| Abstrakt: |
The internet of things (IoT) is a rapidly expanding network of smart digital devices that can communicate with one another and be controlled remotely over the internet. Moreover, IoT devices are cheap and can be used to control and monitor activities remotely. Due to this reason, IoT is widely used in the applications of a smart city. Moreover, the smart devices that are used in IoT-based smart city applications are used to gather information from devices, humans, and other sources for analyzing purposes. Hence, it is crucial to conduct the face recognition process to ensure the safety of the city. Several works were conducted by the researchers to recognize the face accurately. Typically, the effectiveness of achieving face recognition is still an intricate one. To tackle those issues, we have proposed a novel condition convolutional neural network (condition-CNN)-based bee foraging learning (BFL)-based particle swarm optimization (PSO) algorithm (CCNNBFLPSO). To recognize the facial images from the face image datasets, the proposed CCNNBFLPSO model is used. To ensure the prediction accuracy condition, CNN uses the conditional probability weight matrix (CPWM) to estimate the higher and lower class level of image features. Meanwhile, the learning of CPWM can be performed by utilizing the adopted BPL-PSO approach. For experimental purposes, we have taken three datasets namely the CVL face database, the MUCT database, and the CMU multi-PIE face database. The training time and the training accuracy are analyzed for all the three datasets, and comparative studies are performed with state-of-art works such as LBPH, FoL TDL, and TPS approaches. The training and validation loss functions are analyzed with baseline CNNs, B-CNN, and condition-CNN. The proposed approach accomplishes better face recognition accuracy and F1-score of about 99.9% and 99.9%, respectively. [ABSTRACT FROM AUTHOR] |
