SARC: Search and rescue optimization-based coding scheme for channel fault tolerance in wireless networks

Sreethar, S.; Nandhagopal, N.; Karuppusamy, S. Anbu; Dharmalingam, Mala

doi:10.1007/s11276-021-02702-2

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…At the final stage, the SRO algorithm is exploited to fine tune the parameters involved in the M-WKELM model [18]. SRO algorithm which is simulated as the research shown by humans at the searching and rescue methods.…”

Section: Parameter Optimization Using Sro Algorithmmentioning

confidence: 99%

Search and Rescue Optimization with Machine Learning Enabled Cybersecurity Model

Mengash¹,

Eltahir²,

Al‐Wesabi³

et al. 2023

Computer Systems Science and Engineering

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Presently, smart cities play a vital role to enhance the quality of living among human beings in several ways such as online shopping, e-learning, ehealthcare, etc. Despite the benefits of advanced technologies, issues are also existed from the transformation of the physical word into digital word, particularly in online social networks (OSN). Cyberbullying (CB) is a major problem in OSN which needs to be addressed by the use of automated natural language processing (NLP) and machine learning (ML) approaches. This article devises a novel search and rescue optimization with machine learning enabled cybersecurity model for online social networks, named SRO-MLCOSN model. The presented SRO-MLCOSN model focuses on the identification of CB that occurred in social networking sites. The SRO-MLCOSN model initially employs Glove technique for word embedding process. Besides, a multiclass-weighted kernel extreme learning machine (M-WKELM) model is utilized for effectual identification and categorization of CB. Finally, Search and Rescue Optimization (SRO) algorithm is exploited to fine tune the parameters involved in the M-WKELM model. The experimental validation of the SRO-MLCOSN model on the benchmark dataset reported significant outcomes over the other approaches with precision, recall, and F1-score of 96.24%, 98.71%, and 97.46% respectively.

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Section: Parameter Optimization Using Sro Algorithmmentioning

confidence: 99%

Search and Rescue Optimization with Machine Learning Enabled Cybersecurity Model

Mengash¹,

Eltahir²,

Al‐Wesabi³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

show abstract

“…Accordingly, SAR algorithm has attracted the authors' attention to be used in optimizing various problems including the engineering applications, 19 proposed a modified version of SAR algorithm called FSAR using Fuzzy logic mutation, to optimize the feature selection in medical data classification. Sreethar et al 20 employed SAR algorithm to optimize the fault-tolerant coding process in wireless networks where the authors utilized a SARC scheme to detect and correct channel errors with low computational cost.…”

Section: Introductionmentioning

confidence: 99%

Modified search and rescue optimization algorithm for identifying the optimal parameters of high efficiency triple‐junction solar cell/module

Houssein

Nassef

Fathy

et al. 2022

Intl J of Energy Research

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Summary The Search and Rescue optimization algorithm (SAR) is a recent metaheuristic inspired by the explorations behavior of humans during search and rescue operations. Similar to many of the metaheuristic algorithms (MAs), SAR may suffer from drawbacks, such as being trapped in minimum local regions and improper balance between exploitation and exploration phases. To overcome these weaknesses, this study proposes an alternative algorithm of Search And Rescue optimization algorithm (SAR) called (mSAR) to improve its diversity and provide a soft balance between the exploration and exploitation stages of the original SAR algorithm. The mSAR introduces an adaptive strategy to boost the algorithm performance. To assess its reliability, the proposed approach is validated through IEEE CEC'2020 test suite against six state‐of‐the‐art algorithms namely the Adaptive guided differential evolution algorithm (AGDE), Evolution strategy with covariance matrix adaptation (CMA‐ES), the Whale optimization algorithm (WOA), Harris hawks optimization (HHO), Archimedes optimization algorithm (AOA), Cuckoo search algorithm (CS) besides the original SAR algorithm. A robust strategy based on a mSAR to create an equivalent circuit for a high‐efficiency triple‐junction solar cell/module (TJS/M) has been proposed. The suggested strategy is used to determine the best parameters of the TJS/M model based on the measurement datasets. During the optimization process, the aim is to minimize the integral time absolute error (ITAE) between the measured and estimated currents. The suggested mSAR is compared with other optimizers considering statistical tests of Wilcoxon sign rank, Friedman, and ANOVA. For the case of TJSC based PV module, the best (minimum) objective function is achieved at a value of 0.00178 by the proposed mSAR, whereas the worst value at 0.02476 is obtained by the CS. Furthermore, for the single TJSC, the minimum objective function is achieved at value of 0.04277 by the proposed mSAR whereas the worst value at 0.19987 is obtained by the CS. The best average cost value at 0.00663 is achieved by the proposed mSAR, whereas the worst value at 0.0402 is obtained by the HHO. The best SD value at 0.0061 is achieved by the proposed mSAR, whereas the worst value at 0.03845 is obtained by the AOA. The obtained results proved the superiority of the proposed mSAR in determining the best parameters of the TJS model.

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Detection of Parkinson’s Disease in Brain MRI Images Using Deep Learning Algorithms

Kalyan Chakravarthy,

Hima Bindu,

Jafar Ali Ibrahim

et al. 2023

Smart Innovation, Systems and Technologies

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SARC: Search and rescue optimization-based coding scheme for channel fault tolerance in wireless networks

Cited by 4 publications

References 24 publications

Search and Rescue Optimization with Machine Learning Enabled Cybersecurity Model

Search and Rescue Optimization with Machine Learning Enabled Cybersecurity Model

Modified search and rescue optimization algorithm for identifying the optimal parameters of high efficiency triple‐junction solar cell/module

Detection of Parkinson’s Disease in Brain MRI Images Using Deep Learning Algorithms

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