Krishnaraj Chadaga

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Dept. of Computer Science and Engineering, Manipal Institute of Technology, Manipal University, Manipal, Karnataka State. Pin 576104
India

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Research activity per year

Education

B.E., M.Tech., Ph.D

Research Interests

Area of Interest

Artificial Intelligence, Machine Learning, Data Science, Health Informatics and Casual Inference.

Area of Expertise

Artificial Intelligence, Machine Learning, Data Science, Health Informatics and Casual Inference.

Area of Research

Artificial Intelligence, Machine Learning, Data Science, Health Informatics and Casual Inference.

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Dive into the research topics where Krishnaraj Chadaga is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

1 Similar Profiles

60 Article
15 Conference contribution
4 Review article
1 Conference article

AlzStack: Forecasting early-onset Alzheimer's with an explainable AI system using multiple data balancing techniques
Modali, V. A., Pavanya, M., Arjunan, R. V., Cenitta, D., Sampathila, N., Kamath, R. & Chadaga, K., 06-2026, In: Global Epidemiology. 11, 100235.
Research output: Contribution to journal › Article › peer-review

Open Access
Countering synthetic realities: Advances, challenges, and future directions in deepfake image detection
Prabhu, O., Naik, S. S., BK, P., Prabhu, S. & Chadaga, K., 06-2026, In: Systems and Soft Computing. 8, 200476.
Research output: Contribution to journal › Review article › peer-review

Open Access
Explainable machine learning models for predicting current and voltage in photovoltaic systems
Dinakar, A., Cenitta, D., Arjunan, R. V., Bhandage, V. & Chadaga, K., 05-2026, In: Energy Conversion and Management: X. 30, 101627.
Research output: Contribution to journal › Article › peer-review

Open Access
Proposed dense variational autoencoder model integrated with contrastive learning for foot ulcer classification
Shandilya, G., Gupta, S., Gupta, D., Juneja, S., Chadaga, K., Nauman, A. & Al-Masri, A. A., 12-2026, In: Scientific Reports. 16, 1, 4171.
Research output: Contribution to journal › Article › peer-review

Open Access
Sickle cell disease detection in low-resource conditions using transfer-learning and contrastive-learning coupled with XAI
Patel, J., Muralikrishna, H., Chadaga, K., Thalengala, A. & Sampathila, N., 12-2026, In: Scientific Reports. 16, 1, 6104.
Research output: Contribution to journal › Article › peer-review

Open Access

Krishnaraj Chadaga

Personal profile

Education

Research Interests

Fingerprint

Collaborations and top research areas from the last five years

Dive into details

AlzStack: Forecasting early-onset Alzheimer's with an explainable AI system using multiple data balancing techniques

Countering synthetic realities: Advances, challenges, and future directions in deepfake image detection

Explainable machine learning models for predicting current and voltage in photovoltaic systems

Proposed dense variational autoencoder model integrated with contrastive learning for foot ulcer classification

Sickle cell disease detection in low-resource conditions using transfer-learning and contrastive-learning coupled with XAI

Krishnaraj Chadaga

Personal profile

Education

Research Interests

Fingerprint

Collaborations and top research areas from the last five years

Research output

AlzStack: Forecasting early-onset Alzheimer's with an explainable AI system using multiple data balancing techniques

Countering synthetic realities: Advances, challenges, and future directions in deepfake image detection

Explainable machine learning models for predicting current and voltage in photovoltaic systems

Proposed dense variational autoencoder model integrated with contrastive learning for foot ulcer classification

Sickle cell disease detection in low-resource conditions using transfer-learning and contrastive-learning coupled with XAI