Dr. Krishnaraj Chadaga, Ph.D

About

I am Dr. Krishnaraj Chadaga. I am working as an Assistant Professor in the School of Computer Engineering, Manipal Institute of Technology.

Employment

• Manipal Academy of Higher Education Assistant Professor (Full time)
  2024 - Present
• Informatica Associate Software tester
  2020 - 2021
• Dell EMC Software Developement Intern
  2019 - 2020

Education

• Manipal Academy of Higher Education PhD
  2021 - 2024

Projects & Funding

Projects & funding information is unavailable.

Publications (26)

• Explainable machine learning models for predicting current and voltage in photovoltaic systems
  Energy Conversion and Management: X 2026 DOI: 10.1016/j.ecmx.2026.101627
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• DVTPRED: an explainable machine learning framework for the early prediction of deep vein thrombosis
  Journal of Intelligent Systems 2026 DOI: 10.1515/jisys-2025-0051
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• Machine Learning Techniques for the Detection of Melasma Disease: A Comprehensive Review
  2026 DOI: 10.1007/978-981-95-7289-2_9
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• Quantifying Resilience of CNN-Based Brain Tumor Classification Under FGSM, PGD, and BIM Attacks
  IEEE Access 2026 DOI: 10.1109/ACCESS.2026.3676776
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• An interpretable machine learning framework for pediatric appendicitis and peritonitis diagnosis
  Healthcare Analytics 2026 DOI: 10.1016/j.health.2026.100483
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• A unified model of explainable regression and AES-based data security for soybean yield prediction
  Smart Agricultural Technology 2025 DOI: 10.1016/j.atech.2025.101506
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• Explainable artificial intelligence-driven gestational diabetes mellitus prediction using clinical and laboratory markers
  Cogent Engineering 2024 DOI: 10.1080/23311916.2024.2330266
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• An Explainable Decision Support Framework for Differential Diagnosis Between Mild COVID-19 and Other Similar Influenzas
  IEEE Access 2024 DOI: 10.1109/ACCESS.2024.3405071
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• Classification of Surya Namaskar Yoga Asanas: A Sequential Combination of Predominant Poses for Physical and Mental Health
  IEEE Access 2024 DOI: 10.1109/ACCESS.2024.3429569
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• Empowering Glioma Prognosis With Transparent Machine Learning and Interpretative Insights Using Explainable AI
  IEEE Access 2024 DOI: 10.1109/ACCESS.2024.3370238
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