Machine learning is penetrating most of the classification and recognition tasks performed by a computer. This paper proposes the classification of flower images using a powerful artificial intelligence tool, convolutional neural networks (CNN). A flower image database with 9500 images is considered for the experimentation. The entire database is sub categorized into 4. The CNN training is initiated in five batches and the testing is carried out on all the for datasets. Different CNN architectures were designed and tested with our flower image data to obtain better accuracy in recognition. Various pooling schemes were implemented to improve the classification rates. We achieved 97.78% recognition rate compared to other classifier models reported on the same dataset.
Multi-Level Inverter (MLI) has gained significant significance in medium power and medium voltage AC drive applications. Various topologies have been developed in the design of MLIs. Hybrid topologies are emerging to minimize component requirements and reduce switching losses. This paper introduces a comprehensive analysis and functioning of the asymmetrical seven level multi-level inverter topology. The modeling process is executed using the MATLAB Simulink environment.
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