Identity recognition is a research hotspot in the information age. Nowadays, more and more occasions require identity recognition, especially in smart home. Identity recognition of the head of the household can avoid many troubles, such as home identification and network information authentication. Nowadays, in smart home identification, especially based on face recognition, system authentication is basically through feature matching. Although this method is convenient and quick to use, it lacks intelligence. Nowadays, for the make-up, facelift, posture, and other differences, the accuracy of the system is greatly reduced. In this paper, the face recognition method is used for identity authentication. Firstly, the AdaBoost learning algorithm is used to construct the face detection and eye detection classifier to realize the detection and localization of the face and eyes. Secondly, the two-dimensional discrete wavelet transform is used to extract facial features and construct a personal face dynamic feature database. Finally, an improved elastic template matching algorithm is used to establish an intelligent classification method for dynamic face elasticity models. The simulation shows that the proposed method can intelligently adapt to various environments without reducing the accuracy.
According to the World Health Organization, heart disease is the biggest cause of death worldwide. It may be possible to bring down the overall death rate of individuals if cardiovascular disease can be detected in its earlier stages. If the cardiac disease is detected at an earlier stage, there is a greater possibility that it may be successfully treated and managed under the guidance of a physician. Recent advances in areas such as the Internet of Things, cloud storage, and machine learning have given rise to renewed optimism over the capacity of technology to bring about a paradigm change on a global scale. At the bedside, the use of sensors to capture vital signs has grown increasingly commonplace in recent years. Patients are manually monitored using a monitor located at the patient’s bedside; there is no automatic data processing taking place. These results, which came from an investigation of cardiovascular disease carried out across a large number of hospitals, have been used in the development of a protocol for the early, automated, and intelligent identification of heart disorders. The PASCAL data set is prepared by collecting data from different hospitals using the digital stethoscope. This data set is publicly available, and it is used by many researchers around the world in experimental work. The proposed strategy for doing research includes three steps. The first stage is known as the data collection phase, the data is collected using biosensors and IoT devices through wireless sensor networks. In the second step, all of the information pertaining to healthcare is uploaded to the cloud so that it may be analyzed. The last step in the process is training the model using data taken from already-existing medical records. Deep learning strategies are used in order to classify the sound that is produced by the heart. The deep CNN algorithm is used for sound feature extraction and classification. The PASCAL data set is essential to the functioning of the experimental environment. The deep CNN model is performing most accurately.
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