Analysis and design of optimal deep neural network model for image recognition using hybrid cuckoo search with self-adaptive particle swarm intelligence

Analysis and design of optimal deep neural network model for image recognition using hybrid cuckoo search with self-adaptive particle swarm intelligence

연구 분야: Artificial Intelligence

학회: Signal, Image and Video Processing

Image recognition involves identifying objects in digital photos via computer algorithms and machine learning. Integrating cooperative behaviours and adaptive dynamics, bio-inspired swarm intelligence optimizes multiple algorithms for efficient solutions to complex challenges. CSO and SaPSO is combined in this framework for enhanced optimization efficiency, promising faster convergence and better solutions. Employing Deep Convolution Generative Adversarial Networks (DC-GAN) for classification, the study achieves an outstanding 99.5% accuracy using Python. Through feature extraction, accuracy reaches 99%, indicating precise classification with minimal error. Key terms: DNN, Image Recognition, Bio-Inspired Swarm Intelligence, Hybrid Cuckoo Search, Self-Adaptive Particle Swarm Intelligence.