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Music genre classification with parallel convolutional neural networks and capuchin search algorithm

SCIENTIFIC REPORTS 2025年第1期15卷 1-17页

作者： Zhang, Yuxin Li, Teng Jilin Univ Arts Conservatory Mus Changchun 130000 Jilin Peoples R China Pingdingshan Polytenchn Coll Acad Arts Pingdingshan 467000 Henan Peoples R China

With the primary objective of creating playlists that suggest songs, interest in music genre categorization has grown thanks to high-tech multimedia tools. To develop a strong music classifier that can quickly classify unlabeled music and enhance consumers' experiences with media players and music files, machine learning and deep learning ideas are required. This study presents a unique method that blends convolutional neural network (CNN) models as an ensemble system to detect musical genres. The method makes use of discrete wavelet transform (DWT), mel frequency cepstral coefficients (MFCC), and short-time fourier transform (STFT) characteristics to provide a comprehensive framework for expressing stylistic qualities in music. To do this, each model's hyperparameters are generated using the capuchin search algorithm (CapSA). Preprocessing the original signals, feature description utilizing DWT, MFCC, and STFT signal matrices, CNN model optimization to extract signal features, and music genre identification based on combined features make up the four main components of the technique. By integrating many signal processing techniques and CNN models, this study advances the field of music genre classification and provides possible insights into the blending of diverse musical components for improved classification accuracy. The GTZAN and Extended-Ballroom datasets were the two used in the studies. The average classification accuracy of 96.07 and 96.20 for each database, respectively, show how well our suggested strategy performs when compared to earlier, comparable methods.

关键词： Music genre classification Convolutional neural network Deep learning capuchin search algorithm

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Design of High Gain Metasurface Antenna Using Hybrid African Vulture's Optimization and capuchin search algorithm for RF Energy Harvesting (Jul, 10.1007/s11277-023-10583-1, 2023)

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WIRELESS PERSONAL COMMUNICATIONS 2023年第1期132卷 95-95页

作者： Kumar, M. Vinod Sharma, Dinesh Sri Vasavi Engn Coll Tadepalligudem 534101 Andhra Pradesh India Panjab Univ CCET Chandigarh Dept ECE Chandigarh 160019 India

In general, Metasurface Antennas (MSA) are designed to diminish the antenna shape by enhancing the operating band and directivity. As the efficiency decreases, the design complexity of MSA increases. In order to enhance the antenna design, a high-gain MSA is designed using the hybrid African Vulture's Optimization algorithm (AVOA), and the capuchin search algorithm (CapSA) is used for Radio Frequency (RF) energy harvesting. The dimensions of the designed antenna are \(1.66\lambda_capuchin search algorithm \times 1.25\lambda_capuchin search algorithm \times 0.02\lambda_capuchin search algorithm\) with a resonating frequency of 5 GHz. To design the high gain MSA, the proposed Hybrid African Vulture’s Optimization and capuchin search algorithm (Hyb-AVOA-CapSA) is used to enhance the antenna parameters such as radiation efficiency, Bandwidth, gain, and return loss. Therefore, the proposed MSA design has achieved high efficiency and profit. Finally, the simulation has done on HFSS19 and ADS2020 version software; and evaluated using MATLAB. The proposed antenna gives a better efficiency of 70.12%, and resonate at 1.5 GHz of the axial ratio bandwidth at 5 GHz resonant frequency. The gain of the proposed antenna has increased from 6.86 to 7.6 dBi. While examining the comparative outcomes, the proposed approach has attained 22.4%, 23.7% high gain, and 18.85%, 12.6% lower return loss than the compared methods. Thus, the designed MSA is applied in RF energy harvesting applications because of its compact, low-profile, and simple structure. The Rectenna design uses a voltage doubler circuit at the receiver end and produces 5.55 V.

