A generalized Poisson model for gene expression profiling using RNA sequence data

An RNA-Seq experiment is a useful tool in characterizing and quantify- ing transcriptomes into read counts and identifying di erentially expressed (DE) genes under di erent treatment conditions. However, analyzing RNA- Seq data in the quest of di erentially expressed genes is not straight forward. Focusing on the experiment of interest, di erent approaches can be made in identifying DE genes. Here, we propose to use a two parameter generalized poisson (GP) model to address the non-uniformity of read counts than the traditional poisson model and apply it to Arabidopsis pilot survey data by TCC(http://bioconductor .org/packages/release/bioc/html/TCC.html). A comparison study has also been performed with built in R-packages edgeR and DESeq with their default settings to understand the performance of GP model. Here, 28 new di erentially expressed genes have been identi ed by GP model more than edgeR and DESeq for Arabidopsis data and these genes can be a potential source of information in treating bacterial infection to the experimenters. Therefore, the approach of using GP model in real data set evident a signi cant performance to the in built methods of R-packages.Thesis (M.S.

A Personalized Travel Recommendation System Using Social Media Analysis

Personalization of recommender systems enables customized services to users. Social media is one resource that aids personalization. This study explores the use of twitter data to personalize travel recommendations. A machine learning classification model is used to identify travel related tweets. The travel tweets are then used to personalize recommendations regarding places of interest for the user. Places of interest are categorized as: historical buildings, museums, parks, and restaurants. To better personalize the model, travel tweets of the user\u27s friends and followers are also mined. Volunteer twitter users were asked to provide their twitter handle as well as rank their travel category preferences in a survey. We evaluated our model by comparing the predictions made by our model with the users choices in the survey. The evaluations show 68% prediction accuracy. The accuracy can be improved with a better travel-tweet training dataset as well as a better travel category identification technique using machine learning. The travel categories can be increased to include items like sports venues, musical events, entertainment, etc. and thereby fine-tune the recommendations. The proposed model lists \u27n\u27 places of interest from each category in proportion to the travel category score generated by the model

Identifying Buildings with Ramp Entrances Using Convolutional Neural Networks

The Americans with Disabilities Act (ADA) is a civil rights law that was signed into law in 1992 by President George H.W. Bush. The law requires wheelchair access be made available for buildings built after 1992. Buildings under the law include retail stores, hotels, banks and most other public buildings. However, there are a large percentage of buildings built before 1992 that are not wheelchair accessible. In addition, ADA does not require the location of ramp to be at the front of the building. This is an inconvenience for individuals who use wheelchairs to access a building, as a) the building may not have a ramp or b) they may have to roll around the building to where the ramp may be located. Hence, in this paper, we describe a prototype artificial intelligent system, which takes the input of a building image, and produces the output prediction for whether the building has a ramp. The system uses a deep learning technique, convolution neural network (CNN) to classify building images. We evaluated our method on a sample dataset of building images that we collected and building images from online sources. Training and validation accuracies were very high, 98.9 and 95.6 percentages respectively