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1.
Advances in Data Analysis and Classification ; 2023.
Article in English | Scopus | ID: covidwho-20234699

ABSTRACT

This paper deals with a clustering approach based on mixture models to analyze multidimensional mobility count time-series data within a multimodal transport hub. These time series are very likely to evolve depending on various periods characterized by strikes, maintenance works, or health measures against the Covid19 pandemic. In addition, exogenous one-off factors, such as concerts and transport disruptions, can also impact mobility. Our approach flexibly detects time segments within which the very noisy count data is synthesized into regular spatio-temporal mobility profiles. At the upper level of the modeling, evolving mixing weights are designed to detect segments properly. At the lower level, segment-specific count regression models take into account correlations between series and overdispersion as well as the impact of exogenous factors. For this purpose, we set up and compare two promising strategies that can address this issue, namely the "sums and shares” and "Poisson log-normal” models. The proposed methodologies are applied to actual data collected within a multimodal transport hub in the Paris region. Ticketing logs and pedestrian counts provided by stereo cameras are considered here. Experiments are carried out to show the ability of the statistical models to highlight mobility patterns within the transport hub. One model is chosen based on its ability to detect the most continuous segments possible while fitting the count time series well. An in-depth analysis of the time segmentation, mobility patterns, and impact of exogenous factors obtained with the chosen model is finally performed. © 2023, Springer-Verlag GmbH Germany, part of Springer Nature.

2.
International Journal of Advanced Computer Science and Applications ; 14(3):924-934, 2023.
Article in English | Scopus | ID: covidwho-2292513

ABSTRACT

In this paper, a COVID-19 dataset is analyzed using a combination of K-Means and Expectation-Maximization (EM) algorithms to cluster the data. The purpose of this method is to gain insight into and interpret the various components of the data. The study focuses on tracking the evolution of confirmed, death, and recovered cases from March to October 2020, using a two-dimensional dataset approach. K-Means is used to group the data into three categories: "Confirmed-Recovered”, "Confirmed-Death”, and "Recovered-Death”, and each category is modeled using a bivariate Gaussian density. The optimal value for k, which represents the number of groups, is determined using the Elbow method. The results indicate that the clusters generated by K-Means provide limited information, whereas the EM algorithm reveals the correlation between "Confirmed-Recovered”, "Confirmed-Death”, and "Recovered-Death”. The advantages of using the EM algorithm include stability in computation and improved clustering through the Gaussian Mixture Model (GMM). © 2023,International Journal of Advanced Computer Science and Applications. All Rights Reserved.

3.
Am J Epidemiol ; 2023 Apr 21.
Article in English | MEDLINE | ID: covidwho-2296490

ABSTRACT

Serological assays used to estimate SARS-CoV-2 seroprevalence often rely on manufacturer cut-offs established based on severe cases. We conducted a household-based serosurvey of 4,677 individuals in Chennai, India from January to May, 2021. Samples were tested for SARS-CoV-2 IgG antibodies to the spike (S) and nucelocapsid (N) proteins. We calculated seroprevalence, defining seropositivity using manufacturer cut-offs and using a mixture model based on measured IgG. Using manufacturer cut-offs, there was a five-fold difference in seroprevalence estimated by each assay. This difference was largely reconciled using the mixture model, with estimated anti-S and anti-N IgG seroprevalence 64.9% (95% Credible Interval [CrI], 63.8-66.0) and 51.5% (95% CrI, 50.2-52.9) respectively. Age and socioeconomic factors showed inconsistent relationships with anti-S and anti-N IgG seropositivity using manufacturer cut-offs. In the mixture model, age was not associated with seropositivity, and improved household ventilation was associated with lower seropositivity odds. With global vaccine scale-up, the utility of the more stable anti-S IgG assay may be limited due to the inclusion of the S protein in several vaccines. SARS-CoV-2 seroprevalence estimates using alternative targets must consider heterogeneity in seroresponse to ensure seroprevalence is not underestimated and correlates not misinterpreted.

