Health informatization as an urgent problem has not yet been resolved. Quality medical care, intellectual level, communication infrastructure, management process, control of equipment, sensors are presented as a “smart-digital hospital” cyberphysical system with modern information technologies. The most important condition for “smart-digital” hospitals is that health workers inform patients about patients using the Internet of Things and mobile applications, and ensure their comfort.
Cardiovascular disease is one of the leading causes of death worldwide. One of the reasons for the large number of deaths from heart disease is the lack of regular cardiac monitoring. Electrocardiograph (ECG) is a method of monitoring heart conditions. There are still many ECG examinations in hospitals that are carried out directly in certain places and the results can only be seen at that time. Meanwhile, when negligence or carelessness occurs, it can endanger the patient due to delays in handling. One of the efforts that can be made to improve service to heart patients is by having ECG monitoring based on IoT (Internet of things). The purpose of this study is to analyze the ECG signals sent and received by IoT media so that they are useful for the diagnostic process. The contribution of this research is to know the shape of the ECG signal that is sent and received through IoT media. The procedure to achieve this goal is with the AD8232 sensor whose output will be processed through the microcontroller and displayed on computer and smartphones via IoT. From this research, the results obtained that the average value of lost data at BPM 60 and BPM 120 is quite good, namely 21.26% and 24.44%.hile the average time delay value at BPM 60 and BPM 120 is also quite good, namely 0.023 s and 0.03 s. So, it can be concluded that the sending of IoT-based ECG signals affects the form of signals sent and received. The results of this study are expected to be developed in further research with development in the form of adding leads or adding BPM parameters.
Aim: Clustering belongs to unsupervised learning, which divides the data objects in the data set into multiple clusters or classes, so that the objects in the same cluster have high similarity. Background: The clustering of spatial data objects can be solved by optimization based on the clustering objective function. Objective: Study on Intelligent Analysis and Processing Technology of Computer Big Data Based on Clustering Algorithm. Method: First, a new dynamic self-organizing feature mapping model is proposed, and the training algorithm of the model is given. Then, the spectral clustering technology and related concepts are introduced. The spectral clustering algorithm is studied and analyzed, and a spectral clustering algorithm that automatically determines the number of clusters is proposed. Furthermore, an algorithm for constructing a discrete Morse function to find the optimal solution is proposed, proving that the constructed function is the optimal discrete Morse function. At the same time, two optimization models based on the discrete Morse theory are constructed. Finally, the optimization model based on discrete Morse theory is applied to cluster analysis, and a density clustering algorithm based on discrete Morse optimization model is proposed. Results: This study is focused on designing and implementing partitional based clustering algorithm based on big data, that is suitable for clustering huge datasets to meet low computational requirements. The experiments are conducted in terms of time and space complexity and it is observed that the measure of clustering quality and the run time is capable of running in very less time without negotiating the quality of clustering. The results show that the experiments are carried out on the artificial data set and the UCI data set. Conclusion: Efficiency and superiority of the new model are verified by comparing with the clustering results of the DBSCAN algorithm.
Auscultation is a technique or method most often used by medical personnel in the initial examination of patients. One way is to use a stethoscope. However, this method has its drawbacks because the diagnosis is carried out subjectively and cannot be relied on with the accuracy to diagnose the symptoms of heart defects. Thus, the purpose of this study is to create an IoT system for electronic stethoscopes with BPM value output and make analog filters to eliminate noise interference which was a major obstacle in previous studies. The contribution to this study is to make it easier for medical users to monitor vital conditions, namely BPM remotely and produce BPM values in real-time. The method used in this study was to use a mic condensor placed on the patient's chest to detect pressure changes that occurred. This change in pressure causes a change in the voltage output value on the condensor mic. Output dari mic condenser masuk dan diproses di rangkaian PSA. Output sinyal dari PSA masuk ke mikrokontroler yang telah diprogram. Hasil yang dipeoleh dari pengukuran mengasilkan nilai error pengukuran nilai BPM dari 5 responden dan diperoleh nilai error yang dihasilkan dari responden 1 diperoleh error sebesar 0.33 BPM, responden 2 diperoleh nilai error sebesar 0,67 BPM, responden 3 memiliki nilai error sebesar 0,5 BPM, responden 4 nilai error sebesar 0,67 dan responden 5 mempunyai nilai error sebesar 0,67 BPM. The results of the statistical test were also obtained P-Value>0.05 which explained that the resulting value did not have a significant difference and could be used for medical purposes. This research can help make it easier for doctors to analyze and diagnose symptoms of heart defects because this system is equipped with the detection of disease symptoms.
The volume of information in the 21st century is growing at a rapid pace. Big data technologies are used to process modern information. This article discusses the use of big data technologies to implement monitoring of social processes. Big data has its characteristics and principles, which reflect here. In addition, we also discussed big data applications in some areas. Particular attention in this article pays to the interactions of big data and sociology. For this, there consider digital sociology and computational social sciences. One of the main objects of study in sociology is social processes. The article shows the types of social processes and their monitoring. As an example, there is implemented monitoring of social processes at the university. There are used following technologies for the realization of social processes monitoring: products 1010data (1010edge, 1010connect, 1010reveal, 1010equities), products of Apache Software Foundation (Apache Hive, Apache Chukwa, Apache Hadoop, Apache Pig), MapReduce framework, language R, library Pandas, NoSQL, etc. Despite this, this article examines the use of the MapReduce model for social processes monitoring at the university.
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