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Found 7 papers, 2 papers with code
Fruit Quality and Defect Image Classification with Conditional GAN Data Augmentation
Contemporary Artificial Intelligence technologies allow for the employment of Computer Vision to discern good crops from bad, providing a step in the pipeline of selecting healthy fruit from undesirable fruit, such as those which are mouldy or gangrenous.
Chatbot Interaction with Artificial Intelligence: Human Data Augmentation with T5 and Language Transformer Ensemble for Text Classification
We find that all models are improved when training data is augmented by the T5 model, with an average increase of classification accuracy by 4. 01%.
Look and Listen: A Multi-modality Late Fusion Approach to Scene Classification for Autonomous Machines
The image and the audio datasets are first classified independently, using a fine-tuned VGG16 and an evolutionary optimised deep neural network, with accuracies of 89. 27% and 93. 72%, respectively.
LSTM and GPT-2 Synthetic Speech Transfer Learning for Speaker Recognition to Overcome Data Scarcity
In speech recognition problems, data scarcity often poses an issue due to the willingness of humans to provide large amounts of data for learning and classification.
On the effects of pseudorandom and quantum-random number generators in soft computing
In CIFAR-10, the QRNG outperforms PRNG by + 0. 92%.
On the Effects of Pseudo and Quantum Random Number Generators in Soft Computing
In 50 Dense Neural Networks (25 PRNG/25 QRNG), QRNG increases over PRNG for accent classification at +0. 1%, and QRNG exceeded PRNG for mental state EEG classification by +2. 82%.
A Deep Evolutionary Approach to Bioinspired Classifier Optimisation for Brain-Machine Interaction
This study suggests a new approach to EEG data classification by exploring the idea of using evolutionary computation to both select useful discriminative EEG features and optimise the topology of Artificial Neural Networks.
