CSVC-Net: Code-Switched Voice Command Classification using Deep CNN-LSTM Network

Yasmeen, Arowa; Rahman, Fariha Ishrat; Ahmed, Sharmin; Kabir, Md. Hasanul

doi:10.1109/icievicivpr52578.2021.9564183

Cited by 8 publications

(1 citation statement)

References 18 publications

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“…Conventional machine learning algorithms rely on training and test data from the same feature space [30]. Yet, developments in deep neural networks have made it possible to reuse architectures trained to extract features from training data of one domain to extract features from another domain.…”

Section: Feature Extractionmentioning

confidence: 99%

An Efficient Transfer Learning-based Approach for Apple Leaf Disease Classification

Ashmafee

Ahmed

et al. 2023

2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)

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Correct identification and categorization of plant diseases are crucial for ensuring the safety of the global food supply and the overall financial success of stakeholders. In this regard, a wide range of solutions has been made available by introducing deep learning-based classification systems for different staple crops. Despite being one of the most important commercial crops in many parts of the globe, research proposing a smart solution for automatically classifying apple leaf diseases remains relatively unexplored. This study presents a technique for identifying apple leaf diseases based on transfer learning. The system extracts features using a pretrained EfficientNetV2S architecture and passes to a classifier block for effective prediction. The class imbalance issues are tackled by utilizing runtime data augmentation. The effect of various hyperparameters, such as input resolution, learning rate, number of epochs, etc., has been investigated carefully. The competence of the proposed pipeline has been evaluated on the apple leaf disease subset from the publicly available 'PlantVillage' dataset, where it achieved an accuracy of 99.21%, outperforming the existing works.

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Section: Feature Extractionmentioning

confidence: 99%