# Deep Machine Learning of MobileNet, Efficient, and Inception Models
###### Abstract  
Today, specific convolution neural network (CNN) models assigned to specific tasks are often used. In this article, the authors explored three models: MobileNet, EfficientNetB0, and InceptionV3 combined. The authors were interested in investigating how quickly an artificial intelligence model can be taught with limited computer resources. Three types of training bases were investigated, starting with a simple base verifying five colours, then recognizing two different orthogonal elements, followed by more complex images from different families. This research aimed to demonstrate the capabilities of the models based on training base parameters such as the number of images and epoch types. Architectures proposed by the authors in these cases were chosen based on simulation studies conducted on a virtual machine with limited hardware parameters. The proposals present the advantages and disadvantages of the different models based on the TensorFlow and Keras libraries in the Jupiter environment based on the Python programming language. An artificial intelligence model with a combination of MobileNet, proposed by Siemens, and Efficient and Inception, selected by the authors, allows for further work to be conducted on image classification, but with limited computer resources for industrial implementation on a programmable logical controller (PLC). The study showed a 90% success rate, with a learning time of 180 s.  
artificial intelligence; deep learning; MobileNet; EfficientNetB0; Inception +
Footnote †: journal: 22  
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traditional CNN models. In an example of bird species classification, the MobileNet model outperformed other models in terms of accuracy. Similarly, in a study of tomato seed variety classification, the MobileNet model achieved the highest classification accuracy [8]. In a study of rice plant leaf classification, the MobileNet model, with 150 epochs, resulted in the highest accuracy [9]. These findings highlight the effectiveness of the MobileNet model in various image classification tasks. Another example of CNN applications is the EfficientNetB0 model, which has shown promising results in skin disease diagnosis [10], haemoglobin level classification for anaemia diagnosis [11], or white blood cell classification [12]. In the diagnosis of anaemia, the EfficientNetB0 model achieved a high accuracy of 97.52%, and in the classification of white blood cells, an accuracy of 99.02%. In medical applications, several papers, including [13], have proposed a CNN-based brain tumour classification model (BCM-CNN) based on CNN hyperparameter optimisation using an adaptive dynamic sinusoidal cosine grey wolf optimiser (ADSCFGWO) algorithm. The training model was built using Inception-ResnetV2. This model uses common pre-trained models (Inception-ResnetV2) to improve brain tumour diagnosis, resulting in binary 0 or 1 (0: normal, 1: tumour). The results of these experiments showed that, as a classifier, BCM-CNN achieved an accuracy of 99.98% in the BRaTS 2021 dataset. More than one learning model can be used for research. For example, paper [14] proposes a technique to segment organs of the gastrointestinal tract (small intestine, large intestine, and stomach) to help radio oncologists treat cancer patients more quickly and accurately. The proposed model uses the segmentation of small-size images to extract local features more efficiently. It uses six transfer learning models as the backbone of U-Net topology. The six transfer learning models used are Inception V3, SeResNet50, VGG19, DenseNet121, InceptionResNetV2, and EfficientNet B0. The results show that the suggested model outperformed all the other transfer learning models. In the industrial sector, a solution has been proposed by the authors of [15], where the learning model directed electromagnetic leakage information in AES encryption chips, learning the attack response model of cryptographic FPGA chips.