4.
2022 Scholar's Yearly Symposium of Technology, Engineering and Mathematics, SYSTEM 2022 ; 3360:55-63, 2022.
Article in English | Scopus | ID: covidwho-2276732

ABSTRACT

The global spread of the COVID-19 virus has become one of the greatest challenges that humanity has faced in recent years. The unprecedented circumstances of forced isolation and uncertainty that it has imposed on us continue to impact our mental well-being, whether or not we have been directly affected by the virus. Over a period of nearly three years (2017-2020), data was collected from multiple administrations of the Rorschach test, one of the most renowned and extensively studied psychological tests. This study involved the clustering of data, collected through the RAP3 software, to analyze the distinctive trends in data recorded before and after the pandemic. This was achieved through the implementation of the well-established machine learning algorithm, Expectation-Maximization. The proposed solution effectively identifies the key variables that significantly influence the subject's score and provides a reliable solution. Additionally, the solution offers an intuitive visualization that can assist psychologists in accurately interpreting shifts in trends and response distributions within a large amount of data in the two periods. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

5.
International Journal of Mental Health Promotion ; 25(4):563-577, 2023.
Article in English | Scopus | ID: covidwho-2288110

ABSTRACT

Objective: In this research, we tried to explore how short-term mindfulness (STM) intervention affects adoles-cents' anxiety, depression, and negative and positive emotion during the COVID-19 pandemic. Design: 10 classes were divided into experiment groups (5 classes;n = 238) and control (5 classes;n = 244) randomly. Hospital Anxiety and Depression Scale (HADS) and Positive and Negative Affect Schedule (PANAS) were used to measure par-ticipants' dependent variables. In the experiment group, we conducted STM practice interventions every morning in their first class from March to November 2020. No interventions were conducted in the control group. Methods: Paired-sample t-tests were used to identify if a significant difference exists between every time point of the experimental and control groups. Repeated ANOVA and Growth Mixture Model (GMM) were used to analyze the tendency of positive and negative emotions, anxiety, and depression in the experimental group. Results and Conclusions: (1) With the intervention of STM, there was a significant decrease in negative emotions and an increase in positive emotions in the experimental group, whereas there were non-significant differences in the control group. (2) To explore the heterogeneity trajectories of dependent variables, we built a GMM and found there were two latent growth classes in the trajectories. (3) The results of the models showed their trajectories were downward, which meant that the levels of anxiety, depression, and negative emotions of participants decreased during the STM training period. Nonetheless, the score of positive affect showed upward in three loops of intervention, which indicated that the level of the participants' positive affect increased through the STM inter-vention. (4) This research indicated that STM should be given increasing consideration to enhance mental health during the worldwide outbreak of COVID-19. © 2023, Tech Science Press. All rights reserved.

6.
Thermal Science ; 27(1):405-410, 2023.
Article in English | Scopus | ID: covidwho-2248964

ABSTRACT

Statistical classification is recently considered one of the most important and most common methods in machine learning models and consists of building mod-els that define the target of research interest. There are many classification methods that can be used to predict the value of a response. In this article, we are interested in machine learning applications to classify the new deaths due to Covid-19. Under consideration BIC criterion, the experimental results have shown that the E (Equal variance) with four is the best mixture model. The con-vergence in the algorithm of expectation-maximization is satisfied after 167 itera-tions. The World Health Organization has presented the source of data over the period of March 2, 2020 to August 5, 2020. © 2023 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, Belgrade, Serbia. This is an open access article distributed under the CC BY-NC-ND 4.0 terms and conditions.

7.
J Am Stat Assoc ; 118(541): 43-55, 2023.
Article in English | MEDLINE | ID: covidwho-2282129

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused over six million deaths in the ongoing COVID-19 pandemic. SARS-CoV-2 uses ACE2 protein to enter human cells, raising a pressing need to characterize proteins/pathways interacted with ACE2. Large-scale proteomic profiling technology is not mature at single-cell resolution to examine the protein activities in disease-relevant cell types. We propose iProMix, a novel statistical framework to identify epithelial-cell specific associations between ACE2 and other proteins/pathways with bulk proteomic data. iProMix decomposes the data and models cell-type-specific conditional joint distribution of proteins through a mixture model. It improves cell-type composition estimation from prior input, and utilizes a non-parametric inference framework to account for uncertainty of cell-type proportion estimates in hypothesis test. Simulations demonstrate iProMix has well-controlled false discovery rates and favorable powers in non-asymptotic settings. We apply iProMix to the proteomic data of 110 (tumor adjacent) normal lung tissue samples from the Clinical Proteomic Tumor Analysis Consortium lung adenocarcinoma study, and identify interferon α/γ response pathways as the most significant pathways associated with ACE2 protein abundances in epithelial cells. Strikingly, the association direction is sex-specific. This result casts light on the sex difference of COVID-19 incidences and outcomes, and motivates sex-specific evaluation for interferon therapies.

