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All Journal Bulletin of Electrical Engineering and Informatics
Abhishek Kumar
Chitkara University
Electrical & Electronics Engineering

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Hyper parameter tuning based gradient boosting algorithm for detection of diabetic retinopathy: an analytical review Parul Datta; Prasenjit Das; Abhishek Kumar
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3559

Abstract

The pipelines of approaches for classifying diabetic retinopathy were examined in this study. The effort entails developing appropriate transformations and estimators that can be used to automate the process of diabetic retinopathy detection. The segmentation of the blood vessels was done using a hybrid algorithm that uses Otsu and median filter to get the region of interest. Further, ten classifiers were investigated in order to develop an automated pipeline for diabetic retinopathy detection. The ten classifiers were reviewed based on earlier work in a similar setting and on an exploration of new ways for identifying diabetic retinopathy. To overcome the challenge of low volume of dataset, data argumentation was done so that a generic classifier can be configured. Extensive hyper parameter tuning was performed, and it was shown that the gradient boosting approach is the most stable technique for detecting diabetic retinopathy. This was validated using a 10K fold cross validation method on many metrics (accuracy, recall, precision, and v-measure score). Hyper-parameter tuning helped in achieving accuracy of 0.96.
Twitter sentimental analysis from time series facts: the implementation of enhanced support vector machine Abhishek Kumar; Vishal Dutt; Vicente García-Díaz; Sushil Kumar Narang
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.3078

Abstract

Sentiment analysis through textual data mining is an indispensable system used to extract the contextual social information from the texts submitted by the intended users. Now days, world wide web is playing a vital source of textual content being shared in different communities by the people sharing their own sentiments through the websites or web blogs. Sentiment analysis has become a vital field of study since based on the extracted expressions, individuals or the businesses can access or update their reviews and take significant decisions. Sentimental mining is typically used to classify these reviews depending on its assessment as whether these reviews come out to be neutral, positive or negative. In our study, we have boosted feature selection technique with strong feature normalization for classifying the sentiments into negative, positive or neutral. Afterwards, support vector machine (SVM) classifier powered with radial basis kernel with adjusted hyper plane parameters, was employed to categorize reviews. Grid search with cross validation as well as logarithmic scale were employed for optimal values of hyper parameters. The classification results of this proposed system provides optimal results when compared to other state of art classification methods.
Co-Authors Parul Datta Prasenjit Das Sushil Kumar Narang Vicente García-Díaz Vishal Dutt