Namib beetle optimization algorithm: A new meta‐heuristic method for feature selection and dimension reduction

Chahardoli, Meysam; Eraghi, Nafiseh Osati; Nazari, Sara

doi:10.1002/cpe.6524

Cited by 35 publications

(19 citation statements)

References 41 publications

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“…Then, the segmented image is given to the feature extraction, where the suitable features are extracted. Here, the extracted features are Local Ternary Pattern (LTP) [ 22 ], Gray Level Co-occurrence Matrix (GLCM) features [ 23 ], Local Gabor XoR Pattern (LGXP) [ 24 ], statistical features such as mean, variance, standard deviation, kurtosis, and skewness. Moreover, the extracted features are then given to the detection unit, where the COVID detection is categorized into COVID and non-COVID.…”

Section: Proposed Csjso_deep Lstm For Covid-19 Prediction In Fog Layermentioning

confidence: 99%

COVID-19 prediction using Caviar Squirrel Jellyfish Search Optimization technique in fog-cloud based architecture

Amgothu,

Koppu

2023

PLoS ONE

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In the pandemic of COVID-19 patients approach to the hospital for prescription, yet due to extreme line up the patient gets treatment after waiting for more than one hour. Generally, wearable devices directly measure the preliminary data of the patient stored in capturing mode. In order to store the data, the hospitals require large storage devices that make the progression of data more complex. To bridge this gap, a potent scheme is established for COVID-19 prediction based fog-cloud named Caviar Squirrel Jellyfish Search Optimization (CSJSO). Here, CSJSO is the amalgamation of CAViar Squirrel Search Algorithm (CSSA) and Jellyfish Search Optimization (JSO), where CSSA is blended by the Conditional Autoregressive Value-at-Risk (CAViar) and Squirrel Search Algorithm (SSA). This architecture comprises the healthcare IoT sensor layer, fog layer and cloud layer. In the healthcare IoT sensor layer, the routing process with the collection of patient health condition data is carried out. On the other hand, in the fog layer COVID-19 detection is performed by employing a Deep Neuro Fuzzy Network (DNFN) trained by the proposed Remora Namib Beetle JSO (RNBJSO). Here, RNBJSO is the combination of Namib Beetle Optimization (NBO), Remora Optimization Algorithm (ROA) and Jellyfish Search optimization (JSO). Finally, in the cloud layer, the detection of COVID-19 employing Deep Long Short Term Memory (Deep LSTM) trained utilizing proposed CSJSO is performed. The evaluation measures utilized for CSJSO_Deep LSTM in database-1, such as Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) observed 0.062 and 0.252 in confirmed cases. The measures employed in database-2 are accuracy, sensitivity and specificity achieved 0.925, 0.928 and 0.925 in K-set.

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Section: Proposed Csjso_deep Lstm For Covid-19 Prediction In Fog Layermentioning

confidence: 99%

COVID-19 prediction using Caviar Squirrel Jellyfish Search Optimization technique in fog-cloud based architecture

Amgothu,

Koppu

2023

PLoS ONE

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“…The optimization using NBOA [32] is discussed. Here, NEGCNs weight parameters C and F are optimized using NBOA.…”

Section: E Optimization Using Namib Beetle Optimization Algorithmmentioning

confidence: 99%

English Speaking Assessment Algorithm Based on Deep Learning

Xiaoli Hu

2024

jes

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English as a Foreign Language (EFL) students perform when speaking in public. An increasingly globalized world, effective public speaking is critical, but EFL students struggle to perform it, despite importance of qualities such as eye contact, speech pauses, there is presently no objective examination of such elements. A summative assessment has historically been the predominant form of evaluation in college English speaking assessments. Exam-centric teaching has considerable negative effect on foreign language training. In this research work, English Speaking Assessment Algorithm Based on Deep Learning (ESA-NEGCN-NBOA) is proposed. Initially, input video data are gathered from the multiple video dataset (MVD).The input video data is then pre-processed using Deep Attentional Guided Image Filtering (DAGIF) to remove presence of signal-dependent noise and improve lack of pixels from the regions and enhanced the video data. The data that has been pre-processed is utilized to Feature extraction using New General Double Integral Transform (NGDIT), which extract the significant attributes such as mel-frequency cepstral coefficients, energy, speech rate and pitch. Then NEGCN is proposed to improve students spoken English performance by assessmenting the English speakers. In general, NEGCN doesn’t express some adaption of optimization approaches for determining optimal parameters to promise exact improvement of assessment. Therefore, NBOA is proposed to enhance weight parameter of NEGCN for English speaking assessments, which precisely assess the English speaking. Performance measures such as accuracy, assessment error, evaluation time, pretest and posttest are examined when the proposed ESA-NEGCN-NBOA method is put into practice. The proposed ESA-NEGCN-NBOA method attains21.36%, 23.42% and 19.29% higher accuracy, 23.36%, 18.42% and 28.27% lower evaluation error, 20.36%, 27.42%, 28.17% lesser evaluation time analysed with existing techniques, likes innovative strategy towards oral English assessment utilizing machine learning, data mining, blockchain methods(IST-OEA-ML), machine learning assessment system for spoken English depend on linear predictive coding (AS-SE-LPC-ML), multimodal transfer learning for oral presentation assessment (MM-TL-OPA) respectively.

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“…( 2016 ) 338 Naked Moled Rat (NMR) Salgotra and Singh ( 2019 ) 339 Namib Beetle Optimization (NBO) Chahardoli et al. ( 2022 ) 340 Natural Aggregation Algorithm (NAA) Luo et al. ( 2016 ) 341 Natural Forest Regeneration Algorithm (NFR) Moez et al.…”

Section: Metaheuristicsmentioning

confidence: 99%

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

2023

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As the world moves towards industrialization, optimization problems become more challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms (MAs) have been developed to date, with over 350 of them appearing in the last decade. The literature has grown significantly in recent years and should be thoroughly reviewed. In this study, approximately 540 MAs are tracked, and statistical information is also provided. Due to the proliferation of MAs in recent years, the issue of substantial similarities between algorithms with different names has become widespread. This raises an essential question: can an optimization technique be called ‘novel’ if its search properties are modified or almost equal to existing methods? Many recent MAs are said to be based on ‘novel ideas’, so they are discussed. Furthermore, this study categorizes MAs based on the number of control parameters, which is a new taxonomy in the field. MAs have been extensively employed in various fields as powerful optimization tools, and some of their real-world applications are demonstrated. A few limitations and open challenges have been identified, which may lead to a new direction for MAs in the future. Although researchers have reported many excellent results in several research papers, review articles, and monographs during the last decade, many unexplored places are still waiting to be discovered. This study will assist newcomers in understanding some of the major domains of metaheuristics and their real-world applications. We anticipate this resource will also be useful to our research community.

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Namib beetle optimization algorithm: A new meta‐heuristic method for feature selection and dimension reduction

Cited by 35 publications

References 41 publications

COVID-19 prediction using Caviar Squirrel Jellyfish Search Optimization technique in fog-cloud based architecture

COVID-19 prediction using Caviar Squirrel Jellyfish Search Optimization technique in fog-cloud based architecture

English Speaking Assessment Algorithm Based on Deep Learning

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

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