8.
Biostatistics ; 2023 Mar 06.
Article in English | MEDLINE | ID: covidwho-2281852

ABSTRACT

Naive estimates of incidence and infection fatality rates (IFR) of coronavirus disease 2019 suffer from a variety of biases, many of which relate to preferential testing. This has motivated epidemiologists from around the globe to conduct serosurveys that measure the immunity of individuals by testing for the presence of SARS-CoV-2 antibodies in the blood. These quantitative measures (titer values) are then used as a proxy for previous or current infection. However, statistical methods that use this data to its full potential have yet to be developed. Previous researchers have discretized these continuous values, discarding potentially useful information. In this article, we demonstrate how multivariate mixture models can be used in combination with post-stratification to estimate cumulative incidence and IFR in an approximate Bayesian framework without discretization. In doing so, we account for uncertainty from both the estimated number of infections and incomplete deaths data to provide estimates of IFR. This method is demonstrated using data from the Action to Beat Coronavirus erosurvey in Canada.

9.
Int J Environ Res Public Health ; 19(22)2022 Nov 14.
Article in English | MEDLINE | ID: covidwho-2249182

ABSTRACT

Tracking the progress of an infectious disease is critical during a pandemic. However, the incubation period, diagnosis, and treatment most often cause uncertainties in the reporting of both cases and deaths, leading in turn to unreliable death rates. Moreover, even if the reported counts were accurate, the "crude" estimates of death rates which simply divide country-wise reported deaths by case numbers may still be poor or even non-computable in the presence of small (or zero) counts. We present a novel methodological contribution which describes the problem of analyzing COVID-19 data by two nested Poisson models: (i) an "upper model" for the cases infected by COVID-19 with an offset of population size, and (ii) a "lower" model for deaths of COVID-19 with the cases infected by COVID-19 as an offset, each equipped with their own random effect. This approach generates robustness in both the numerator as well as the denominator of the estimated death rates to the presence of small or zero counts, by "borrowing" information from other countries in the overall dataset, and guarantees positivity of both the numerator and denominator. The estimation will be carried out through non-parametric maximum likelihood which approximates the random effect distribution through a discrete mixture. An added advantage of this approach is that it allows for the detection of latent subpopulations or subgroups of countries sharing similar behavior in terms of their death rates.


Subject(s)
COVID-19 , Communicable Diseases , Humans , COVID-19/epidemiology , Population Density , Pandemics
10.
Comput Electr Eng ; 102: 108224, 2022 Sep.
Article in English | MEDLINE | ID: covidwho-2247861

ABSTRACT

Due to the COVID-19 epidemic and the curfew caused by it, many people have sought to find an ADPS on the internet in the last few years. This hints to a new age of medical treatment, all the more so if the number of internet users continues to expand. As a result, automatic illness prediction online applications have attracted the interest of a large number of researchers worldwide. This work aims to develop and implement an automated illness prediction system based on speech. The system will be designed to forecast the sort of ailment a patient is suffering from based on his voice, but this was not feasible during the trial, therefore the diseases were divided into three categories (painful, light pain and psychological pain), and then the diagnose process were implemented accordingly. The medical dataset named "speech, transcription, and intent" served as the baseline for this study. The smoothness, MFCC, and SCV properties were used in this work, which demonstrated their high representation to human being medical situations. The noise reduction forward-backward filter was used to eliminate noise from wave files captured online in order to account for the high level of noise seen in the deployed dataset. For this study, a hybrid feature selection method was created and built that combined the output of a genetic algorithm (GA) with the inputs of a NN algorithm. Classification was performed using SVM, neural network, and GMM. The greatest results obtained were 94.55% illness classification accuracy in terms of SVM. The results showed that diagnosing illness through speech is a difficult process, especially when diagnosing each type of illness separately, but when grouping the different illness types into groups, depending on the amount of pain and the psychological situation of the patient, the results were much higher.

