Feature selection becomes prominent, especially in the data sets with many variables and features. It will eliminate unimportant variables and improve the accuracy as well as the performance of classification. Random Forest has emerged as a quite useful algorithm that can handle the feature selection issue even with a higher number of variables. In this paper, we use three popular datasets with a higher number of variables (Bank Marketing, Car Evaluation Database, Human Activity Recognition Using Smartphones) to conduct the experiment. There are four main reasons why feature selection is essential. First, to simplify the model by reducing the number of parameters, next to decrease the training time, to reduce overfilling by enhancing generalization, and to avoid the curse of dimensionality. Besides, we evaluate and compare each accuracy and performance of the classification model, such as Random Forest (RF), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Linear Discriminant Analysis (LDA). The highest accuracy of the model is the best classifier. Practically, this paper adopts Random Forest to select the important feature in classification. Our experiments clearly show the comparative study of the RF algorithm from different perspectives. Furthermore, we compare the result of the dataset with and without essential features selection by RF methods varImp(), Boruta, and Recursive Feature Elimination (RFE) to get the best percentage accuracy and kappa. Experimental results demonstrate that Random Forest achieves a better performance in all experiment groups.

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Selecting critical features for data classification based on machine learning methods

Multi-sink distributed power control algorithm for Cyber-physical-systems in coal mine tunnels

IoT information sharing security mechanism based on blockchain technology

With the booming development of medical informatization and the ubiquitous connections in the fifth generation mobile communication technology (5G) era, the heterogeneity and explosive growth of medical data have brought huge challenges to data access, security and privacy, as well as information processing in Internet of Medical Things (IoMT). This article provides a comprehensive review of how to realize the timely processing and analysis of medical big data and the sinking of high-quality medical resources under the constraints of the existing medical environment and medical-related equipment. We mainly focus on the advantages brought by the cloud computing, edge computing and artificial intelligence technologies to the IoMT. We also explore how to rationalize the use of medical resources and the security and privacy of medical data, so that high-quality medical services can be provided to patients. Finally, we discuss the current challenges and possible future research directions in the edge-cloud computing and artificial intelligence related IoMT.

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Edge-Cloud Computing and Artificial Intelligence in Internet of Medical Things: Architecture, Technology and Application

Design and application of fog computing and Internet of Things service platform for smart city

This paper investigates H∞ control method for a class of singular network control systems (SNCS) based on singular plant. Considering the network delay, external disturbance, impulse behavior and structural instability of singular plant, the H∞ control of SNCS with state feedback and dynamic output feedback are investigated respectively by approach of linear matrix inequality (LMI). The existence H∞ control law, the solving H∞ control law and disturbance degree are presented respectively. Simulation results illustrate the effectiveness and feasibility of the given approach. KEYWORDS: singular networked controls; H∞ control; linear matrix inequality; network delay DOI: http://dx.doi.org/10.5755/j01.itc.47.1.19982

/pdf/h-control-of-network-control-system-for-singular-plant-ku3kv78zkz.pdf

H∞ Control of Network Control System for Singular Plant

To reduce the data redundancy and energy consumption caused by the explosive growth of data in wireless sensor networks, this paper presents a node data image based mean filtering algorithm. In the provided approach, the nodes are divided into active and de-active nodes to eliminate the data of the sleep nodes and data redundancy. The nodes of wireless sensor network are clustered, and the data obtained from the sensor network are modeled. Simulation results show that the proposed algorithm is more effective than that of k-medoids, which can reduce the redundant data. Moreover, it reduces the energy consumption, and improves the life cycle of wireless sensor networks. DOI: http://dx.doi.org/10.5755/j01.itc.47.3.20565

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Xunli Fan

Papers

Multi-sink distributed power control algorithm for Cyber-physical-systems in coal mine tunnels

H∞ Control of Network Control System for Singular Plant

Low Energy Consumption and Data Redundancy Approach of Wireless Sensor Networks with Bigdata