关键词： African vulture’s Optimization algorithm capuchin search algorithm Gain Metasurface antenna RF energy harvesting

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Cognitively Enhanced Versions of capuchin search algorithm for Feature Selection in Medical Diagnosis: a COVID-19 Case Study

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COGNITIVE COMPUTATION 2023年第6期15卷 1884-1921页

作者： Braik, Malik Awadallah, Mohammed A. Al-Betar, Mohammed Azmi Hammouri, Abdelaziz I. Alzubi, Omar A. Al Balqa Appl Univ Dept Comp Sci Salt Jordan Al Aqsa Univ Dept Comp Sci POB 4051 Gaza Palestine Ajman Univ Coll Engn & Informat Technol Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates Al Balqa Appl Univ Al Huson Univ Coll Dept Informat Technol Irbid Jordan

Feature selection (FS) is a crucial area of cognitive computation that demands further studies. It has recently received a lot of attention from researchers working in machine learning and data mining. It is broadly employed in many different applications. Many enhanced strategies have been created for FS methods in cognitive computation to boost the performance of the methods. The goal of this paper is to present three adaptive versions of the capuchin search algorithm (CSA) that each features a better search ability than the parent CSA. These versions are used to select optimal feature subset based on a binary version of each adapted one and the k-Nearest Neighbor (k-NN) classifier. These versions were matured by applying several strategies, including automated control of inertia weight, acceleration coefficients, and other computational factors, to ameliorate search potency and convergence speed of CSA. In the velocity model of CSA, some growth computational functions, known as exponential, power, and S-shaped functions, were adopted to evolve three versions of CSA, referred to as exponential CSA (ECSA), power CSA (PCSA), and S-shaped CSA (SCSA), respectively. The results of the proposed FS methods on 24 benchmark datasets with different dimensions from various repositories were compared with other k-NN based FS methods from the literature. The results revealed that the proposed methods significantly outperformed the performance of CSA and other well-established FS methods in several relevant criteria. In particular, among the 24 datasets considered, the proposed binary ECSA, which yielded the best overall results among all other proposed versions, is able to excel the others in 18 datasets in terms of classification accuracy, 13 datasets in terms of specificity, 10 datasets in terms of sensitivity, and 14 datasets in terms of fitness values. Simply put, the results on 15, 9, and 5 datasets out of the 24 datasets studied showed that the performance levels of the

关键词： Feature selection capuchin search algorithm Transfer function Optimization

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A Hybrid Multi-gene Genetic Programming with capuchin search algorithm for Modeling a Nonlinear Challenge Problem: Modeling Industrial Winding Process, Case Study

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NEURAL PROCESSING LETTERS 2021年第4期53卷 2873-2916页

作者： Braik, Malik Al Balqa Appl Univ Dept Comp Sci Salt Jordan

Motivated by the increasing complexity and operational productivity of industrial processes, the need for efficient modeling schemes for industrial systems is highly demanded. This study presents a new simulator model for a real winding process based on a combination of Multi-gene Genetic Programming (MGP) and capuchin search algorithm (CapSA), referred to as MGP-CapSA modeling approach. CapSA is a meta-heuristic algorithm used to optimize the coefficients of the regression equations of the MGP technique. The winding process has tensions in the web between reels 1 and 2 and between reels 2 and 3. On this basis, two mathematical models were developed by the MGP-CapSA method to estimate the tensions in the web for this process. The efficacy and superiority of the proposed MGP-CapSA method were verified by extensive experiments and hypothesis testing, and the proposed method was then compared with other well-known intelligent and conventional modeling methods. The proposed MGP-CapSA method can be exploited to enhance control performance and achieve robust fault-tolerant system. A comparison of the MGP-CapSA method with other promising modeling methods corroborates the performance level of MGP-CapSA over those competitors. The results demonstrate that MGP-CapSA is a suitable method for generating robust models for complex nonlinear systems.

