Poulami Dash ¹, V. Vaidhehi ¹

Affiliations
1 Department of Computer Science, Christ University, Hosur Road, Bengaluru – 560029, Karnataka, IN

Abstract

Objective: Academic advising requires a lot of expertise, time and responsibility. To assist the human advisors in an efficient way, upcoming of computerized advising system is a necessity Methods/Statistical Analysis: Course Advisory System has been implemented using WEKA tool to recommend subjects for 8th class students of ICSE board. Machine learning algorithms – Naïve Bayes, J48, PART, Random Forest and KNN have been modeled and tested on the data set. The performance of each classifier has been compared and analyzed Findings: It is inferred that no advising system has been developed to assist school students in subject selection. Research work based on Indian students’ requirements is minimal. Research work based on students’ data caters more on binary class problems whereas the addressing of multi class problems is minimal. This work proposes an advising system for the school students of 8th standard of ICSE board to choose their electives. Application/Improvements: This work focuses on Indian educational system of school students. The approach takes care of the school students which will add its advantage to the existing systems. As school students are more vulnerable by taking wrong decisions, the course Advisory system will assist them in analyzing their academic history and help them choose their electives wisely. The classification algorithms might give a better accuracy with increasing instances. The Course advisory system can be enhanced using ensemble approach.

Keywords

Course Advisory System, Feature Selection Algorithms, ICSE School Students, Machine Learning Algorithms, Subject Selection

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Varun Teja Gundabathula ¹, V. Vaidhehi ¹

Affiliations
1 Department of Computer Science, Christ University, Bengaluru - 560029, Karnataka, IN

Abstract

Objectives: The study presents best machine learning models that can be employed on terrorism related data to predict the most accurate terrorist group responsible for an attack based on historic data. Methods: This paper analyses terrorism challenges faced in India by modelling the behaviour of terrorist groups using famous machine learning algorithms like J48, IBK, Naive Bayes and ensemble approach using vote. Findings: The results of the evaluation models show the accuracy percentage of various models employed and their relevance to the dataset. It was found that when the classification models are created on a data that has class imbalance problem the percentage of correctly classified instances will be very less. The paper establishes that sampling plays a main role in determining the accuracy percentage of classifier models and gives better results. The study also shows the accuracy percentage of correctly classified instances for various algorithms and shows the ideal one for the dataset. Application/Improvement: The defined model is a new approach to classify data that has major class imbalance problem by using sampling techniques like oversampling, under sampling and ensemble models.

Keywords

Classification, Data Mining, Ensemble, Global Terrorism Database

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