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All Journal APTIKOM Journal on Computer Science and Information Technologies (CSIT) Wallacea Plant Protection Journal
Singh, Sandeep Kumar
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Education Electrical & Electronics Engineering Energy Materials Science & Nanotechnology

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SURVEY BASED CLASSIFICATION OF BUG TRIAGE APPROACHES Yadav, Asmita; Singh, Sandeep Kumar
APTIKOM Journal on Computer Science and Information Technologies Vol 1 No 1 (2016): APTIKOM Journal on Computer Science and Information Technologies (CSIT)
Publisher : APTIKOM Publisher

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Abstract

This paper presents a comprehensive survey of bug triaging approaches in three classes namely machine learning based, meta-data based and profile based. All approaches under three categories are critically compared and some potential future directions and challenges are reported. Findings from the survey show that there is a lot of scope to work in cold-start problem, developer- profiling, load balancing, and reopened bug analysis.
DenseNet121 based pest identification in plants Negi, Nisha; Singh, Sandeep Kumar
Wallacea Plant Protection Journal Vol. 1 No. 2 (2025)
Publisher : Department of Plant Pest and Diseases, Faculty of Agriculture, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64128/wppj.v1i2.46088

Abstract

Smart agriculture has benefited greatly from the widespread use of deep learning, which has proven critical to the industry. Reliability of data annotation and poor data quality, on the other hand, will severely limit the performance of intelligent applications because deep learning models are limited by these factors. We approaches, distance-entropy to distinguish the good and bad data from the perspective of information. DenseNet-121 was used as the backbone network and the IP06 dataset was used in trials. The findings highlight the frequency of duplicate data by demonstrating that almost 50% of the dataset has sufficient redundancy to produce test accuracy scores that are comparable. In addition, a thorough examination of representative samples resulted in the development of recommendations for enhancing dataset efficiency. These recommendations provide a useful road map for data-driven smart agriculture research, advancing knowledge and the use of data to advance agricultural innovation and sustainability.
Co-Authors Negi, Nisha Yadav, Asmita