Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
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This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Hemalatha, S.
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Computer Science & IT Engineering

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A relation network for plant disease detection based on fewshot learning Hemalatha, S.; Jayachandran, Jai Jaganath Babu
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4499-4508

Abstract

Accurate and timely disease detection remains a critical challenge in plant health management. Conventional methods often struggle to effectively differentiate between healthy and diseased plants, leading to compromised agricultural productivity and food security. In response to this pressing issue, this paper presents an innovative solution in the form of a novel few-shot learning (FSL) classifier, based on relation network (RN) specifically designed for precise plant disease detection from limited image samples. Leveraging inherent relationships between samples, the proposed relation network for plant disease classification (RN-PDC) enhances the detection performance by capturing intricate patterns within the data. Through comprehensive evaluation on a public image data subset, RN-PDC achieves exceptional detection accuracies of 0.9984 and 0.9967 in binary and multiclass classifications, respectively. This advancement holds great promise for revolutionizing disease diagnosis in the field of plant health, ultimately fostering more productive and sustainable agricultural practices. 
Fake review detection using enhanced ensemble support vector machine system on e-commerce platform Joseph, Seenia; Hemalatha, S.
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp478-485

Abstract

Due to the quick growth of online marketing transactions, including buying and selling, fake reviews are created to promote the product market and mislead new customers. E-commerce customers can post reviews and comments on the goods or services they obtained. Before making a purchase, new customers frequently read the feedback and comments posted on the website. Nowadays customers find it very difficult to identify whether the reviews are fake or not, but doing so is essential. So, it's very crucial to develop an online spam detection system to help both consumers and producers in their decision-making. The reviewer's behaviour and important review characteristics can help you identify fake reviews. The importance of this study is to develop a fake review detection system on e-commerce platforms using an enhanced ensemble support vector machine system in which the Euclidean distance is replaced with the Mahalanobis distance metric. Review texts collected from Amazon and Yelp were given as input data sets into the constructed model and classified as fake or real.
Co-Authors Jayachandran, Jai Jaganath Babu Joseph, Seenia