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Implementation of convolutional neural network (CNN) Architecture in classifying tomato ripeness level / Mark Angelo A. Cabacungan, Ian P. Caballero, Kimberly P. Caluya, Princess Mae G. Delos Reyes, Vincent O. Moratal, Donna Faye D. Semana, Eula Kate A. Ubay

TextPublication details: City of Batac : MMSU, 2024Description: xxvii, 105 leaves : 29 cm

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Holdings
Item type	Home library	Shelving location	Call number	Status	Notes	Date due	Barcode
Thesis/Dissertation	MMSU Main Library	Theses and Dissertation Section		Available	Room Use Only		UTHESIS-6927

Previous	No cover image available	No cover image available	No cover image available	No cover image available	No cover image available	No cover image available	No cover image available	Next
Previous	Enhancement of web-based cashless canteen ordering system using local network at Bingao National High School /	Development of Iot-based and controlled environment nursery for improved germination and growth of garlic /	Development of an IOT-enhanced fire safety system with cloud monitoring and location profiling /	Implementation of convolutional neural network (CNN) Architecture in classifying tomato ripeness level /	Development of a gait assessment device using deep convolutional neural network and pressure distribution insole /	Streamlining of document management system for Mariano Marcos State University- College of Engineering /	Enhancing precision Agriculture at MMSU i4.0: comparative analysis of machine learning based decision tree classifier and hybrid genetic algorithm with simulated annealing for crop yield prediction /	Next

UTHESIS (Bachelor of Science in Computer Engineering)

Bibliography: leaves 89-94

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