Article Per Year (5 Year)
p-Index From 2021 - 2026
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All Journal Indonesian Journal of Electrical Engineering and Computer Science International Journal of Applied and Scientific Research (IJASR)
singh, Vijay
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Religion Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Social Sciences

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CriteriaChecker: a knowledge graph approach to enhance integrity and ethics in academic publication Sharma, Garima; Tripathi, Vikas; Singh, Vijay
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp973-986

Abstract

Academic writing is an integral part of scientific communities. This is a formal style of writing used by researchers and scholars to communicate critical analysis and evidence based arguments. This work showcased a graph-based approach for scraping, extracting, representing and evaluating the available academic writing forgery detection criteria and further enhancing the model by proposing a set of new age criteria. The proposed work is based on knowledge graphs and graph analytics capable of selecting subset of 16 criteria from the available superset of a cent of criterias provided by Bealls, Cabells, Shreshtha, and Think.Check.Submit, Scopus, and other relevant authors. The process for detecting the influencial parameters consists of 04 phases: dataset preparation, knowledge graph representation and making inferences through graph analytics and evaluation of results. The experimental results are then compared to the retraction database that consisting of information about retracted articles. The work enables the construction of an experiential knowledge graph that effectively identifies influential criteria, enhancing this list by incorporating new age criteria into current influential set and concluding in result by successfully detecting the academic predatory behavior.
Addressing Environmental Challenges through Artificial Intelligence (AI)-Powered Natural Disaster Management Singh, Vijay; Agnihotri, Aastha
International Journal of Applied and Scientific Research Vol. 2 No. 5 (2024): May 2024
Publisher : MultiTech Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59890/ijasr.v2i5.1413

Abstract

Recent advancements in AI offer promising tools for enhancing disaster management which is crucial given the increasing frequency of climate-related disasters. The study aims to evaluate how AI technologies can be utilized to improve disaster preparedness, response, and recovery efforts, thus aiding in environmental resilience and sustainability. This paper examines the intersection of artificial intelligence (AI) and environmental sustainability, with a focus on the role of AI in managing natural disasters. By reviewing secondary data and existing research, the paper explores various AI applications such as predictive modeling, real-time monitoring, and decision support systems. The analysis reveals that AI can significantly enhance early warning systems, optimize the allocation of resources, and ensure timely interventions during emergencies. The findings highlight the importance of integrating AI technologies into disaster management strategies to foster environmental sustainability amidst growing climate-related risks. The paper also discusses the challenges and ethical considerations of implementing AI in this field and underscores the need for interdisciplinary collaboration and stakeholder engagement for successful implementation.
Co-Authors Agnihotri, Aastha Sharma, Garima Tripathi, Vikas