An emotion prediction model using 3-layer CNN plus AFME algorithm on speech and image data detects seven basic emotions and sarcasm at 85-96% accuracy, addressing cultural challenges in Black African conversational AI.
Real Time Emotion Detection of Humans Using Mini -Xception Algorithm,
2 Pith papers cite this work. Polarity classification is still indexing.
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Empirical comparison shows APPLE, FedGC, and FedProto outperform other PFL algorithms on MNIST, SignMNIST, and Digit5 using accuracy, precision, recall, and F1 score.
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Evaluation of Conversational Agents: Understanding Culture, Context and Environment in Emotion Detection
An emotion prediction model using 3-layer CNN plus AFME algorithm on speech and image data detects seven basic emotions and sarcasm at 85-96% accuracy, addressing cultural challenges in Black African conversational AI.
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Pattern Recognition Tasks with Personalized Federated Learning
Empirical comparison shows APPLE, FedGC, and FedProto outperform other PFL algorithms on MNIST, SignMNIST, and Digit5 using accuracy, precision, recall, and F1 score.