An Optimized E-Lecture Video Retrieval based on Machine Learning Classification
Lakshmi Haritha Medida1, Kasarapu Ramani2

1Lakshmi Haritha Medida*, Research Scholar, CSE, JNTUA, Ananthapuramu, India.
2Kasarapu Ramani, Department of Intelligent Computing Research Centre,  IT, Sree Vidyanikethan Engg College, Tirupati, India.
Manuscript received on July 26, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 4820-4827 | Volume-8 Issue-6, August 2019. | Retrieval Number: F9114088619/2019©BEIESP | DOI: 10.35940/ijeat.F9114.088619
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Abstract: The advent of internet has lead to colossal development of e-learning frameworks. The efficiency of such systems however relies on the effectiveness and fast content based retrieval approaches. This paper presents a methodology for efficient search and retrieval of lecture videos based on Machine Learning (ML) text classification algorithm. The text transcript is generated exclusively from the audio content extracted from the video lectures. This content is utilized for the summary and keyword extraction which is used for training the ML text classification model. An optimized search is achieved based on the trained ML model. The performance of the system is compared by training the system using Naive Bayes, Support Vector Machine and Logistic Regression algorithms. Performance evaluation was done by precision, recall, F-score and accuracy of the search for each of the classifiers. It is observed that the system trained on Naive Bayes classification algorithm achieved better performance both in terms of time and also with respect to relevancy of the search results.
Keywords: Logistic Regression, Machine Learning, Naive Bayes, Support Vector Machine, Text Classification, Video retrieval.