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Recalling-Enhanced Recurrent Neural Network optimized with chimp optimization algorithm based speech enhancement for hearing aids

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INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2024年第1期18卷 123-134页

作者： Rai, Rahul R. Mathivanan, M. VTU SJB Inst Technol Dept Elect & Commun Engn Belagavi India ACS Coll Engn Dept Elect & Commun Engn Bengaluru Karnataka India

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.

关键词： Speech enhancement hearing aids MS-SNSD dataset ternary pattern and discrete wavelet transforms Recalling-Enhanced Recurrent Neural Network chimp optimization algorithm

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An enhanced chimp optimization algorithm for continuous optimization domains

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COMPLEX & INTELLIGENT SYSTEMS 2022年第1期8卷 65-82页

作者： Jia, Heming Sun, Kangjian Zhang, Wanying Leng, Xin Sanming Univ Coll Informat Engn Sanming 365004 Fujian Peoples R China Northeast Forestry Univ Coll Mech & Elect Engn Harbin 150040 Heilongjiang Peoples R China

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.

关键词： chimp optimization algorithm Highly disruptive polynomial mutation Spearman’ s rank correlation coefficient Beetle antennae operator Continuous optimization domains

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Improved chimp optimization algorithm with multi-strategy integration 9

Improved Chimp optimization algorithm with multi-strategy in...

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9th International Forum on Electrical Engineering and Automation (IFEEA)

作者： Li, Ya-mei Jin, Tian-cheng Liu, Shang-lin Liu, Su Liaoning Tech Univ Fac Elect & Control Engn Fuxin Liaoning Peoples R China Liaoning Tech Univ Fac Business Adm Fuxin Liaoning Peoples R China

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.

关键词： chimp optimization algorithm Nonlinear control parameter strategy Cauchy variation

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Optimal Design of Microgrid Using chimp optimization algorithm

Optimal Design of Microgrid Using Chimp Optimization Algorit...

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IEEE IFAC International Conference on Automation (ICA) / 24th Congress of the Chilean-Association-of-Automatic-Control (ACCA)

作者： Kharrich, Mohammed Mohammed, Omar Hazem Kamel, Salah Aljohani, Mansour Akherraz, Mohammed Mosaad, Mohamed, I Mohammed V Univ Mohammadia Sch Engineers Dept Elect Engn Rabat Morocco Northern Tech Univ Tech Power Engn Tech Coll Mosul Iraq Aswan Univ Dept Elect Engn Aswan Egypt Yanbu Ind Coll Dept Elect & Elect Engn Yanbu Saudi Arabia

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.

关键词： chimp optimization algorithm GWO Hybrid Power Systems IWO Microgrid Net Present Cost

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A hybrid chimp optimization algorithm and generalized normal distribution algorithm with opposition-based learning strategy for solving data clustering problems

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Iran Journal of Computer Science 2024年第1期7卷 65-101页

作者： Haeri Boroujeni, Sayed Pedram Pashaei, Elnaz School of Computing Clemson University Clemson 29634 SC United States Department of Medical and Molecular Genetics Indiana University School of Medicine Indianapolis United States

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 trap in local optima and have a slow convergence rate in solving high-dimensional problems. To address these issues, many successful meta-heuristic optimization algorithms and intelligence-based methods have been introduced to attain the optimal solution in a reasonable time. In this study, we attempt to conceptualize a powerful approach using the three main components: chimp optimization algorithm (ChOA), Generalized Normal Distribution algorithm (GNDA), and Opposition-Based Learning (OBL) method. First, two versions of ChOA with two different dynamic coefficients and seven chaotic maps, entitled ChOA(I) and ChOA(II), are presented to achieve the best possible result for data clustering purposes. Second, a novel combination of ChOA and GNDA algorithms with the OBL strategy is devised to solve the major shortcomings of the original algorithms. Lastly, the proposed ChOAGNDA method is a Selective Opposition (SO) algorithm based on ChOA and GNDA, which can be used to tackle large and complex real-world optimization problems, particularly data clustering applications. In this study, eight benchmark datasets, including five datasets of the UCI machine learning repository and three challenging shape datasets, are used to investigate the general performance of the proposed method. The results are evaluated against several popular and recent state-of-the-art clustering techniques. Experimental results illustrate that the proposed work significantly outperforms other existing methods in terms of the Sum of Intra-Cluster Distances (SICD), Error Rate (ER), and convergence rate. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： chimp optimization algorithm Data clustering Generalized normal distribution algorithm K-means Meta-heuristic optimization algorithm Opposition-based learning

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Spiral Shape - chimp optimization algorithm for Secure Cluster-Based Routing in Wireless Sensor Network 2

Spiral Shape - Chimp Optimization Algorithm for Secure Clust...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Vennam, Preethi Mouleeswaran, S.K. Dayananda Sagar University Department of Computer Science and Enigineering Bengaluru India