关键词： Multi-gene genetic programming capuchin search algorithm Genetic programming Winding problem

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Optimization of K-means clustering method using hybrid capuchin search algorithm

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JOURNAL OF SUPERCOMPUTING 2024年第2期80卷 1728-1787页

作者： Qtaish, Amjad Braik, Malik Albashish, Dheeb Alshammari, Mohammad T. T. Alreshidi, Abdulrahman Alreshidi, Eissa Jaber Univ Hail Coll Comp Sci & Engn Dept Informat & Comp Sci Hail 81481 Saudi Arabia Al Balqa Appl Univ Prince Abdullah bin Ghazi Fac Informat & Commun Te Comp Sci Dept Salt Jordan

This work presents Hybrid capuchin search algorithm (HCSA) as a meta-heuristic method to deal with the vexing problems of local optima traps and initialization sensitivity of the K-means clustering technique. This study was inspired by the popularity and permanence of meta-heuristics in presenting convincing solutions, which sparked various efficient methods and computational tools to tackle difficult and practical real-world problems. The movement behavior of CSA is strengthened using the Chameleon Swarm algorithm to support the search agents of CSA to more effectively explore and exploit each potential region of the search space. This increases the capacity of both exploitation and exploration of the traditional CSA. Besides, the search agents of CSA utilize the rotation mechanism in CS to migrate to new spots outside the nearby regions to perform global search. This mechanism improves the search proficiency of CSA as well as the intensification and diversity abilities of the search agents. These expansion aptitudes of CSA expand its exploitation potential and broaden the range of search scopes, sizes, and directions in conducting clustering activities. A total of 16 different datasets from diverse sources, each with a different level of complexity, characteristics, and dimension, are used to assess the performance of the developed HCSA method on clustering tasks. According to the experimental results, the proposed HCSA performs statistically significantly better than the K-means clustering algorithm and eight meta-heuristics-based clustering in terms of both distance and performance metric measures.

关键词： capuchin search algorithm Chameleon swarm algorithm Clustering K-means Meta-heuristics

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A novel meta-heuristic search algorithm for solving optimization problems: capuchin search algorithm

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NEURAL COMPUTING & APPLICATIONS 2021年第7期33卷 2515-2547页

作者： Braik, Malik Sheta, Alaa Al-Hiary, Heba Al Balqa Appl Univ Salt Jordan Southern Connecticut State Univ Dept Comp Sci 501 Crescent St New Haven CT 06515 USA

Meta-heuristic search algorithms were successfully used to solve a variety of problems in engineering, science, business, and finance. Meta-heuristic algorithms share common features since they are population-based approaches that use a set of tuning parameters to evolve new solutions based on the natural behavior of creatures. In this paper, we present a novel nature-inspired search optimization algorithm called the capuchin search algorithm (CapSA) for solving constrained and global optimization problems. The key inspiration of CapSA is the dynamic behavior of capuchin monkeys. The basic optimization characteristics of this new algorithm are designed by modeling the social actions of capuchins during wandering and foraging over trees and riverbanks in forests while searching for food sources. Some of the common behaviors of capuchins during foraging that are implemented in this algorithm are leaping, swinging, and climbing. Jumping is an effective mechanism used by capuchins to jump from tree to tree. The other foraging mechanisms exercised by capuchins, known as swinging and climbing, allow the capuchins to move small distances over trees, tree branches, and the extremities of the tree branches. These locomotion mechanisms eventually lead to feasible solutions of global optimization problems. The proposed algorithm is benchmarked on 23 well-known benchmark functions, as well as solving several challenging and computationally costly engineering problems. A broad comparative study is conducted to demonstrate the efficacy of CapSA over several prominent meta-heuristic algorithms in terms of optimization precision and statistical test analysis. Overall results show that CapSA renders more precise solutions with a high convergence rate compared to competitive meta-heuristic methods.

