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).

No abstract

No abstract

Alongside the rapid progress of Wireless sensor networks (WSNs) technologies, sensors and networks can rapidly be victim of distributed attacks. Attackers can perform intrusions to breakdown the network during the routing process, intercept gathered data by dropping or re-sharing them. To avoid the increasing of security issues, many attack identification models were proposed in WSNs in which detection systems are deployed to collect sensed data and categorize them using machine learning and stochastic binary-classification techniques. In this work, a new method is introduced to analyze and classify WSN dataset. We aim to design an anomaly identification approach to improve the sensor network security, it efficiency with high accuracy. To reach this goal, machine learning approaches are used to define a detection system which learn from routing dataset to identify network malicious entries. The proposed models is based on Hidden Markov Model (HMM), Gaussian Mixture Model (GMM) stochastic assumptions. Also, dimensionality reduction technique was deployed to select the most relevant features for training. The experimentation phase was realized on own made dataset reflecting different network situations of normal and attacked cases. The outcomes performances of the proposed method were obtained with classification accuracy of 92.18% using 2HMM/3GMMclassifier. This result demonstrate the quality of our proposed approach compared with existing literature and its usefulness to improve the security of WSN.