Background noise often distorts the speech signals obtained in a real-world environment. This deterioration occurs in certain applications, like speech recognition, hearing aids. The aim of Speech enhancement (SE) is ...
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Background noise often distorts the speech signals obtained in a real-world environment. This deterioration occurs in certain applications, like speech recognition, hearing aids. The aim of Speech enhancement (SE) is to suppress the unnecessary background noise in the obtained speech signal. The existing approaches for speech enhancement (SE) face more challenges like low Source-distortion ratio and memory requirements. In this manuscript, Recalling-Enhanced Recurrent Neural Network (R-ERNN) optimized with chimp optimization algorithm based speech enhancement is proposed for hearing aids (R-ERNN-COA-SE-HA). Initially, the clean speech and noisy speech are amassed from MS-SNSD dataset. The input speech signals are encoded using vocoder analysis, and then the Sample RNN decode the bit stream into samples. The input speech signals are extracted using Ternary pattern and discrete wavelet transforms (TP-DWT) in the training phase. In the enhancement stage, R-ERNN forecasts the associated clean speech spectra from noisy speech spectra, then reconstructs a clean speech waveform. chimp optimization algorithm (COA) is considered for optimizing the R-ERNN which enhances speech. The proposed method is implemented in MATLAB, and its efficiency is evaluated under some metrics. The R-ERNN-COA-SE-HA method provides 23.74%, 24.81%, and 19.33% higher PESQ compared with existing methods, such as RGRNN-SE-HA, PACDNN-SE-HA, ARN-SE-HA respectively.
chimp optimization algorithm (ChOA) is a recently proposed metaheuristic. Interestingly, it simulates the social status relationship and hunting behavior of chimps. Due to the more flexible and complex application fie...
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chimp optimization algorithm (ChOA) is a recently proposed metaheuristic. Interestingly, it simulates the social status relationship and hunting behavior of chimps. Due to the more flexible and complex application fields, researchers have higher requirements for native algorithms. In this paper, an enhanced chimp optimization algorithm (EChOA) is proposed to improve the accuracy of solutions. First, the highly disruptive polynomial mutation is used to initialize the population, which provides the foundation for global search. Next, Spearman's rank correlation coefficient of the chimps with the lowest social status is calculated with respect to the leader chimp. To reduce the probability of falling into the local optimum, the beetle antennae operator is used to improve the less fit chimps while gaining visual capability. Three strategies enhance the exploration and exploitation of the native algorithm. To verify the function optimization performance, EChOA is comprehensively analyzed on 12 classical benchmark functions and 15 CEC2017 benchmark functions. Besides, the practicability of EChOA is also highlighted by three engineering design problems and training multilayer perceptron. Compared with ChOA and five state-of-the-art algorithms, the statistical results show that EChOA has strong competitive capabilities and promising prospects.
Aiming at the shortcomings of chimp optimization algorithm, such as easy to fall into local optimum, slow convergence speed and low convergence accuracy, an improved chimp optimization algorithm based on multi-strateg...
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ISBN:
(纸本)9781665464215
Aiming at the shortcomings of chimp optimization algorithm, such as easy to fall into local optimum, slow convergence speed and low convergence accuracy, an improved chimp optimization algorithm based on multi-strategy fusion is proposed. Firstly, the population was initialized by nonlinear control parameter strategy, which enhance the quality of the initial individuals and the diversity of the population, and lay the foundation for the global optimization of the algorithm. Secondly, by improving position updating process of chimp, the leader position of the attacker chimpanzee is reflected and the global optimization ability of the algorithm is improved. Finally, The Cauchy-Gauss mutation strategy was used to improve the ability of maintaining population diversity, improve the convergence accuracy and speed of the algorithm. Eleven bench mark test functions with different characteristics are optimizated. The test results and Wilcoxon's signed rank test results both show that the improved algorithm has better optimization accuracy, convergence performance and stability.
This paper focuses on connectivity-based data clustering for categorizing similar and dissimilar data into distinct groups. Although classical clustering algorithms such as K-means are efficient techniques, they often...
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The hybrid power systems become necessary, mainly in non-electrified areas as in Africa, where millions of peoples have not access to electricity. This study solves the design problem of the microgrid systems, contain...
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ISBN:
(纸本)9781665401272
The hybrid power systems become necessary, mainly in non-electrified areas as in Africa, where millions of peoples have not access to electricity. This study solves the design problem of the microgrid systems, containing PV panels, wind turbines and battery storage system. This paper focuses to apply a recent algorithm named chimp optimization algorithm (ChOA) and comparing its performance with two algorithms called IWO and GWO. The microgrid system design is based on optimizing of net present cost function, respecting some constraints. The results showed that the recent ChOA algorithm is better than the IWO and GWO, however, the suggested system is very suitable in the Dakhla location, where the metrological conditions are suitable.
A Wireless Sensor Network (WSN) is a self-organization network that contains several tiny sensor nodes for tracking and monitoring an application in a wide range. Still, security and consumption of energy are two majo...
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Financial crises are typically the result of a combination of economic, political, and market factors, and they can be triggered by events that are challenging to foresee. Financial Crisis Prediction (FCP) has played ...
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The high prevalence of breast cancer in women has increased dramatically in recent times. Physician's knowledge in breast cancer diagnosis and detection using computerized algorithms for extraction and segmentatio...
