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Advancements in underwater robotic fish have generated new requirements for diverse underwater scenarios, presenting challenges in attaining efficient and precise control, particularly in the realm of classical trajectory tracking. In response to the inherently nonlinear and underactuated characteristics of underwater robot control design, this study introduces a trajectory tracking backstepping control method for the planar motion of underactuated underwater robotic systems. The method is grounded in dung beetle optimization (DBO) backstepping control. Firstly, a dynamic model of a single-node tail-actuated robotic fish is introduced, and the model is averaged. Based on the averaged model and Lyapunov functions, the design of the backstepping control scheme is derived to ensure the stability of the control system. Subsequently, the derived backstepping control is further optimized through the application of the DBO optimization algorithm, then the optimal backstepping control (OBC) approach is presented. Finally, the proposed control scheme is applied to the simulation experiments with the robotic fish. The simulation results for straight-line tracking indicate that OBC is superior to the PID method in terms of overshoot performance, reducing the average overshoot from 0.23 to 0.02. Additionally, OBC reduces the average velocity error from 0.043 m/s (backstepping control) to 0.035 m/s, which is lower than that of the PID method, with an average velocity error of 0.054 m/s. In turn tracking, the simulation results reveal that OBC reduces the average velocity error from 0.067 m/s (backstepping control) to 0.055 m/s and demonstrates better performance than the PID method, with an average velocity error of 0.066 m/s. Under various disturbance conditions, the simulations reveal that OBC exhibits superior performance when compared to other control methods. [ABSTRACT FROM AUTHOR]

Resistivity imaging logging has become a direct and precise method for visualizing the structural complexities of reservoir fractures and dissolution pores. The current use of Otsu's thresholding for segmentation results in poor segmentation quality and significant noise. Accurate segmentation of sub-images containing fracture and dissolution pore targets is essential for automated structure identification and subsequent parameter calculation. This study leverages the rapid convergence and robust global optimization capabilities of the dung beetle optimizer to develop enhanced image segmentation approaches. Specifically, it introduces a refined K -means algorithm for multi-category image segmentation and an Otsu algorithm for multi-threshold image segmentation, both optimized by the dung beetle optimizer. Compared to conventional binary segmentation algorithms, this new algorithm effectively isolates noise and extracts multi-category information. Using the segmented sub-images, this paper integrates mathematical morphology techniques to compute parameters such as area, perimeter, tortuosity length, and pore shape factor for identified targets. Additionally, principal component analysis is used to derive recognition factors for fractures and dissolution pores. Applications show that this factor can identify matrix, fracture, and dissolution pore targets in complex background images. By combining parameter information of the target area, the method effectively removes false information in resistivity imaging and segments sub-images of fractures and dissolution pores, calculating fracture area ratio, dissolution pore area ratio, and total area ratio. [ABSTRACT FROM AUTHOR]

As the number of thresholds increases in the multi-threshold segmentation of digital images, the complexity of determining the ideal thresholds rises sharply, posing significant challenges for conventional approaches. Dung Beetle Optimization (DBO) is a metaheuristic algorithm that mimics the behaviors of dung beetles, including rolling dung balls, female beetles laying eggs, small beetles searching for food, and thief beetles stealing. However, the original DBO suffers from slow convergence rate and suboptimal solutions. This paper proposes an improved DBO algorithm, named DBO with composite population initialization and multi-strategy learning (CMDBO), to address the issues. The improvements include initializing the population using chaotic mapping and oppositional learning, enabling weaker individuals to learn from better ones, and applying quasi-center oppositional-based learning to enhance convergence rate and solution accuracy. To verify its search performance, CMDBO was tested on CEC2017 function set and compared with several algorithms. Furthermore, CMDBO was applied to multi-threshold image segmentation. Experimental results indicate that the proposed CMDBO achieved the best overall performance in terms of convergence speed and solution accuracy. [ABSTRACT FROM AUTHOR]

