Clustered Single-Board Devices with Docker Container Big Stream Processing燗rchitecture

Abstract

The expanding amounts of information created by Internet of Things (IoT) devices places a strain on cloud computing, which is often used for data analysis and storage. This paper investigates a different approach based on edge cloud applications, which involves data filtering and processing before being delivered to a backup cloud environment. This Paper suggest designing and implementing a low cost, low power cluster of Single Board Computers (SBC) for this purpose, reducing the amount of data that must be transmitted elsewhere, using Big Data ideas and technology. An Apache Hadoop and Spark Cluster that was used to run a test application was containerized and deployed using a Raspberry Pi cluster and Docker.To obtain system data and analyze the setup's performance a Prometheusbased stack monitoring and alerting solution in the cloud based market is employed. This Paper assesses the system's complexity and demonstrates how containerization can improve fault tolerance and maintenance ease, allowing the suggested solution to be used in industry. An evaluation of the overall performance is presented to highlight the capabilities and limitations of the suggested architecture, taking into consideration the suggested solution's resource use in respect to device restrictions.

Epilepsy Identification using Hybrid CoPrO-DCNN Classifier

“…There is a notable increase in negative entropy values for epileptic conditions, indicating a higher degree of disorder [95] and complexity during seizures. Kurtosis describes the shape of [96] the distribution. Higher kurtosis in epileptic conditions, especially for Det1 to Det3, [97], [98] suggests a more peaked and heavy-tailed distribution, potentially reflecting abnormal EEG patterns during seizures.…”

“…An increase in theta/delta variance suggests [117] greater variability in the balance between theta and delta activity, which could reflect dynamic changes in brain states associated with epileptic activity. A high positive skewness in theta/alpha ratio indicates an asymmetric distribution [118], [119] with a longer tail towards higher values, suggesting dominance of theta activity over alpha activity. An increase in alpha/theta ApEntropy suggests [120] greater irregularity or unpredictability in the relative balance between alpha and theta activity.…”