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Model for Classification of Poems in Hindi Language Based on Ras
The developed model will classify poem into Shringar, Hasya, Adbhuta, Shanta, Raudra, Veera, Karuna, Bhayanaka, Vibhasta rasas, which will use mix of part-of-speech-based feature and emotional…
Multi - Class Document Classification: Effective and Systematized Method to Categorize Documents
This research work is combining approach of Natural Language Processing and Machine Learning for content-based classification of documents that is successful in classifying documents with more than 70% of accuracy for major Indian Languages and more than 80% accuracy for English Language.
Emotion Classification with Reduced Feature Set SGDClassifier, Random Forest and Performance Tuning
This research work is classifying emotions written in Hindi in form of poem with 4 categories namely Karuna, Shanta, Shringar and Veera, the model is build with Random Forest, SGDClassifier and was trained with 134 poetries and tested with 46 Poetries for both types of features.
Data Classification with k-fold Cross Validation and Holdout Accuracy Estimation Methods with 5 Different Machine Learning Techniques
The result of the experiment shows that the results of SVM, NB and random forest methods are better as compared to DTT and K-NN for used data set available in this experiment.
Significance of stop word elimination in meta search engine
The result shows that retrieved links in new meta search engine, with and without stop words, is more than 90% same where as by existingmeta search engine like dogpile differ with more the 90%.
Automatic Multiclass Document Classification of Hindi Poems using Machine Learning Techniques
Experiments shows that Naïve Bayes with 64% accuracy and Random Forest with 56% are performing better as compared to other algorithms for Hindi Poem Classification.
Search Engine Optimization (SEO) using HTML Meta-Tags
Search Engines are information providers; they are also referred as crawlers; there are different strategies for listings, either organic or PPC (Pay per Click).
Issues in Real Time Knowledge Discovery through Data Stream Mining
This paper represents the current issues with this growing technique of data stream mining, used to find the hidden pattern from online records of business transaction and many fields where data are frequently changes.
A Study of Current State of Work done for Classification in Indian Languages
The purpose of this paper is to study current work done in various Indian languages, and analyze the current situation and future scope to research in classification and related work on Indian languages.
Comparative Study of Different Classification Algorithms for Stream Data Mining Using MOA