Feature Selection (FS) is a key pre-processing step in pattern recognition and data mining tasks, which can effectively avoid the impact of irrelevant and redundant features on the performance of classification models. In recent years, meta-heuristic algorithms have been widely used in FS problems, so a Hybrid Binary Chaotic Salp Swarm Dung Beetle Optimization (HBCSSDBO) algorithm is proposed in this paper to improve the effect of FS. In this hybrid algorithm, the original continuous optimization algorithm is converted into binary form by the S-type transfer function and applied to the FS problem. By combining the K nearest neighbor (KNN) classifier, the comparative experiments for FS are carried out between the proposed method and four advanced meta-heuristic algorithms on 16 UCI (University of California, Irvine) datasets. Seven evaluation metrics such as average adaptation, average prediction accuracy, and average running time are chosen to judge and compare the algorithms. The selected dataset is also discussed by categorizing it into three dimensions: high, medium, and low dimensions. Experimental results show that the HBCSSDBO feature selection method has the ability to obtain a good subset of features while maintaining high classification accuracy, shows better optimization performance. In addition, the results of statistical tests confirm the significant validity of the method. [ABSTRACT FROM AUTHOR]

Many industries, including healthcare, the military, finance, and more, need extra protection for the interchange of picture data since images are now sent across open channels that might be attacked. In order to protect the system against differential and brute force assaults, the security aspects are crucial. The transmission of multimedia, including digital pictures, text, audio, and video, relies heavily on encryption to maintain secrecy, integrity, and confidentiality while preventing unwanted access to critical information. Even while chaos-based cryptosystems aren't as widely used as AES, DES, or RSA, they've been a hot topic of study recently and can enhance the security of public key cryptosystems when combined with them. With the rise of deep convolutional neural networks (CNNs) as the go-to machine learning technology for many uses, there have been several efforts to use CNNs to decipher encrypted data. On the other hand, prior research has paid little attention to protecting model parameters and has instead concentrated on protecting data. Additionally, they provide high-level implementations without thoroughly analyzing the trade-offs between speed, security, and accuracy in the ECC implementation of common CNN basic operators like non-linear activation, convolution, along with pooling. The goal of this research is to develop and construct a cryptosystem based on Chaos that can effectively encrypt images and withstand differential assaults. In order to create the first layer of encryption, the system first divides the original picture into smaller pieces and rearranges them. A logistic map is used to generate a one-dimensional sequence, which is then multiplied over the highest pixel value and processed bit by bit as part of the encryption process. We use the outcome to encrypt the picture, and then apply the same procedure to decode it. For efficient key generation during picture encryption, the suggested model uses the ECC approach to produce a Dung Beetle optimization (DBO). For improved security performance, the chaotic map notion is introduced to the robust optimization approach. The results of the investigation demonstrate that the suggested approach provides significantly improved security performance while leaving picture quality unaffected. The histogram, Pearson's correlation analysis, peak signal-to-noise ratio (PSNR), entropy, number of pixels change rate (NPCR), and unified average fluctuation in intensity (UACI) are used to assess the encryption outcomes. Our findings prove that the suggested strategy is safe, dependable, efficient, and adaptable. [ABSTRACT FROM AUTHOR]

