Early COVID-19 Symptoms Identification Using Hybrid Unsupervised Machine Learning Techniques

The COVID-19 virus exhibits pneumonia-like symptoms,including fever,cough,and shortness of breath,and may be fatal.Many COVID-19 contraction experiments require comprehensive clinical procedures at medical facilities.Clinical studies help to make a correct diagnosis of COVID-19,where the disease has already spread to the organs in most cases.Prompt and early diagnosis is indispensable for providing patients with the possibility of early clinical diagnosis and slowing down the disease spread.Therefore,clinical investigations in patients with COVID-19 have revealed distinct patterns of breathing relative to other diseases such as flu and cold,which are worth investigating.Current supervised Machine Learning(ML)based techniques mostly investigate clinical reports such as X-Rays and Computerized Tomography(CT)for disease detection.This strategy relies on a larger clinical dataset and does not focus on early symptom identification.Towards this end,an innovative hybrid unsupervised ML technique is introduced to uncover the probability of COVID-19 occurrence based on the breathing patterns and commonly reported symptoms,fever,and cough.Specifically,various metrics,including body temperature,breathing and cough patterns,and physical activity,were considered in this study.Finally,a lightweight ML algorithm based on the K-Means and Isolation Forest technique was implemented on relatively small data including 40 individuals.The proposed technique shows an outlier detection with an accuracy of 89%,on average.

A Machine Learning Approach for Early COVID-19 Symptoms Identification

Symptom identification and early detection are the first steps towards a health condition diagnosis.TheCOVID-19 virus causes pneumonialike symptoms such as fever,cough,and shortness of breath.ManyCOVID-19 contraction tests necessitate extensive clinical protocols in medical settings.Clinical studies help with the accurate analysis of COVID-19,where the virus has already spread to the lungs in most patients.The majority of existing supervised machine learning-based disease detection techniques are based on clinical data like x-rays and computerized tomography.This is heavily reliant on a larger clinical study and does not emphasize early symptom detection.The aim of this study is to investigate anomalies in patient physiological data for early COVID-19 symptoms identification.In this context,two of the most prevalent symptoms,fever and cough,were examined in a two-fold manner utilizing an unsupervised machine learningmodel.To examine disease progression,physiological features from a chest-worn device were analyzed.First,a Single Vector Activity Index(SVAI)parameter is proposed to monitor the breathing and cough patterns.Second,the dataset’s variance is examined using the DBSCAN method for clustering and outlier detection.Finally,the model accuracy is evaluated to identify outliers on real-time data based on feature dissimilarities,yielding an overall detection accuracy of 90.34%.

Dynamic Stability Improvement of Decentralized Wind Farms by Effective Distribution Static Compensator

Dynamic instability of decentralized wind energy farms is a major issue to deliver continuous green energy to electricity consumers.This instability is caused by variations of voltage and frequency parameters due to intermittencies in wind power.Previously,droop control and inverter-based schemes have been proposed to regulate the voltage by balancing reactive power,while inertial control,digital mapping tech-nique of proportional-integral-differential(PID)controller and efficiency control strategy have been developed to regulate the frequency.In this paper,voltage stability is improved by a new joint strategy of distribution static compensator(DSTATCOM)six-pulse controller based reactive power management among decentralized wind turbines and controlled charging of capacitor bank.The frequency stability is ensured by a joint coordinated utilization of capacitor bank and distributed wind power turbines dispatching through a new DSTATCOM six-pulse controller scheme.In both strategies,power grid is contributed as a backup source with less priority.These new joint strategies for voltage and frequency stabilities will enhance the stable active power delivery to end users.A system test case is developed to verify the proposed joint strategies.The test results of the proposed new schemes are proved to be effective in terms of stability improvement of voltage,frequency and active power generation.