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Anita Kushwaha
Birla Institute of Technology

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A Graph Based Approach to Identify Objects using Identifying Attribute Anita Kushwaha; R.S. Pandey
Indonesian Journal of Electrical Engineering and Computer Science Vol 6, No 2: May 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v6.i2.pp438-446

Abstract

We have proposed a method to identify objects in database schema using association degree with other objects. We have also used identifying attribute of associations in graph to specify a unique path to resolve ambiguity of Fuzzy Object Functional Dependencies. Recently Fuzzy Concepts were used in Object Oriented Data Models. The Object Identifier allows distinguishing between similar objects. Functional Dependencies play a dominant role to uniquely identify objects. Moreover object identification has now become a modeling concept rather than database concept so starting a search for objects with a set of values is possible. We have also investigated the presence of identifying attributes in fuzzy object schema and its implications.
Imbalanced dataset classification using fuzzy ARTMAP and computational intelligence techniques Anita Kushwaha; Ravi Shanker Pandey
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp909-916

Abstract

Recently, fuzzy adaptive resonance theory mapping (ARTMAP) neural networks are applied to solving complex problems due to their plasticity-stability capability and resonance property. An imbalanced dataset occurs when there is the presence of one class containing a greater number of instances than other classes. It is skewed representation of data. Many standard algorithms have failed in mitigating imbalanced dataset problems. There are four paradigms used-data level, algorithm level, cost-sensitive, and ensemble method in solving imbalanced dataset problems. Here we put forward a method to solve the imbalanced dataset problem by a brain-neuron framework and an ensemble of a special type of artificial neural network (ANN) called fuzzy ARTMAP thereafter we applied a clustering algorithm known as fuzzy C-means clustering to handle missing value and also propose to make fuzzy ARTMAP cost-sensitive. Results indicate 100% accuracy in classification.
Co-Authors R.S. Pandey Ravi Shanker Pandey