Accurate prediction of air quality is crucial for assessing the state of the atmospheric environment, especially considering the nonlinearity, volatility, and abrupt changes in air quality data. This paper introduces an air quality index (AQI) prediction model based on the Dung Beetle Algorithm (DBO) aimed at overcoming limitations in traditional prediction models, such as inadequate access to data features, challenges in parameter setting, and accuracy constraints. The proposed model optimizes the parameters of Variational Mode Decomposition (VMD) and integrates the Informer adaptive sequential prediction model with the Convolutional Neural Network-Long Short Term Memory (CNN-LSTM). Initially, the correlation coefficient method is utilized to identify key impact features from multivariate weather and meteorological data. Subsequently, penalty factors and the number of variational modes in the VMD are optimized using DBO. The optimized parameters are utilized to develop a variationally constrained model to decompose the air quality sequence. The data are categorized based on approximate entropy, and high-frequency data are fed into the Informer model, while low-frequency data are fed into the CNN-LSTM model. The predicted values of the subsystems are then combined and reconstructed to obtain the AQI prediction results. Evaluation using actual monitoring data from Beijing demonstrates that the proposed coupling prediction model of the air quality index in this paper is superior to other parameter optimization models. The Mean Absolute Error (MAE) decreases by 13.59%, the Root-Mean-Square Error (RMSE) decreases by 7.04%, and the R-square (R2) increases by 1.39%. This model surpasses 11 other models in terms of lower error rates and enhances prediction accuracy. Compared with the mainstream swarm intelligence optimization algorithm, DBO, as an optimization algorithm, demonstrates higher computational efficiency and is closer to the actual value. The proposed coupling model provides a new method for air quality index prediction. [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN) performs monitoring of each aspect of the area of interest by detecting the surrounding physical phenomena with sensor nodes and transferring the information to the gateway through the corresponding system. Several researcher workers have introduced localization methods to accomplish high accuracy of localization. An intelligent optimization technique has attracted various researcher workers due to its advantages such as strong optimization capability and few parameters to optimize the localization performance of the DV-Hop method. Sink node localization (NL) using metaheuristics in WSN includes applying optimization techniques inspired by human behavior or natural phenomena to define the geographical coordinates of the sink nodes within the network coverage region. WSNs can accomplish better localization performance, especially in dynamic or complex environments, improving the efficiency and reliability of network management and data transmission by leveraging metaheuristics. In this view, this manuscript develops a Dung Beetle Optimization based Sink Node Localization Approach (DBO-SNLA) for WSN. In the DBO-SNLA technique, the DBO algorithm involved is based on the social behavior of dung beetle populations and is developed with five updated rules to assist in finding high-quality solutions. In addition, the DBO-SNLA technique addresses the issues of defining the sink node location with lowest localization error once the data between the nodes is transferred wirelessly. Finally, the localization errors are calculated and the location of the different unknown nodes is computed. A detailed set of simulation takes place to examine the performance of the DBO-SNLA technique. The empirical analysis stated the betterment of the DBO-SNLA method than other techniques [ABSTRACT FROM AUTHOR]

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Advancements in underwater robotic fish have generated new requirements for diverse underwater scenarios, presenting challenges in attaining efficient and precise control, particularly in the realm of classical trajectory tracking. In response to the inherently nonlinear and underactuated characteristics of underwater robot control design, this study introduces a trajectory tracking backstepping control method for the planar motion of underactuated underwater robotic systems. The method is grounded in dung beetle optimization (DBO) backstepping control. Firstly, a dynamic model of a single-node tail-actuated robotic fish is introduced, and the model is averaged. Based on the averaged model and Lyapunov functions, the design of the backstepping control scheme is derived to ensure the stability of the control system. Subsequently, the derived backstepping control is further optimized through the application of the DBO optimization algorithm, then the optimal backstepping control (OBC) approach is presented. Finally, the proposed control scheme is applied to the simulation experiments with the robotic fish. The simulation results for straight-line tracking indicate that OBC is superior to the PID method in terms of overshoot performance, reducing the average overshoot from 0.23 to 0.02. Additionally, OBC reduces the average velocity error from 0.043 m/s (backstepping control) to 0.035 m/s, which is lower than that of the PID method, with an average velocity error of 0.054 m/s. In turn tracking, the simulation results reveal that OBC reduces the average velocity error from 0.067 m/s (backstepping control) to 0.055 m/s and demonstrates better performance than the PID method, with an average velocity error of 0.066 m/s. Under various disturbance conditions, the simulations reveal that OBC exhibits superior performance when compared to other control methods.

The Dung beetle optimization (DBO) algorithm, devised by Jiankai Xue in 2022, is known for its strong optimization capabilities and fast convergence. However, it does have certain limitations, including insufficiently random population initialization, slow search speed, and inadequate global search capabilities. Drawing inspiration from the mathematical properties of the Sinh and Cosh functions, we proposed a new metaheuristic algorithm, Sinh–Cosh Dung Beetle Optimization (SCDBO). By leveraging the Sinh and Cosh functions to disrupt the initial distribution of DBO and balance the development of rollerball dung beetles, SCDBO enhances the search efficiency and global exploration capabilities of DBO through nonlinear enhancements. These improvements collectively enhance the performance of the dung beetle optimization algorithm, making it more adept at solving complex real-world problems. To evaluate the performance of the SCDBO algorithm, we compared it with seven typical algorithms using the CEC2017 test functions. Additionally, by successfully applying it to three engineering problems, robot arm design, pressure vessel problem, and unmanned aerial vehicle (UAV) path planning, we further demonstrate the superiority of the SCDBO algorithm. [ABSTRACT FROM AUTHOR]