11.
Int J Environ Res Public Health ; 20(5)2023 03 02.
Article in English | MEDLINE | ID: covidwho-2269462

ABSTRACT

Biosafety laboratory is an important place to study high-risk microbes. In biosafety laboratories, with the outbreak of infectious diseases such as COVID-19, experimental activities have become increasingly frequent, and the risk of exposure to bioaerosols has increased. To explore the exposure risk of biosafety laboratories, the intensity and emission characteristics of laboratory risk factors were investigated. In this study, high-risk microbe samples were substituted with Serratia marcescens as the model bacteria. The resulting concentration and particle size segregation of the bioaerosol produced by three experimental procedures (spill, injection, and sample drop) were monitored, and the emission sources' intensity were quantitatively analyzed. The results showed that the aerosol concentration produced by injection and sample drop was 103 CFU/m3, and that by sample spill was 102 CFU/m3. The particle size of bioaerosol is mainly segregated in the range of 3.3-4.7 µm. There are significant differences in the influence of risk factors on source intensity. The intensity of sample spill, injection, and sample drop source is 3.6 CFU/s, 78.2 CFU/s, and 664 CFU/s. This study could provide suggestions for risk assessment of experimental operation procedures and experimental personnel protection.


Subject(s)
COVID-19 , Laboratories , Humans , Containment of Biohazards , Respiratory Aerosols and Droplets , Risk Factors , Air Microbiology
12.
Thermal Science ; 26:261-270, 2022.
Article in English | Web of Science | ID: covidwho-2227295

ABSTRACT

In light of the global events resulting from the spread of the Corona pandemic and viral mutations, there is a need to examine epidemic data in terms of numbers of infected and deaths, different geographical locations, and the dynamics of disease dissemination virus. In the Kingdom of Saudi Arabia (KSA), since the spread of the virus on March 2, 2020, the number of confirmed cases has increased to 599044 cases until January 13, 2022, of which 262 are critical cases, while the number of recovery cases have reached 55035 cases, and deaths are 8901. It is a serious disease, and its spread is difficult to contain. The number of cases has continued to grow rapidly since the first cases appeared. Guess and Buck's model for forecasting time-series data is an important figure that cannot be crossed when predicting fuzzy time-series, although several modifications have been made to the model to improve the accuracy of its results. The Gaussian mixture model and the fuzzy method for modelling new cases in Saudi Arabia were used as machine learning methods to classify and predict new cases of the virus in Saudi Arabia. Foggy time series forecasting. The studied datasets from the World Health Organization from May 15 to August 12, 2020 were used.

13.
BMC Public Health ; 23(1): 148, 2023 Jan 21.
Article in English | MEDLINE | ID: covidwho-2232617

ABSTRACT

BACKGROUND: One of the seminal events since 2019 has been the outbreak of the SARS-CoV-2 pandemic. Countries have adopted various policies to deal with it, but they also differ in their socio-geographical characteristics and public health care facilities. Our study aimed to investigate differences between epidemiological parameters across countries. METHOD: The analysed data represents SARS-CoV-2 repository provided by the Johns Hopkins University. Separately for each country, we estimated recovery and mortality rates using the SIRD model applied to the first 30, 60, 150, and 300 days of the pandemic. Moreover, a mixture of normal distributions was fitted to the number of confirmed cases and deaths during the first 300 days. The estimates of peaks' means and variances were used to identify countries with outlying parameters. RESULTS: For 300 days Belgium, Cyprus, France, the Netherlands, Serbia, and the UK were classified as outliers by all three outlier detection methods. Yemen was classified as an outlier for each of the four considered timeframes, due to high mortality rates. During the first 300 days of the pandemic, the majority of countries underwent three peaks in the number of confirmed cases, except Australia and Kazakhstan with two peaks. CONCLUSIONS: Considering recovery and mortality rates we observed heterogeneity between countries. Liechtenstein was the "positive" outlier with low mortality rates and high recovery rates, at the opposite, Yemen represented a "negative" outlier with high mortality for all four considered periods and low recovery for 30 and 60 days.