关键词： Optimization Meta-heuristics Bio-inspired algorithms capuchin search algorithm Optimization techniques

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A hybrid capuchin search algorithm with gradient search algorithm for economic dispatch problem

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SOFT COMPUTING 2023年第22期27卷 16809-16841页

作者： Braik, Malik Awadallah, Mohammed A. A. Al-Betar, Mohammed Azmi Hammouri, Abdelaziz I. I. Al Balqa Appl Univ Dept Comp Sci Salt Jordan Al Aqsa Univ Dept Comp Sci Gaza Palestine Ajman Univ Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates Ajman Univ Coll Engn & Informat Technol Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates Al Hosn Univ Coll Dept Informat Technol Irbid Jordan

This paper presents an effective approach for solving economic load dispatch problems contemplating the scheduling a set of thermal generating units to produce a specific power at low consumption costs. These problems can be thought of as nonlinear, non-convex, and highly constrained optimization problems with a large number of local minima. To cope with the above issues in solving such problems, a new meta-heuristic named capuchin search algorithm was adopted. To boost the search performance of this algorithm as well as to mitigate its early convergence and regression to the local optimum, it was hybridized with another algorithm and improved using several positive amendments. First, a memory element was added to this algorithm to ameliorate its position and velocity update mechanisms in order to exploit the most encouraging candidate solutions. Second, two adaptive parametric functions were used to manage the exploration and exploitation features of this algorithm and balance them appropriately. Finally, the hybridization was made using the gradient-based optimizer to strengthen the intensification ability of this algorithm and balance its searching ability to fulfill sensible search performance. The proficiency of the proposed algorithm was divulged by assessing it on computationally difficult economic load dispatch problems under 6 different tests with a generator of 3, 13, 40, 80, and 140 units, each with different constraints and load conditions. The proposed algorithm provided the best performance among many other competitors. Its superiority and practicality were revealed by obtaining optimal solutions for large-scale test cases such as 40-unit and 140-unit test systems.

关键词： Economic load dispatch capuchin search algorithm Gradient-based optimizer Memory concept Optimization

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A density-based MS disease diagnosis model using the capuchin search algorithm and an ensemble of deep neural networks

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SCIENTIFIC REPORTS 2024年第1期14卷 1-17页

作者： Bai, Lijuan Wu, Jiao Chen, Li Jiang, Xin Song, Zhuyin Peoples Hosp Liaoning Prov Dept Neurol Shenyang 110016 Liaoning Peoples R China Shenzhen Univ Affiliated Hosp 3 Dept Neurol Shenzhen 518001 Guangdong Peoples R China

Multiple sclerosis (MS) is a severe brain disease that permanently destroys brain cells, impacting vision, balance, muscle control, and daily activity. This research employs a weighted combination of deep neural networks and optimization techniques for MS disease diagnosis. This method uses slices of magnetic resonance imaging (MRI) images as input. Then, after the pre-processing operation, the process of segmentation and identification of the region of interest (ROI) is performed using a combination of the fuzzy c-means (FCM) algorithm and the capuchin search algorithm (CapSA) algorithm. When the target view is detected, the features of each ROI are extracted through three techniques: local binary pattern (LBP), multi-linear principal component analysis (MPCA), and gray level co-occurrence matrix (GLCM). Each of these features is then processed by a deep neural network. In each deep neural network, the CapSA algorithm is used to determine the optimal topology structure and adjust the weight vector of the neural network. This means that in this process, the vector and topology of the deep neural network are adjusted using the CapSA algorithm in such a way that the training error is minimized. Finally, after creating the trained models, the weighted combination of the outputs of these three models is used for the final diagnosis. The implementation results showed that our method was successful in achieving 100% precision compared to other comparative methods. Also, in the average accuracy criterion, it showed a performance of 99.51%, which shows the high performance of our method in diagnosing patients.

关键词： Multiple sclerosis Magnetic resonance imaging capuchin search algorithm Deep ensemble learning.

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Research on Optimization Control of Deep Hole Machining Based on capuchin search algorithm to Optimize Fuzzy PID 21

Research on Optimization Control of Deep Hole Machining Base...

