EEG-based emotion recognition is an important task in affective computing; however, conventional machine learning methods are limited in reliably extracting emotional features because brain signals are highly non-stationary and noisy. To overcome the limitation, we present a deep learning framework that integrates CWT-...
K. Archana, G. Thirupati· International Journal of Sci...· 0 citations
ABSTRACT
liver cirrhosis staging is crucial to enhance prognosis among patients and optimize the therapeutic approach. An attention-based deep learning framework for automated cirrhosis stage classification using routinely collected clinical and biochemical features As solution 1: A dataset of 25,000 patient records w...
Patti Kavya, G. Thirupati· International Scientific Jou...· 0 citations
Breast cancer is a leading cause of cancer morbidity and mortality among women globally, emphasizing the need for accurate and timely diagnostic methods. A systematic but innovative two phases transfer learning based deep learning classification framework is developed using popular EfficientNetB7 architecture architect...
M. Kavya, G. Thirupati· International Journal of Sci...· 0 citations
The new high-water mark physiological plant disease diagnosis that is established here is an important step toward ultimately real-world applicability as“adaptive components of precision agriculture to monitor crop health and protect yield.
Rebally.Vijay Kumar, G. Thirupati· International Journal of Sci...· 0 citations
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