Optimization of ZigBee Network Parameters for the Improvement of Quality of Service

Network enabled digital technologies are offering new and exciting opportunities to increase the connectivity of devices for the purpose of home and office automation. ZigBee (IEEE 802.15.4) is such a digital wireless technology that is used for personal area networks. In this paper, an office automation network using the combination of fixed and mobile IEEE 802.15.4 has been deployed and analyzed. The QoS parameters of the network as the performance metrics like throughput, MAC delay and data dropped rate have been investigated. Finally the network has been finalized with the optimized QoS parameters.

Improving Prediction Efficiency of Machine Learning Models for Cardiovascular Disease in IoST-Based Systems through Hyperparameter Optimization

This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST(Internet of Sensing Things)device.Ten distinct machine learning approaches were implemented and systematically evaluated before and after hyperparameter tuning.Significant improvements were observed across various models,with SVM and Neural Networks consistently showing enhanced performance metrics such as F1-Score,recall,and precision.The study underscores the critical role of tailored hyperparameter tuning in optimizing these models,revealing diverse outcomes among algorithms.Decision Trees and Random Forests exhibited stable performance throughout the evaluation.While enhancing accuracy,hyperparameter optimization also led to increased execution time.Visual representations and comprehensive results support the findings,confirming the hypothesis that optimizing parameters can effectively enhance predictive capabilities in cardiovascular disease.This research contributes to advancing the understanding and application of machine learning in healthcare,particularly in improving predictive accuracy for cardiovascular disease management and intervention strategies.

Prediction of COVID-19 Confirmed, Death, and Cured Cases in India Using Random Forest Model

A novel coronavirus(SARS-CoV-2) is an unusual viral pneumonia in patients, first found in late December 2019, latter it declared a pandemic by World Health Organizations because of its fatal effects on public health. In this present, cases of COVID-19 pandemic are exponentially increasing day by day in the whole world. Here, we are detecting the COVID-19 cases, i.e., confirmed, death, and cured cases in India only. We are performing this analysis based on the cases occurring in different states of India in chronological dates. Our dataset contains multiple classes so we are performing multi-class classification. On this dataset, first, we performed data cleansing and feature selection, then performed forecasting of all classes using random forest, linear model, support vector machine,decision tree, and neural network, where random forest model outperformed the others, therefore, the random forest is used for prediction and analysis of all the results. The K-fold cross-validation is performed to measure the consistency of the model.

Mobility Issue on Octagonal Structured ZigBee Network Using Riverbed

Wireless Sensor Network (WSN) is a special type of communication medium through distributed sensor nodes. Popular wireless sensor nodes like ZigBee have splendid interoperability after IEEE 802.15.4 standardization in the domain of wireless personal area network (WPAN). ZigBee has another great feature mobility that makes the ZigBee network more versatile. The mobility feature of ZigBee mobile nodes has a greater impact on network performance than fixed nodes. This impact sometimes turns into more severe because of network structure and mobility model. This study mainly focuses on the performance analysis of the ZigBee mobile node under Random and Octagonal mobility management model with the Tree routing method. The Riverbed academic modeler is used to design, implement and simulate the ZigBee network under certain conditions. This study also presents a competitive performance analysis based on ZigBee mobile nodes transmitter and receiver characteristics under the observation of the mobility model. This indicates that Octagonal mobility model exhibits better performance than the Random mobility model. This study will constitute a new way for further designing and planning a reliable and efficient ZigBee network.