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21st IEEE International Conference on Ubiquitous Computing and Communications / 5th IEEE International Conference on Data Science and Computational Intelligence / 21st IEEE International Conference on Computer and Information Technology

作者： Qin, Chang Hu, Hongzhi Chen, HuaiZhi Yan, BaoXian Wuzhou Univ Guangxi Key Lab Machine Vis & Intelligent Control Wuzhou Peoples R China Guilin Univ Elect Sci & Technol Guangxi Key Lab Automat Testing Guilin Peoples R China Guilin Univ Elect Sci & Technol Sch Elect Engn & Automat Guilin Peoples R China

ISBN: (纸本)9781665477260

A small deep hole drilling control system based on the capuchin search algorithm to optimize fuzzy PID is proposed to improve the efficiency of small deep hole drilling. Based on the difference between the actual axial force and the expected axial force, the system adaptively adjusts the quantization factor and scale factor of the fuzzy PID controller by using the capuchin search algorithm and adjusting the parameters of the controller in real-time to improve the system performance. The experimental results show that the CapSA-optimized fuzzy PID algorithm achieves the shortest stabilization time and slight overshoot. When the algorithm is applied to actual machining, the proposed algorithm has a faster response speed and more stable axial force. It can be adaptive to Change the PID drilling parameters to meet the production requirements of intelligent manufacturing..

关键词： small deep hole machining axial force capuchin search algorithm fuzzy PID control

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An efficient capuchin search algorithm for allocating the renewable based biomass distributed generators in radial distribution network

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2022年第PartB期53卷

作者： Fathy, Ahmed Yousri, Dalia Rezk, Hegazy Ramadan, Haitham S. Jouf Univ Elect Engn Dept Fac Engn Sakaka Saudi Arabia Fayoum Univ Elect Engn Dept Fac Engn Al Fayyum Egypt Prince Sattam Bin Abdulaziz Univ Coll Engn Wadi Alddawasir Dept Elect Engn Al Kharj Saudi Arabia Zagazig Univ Fac Engn Elect Power & Machines Dept Zagazig 44519 Egypt Inst Int Stockage Hydrogene ISTHY F-90400 Meroux Moval France

It is essential to keep the stability of electric distribution network especially during abnormal operating conditions, this guarantees service continuity to the customers. Installing small-scale generating units named distributed generators (DGs) can enhance the stability of the grid via minimizing the system losses and improving the network voltage profile. Many approaches, conventional and metaheuristic, have been conducted in installing the DGs in distribution network, however some shortages like slow convergence and stuck in local optima represent obstacles in identifying the appropriate locations and sizes of DGs. Therefore, this paper proposes an efficient methodology of capuchin search algorithm (CapSA) which is presented for the first time to determine the optimum locations, sizes, and power factors of biomass-based DGs (BDGs) in the radial distribution network. The proposed methodology is implemented via adapting the first rows of CapSA population to integer numbers assigned to the BDGs' locations. The target is minimizing the network active power loss with keeping the power flow, bus voltage, and transmission line in their normal ranges. Two distribution networks of IEEE 33-bus and IEEE 69-bus are analyzed, the CapSA results are compared to chimp optimizer (CO), heap-based optimizer (HBO), seagull optimization algorithm (SOA), differential evolution (DE), and sine cosine algorithm (SCA). Moreover, installing different numbers of BDGs are presented to validate the proposed approach. Furthermore, statistical tests of Friedman and ANOVA are conducted to assess the CapSA performance. In IEEE 33-bus network, the proposed approach minimized the active and reactive power losses to 7.9745 kW and 10.4134 kVAr respectively via installing four BDGs. While for IEEE 69-bus system, the active and reactive losses are minimized to 2.1092 kW and 1.5065 kVAr respectively via the proposed CapSA. The obtained results confirmed the preference of the proposed CapSA in installi

关键词： Biomass distributed generators capuchin search algorithm Radial distribution network Renewable energy

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