This paper introduces an experimental study on the recognition of the person’s face by utilizing three Techniques of extraction: Principle Components Analysis (PCA), Linear Discriminant Analysis (LDA) and Contourlet- Curvelet Transform (CCT). The results of these approaches were observed and compared to discover the perfect scheme for identification of human faces. The tests have been carried out on the faces databases of (ORL),(UMIST), and (JAFFE). The results acquired by the methods were quantified by altering the ratio of train to test photos in three categories: 75/25, 55/45 and 35/65. The evaluation results showed that the CCT extraction method provides better results than the others. The highest recognition rate was recorded for the CCT approach (recognition rate=98.980%) when the (train/test) photos ratio is (75/25). Furthermore, the best recognition rates for the LDA and PCA were 96.391% and 95.127% respectively. The Matlab R2019b program was used for implementing and testing the algorithms.
The concept and growth of superior individualized healthcare technologies are influenced in significant ways by the rising areas of "Artificial Intelligence (AI) and the Internet of Things (IoT)". Most people use wearable devices for mHealth, hence there are many potential applications for the "Internet of Medical Things (IoMT)". Only 5G can provide the necessary support for smart medical devices to perform many different types of demanding computing activities. Today, heart disease was the major mortality on a global scale. For patients who need a greater accurate diagnosis and treatment, the advancement of medical innovation has created new obstacles. Although many studies have focused on diagnosing cardiac disease, the findings are often inaccurate and fail to fulfill patients' expectations of quality of service (QoS). So, this paper introduces a novel "feed-forward Bi-directional long-short term memory (FF-Bi-LSTM) algorithm to predict heart disease more accurately with enhanced QoS in IoMT based on 5G". Linear discriminant analysis (LDA) and min-max normalization are employed, respectively, for preprocessing and feature extraction. Several measures, including precision, recall, accuracy, and f1-score, are used to the assess effectiveness of the suggested strategy. The proposed method also compared to certain existing techniques. These results show that the suggested strategy outperforms existing strategies in terms of improving QoS.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.