Feature Selection Using Modified Sine Cosine Algorithm with COVID-19 Dataset

Živković, Miodrag; Jovanovic, Luka; Ivanovic, Milica; Krdzic, Aleksa; Bačanin, Nebojša; Strumberger, Ivana

doi:10.1007/978-981-16-9605-3_2

Cited by 36 publications

(17 citation statements)

References 35 publications

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“…Based on the previous findings, the ABC algorithm has efficient exploration mechanism which discards individuals that can not be improved in the predefined number of iterations, however it suffers from poor exploitation [73]. Conversely, both FA and SCA meta-heuristics exhibit above average intensification abilities, but they do not employ explicit exploration mechanism which leads to lower diversification capabilities [67,80]. Dynamic FA implementation controls exploitation-exploration balance by shrinking parameter α throughout iterations, while the SCA also uses dynamic parameter r 1 .…”

Section: Motivation and Preliminariesmentioning

confidence: 97%

“…The algorithms from this group have been used in a wide spectrum of different challenges with NP-hardness from the computer science field. These applications include the problem of global numerical optimization [37], scheduling of tasks in the cloud-edge environments [38][39][40], health care systems and pollution prediction [41], the problems of wireless sensors networks including localization and lifetime maximization [42][43][44], artificial neural networks optimization [45][46][47][48][49][50][51][52][53][54][55][56][57], feature selection in general [58,59], text document clustering [48], cryptocurrency values prediction [60], computer-aided medical diagnostics [61][62][63][64], and, finally, the ongoing COVID-19 pandemic related applications [65][66][67].…”

Section: Swarm Intelligencementioning

confidence: 99%

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Multi-Swarm Algorithm for Extreme Learning Machine Optimization

Bačanin

Stoean

Živković

et al. 2022

Sensors

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There are many machine learning approaches available and commonly used today, however, the extreme learning machine is appraised as one of the fastest and, additionally, relatively efficient models. Its main benefit is that it is very fast, which makes it suitable for integration within products that require models taking rapid decisions. Nevertheless, despite their large potential, they have not yet been exploited enough, according to the recent literature. Extreme learning machines still face several challenges that need to be addressed. The most significant downside is that the performance of the model heavily depends on the allocated weights and biases within the hidden layer. Finding its appropriate values for practical tasks represents an NP-hard continuous optimization challenge. Research proposed in this study focuses on determining optimal or near optimal weights and biases in the hidden layer for specific tasks. To address this task, a multi-swarm hybrid optimization approach has been proposed, based on three swarm intelligence meta-heuristics, namely the artificial bee colony, the firefly algorithm and the sine–cosine algorithm. The proposed method has been thoroughly validated on seven well-known classification benchmark datasets, and obtained results are compared to other already existing similar cutting-edge approaches from the recent literature. The simulation results point out that the suggested multi-swarm technique is capable to obtain better generalization performance than the rest of the approaches included in the comparative analysis in terms of accuracy, precision, recall, and f1-score indicators. Moreover, to prove that combining two algorithms is not as effective as joining three approaches, additional hybrids generated by pairing, each, two methods employed in the proposed multi-swarm approach, were also implemented and validated against four challenging datasets. The findings from these experiments also prove superior performance of the proposed multi-swarm algorithm. Sample code from devised ELM tuning framework is available on the GitHub.

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Section: Motivation and Preliminariesmentioning

confidence: 97%

Section: Swarm Intelligencementioning

confidence: 99%

Multi-Swarm Algorithm for Extreme Learning Machine Optimization

Bačanin

Stoean

Živković

et al. 2022

Sensors

Self Cite

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“…Other successful applications of metaheuristics optimizers include tuning of the cloud, edge and fog computing [2,5,15,23,46,59], feature selection challenge [8,19,22,32,37,49,61], dropout regularization [11], a variety of COVID-19 applications [25,58,[62][63][64], tuning artificial neural networks [3,6,7,10,13,18,44], text clustering [21,50] and cryptocurrency price forecast [42].…”

Section: Metaheuristics Optimizationmentioning

confidence: 99%

Artificial Neural Network Tuning by Improved Sine Cosine Algorithm for HealthCare 4.0

Gajevic¹,

Milutinovic²,

Krstovic³

et al. 2023

Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB 2022)

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This paper explores classification of datasets for Healthcare 4.0 using artificial neural networks which are tuned by improved sine cosine algorithm (SCA). Healthcare 4.0 themes include internet of things (IoT), industrial IoT (IIoT), cognitive computing, artificial intelligence, cloud computing, fog computing, edge computing, and other industry 4.0 procedures. Health issues identification are critical since prompt treatment improves the quality of life for individuals affected. One of the most difficult challenges for artificial intelligence (AI) is selecting control parameters that are appropriate for the situation at hand. This paper presents a metaheuristics-based method for training the artificial neural network, by utilizing the SCA.

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“…NP-hard complexity with real world problems is common and hence the application of these algorithms is diverse. Some notable examples are artificial neural network optimization [7][8][9][10]12,14,15,19,21,26,32,36,48,53,54], wireless sensors networks (WSNs) [4,11,13,52,65,75], cryptocurrency trends estimations [44,49], finally the COVID-19 global epidemic-associated applications [22,25,64,66,[69][70][71]73], computer-conducted MRI classification and sickness determination [17,20,24,33,55], cloud-edge and fog computing and task scheduling [3,5,6,16,23,50,67], and lastly securing networks through intrusion detection [2,31,43,62,…”

Section: Swarm Intelligence Applications In Machine Learningmentioning

confidence: 99%

Intrusion Detection by XGBoost Model Tuned by Improved Multi-verse Optimizer

Petrovic¹,

Antonijevic²,

Strumberger³

et al. 2023

Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB 2022)

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Artificial intelligence and internet of things (IoT) fields have contributed to the flourishment of the industry 4.0 concept. The main benefits include the improvements in terms of device communication, productivity, and efficiency. Nevertheless, there is a downside concerning the security of these systems. The amount of devices and their diversity prove a security risk. Due to this intrusion detection systems are paramount. This paper proposes a novel framework exploiting extreme gradient boosting machine learning model which is optimized by a modified version of the multi-verse optimizer metaheuristic. The UNSW-NB intrusion dataset was used for experimental purposes on which the other cutting-edge techniques were tested and compared. The results provide the proof of improvement as the proposed method outperformed all other overall metaheuristic performances. Furthermore, the units for truthfulness and polarity for the case have been established as a standard evaluation system. True and false positives exist alongside the same negative counterparts. The results provided by these metrics have been visualized and used for further comparison proving the superiority of the performance of the proposed solution.

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Feature Selection Using Modified Sine Cosine Algorithm with COVID-19 Dataset

Cited by 36 publications

References 35 publications

Multi-Swarm Algorithm for Extreme Learning Machine Optimization

Multi-Swarm Algorithm for Extreme Learning Machine Optimization

Artificial Neural Network Tuning by Improved Sine Cosine Algorithm for HealthCare 4.0

Intrusion Detection by XGBoost Model Tuned by Improved Multi-verse Optimizer

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