Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

Browse

Filters

2014 - 20153

Settings

Author: search.filters.author.Nisha, K.K.

Search Results

Now showing 1 - 3 of 3
  • No Thumbnail Available
    Item
    An android GPS-based navigation application for blind
    (Association for Computing Machinery, 2014) Nisha, K.K.; Pruthvi, H.R.; Hadimani, S.N.; Guddeti, G.R.M.; Ashwin, T.S.; Domanal, S.G.
    Visual Impairment makes the person depend on another person for all his works and daily chores. Through the application proposed in this paper, we aim to eliminate this dependency of a visually impaired person when travelling from one place to another. The main goal is to provide information regarding the current location, how much distance and time is required to reach the destination as well as provide the user with the directions and turns to be taken while travelling by providing continuous audio feedback in his understandable language. © is held by the author/owner(s).
  • No Thumbnail Available
    Item
    An improved sentiment analysis of online movie reviews based on clustering for box-office prediction
    (Institute of Electrical and Electronics Engineers Inc., 2015) Patil, N.; Pruthvi, H.R.; Nisha, K.K.; Hadimani, N.H.
    With the rapid development of E-commerce, more online reviews for products and services are created, which form an important source of information for both sellers and customers. Research on sentiment and opinion mining for online review analysis has attracted increasingly more attention because such study helps leverage information from online reviews for potential economic impact. The paper discusses applying sentiment analysis and machine learning methods to study the relationship between the online reviews for a movie and the movies box office revenue performance. The paper shows that a simplified version of the sentiment-aware autoregressive model can produce very good accuracy for predicting the box office sale using online review data. Document level sentiment analysis is used which consists of Term Frequency (TF) and Inverse Document Frequency (IDF) values as features along with Fuzzy Clustering which results in positive and negative sentiments. This lead to the creation of a simpler model which could be more efficient to train and use. In addition, a classification model is created using Support Vector Machine (SVM) Classifier for predicting the trend of the box office revenue from the review sentiment. © 2015 IEEE.
  • No Thumbnail Available
    Item
    Feature engineering on forest cover type data with ensemble of decision trees
    (Institute of Electrical and Electronics Engineers Inc., 2015) Pruthvi, H.R.; Nisha, K.K.; Chandana, T.L.; Navami, K.; Mohan, R.
    The paper aims to determine the forest cover type of the dataset containing cartographic attributes evaluated over four wilderness areas of Roosevelt National Forest of Northern Colorado. The cover type data is provided by US Forest service inventory, while Geographic Information System (GIS) was used to derive cartographic attributes like elevation, slope, soil type etc. Dataset was analyzed, pre processed and feature engineering techniques were applied to derive relevant and non-redundant features. A comparative study of various decision tree algorithms namely, CART, C4.5, C5.0 was performed on the dataset. With the new dataset built by applying feature engineering techniques, Random Forest and C5.0 improved the accuracy by 9% compared to the raw dataset. © 2015 IEEE.