done.

Abstract: The motivation of our project is to build a data de-duplication scheme which would enable us to build an object repository in a network. A network is characterized by several hosts, all requesting concurrent access to files and entering new files (objects) into the network. Hence, in order to meet the clients' needs, we need to come up with a computational solution which would enable a central server to service these requests and store data with minimum storage space. We came up with a client-server model where the server would break up files into fixed size blocks, create a mapping between blocks and files and also calculate hash values for all these blocks which would help in the de-duplication process. The difficult part is to implement this system over a network with one server handling concurrent requests for the same file from different client and we aim to solve this problem through this project. The project is somewhat analogous to the Amazon S3 repository which stores de-duplicated objects and provides access to users on an on-demand basis.

Abstract: ReasonableCare database system maintains medical reports, appointment information etc. for students and doctors who are part of the university. The system allows a student to book and cancel appointments with doctor as per availability or the type of health problem. At any time, a student or a doctor has access to his past and upcoming appointments and doctors and nurses can read and update a student medical records. Every new student has to get 3 vaccinations, which if not done by student by the end of first semester will lead to suspension of privileges on student’s records. The system also maintains student health insurance information and amount of co-payment required to be done by student which depends on whether the appointment is with a general practitioner or a specialist or it can depend on the reason of visit to the doctor. If the deductible amount for the current year has been paid in full by student, the health center tracks the health insurance information from student account and bill the insurance company for the remaining balance. Students takes consultations from nurses over the phone which is updated in system and is accessible to both the nurse and student for future reference. Nurses are able to access information of the list of doctors the student has been seeing for consulting or specific doctor in case of a particular requirement.

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Abstract: In last decade lot of efforts had been made by research community to create sign language recognition system which provide a medium of communication for differently-abled people and their machine translations help others having trouble in understanding such sign languages. Computer vision and machine learning can be collectively applied to create such systems. In this paper, we present a sign language recognition system which makes use of depth images that were captured using a Microsoft Kinect® camera. Using computer vision algorithms, we develop a characteristic depth and motion profile for each sign language gesture. The feature matrix thus generated was trained using a multi-class SVM classifier and the final results were compared with existing techniques. The dataset used is of sign language gestures for the digits 0-9.

Abstract: Traffic classification has a vital role in tasks as wide ranging as trend analyses, adaptive networkbased QoS marking of traffic, dynamic access control and lawful interception. Traditionally performed using port and payload based analysis, recent years have seen an increased interest in the development of machine learning techniques for classification. Much of this existing research focuses on the achievable accuracy (classification accuracy) of different machine learning algorithms. These experiments have used different (thus not comparable) datasets and features. The process of defining appropriate features, performing feature selection and the influence of this on classification and computation performance has not been studied. With this project we recognize that real-time traffic classifiers will operate under constraints, which limit the number and type of features that can be calculated. On this basis we define 9 flow features like Flow Duration Packet length Inter-arrival time of packets Number of packets Throughput Packet Size (minimum, maximum, average, standard deviation) which are simple to compute and are well understood within the networking community. We evaluate the classification accuracy and computational performance of K-NN Clustering Algorithm, Naïve Bayes Algorithm and Multi Class Logistic Regression algorithms using the 9 features divided into the three reduced feature vectors.