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The high prevalence of breast cancer in women has increased dramatically in recent times. Physician's knowledge in breast cancer diagnosis and detection using computerized algorithms for extraction and segmentation of features can help. Image segmentation is a critical component of image analysis that has a direct impact on the quality of the results. This article presents Kapur's entropy-based multilevel thresholding using chimp optimization algorithm (ChOA) to estimate optimal values for the lesion segmentation of breast DCE-MRI. An improved ChOA is also developed by incorporating Opposition based-learning (OBL) in it, termed as ChOAOBL, and applied to solve the same problem. The proposed methods are evaluated using 200 Sagittal T2-Weighted fat-suppressed DCE-MRI images of 40 patients. The proposed methods are compared with Improved ChOA (IChOA), Particle Swarm optimization (PSO), Multi-verse Optimizer (MVO), Slime Mould algorithm (SMA), Arithmetic optimizationalgorithm (AOA), Tunicate Swarm algorithm (TSA), Multilevel Otsu Threshold (MLOT), Conventional Markov Random Field (CMRF), Hidden Markov Random Field (HMRF), and Improved Markov Random Field (IMRF). The high sensitivity, accuracy, and Dice Efficient Coefficient (DSC) level of the proposed ChOA-based method are achieved at 90.75%, 98.24%, and 87.09% respectively. The accuracy value of 99.02%, sensitivity 95.73%, and DSC 93.25% are achieved using another proposed ChOAOBL-based segmentation method. The results are analyzed using a one-way ANOVA test followed by Tukey HSD, and Wilcoxon Signed Rank Test. We have also analyzed the overall performance using Multi Criteria Decision Making based on accuracy, precision, specificity, F-measure, sensitivity, false-positive rate, Geometric-Mean (G-mean), and DSC. The proposed methods outperform other compared methods, according to both quantitative and qualitative outcomes.
Speech signals often include paralinguistic features such as pathologies that impair a speaker's capability to communicate. Those cognitive symptoms have various causes depending on the disease. For example, morph...
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Speech signals often include paralinguistic features such as pathologies that impair a speaker's capability to communicate. Those cognitive symptoms have various causes depending on the disease. For example, morphological diseases like cleft lip and palate create hypernasality, while neurodegenerative conditions like Parkinson's disease cause hypokinetic dysarthria. Automatic assessment of abnormal speech supports early diagnosis or disease severity evaluation. Conventional methods rely on manually assessing single aspects like shimmer, jitter, or formant frequencies, which may not fully reflect the disease's manifestations. In this paper, we use deep convolutional neural networks (DCNNs) to recognize disordered speech. Despite DCNNs' many approved benefits, selecting the best structure is challenging. In order to overcome this issue, this research looks into using the chimp optimization algorithm (ChOA) to automatically select the optimal DCNN structure. In order to achieve the goal, three ChOA-based advancements are proposed. First, an internet protocol address-based (IPA-based) encoding method for DCNN layers employing chimp vectors is created. Then an Enfeebled layer with specified chimp vector dimensions is presented for variable-length DCNNs. Eventually, large datasets are partitioned into smaller ones and evaluated at random to recognize abnormal speech signals from patients with Parkinson's disease and cleft lip and palate. In addition to receiver operating characteristic (ROC) and precision-recall curves, five well-known metrics were used: sensitivity, specificity, accuracy, precision, F1-Score. The proposed model accurately diagnoses disordered and normal speech signals, with an accuracy of up to 96.37%, which is 1.62 more accurate than the second-best approach, i.e., VLNSGA-II.
This paper presents an evolved chimp optimization algorithm (ChOA) that uses greedy search (GS) and opposition-based learning (OBL) to respectively increase the ChOA's capabilities for exploration and exploitation...
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This paper presents an evolved chimp optimization algorithm (ChOA) that uses greedy search (GS) and opposition-based learning (OBL) to respectively increase the ChOA's capabilities for exploration and exploitation in addressing real practical engineering-constrained problems. In order to investigate the efficiency of the GSOBL-ChOA, its performance is evaluated by twenty-three standard benchmark functions, 10 benchmark functions from CEC06-2019, a randomly generated landscape, and 12 real practical Constrained optimization Problems (COPs-2020) from a wide variety of engineering fields, including power system design, synthesis and process design, industrial chemical producer, power -electronic design, mechanical design, and animal feed ratio. The findings are compared to those obtained using benchmark optimizers such as CMA-ES and SHADE as state-of-the-art optimization techniques and CEC competition winners;standard ChOA;OBL-GWO, OBL-SSA, and OBL-CSA as the best benchmark OBL-based algorithms. In order to perform a comprehensive assessment, three non-parametric statistical tests, including the Wilcoxon rank-sum, Bonferroni-Dunn and Holm, and Friedman average rank tests, are utilized. The top two algorithms are GSOBL-ChOA and CMA-ES, with scores of forty and eleven, respectively, among 27 mathematical functions. jDE100 obtained the highest score of 100 in the 100-digit challenge, followed closely by DISHchain1e+12, which achieved the highest possible score of 97, and GSOBL-ChOA obtained the fourth-highest score of 93. Finally, GSOBL-ChOA and CMA-ES outperform other benchmarks in five and four real practical COPs, respectively. The source code of the paper can be downloaded using the following link: https://***/ matlabcentral/fileexchange/119108-evolving-chimp-optimization-algorithm-by-weighted-opposition.(c) 2022 Elsevier B.V. All rights reserved.
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