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , COVID-19/epidemiology , Pandemics , Disease Outbreaks , France
14.
BMC Bioinformatics ; 23(1): 551, 2022 Dec 19.
Article in English | MEDLINE | ID: covidwho-2196035

ABSTRACT

BACKGROUND: The genomes of SARS-CoV-2 are classified into variants, some of which are monitored as variants of concern (e.g. the Delta variant B.1.617.2 or Omicron variant B.1.1.529). Proportions of these variants circulating in a human population are typically estimated by large-scale sequencing of individual patient samples. Sequencing a mixture of SARS-CoV-2 RNA molecules from wastewater provides a cost-effective alternative, but requires methods for estimating variant proportions in a mixed sample. RESULTS: We propose a new method based on a probabilistic model of sequencing reads, capturing sequence diversity present within individual variants, as well as sequencing errors. The algorithm is implemented in an open source Python program called VirPool. We evaluate the accuracy of VirPool on several simulated and real sequencing data sets from both Illumina and nanopore sequencing platforms, including wastewater samples from Austria and France monitoring the onset of the Alpha variant. CONCLUSIONS: VirPool is a versatile tool for wastewater and other mixed-sample analysis that can handle both short- and long-read sequencing data. Our approach does not require pre-selection of characteristic mutations for variant profiles, it is able to use the entire length of reads instead of just the most informative positions, and can also capture haplotype dependencies within a single read.


Subject(s)
COVID-19 , SARS-CoV-2 , Wastewater , Humans , RNA, Viral , SARS-CoV-2/genetics , SARS-CoV-2/isolation & purification , Wastewater/virology
15.
J Am Stat Assoc ; 118(541): 43-55, 2023.
Article in English | MEDLINE | ID: covidwho-2069960

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused over six million deaths in the ongoing COVID-19 pandemic. SARS-CoV-2 uses ACE2 protein to enter human cells, raising a pressing need to characterize proteins/pathways interacted with ACE2. Large-scale proteomic profiling technology is not mature at single-cell resolution to examine the protein activities in disease-relevant cell types. We propose iProMix, a novel statistical framework to identify epithelial-cell specific associations between ACE2 and other proteins/pathways with bulk proteomic data. iProMix decomposes the data and models cell-type-specific conditional joint distribution of proteins through a mixture model. It improves cell-type composition estimation from prior input, and utilizes a non-parametric inference framework to account for uncertainty of cell-type proportion estimates in hypothesis test. Simulations demonstrate iProMix has well-controlled false discovery rates and favorable powers in non-asymptotic settings. We apply iProMix to the proteomic data of 110 (tumor adjacent) normal lung tissue samples from the Clinical Proteomic Tumor Analysis Consortium lung adenocarcinoma study, and identify interferon α/γ response pathways as the most significant pathways associated with ACE2 protein abundances in epithelial cells. Strikingly, the association direction is sex-specific. This result casts light on the sex difference of COVID-19 incidences and outcomes, and motivates sex-specific evaluation for interferon therapies.

16.
Alexandria Engineering Journal ; 62:327-333, 2023.
Article in English | Scopus | ID: covidwho-2014736

ABSTRACT

Regarding the pandemic taking place in the world from the spread of the Coronavirus pandemic and viral mutations, the need has arisen to analyze the epidemic data in terms of numbers of infected and deaths, different geographical regions, and the dynamics of the spread of the virus. In China, the total number of reported infections is 224,659 on June 11, 2022. In this paper, the Gaussian Mixture Model and the decision tree method were used to classify and predict new cases of the virus. Although we focus mainly on the Chinese case, the model is general and adapted to any context without loss of validity of the qualitative results. The Chi-Squared (χ2) Automatic Interaction Detection (CHAID) was applied in creating the decision tree structure, the data has been classified into five classes, according to the BIC criterion. The best mixture model is the E (Equal variance) with five components. The considered data sets of the world health organization (WHO) were used from January 5, 2020, to 12, November 2021. We provide numerical results based on the Chinese case. © 2022 THE AUTHORS

17.
STAT ; 11(1), 2022.
Article in English | Web of Science | ID: covidwho-1935735

ABSTRACT

In recent days, a combination of finite mixture model (FMM) and hidden Markov model (HMM) is becoming popular for partitioning heterogeneous temporal data into homogeneous groups (clusters) with homogeneous time points (regimes). The regression mixtures commonly considered in this approach can also accommodate for covariates present in data. The classical fixed covariate approach, however, may not always serve as a reasonable assumption as it is incapable of accounting for the contribution of covariates in cluster formation. This paper introduces a novel approach for detecting clusters and regimes in time series data in the presence of random covariates. The computational challenges related to the proposed model has been discussed, and several simulation studies are performed. An application to United States COVID-19 data yields meaningful clusters and regimes.