One of the limitations of the dung beetle optimization (DBO) is its susceptibility to local optima and its relatively low search accuracy. Several strategies have been utilized to improve the diversity, search precision, and outcomes of the DBO. However, the equilibrium between exploration and exploitation has not been achieved optimally. This paper presents a novel algorithm called the ODBO, which incorporates cat map and an opposition-based learning strategy, which is based on symmetry theory. In addition, in order to enhance the performance of the dung ball rolling phase, this paper combines the global search strategy of the osprey optimization algorithm with the position update strategy of the DBO. Additionally, we enhance the population's diversity during the foraging phase of the DBO by incorporating vertical and horizontal crossover of individuals. This introduction of asymmetry in the crossover operation increases the exploration capability of the algorithm, allowing it to effectively escape local optima and facilitate global search. [ABSTRACT FROM AUTHOR]

Music education has a rich historical background. Nevertheless, the introduction of modern teaching methods is relatively delayed. In recent years, there has been a remarkable acceleration in the advancement of music education. A promising tool that has emerged to revolutionize education as a whole is Virtual Reality (VR) technology, which offers immersive and interactive experiences across various disciplines. At the university level, integrating VR technology into music education opens up exciting opportunities to enhance practical teaching methods and provide students with enriched musical experiences. Virtual Reality together with Internet of Things (IoT) demonstrates its capabilities in various tasks, but its widespread availability in online learning remainders a pressing challenge that needs to be addressed. In pre-processing, it removes noise data using Dynamic Context-Sensitive Filtering (DCSF). VR technology creates an unparalleled learning environment, it transporting students to virtual concert halls, recording studios, or historical music venues. Hence the Multiscale deep bidirectional gated recurrent neural Network (MDBGNN) improves the practical teaching of music course concept, like Music theory, harmony, and rhythm can be visualized and experienced in VR. Finally, Dung Beetle Optimization Algorithm (DBOA) is employed to optimize the weight parameters of MDBGNN. The proposed MDBGNN-DBO-UMC-VRT is implemented in Python. The proposed method is analysed with the help of performance metrics, like precision, accuracy, F1-score, Recall (Sensitivity), Specificity, Error rate, Computation time and RoC. The proposed MDBGNN-DBO-UMC-VRT method attains 13.11%, 18.12% and 18.73% high specificity, 11.13%, 11.04% and 19.51% lower computation Time, 15.29%, 15.365% and 14.551% higher ROC and 13.65%, 15.98%, and 17.15% higher Accuracy compared with existing methods, such as Enhancing Vocal Music Teaching through the Fusion of Artificial Intelligence Algorithms and VR Technology (CNN-UMC-VRT), Exploring the Efficacy of VR Technology in Augmenting Music Art Teaching (BPNN-UMC-VRT) and Implementing an Interactive Music-Assisted Teaching System Using VR Technology (DNN-UMC-VRT) respectively. [ABSTRACT FROM AUTHOR]

Network security situation awareness enables networks to actively and effectively defend against network attacks, relying on the extraction of network situation elements as an initial and decisive step. In existing studies, the stacked sparse autoencoder (SSAE) has been employed to extract features from unlabeled network flows. However, obtaining the optimal hyperparameter combination is challenging due to its numerous hyperparameters. To address this issue, we propose a novel approach named DBO-SSAE that leverages dung beetle optimization (DBO) to select the optimal hyperparameters for SSAE automatically. Applied to the well-known UNSW-NB15 dataset, our model yields an optimal feature subset, which is evaluated across various binary classifiers with different metrics. Experimental results demonstrate that our approach improves accuracy and $\textit{F}_{1}$ -measure by 0.2% to 1.5% while reducing the false negative rate (FNR) and false positive rate (FPR) by 0.06% to 7%, surpassing other feature extraction methods on the same classifier for the UNSW-NB15 dataset. Particularly, in conjunction with a lightweight bidirectional long short-term memory (BiLSTM), our model achieves metrics of 98.84% accuracy, 98.96% $\textit{F}_{1}$ -measure, 1.86% FNR, and 0.6% FPR. This study could provide novel insights into the effective representation of network situation elements and lay the groundwork for a high-efficiency intrusion detection system.

