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The goal of the paper is to present a solution to improve the fault detection accuracy of rolling bearings. The method is based on variational mode decomposition (VMD), multiscale permutation entropy (MPE) and the particle swarm optimization-based support vector machine (PSO-SVM). Firstly, the original bearing vibration signal is decomposed into several intrinsic mode functions (IMF) by using the VMD method, and the feature energy ratio (FER) criterion is introduced to reconstruct the bearing vibration signal. Secondly, the multiscale permutation entropy of the reconstructed signal is calculated to construct multidimensional feature vectors. Finally, the constructed multidimensional feature vector is fed into the PSO-SVM classification model for automatic identification of different fault patterns of the rolling bearing. Two experimental cases are adopted to validate the effectiveness of the proposed method. Experimental results show that the proposed method can achieve a higher identification accuracy compared with some similar available methods (e.g., variational mode decomposition-based multiscale sample entropy (VMD-MSE), variational mode decomposition-based multiscale fuzzy entropy (VMD-MFE), empirical mode decomposition-based multiscale permutation entropy (EMD-MPE) and wavelet transform-based multiscale permutation entropy (WT-MPE)).

In this study, a novel method for target identification in a foliage environment is presented. This method is based on the ultra wideband (UWB) wireless sensor networks (WSNs) model, and the foliage environment is specially considered. The data used to identify the targets are derived from the received signal waveform, so most existing transceivers can be exploited as detecting sensors, which leads to a potential low-cost way to identify targets during the normal communications within the WSNs under foliage environment. The selected bispectra algorithm is applied to extract the feature vector, and chaos particle swarm optimisation-based support vector machine is used as the target classifier. Experiments with real-world data samples indicate that this method has an excellent classification performance in a foliage environment. Moreover, this method shows potential for online training.

Sentiment analysis is crucial in various systems such as opinion mining and predicting. Considerable research has been done to analyze sentiment using various machine learning techniques. However, the high error rates in these studies can reduce the entire system’s efficiency. We introduce a novel big data and machine learning technique for evaluating sentiment analysis processes to overcome this problem. The data are collected from a huge volume of datasets, helpful in the effective analysis of systems. The noise in the data is eliminated using a preprocessing data mining concept. From the cleaned sentiment data, effective features are selected using a greedy approach that selects optimal features processed by an optimal classifier called cat swarm optimization-based long short-term memory neural network (CSO-LSTMNN). The classifiers analyze sentiment-related features according to cat behavior, minimizing error rate while examining features. This technique helps improve system efficiency, analyzed using experimental results of error rate, precision, recall, and accuracy. The results obtained by implementing the greedy feature and CSO-LSTMNN algorithm and the particle swarm optimization (PSO) algorithm are compared; CSO-LSTMNN outperforms PSO in terms of increasing accuracy and decreasing error rate.

The Internet of Things (IoT) is a modern age technology, designed with the vision to connect and also interconnect all the objects everywhere. Technological progressions provide businesses with many comforts as well as helps the attackers and intruders to crack the IoT networks’ security. Numerous Intrusion Detection Systems (IDSs) are created aimed to attack prevention systems. Frequently, security stays to be challenging in the IoT networks. The work addressed here presents the new effective secured IDS aimed at IoT environment, which sustains the data’s confidentiality, integrity, together with its availability. At first, the data has been pre-processed, which helps in acquiring a clear vision about any attack that is about to occur. The methods are handling of missing and Nan values, date and time variables, categorical features and with scaling of data. Next, aimed at acquiring the data’s knowledge, this work has established an Improved Pearson Correlation Coefficient (IPCC), Feature Extraction (FE) method that presents the relation amidst the data by pondering the causative. The features’ extraction is next followed by the relevant features’ selection aimed at maintaining an efficient computational time and also accuracy utilizing Explorated Particle Swarm Optimization (PSO) centred Sea Turtle Foraging Algorithm (EXPSO-STFA). At last, the feature chosen has been trained and then examined over the Look Ahead Artificial Neural Network (LAANN) classification aimed at identifying the attacks. The LAANN method offers lesser error rate and also evades False Alarm Rate’s (FAR’s) chances and also locates the attack much effectively and also reliably. Moreover, the work administers the malicious attacks’ and random behaviour, and also yields an accurate outcome with the help of evaluation parameters such as Accuracy, Specificity, Sensitivity, Precision, F-Measures, FPR, FNR and MCC. Experiential examination exhibits that the work yields 95.65% accuracy, and also attains 98.16% average Attack Detection Rate (ADR), and the work stays to be much scalable and also secured analogized to the existent top-notch techniques.