Prashant Bhat

Work place: Department of Computer Science, School of Computational Sciences and IT, Garden City University, Bengaluru, India



Research Interests: Computer systems and computational processes, Data Mining, Multimedia Information System, Data Structures and Algorithms


Dr. Prashant Bhat is currently working as Assistant Professor in the School of Computational Sciences and IT. He received Ph.D. in Computer Science from RCU Belagavi, M.Sc. (Comp Sci) from KUD and B.Sc. (Comp Sci) from KUD in 2017, 2012 and 2010 respectively. He has more than 6 years of experience in teaching and research. He is specialized in the area of Algorithms, Finite Automata and Theory of Computation, Big Data, Data Science and Computer Networks. His research area is Web Mining, Multimedia Mining, Predictive Analytics and Data Mining. He published more than 52 research papers in various peer reviewed UGC recognized International Journals and conferences. He is a member of professional bodies like International Association of Engineers in Computer Science and Data Mining, Member of Editorial/Review Board of International Journal of Application or Innovation in Engineering and Management and Universal Journal of Mathematics.

Author Articles
Metadata based Classification Techniques for Knowledge Discovery from Facebook Multimedia Database

By Prashant Bhat Pradnya Malaganve

DOI:, Pub. Date: 8 Aug. 2021

Classification is a parlance of Data Mining to genre data of different kinds in particular classes. As we observe, social media is an immense manifesto that allows billions of people share their thoughts, updates and multimedia information as status, photo, video, link, audio and graphics. Because of this flexibility cloud has enormous data. Most of the times, this data is much complicated to retrieve and to understand. And the data may contain lot of noise and at most the data will be incomplete. To make this complication easier, the data existed on the cloud has to be classified with labels which is viable through data mining Classification techniques. In the present work, we have considered Facebook dataset which holds meta data of cosmetic company’s Facebook page. 19 different Meta Data are used as main attributes. Out of those, Meta Data ‘Type’ is concentrated for Classification. Meta data ‘Type’ is classified into four different classes such as link, status, photo and video. We have used two favored Classifiers of Data Mining that are, Bayes Classifier and Decision Tree Classifier. Data Mining Classifiers contain several classification algorithms. Few algorithms from Bayes and Decision Tree have been chosen for the experiment and explained in detail in the present work. Percentage split method is used to split the dataset as training and testing data which helps in calculating the Accuracy level of Classification and to form confusion matrix. The Accuracy results, kappa statistics, root mean squared error, relative absolute error, root relative squared error and confusion matrix of all the algorithms are compared, studied and analyzed in depth to produce the best Classifier which can label the company’s Facebook data into appropriate classes thus Knowledge Discovery is the ultimate goal of this experiment.

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Educational Data Mining: RT and RF Classification Models for Higher Education Professional Courses

By Siddu P. Algur Prashant Bhat Narasimha H Ayachit

DOI:, Pub. Date: 8 Mar. 2016

Computer applications and business administrations have gained significant importance in higher education. The type of education, students get in these areas depend on the geo-economical and the social demography. The choice of a institution in these area of higher education dependent on several factors like economic condition of students, geographical area of the institution, quality of educational organizations etc. To have a strategic approach for the development of importing knowledge in this area requires understanding the behavior aspect of these parameters. The scientific understanding of these can be had from obtaining patterns or recognizing the attribute behavior from previous academic years. Further, applying data mining tool to the previous data on the attributes identified will throw better light on the behavioral aspects of the identified patterns. In this paper, an attempt has been made to use of some techniques of education data mining on the dataset of MBA and MCA admission for the academic year 2014-15. The paper discusses the result obtained by applying RF and RT techniques. The results are analyzed for the knowledge discovery and are presented.

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Web Video Object Mining: Expectation Maximization and Density Based Clustering of Web Video Metadata Objects

By Siddu P. Algur Prashant Bhat

DOI:, Pub. Date: 8 Jan. 2016

Nowadays YouTube becoming most popular video sharing website, and is established in 2005. The YouTube official website is providing different categories videos including Science and Technology, Films and Animation, News and politics, Movies, Comedy, Sports, Music etc. Each video hosted in website such as YouTube have its own identity and features. The identity and features of each video can be described by web video metadata objects such as- URL of each video, category, length of the video, rating information, view counts, comment information, key words etc. Using extracted web video metadata objects, we present an in-depth and systematic clustering study on the metadata objects of YouTube videos using Expectation Maximization (EM) and Density Based (DB) clustering approach. Distinct web video metadata object clusters are formed based on different category of web videos. The resultant clusters are analyzed in depth as a step in the KDD process.

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