A Mutated Salp Swarm Algorithm for Optimization of Support Vector Machine Parameters

Heart <span>diseases have a severe impact on human life and health. Cardiovascular deaths and diseases have increased at a fast rate worldwide. The early prediction of these diseases is necessary to prevent deaths. Now a day; a considerable amount of medical information is available and collected as databases. An efficient technique is required to analyse this data and predict the disease. Clustering can help medical practitioners in diagnosis by classifying the patient’s data collected for a disease. Clustering techniques can analyse such data based on each patient-generated and predict disease. A new prediction model based on salp swarm algorithm and support vector machine is proposed in this research for predicting heart diseases. Salp swarm algorithm is used to select the useful features from the database. Support vector machine classifier is used to predict heart diseases. Results obtained are compared with the other algorithms available in the literature. It is observed that the proposed approach produces better results with accuracy 98.75% and 98.46% with the dataset 1 and 2, respectively. In addition to this, the algorithm converges in significantly less time in comparison to other algorithms. This algorithm might become a perfect supporting tool for medical </span>practitioners.

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Short-term wind power prediction based on hybrid variational mode decomposition and least squares support vector machine optimized by improved salp swarm algorithm model

Journal of Physics Conference Series ◽

10.1088/1742-6596/2125/1/012012 ◽

2021 ◽

Vol 2125 (1) ◽

pp. 012012

Author(s):

Zhongde Su ◽

Huacai Lu

Keyword(s):

Support Vector Machine ◽

Wind Power ◽

Support Vector ◽

Variational Mode Decomposition ◽

Power Prediction ◽

Short Term ◽

Salp Swarm Algorithm ◽

Mode Decomposition ◽

Wind Power Prediction ◽

Swarm Algorithm

Abstract To improve the accuracy of wind power prediction, a short-term wind power prediction model based on variational mode decomposition (VMD) and improved salp swarm algorithm (ISSA) optimized least squares support vector machine (LSSVM) is proposed. In the model, the variational modal decomposition is used to decompose the wind power sequence into multiple eigenmode components with limited bandwidth. The improved salp swarm algorithm is employed to tune the regularization parameter and kernel parameter in LSSVM. The proposed wind power prediction strategy using mean one-hour historical wind power data collected from a wind farm located in zhejiang, China. Compared with other prediction models illustrate the better prediction performance of VMD-ISSA-LSSVM.

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Recognition of polycyclic aromatic hydrocarbons using fluorescence spectrometry combined with bird swarm algorithm optimization support vector machine

Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy ◽

10.1016/j.saa.2019.117404 ◽

2020 ◽

Vol 224 ◽

pp. 117404 ◽

Cited By ~ 5

Author(s):

Shutao Wang ◽

Shiyu Liu ◽

Xiange Che ◽

Zhifang Wang ◽

Jingkun Zhang ◽

...

Keyword(s):

Support Vector Machine ◽

Polycyclic Aromatic Hydrocarbons ◽

Aromatic Hydrocarbons ◽

Fluorescence Spectrometry ◽

Support Vector ◽

Algorithm Optimization ◽

Polycyclic Aromatic ◽

Swarm Algorithm ◽

Bird Swarm Algorithm

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Software fault prediction using particle swarm algorithm with genetic algorithm and support vector machine classifier

Software Practice and Experience ◽

10.1002/spe.2784 ◽

2020 ◽

Vol 50 (4) ◽

pp. 407-427 ◽

Cited By ~ 1

Author(s):

Hiba Alsghaier ◽

Mohammed Akour

Keyword(s):

Genetic Algorithm ◽

Support Vector Machine ◽

Support Vector Machine Classifier ◽

Particle Swarm ◽

Fault Prediction ◽

Support Vector ◽

Particle Swarm Algorithm ◽

Software Fault Prediction ◽

Software Fault ◽

Swarm Algorithm

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A Novel Intrusion Detection System for RPL Based IoT Networks with Bio-Inspired Feature Selection and Ensemble Classifier

10.21203/rs.3.rs-442429/v1 ◽

2021 ◽

Author(s):

Jayaprakash Pokala ◽

B. Lalitha

Keyword(s):

Feature Selection ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Detection System ◽

Ensemble Classifier ◽

Support Vector ◽

Routing Attacks ◽

Salp Swarm Algorithm ◽

Network Intrusion ◽

Swarm Algorithm

Abstract Internet of Things (IoT) is the powerful latest trend that allows communications and networking of many sources over the internet. Routing protocol for low power and lossy networks (RPL) based IoT networks may be exposed to many routing attacks due to resource-constrained and open nature of the IoT nodes. Hence, there is a need for network intrusion detection system (NIDS) to protect RPL based IoT networks from routing attacks. The existing techniques for anomaly-based NIDS (ANIDS) subjects to high false alarm rate (FAR). Therefore, a novel bio-inspired voting ensemble classifier with feature selection technique is proposed in this paper to improve the performance of ANIDS for RPL based IoT networks. The proposed voting ensemble classifier combines the results of various base classifiers such as logistic Regression, support vector machine, decision tree, bidirectional long short-term memory and K-nearest neighbor to detect the attacks accurately based on majority voting rule. The optimized weights of base classifiers are obtained by using the feature selection method called simulated annealing based improved salp swarm algorithm (SA-ISSA), which is the hybridization of particle swarm optimization, opposition based learning and salp swarm algorithm. The experiments are performed with RPL-NIDDS17 dataset that contains seven types of attack instances. The performance of the proposed model is evaluated and compared with existing feature selection and classification techniques in terms of accuracy, attack detection rate (ADR), FAR and so on. The proposed ensemble classifier shows better performance with higher accuracy (96.4%), ADR (97.7%) and reduced FAR (3.6%).

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