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International Journal of Systems Assurance Engineering and Management
JournalCitation impact & published research
International Journal of Systems Assurance Engineering and Management is a journal indexed in ScholarIQ from OpenAlex. ScholarIQ records 3,289 works, 30,426 citations, an h-index of 58 and an APC (USD) of 2,990.
3,289
Works
30,426
Citations
58
h-index
2,990
APC (USD)
IDs:OpenAlex
What are the most-cited papers on International Journal of Systems Assurance Engineering and Management?
ScholarIQmost cited works
Software reliability prediction using machine learning techniques
Arunima Jaiswal, Ruchika Malhotra
International Journal of Systems Assurance Engineering and Management. 201648 Citations
Improved face recognition method using SVM-MRF with KTBD based KCM segmentation approach
Rangayya, Virupakshappa Virupakshappa, Nagabhushan Patil
International Journal of Systems Assurance Engineering and Management. 202226 Citations
An efficient parameter estimation of software reliability growth models using gravitational search algorithm
Ankur Choudhary, Anurag Singh Baghel, Om Prakash Sangwan
International Journal of Systems Assurance Engineering and Management. 201618 Citations
Modified Otsu thresholding based level set and local directional ternary pattern technique for liver tumor segmentation
Deepak S. Uplaonkar, Virupakshappa Virupakshappa, Nagabhushan Patil
International Journal of Systems Assurance Engineering and Management. 202218 Citations
Wafer defect identification with optimal hyper-parameter tuning of support vector machine using the deep feature of ResNet 101
Santi Kumari Behera, Shishir Dash, Rajat Amat, Prabira Kumar Sethy
International Journal of Systems Assurance Engineering and Management. 202313 Citations
Related on ScholarIQ
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Modified Otsu thresholding based level set and local directional ternary pattern technique for liver tumor segmentation
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Wafer defect identification with optimal hyper-parameter tuning of support vector machine using the deep feature of ResNet 101
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Imbalcbl: addressing deep learning challenges with small and imbalanced datasets
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