18.
Ieee Journal of Selected Topics in Signal Processing ; 16(2):300-306, 2022.
Article in English | English Web of Science | ID: covidwho-1883132

ABSTRACT

We investigate the problem of mathematical modeling of new corona virus (COVID-19) spread in practical scenarios in various countries, specifically in India, the United States of America (USA), France, Brazil, and Turkey. We propose a mathematical model to characterize COVID-19 disease and predict the new/upcoming wave of COVID-19. This prediction is very much required to prepare medical set-ups and proceed with future plans of action. A mixture Gaussian model is proposed to characterize the COVID-19 disease. Specifically, the data corresponding to new active cases of COVID-19 per day is considered, and then we try to fit the data to a mathematical function. It is observed that the Gaussian mixture model is suitable to characterize the new active cases of COVID-19. Further, it is assumed that there are N waves of COVID-19 and the information of each upcoming wave is present in the current and previous waves as well. By using this concept, prediction of the upcoming wave can be performed. A close match between analytical results and the available results shows the correctness of the considered model.

19.
Epidemics ; 39: 100572, 2022 06.
Article in English | MEDLINE | ID: covidwho-1821233

ABSTRACT

Serosurveys are an important tool to estimate the true extent of the current SARS-CoV-2 pandemic. So far, most serosurvey data have been analyzed with cutoff-based methods, which dichotomize individual measurements into sero-positives or negatives based on a predefined cutoff. However, mixture model methods can gain additional information from the same serosurvey data. Such methods refrain from dichotomizing individual values and instead use the full distribution of the serological measurements from pre-pandemic and COVID-19 controls to estimate the cumulative incidence. This study presents an application of mixture model methods to SARS-CoV-2 serosurvey data from the SEROCoV-POP study from April and May 2020 in Geneva (2766 individuals). Besides estimating the total cumulative incidence in these data (8.1% (95% CI: 6.8%-9.9%)), we applied extended mixture model methods to estimate an indirect indicator of disease severity, which is the fraction of cases with a distribution of antibody levels similar to hospitalized COVID-19 patients. This fraction is 51.2% (95% CI: 15.2%-79.5%) across the full serosurvey, but differs between three age classes: 21.4% (95% CI: 0%-59.6%) for individuals between 5 and 40 years old, 60.2% (95% CI: 21.5%-100%) for individuals between 41 and 65 years old and 100% (95% CI: 20.1%-100%) for individuals between 66 and 90 years old. Additionally, we find a mismatch between the inferred negative distribution of the serosurvey and the validation data of pre-pandemic controls. Overall, this study illustrates that mixture model methods can provide additional insights from serosurvey data.


Subject(s)
COVID-19 , SARS-CoV-2 , Adult , COVID-19/epidemiology , Humans , Pandemics , Seroepidemiologic Studies , Young Adult
20.
94th IEEE Vehicular Technology Conference (VTC-Fall) ; 2021.
Article in English | Web of Science | ID: covidwho-1819860

ABSTRACT

Global pandemics such as Covid-19 have led to massive loss of human lives and strict lockdown measures worldwide. To return to a certain level of normalcy, community awareness on avoiding high population density areas is significantly important for infection prevention and control. With the availability of new telecommunication technologies, it is possible to provide highly informative population clustering data back to people using wireless aerial agents (WAAs) placed in a local area. Hence, a service architecture that allows users to access the localization of population clusters is proposed. Further, a convex hull-based clustering method, enhanced population clustering (E-PC), is proposed. This method refined the result of conventional clustering methods such as K-means and Gaussian mixture model (GMM). Moreover, the potential in E-PC to achieve the same or higher results compared to the original K-means and GMM, while consuming lesser data points, is demonstrated. On average, E-PC improved the cluster detection performance in both K-means and GMM by 18.93% under different environments such as remote, rural, suburban, and urban in terms of silhouette score. Further, E-PC allows a 15% data reduction which results in decreasing the computational cost and energy consumption of the WAAs.

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