Existing studies in skeleton‐based action recognition mainly utilise skeletal data taken from a single camera. Since the quality of skeletal tracking of a single camera is noisy and unreliable, however, combining data from multiple cameras can improve the tracking quality and hence increase the recognition accuracy. In this study, the authors propose a method called weighted averaging fusion which merges skeletal data of two or more camera views. The method first evaluates the reliability of a set of corresponding joints based on their distances to the centroid, then computes the weighted average of selected joints, that is, each joint is weighted by the overall reliability of the camera reporting the joint. Such obtained, fused skeletal data are used as the input to the action recognition step. Experiments using various frame‐level features and testing schemes show that more than 10% improvement can be achieved in the action recognition accuracy using these fused skeletal data as compared with the single‐view case.
The process of manually prescribing drugs by doctors can cause several problems, including doctors not knowing what drugs are available and it takes time to find out what drugs are available in the pharmacy. Speech recognition is now widely used in various ways, which can help facilitate work. The application of speech recognition can be done in the e-prescribing application with the neural network method using the Convolutional Neural Network (CNN) algorithm, which is the basic method of deep learning. This study aims to facilitate physicians in filling out drug data in e-prescribing applications using speech recognition. The data used in this study were obtained from the open source dataset provided by Google and collected independent datasets. From the results of experiments that have been carried out, the accuracy achieved with 40 epochs and 40 direct impressions with different words is 90%. Where words are successfully recognized 36 words out of 40 words
The aim of this research is to analyze the number of criminal cases in Indonesia by utilizing unsupervised learning techniques. The unsupervised learning technique used is data mining by mapping clusters of regions in Indonesia. Sources of data were obtained from the Operations Control Bureau, National Police Headquarters of the Republic of Indonesia through processed data from the Central Statistics Agency (abbreviated as BPS) with data url: https://www.bps.go.id. The data mining method used to map the form of calcter is k-medoid. The data used is data on the number of crimes according to the regional police (2017-2019) which consists of 34 records. The attribute used is the number of crimes in the past three years based on the regional police for each province. The mapping label used is the high cluster (D1) and the low cluster (D2) on the number of criminal acts in Indonesia. The mapping analysis process uses the help of Rapid Miner software. In determining the amount of calcter (k = 2) is done using the Davies Bouldin Index (DBI) parameter with a value of 0.876 (the smaller the better). The results showed that six provinces were in the high cluster (D1) and twenty-eight provinces were in the low cluster (D2). The final centroid in each cluster is 16,008; 21,498; 21,616 (cluster_0 / D1) and 6,994; 7,311; 6,785 (cluster_1 / D2). The six provinces in the high cluster of criminal cases are North Sumatra, South Sumatra, Metro Jaya, West Java, East Java and South Sulawesi. The results of the research are expected to provide information for the government to reduce the number of criminal acts in Indonesia based on the number of clusters that exist.
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