Synergistic fibroblast optimization: a novel nature-inspired computing algorithm

Dhivyaprabha, T T; Krishnaveni, M.

doi:10.1631/fitee.1601553

Cited by 27 publications

(7 citation statements)

References 43 publications

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“…Therefore, in the following sections, the algorithms which have been developed in ten years span and applied to feature selection problems are discussed. [40] Backtracking search optimization 2013 SFS [41] Stochastic fractal search 2014 SFO [42] Synergistic fibroblast optimization 2018…”

Section: B Metaheuristic Algorithmsmentioning

confidence: 99%

Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019)

et al. 2021

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Feature selection is a critical and prominent task in machine learning. To reduce the dimension of the feature set while maintaining the accuracy of the performance is the main aim of the feature selection problem. Various methods have been developed to classify the datasets. However, metaheuristic algorithms have achieved great attention in solving numerous optimization problem. Therefore, this paper presents an extensive literature review on solving feature selection problem using metaheuristic algorithms which are developed in the ten years (2009-2019). Further, metaheuristic algorithms have been classified into four categories based on their behaviour. Moreover, a categorical list of more than a hundred metaheuristic algorithms is presented. To solve the feature selection problem, only binary variants of metaheuristic algorithms have been reviewed and corresponding to their categories, a detailed description of them explained. The metaheuristic algorithms in solving feature selection problem are given with their binary classification, name of the classifier used, datasets and the evaluation metrics. After reviewing the papers, challenges and issues are also identified in obtaining the best feature subset using different metaheuristic algorithms. Finally, some research gaps are also highlighted for the researchers who want to pursue their research in developing or modifying metaheuristic algorithms for classification. For an application, a case study is presented in which datasets are adopted from the UCI repository and numerous metaheuristic algorithms are employed to obtain the optimal feature subset.

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Section: B Metaheuristic Algorithmsmentioning

confidence: 99%

Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019)

et al. 2021

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“…The ever-increasing complexity of the newly developed DL methods has raised an emerging resurgence of research on FIGURE 10: Meta-heuristic algorithms with Sine cosine algorithm [99], Find fix exploit analyse [100], Electrosearch algorithm [101], Selfish heard algorithm [102], Emperor Penguins colony [103], Butterfly optimization algorithm [104], Group counseling optimization [105], Volleyball premier league algorithm [106], Jaya algorithm [107], Gaining sharing knowledge based [108], Differential search algorithm [109], Backtracking search optimization [110], Stochastic fractal search [111], Synergistic fibroblast optimization [112]. HO.…”

Section: E Hyperparameter Optimization Of DL Architecturesmentioning

confidence: 99%

Convergence of Photovoltaic Power Forecasting and Deep Learning: State-of-Art Review

et al. 2021

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Deep learning (DL)-based PV Power Forecasting (PVPF) emerged nowadays as a promising research direction to intelligentize energy systems. With the massive smart meter integration, DL takes advantage of the large-scale and multi-source data representations to achieve a spectacular performance and high PV forecastability potential compared to classical models. This review article taxonomically dives into the nitty-gritty of the mainstream DL-based PVPF methods while showcasing their strengths and weaknesses. Firstly, we draw connections between PVPF and DL approaches and show how this relation might cross-fertilize or extend both directions. Then, fruitful discussions are conducted based on three classes: discriminative learning, generative learning, and deep reinforcement learning. In addition, this review analyzes recent automatic architecture optimization algorithms for DL-based PVPF. Next, the notable DL technologies are thoroughly described. These technologies include federated learning, deep transfer learning, incremental learning, and big data DL. After that, DL methods are taxonomized into deterministic and probabilistic PVPF. Finally, this review concludes with some research gaps and hints about future challenges and research directions in driving the further success of DL techniques to PVPF applications. By compiling this study, we expect to help aspiring stakeholders widen their knowledge of the staggering potential of DL for PVPF.

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“…Totally, 7,700 real time speech signals are utilized to construct the model, in which, 70% data are used for training phase and the remaining 30% data are used for testing phase. The performance metrics namely, accuracy, sensitivity, specificity, precision and F-measure are calculated to evaluate the efficiency of RNN classifier [20]. It is compared with different types of Artificial Neural Network (ANN) algorithms, such as, Feed Forward Neural Network (FFNN), Backpropagation Neural Network (BPN) and Random Vector Functional Link Network (RVFLN) to conduct better investigation of this study and the obtained results are tabulated in Table 3.…”

Section: Experimental Observationsmentioning

confidence: 99%

Articulation Battery Test For Tamil Speech Disorder Signals

Krishnaveni¹,

Subashini²,

Dhivyaprabha³

2021

JUSST

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Articulation disorder is referred as difficulty occurs in the pronunciation of specific speech sounds. An irregular coordination of the movement of tongue, lips, palate, jaw, respiratory system, vocal tract, height of the larynx, air flow through nasal leads to the incorrect production of speech sounds. The objective of this paper is to propose a computational model based on Recurrent Neural Network (RNN) algorithm to categorize the phonological patterns of Tamil speech articulation disorder signals into four predefined groups, namely, substitution, omission, distortion and addition. The methodology of the proposed work is described as follows. (1) List of articulation disorder test words suggested by Speech Language Pathologists (SLPs) is selected for this experimental study. (2) Real time speech signals that comprise of Tamil vowels (Uyir eluthukkal) and consonants (Meiyeluthukkal) are collected from people with articulation disorder. (3) Acoustic noise and weak signals are eliminated by applying Low pass filter to acquire the filtered speech signal. (4) Mel-Frequency Cepstral Coefficients (MFCCs) technique is implemented to extract the prominent features from denoised signals. (5) Principal Component Analysis (PCA) method is employed to choose fine-tune feature subset. (6) The refined features are employed to calibrate RNN model for classification. Results show that RNN model achieves 90.25% classification accuracy when compared to other artificial neural network algorithms.

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Synergistic fibroblast optimization: a novel nature-inspired computing algorithm

Cited by 27 publications

References 43 publications

Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019)

Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019)

Convergence of Photovoltaic Power Forecasting and Deep Learning: State-of-Art Review

Articulation Battery Test For Tamil Speech Disorder Signals

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