ISBN: (纸本)9798350317558

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 major threats due to open sources and restricted energy sources. In the research, the Spiral Shape-chimp optimization algorithm (SS-ChOA) method is proposed to perform secure cluster-based routing in WSN. The SS-ChOA is selected due to its high convergence rate and avoiding local optima in the exploitation phase. The proposed SS-ChOA is used to choose secure Cluster Heads (CHs) and routing for achieving secure communication across the network. The proposed algorithm provided maximized security against malicious nodes while maximizing energy efficiency. The performance of the SS-ChOA method is evaluated through Packet Delivery Ratio (PDR), throughput, End-to-end Delay (EED), and energy consumption. SS-ChO algorithm obtained less energy consumption of 0.35 J, 0.43 J, 0.57 J, 0.66 J, and 0.73 J by varying nodes from 100 to 500. The proposed algorithm performed more efficiently than other existing algorithms like Multi-objective Trust based Crow Search algorithm (MTCSA) - Hybrid Encryption algorithm (HEA), Multi Objective Trust Aware Hybrid optimization (MOTAHO), and Multi-Objective Trust Centric Artificial Algae algorithm (M-TCAAA). © 2024 IEEE.

关键词： chimp optimization algorithm cluster heads malicious nodes security spiral shape wireless sensor network

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Exploiting Pattern Recognition using chimp optimization algorithm with Machine Learning for Financial Crisis Prediction

Exploiting Pattern Recognition using Chimp Optimization Algo...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Kalaivani, R. Saravanan, A. Annamalai University Department of Computer and Information Sciences India

ISBN: (纸本)9798350313987

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 a most important part in the financial industry. While predicting financial crises is challenging, some attempts have been made by researchers and organizations to use Machine Learning (ML) techniques to find potential risks and patterns that could be related to impending financial crises. ML techniques can analyze enormous quantities of data and recognize complex relationships that mayn't be instantly evident to human analysts. Therefore, this study offers the design of Enhancing Financial Crisis Prediction using the chimp optimization algorithm with Machine Learning (EFCP-COAML) algorithm. The primary objective of the EFCP-COAML technique is to predict the automated and accurate prediction of the financial crisis. For accomplishing this, the EFCP-COAML technique comprises two major processes such as classification and hyperparameter tuning. At first, the EFCP-COAML method uses a Kernel Extreme Learning Machine (KELM) based prediction procedure. In the next phase, the COA is employed for the parameter selection of the KELM model which in turn enhances the predictive performance. An extensive set of tests were accomplished to demonstrate the superior accomplishment of the EFCP-COAML technique. The simulation results inferred the advancement of the EFCP-COAML approach over other present techniques with greatest accuracy of 94.01%. © 2023 IEEE.

关键词： Artificial intelligence chimp optimization algorithm Financial crisis prediction Machine learning

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Breast DCE-MRI segmentation for lesion detection using chimp optimization algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期204卷

作者： Si, Tapas Patra, Dipak Kumar Mondal, Sukumar Mukherjee, Prakash Bankura Unnayani Inst Engn Dept Comp Sci & Engn Bankura 722146 West Bengal India Raja Narendralal Khan Womens Coll Res Ctr Nat & Appl Sci Dept Comp Sci Midnapore 721102 West Bengal India Raja Narendralal Khan Womens Coll Dept Math Midnapore 721102 West Bengal India Hijli Coll Dept Math Kharagpur 721306 West Bengal India

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 optimization algorithm (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.

关键词： Breast DCE-MRI Segmentation Entropy Multi-level thresholding chimp optimization algorithm Opposition-based learning

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Evolving chimp optimization algorithm by weighted opposition-based technique and greedy search for multimodal engineering problems

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APPLIED SOFT COMPUTING 2023年 132卷

作者： Bo, Qiuyu Cheng, Wuqun Khishe, Mohammad Hebei Agr Univ Inst Urban & Rural Construction Baoding Hebei Peoples R China Tsinghua Univ State Key Lab Hydrosci & Engn Beijing 100084 Peoples R China Imam Khomeini Marine Sci Univ Dept Elect Engn Nowshahr Iran

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.

关键词： Metaheuristics chimp optimization algorithm Opposition-based learning Greedy search

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Diagnose Parkinson?s disease and cleft lip and palate using deep convolutional neural networks evolved by IP-based chimp optimization algorithm

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2022年第0期77卷 000-000页

作者： Chen, Feng Yang, Chunyan Khishe, Mohammad Wenzhou Med Univ Fac Informat Wenzhou 325035 Zhejiang Peoples R China Ctr China Normal Univ Fac Artificial Intelligence Educ Wuhan 430079 Hubei Peoples R China Imam Khomeini Marine Sci Univ Dept Elect Engn Nowshahr Iran

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.

关键词： Pathological speech Deep convolutional neural networks Parkinson?s disease Cleft lip and palate chimp optimization algorithm

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