KEYWORDS: Internet of things, Machine learning, Detection and tracking algorithms, Speech recognition, Object detection, Deep learning, Data processing, Image processing, Facial recognition systems
The Internet of Things (IoT) and Machine Learning (ML) are two very hot technologies these days. IoT requires a lot of data processing, and ML is a useful means of processing data. Therefore, the combination of IoT and ML has become a very promising research direction. This paper is a investigation of the combination of IoT and ML. It first introduces the development history of IoT and ML, then introduces some achievements that have emerged in the field of ML and IoT combination. After that, the paper refers some ML technologies which will play important roles in IoT. In this process, this paper also proposes a scheme to improve the accuracy of YOLO algorithm by identifying picture groups. Finally, the paper discusses the existing problems and future development directions of the combination of IoT and ML and provides some references and suggestions for scholars who study the combination of ML and IoT technology.
Emotion recognition plays an important role in medicine, criminal investigation, human-computer interaction and other fields. Emotion recognition through machine learning has become a promising research direction. VGG network is a kind of classic convolutional neural networks (CNN) which can be used for emotion recognition, and VGG-16 is one of the classic architectures of VGG. However, VGG-16 has too many parameters and it seems that VGG-16 doesn't perform well on FER2013 because of overfitting. To solve these problems, this paper proposes a CNN network based on VGG-16 variant called VGG-LIGHT. This network inherits characters of small kernel size and similar architectures between different convolutional layers of VGG. Compared with traditional VGG architecture, VGG-LIGHT has fewer parameters. Compared with other lightweight network, VGG-LIGHT is more efficient and needs less training time. The paper also built a medical data set which is composed by faces of different patients and verified the proposed network by comparing its results with Deepface, a famous framework for face analysis. The experimental results show that the recognition results of VGG-LIGHT are in good agreement with Deepface.
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