Given the inherent volatility and intermittency of photovoltaic power generation, enhancing the precision of photovoltaic power predictions becomes imperative to ensure the stability of power systems and to elevate power quality. This article introduces an intelligent photovoltaic power prediction model based on the Extreme Learning Machine (ELM) with the Adaptive Spiral Dung Beetle Optimization (ASDBO) algorithm. The model aims to accurately predict photovoltaic power generation under multi-factor correlation conditions, including environmental temperature and solar irradiance. The computational efficiency in high-dimensional data feature conditions is enhanced by using the Pearson correlation analysis to determine the state input of the ELM. To address local optimization challenges in traditional Dung Beetle Optimization (DBO) algorithms, a spiral search strategy is implemented during the dung beetle reproduction and foraging stages, expanding the exploration capabilities. Additionally, during the dung beetle theft stage, dynamic adaptive weights update the optimal food competition position, and the levy flight strategy ensures search randomness. By balancing convergence accuracy and search diversity, the proposed algorithm achieves global optimization. Furthermore, eight benchmark functions are chosen for performance testing to validate the effectiveness of the ASDBO algorithm. By optimizing the input weights and implicit thresholds of the ELM through the ASDBO algorithm, a prediction model is established. Short-term prediction experiments for photovoltaic power generation are conducted under different weather conditions. The selected experimental results demonstrate an average prediction accuracy exceeding 93%, highlighting the effectiveness and superiority of the proposed methodology for photovoltaic power prediction. [ABSTRACT FROM AUTHOR]

Introduction: ovarian cancer ranked as the seventh most common cancer and the eighth leading cause of cancer-related mortality among women globally. Early detection was crucial for improving survival rates, emphasizing the need for better screening techniques and increased awareness. Microarray gene data, containing numerous genes across multiple samples, presented both opportunities and challenges in understanding gene functions and disease pathways. This research focused on reducing feature selection time in large gene expression datasets by applying a hybrid bio-inspired method, HGDBO. The goal was to enhance classification accuracy by optimizing gene subsets for improved gene expression analysis. Method: the study introduced a novel hybrid feature selection method called HGDBO, which combined the Dung Beetle Optimization (DBO) algorithm with the Genetic Algorithm (GA) to improve microarray data analysis. The HGDBO method leveraged the exploratory strengths of DBO and the exploitative capabilities of GA to identify relevant genes for disease classification. Experiments conducted on multiple microarray datasets showed that the hybrid approach offered superior classification performance, stability, and computational efficiency compared to traditional methods. Ovarian cancer classification was performed using Naïve Bayes (NB) and Random Forest (RF) algorithms. Results and Discussion: the Random Forest model outperformed the Naïve Bayes model across all metrics, achieving higher accuracy (0,96 vs. 0,91), precision (0,95 vs. 0,91), recall (0,97 vs. 0,90), F1 score (0,95 vs. 0,91), and specificity (0,97 vs. 0,86). Conclusions: these results demonstrated the effectiveness of the HGDBO method and the Random Forest classifier in improving the analysis and classification of ovarian cancer using microarray gene data. [ABSTRACT FROM AUTHOR]

This paper aims to enhance the accuracy of predicting the mechanical behavior of wood subjected to thermal modification using an improved dung beetle optimization (IDBO) model. The IDBO algorithm improves the original DBO algorithm via three main steps: (1) using piece-wise linear chaotic mapping (PWLCM) to generate the initial dung beetle species and increase its heterogeneity; (2) adopting an adaptive nonlinear decreasing producer ratio model to control the number of producers and boost the algorithm's convergence rate; and (3) applying a dimensional learning-enhanced foraging (DLF) search strategy that optimizes the algorithm's ability to explore and exploit the search space. The IDBO algorithm is evaluated on 14 benchmark functions and outperforms other algorithms. The IDBO algorithm is then applied to optimize a back-propagation (BP) neural network for predicting five mechanical property parameters of heat-treated larch-sawn timber. The results indicate that the IDBO-BP model significantly reduces the error compared with the BP, tent-sparrow search algorithm (TSSA)-BP, grey wolf optimizer (GWO)-BP, nonlinear adaptive grouping grey wolf optimizer (IGWO)-BP and DBO-BP models, demonstrating its superiority in predicting the physical characteristics of lumber after heat treatment. [ABSTRACT FROM AUTHOR]