Research Journal of Pharmacy and Technology

K. Meenakshi ¹, M. Safa ¹, T. Karthick ¹, N. Sivaranjani ¹

Affiliations
1 Department of Information Technology, SRM University, Chennai, IN

Abstract

Machine learning is a kind of data analysis technique which provides a flexible way of learning information about the data, so that necessary action can be predicted accurately. Machine learning techniques provide the way of analyzing and predicting the valuable information from the available data, so that further actions can be carried out accurately. There is several kind of machine learning approaches are available based on their behavior and working procedure. In this analysis work different kind of methodologies are discussed which are used to learn the knowledge about the program. The various machine learning approaches differs in their working procedure and inputs and output processed by them. Various applications adapted the machine learning approaches for learning the information which are discussed in detail in this paper. The analysis work provides the detailed view of working procedure of different research works implemented by various authors. It also gives overview of merits and demerits of different machine learning techniques that are proposed earlier. The main goal of this analysis work is to identify the better machine learning approach which can lead to accurate learning with less false positive rate. The analysis of the work concluded with the performance results of different approaches that carried down and shows better approach which can lead to more accurate learning of health care data.

Keywords

Machine Learning, Classification, Clustering, Predicting Pattern, Accuracy.

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Sundara Kanchana ¹, K. Meenakshi ¹, Velappa Ganapathy ¹

Affiliations
1 School of Computing, SRM University, Kancheepuram-603203, Tamil Nadu, IN

Abstract

Genre classification of Tamil songs is done based on mood, emotion etc. of the listener. The musical genre classification is based on three levels as base, mood and style. Our proposed methodology is comparison of genre based Tamil songs classification using Term Frequency and Inverse Document Frequency (tf-idf). So for this methodology has been applied for English and Korean songs only. In this paper, we are classifying the Tamil songs using the method based on tf-idf scores. The classifier establishes a relation between the features of the training samples and related categories. The frequency of word usage is identified by term frequency and inverse document frequency. Support Vector Machine (SVM) algorithm and Naïve Bayes algorithm (NB) are used in Weka classification tool for analysis. We have compared the experimental results of both the algorithms in musical genre classification. From the experimental values obtained using various parameters, we have shown that the Naïve Bayes algorithm has classified the genre marginally better than Support Vector Machine algorithm. We have considered 2000 Tamil songs for testing the genre classification. At present we have considered two genres for classification and the same classification can be extended to other genres in future.

Keywords

tf–idf, Genre, Lyrics, Tamil Songs, Base, Mood, Style.

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