A Binary Waterwheel Plant Optimization Algorithm for Feature Selection

The vast majority of today’s data is collected and stored in enormous databases with a wide range of characteristics that have little to do with the overarching goal concept. Feature selection is the process of choosing the best features for a classification problem, which improves the classification’s accuracy. Feature selection is considered a multi-objective optimization problem with two objectives: boosting classification accuracy while decreasing the feature count. To efficiently handle the feature selection process, we propose in this paper a novel algorithm inspired by the behavior of waterwheel plants when hunting their prey and how they update their locations throughout exploration and exploitation processes. The proposed algorithm is referred to as the binary waterwheel plant algorithm (bWWPA). In this particular approach, the binary search space as well as the technique’s mapping from the continuous to the discrete spaces are both represented in a new model. Specifically, the fitness and cost functions that are factored into the algorithm’s evaluation are modeled mathematically. To assess the performance of the proposed algorithm, a set of extensive experiments were conducted and evaluated in terms of 30 benchmark datasets that include low, medium, and high dimensional features. In comparison to other recent binary optimization algorithms, the experimental findings demonstrate that the bWWPA performs better than the other competing algorithms. In addition, a statistical analysis is performed in terms of the one-way analysis-of-variance (ANOVA) and Wilcoxon signed-rank tests to examine the statistical differences between the proposed feature selection algorithm and compared algorithms. These experiments’ results confirmed the proposed algorithm’s superiority and effectiveness in handling the feature selection process

Route Planning for Autonomous Mobile Robots Using a Reinforcement Learning Algorithm

This research suggests a new robotic system technique that works specifically in settings such as hospitals or emergency situations when prompt action and preserving human life are crucial. Our framework largely focuses on the precise and prompt delivery of medical supplies or medication inside a defined area while avoiding robot collisions or other obstacles. The suggested route planning algorithm (RPA) based on reinforcement learning makes medical services effective by gathering and sending data between robots and human healthcare professionals. In contrast, humans are kept out of the patients’ field. Three key modules make up the RPA: (i) the Robot Finding Module (RFM), (ii) Robot Charging Module (RCM), and (iii) Route Selection Module (RSM). Using such autonomous systems as RPA in places where there is a need for human gathering is essential, particularly in the medical field, which could reduce the risk of spreading viruses, which could save thousands of lives. The simulation results using the proposed framework show the flexible and efficient movement of the robots compared to conventional methods under various environments. The RSM is contrasted with the leading cutting-edge topology routing options. The RSM’s primary benefit is the much-reduced calculations and updating of routing tables. In contrast to earlier algorithms, the RSM produces a lower AQD. The RSM is hence an appropriate algorithm for real-time systems

Advanced Dipper-Throated Meta-Heuristic Optimization Algorithm for Digital Image Watermarking

Recently, piracy and copyright violations of digital content have become major concerns as computer science has advanced. In order to prevent unauthorized usage of content, digital watermarking is usually employed. This work proposes a new approach to digital image watermarking that makes use of the discrete cosine transform (DCT), discrete wavelet transform (DWT), dipper-throated optimization (DTO), and stochastic fractal search (SFS) algorithms. The proposed approach involves computing the discrete wavelet transform (DWT) on the cover image to extract its sub-components, followed by the performance of a discrete cosine transform (DCT) to convert these sub-components into the frequency domain. Finding the best scale factor for watermarking is a significant challenge in most watermarking methods. The authors used an advanced optimization algorithm, which is referred to as DTOSFS, to determine the best two parameters—namely, the scaling factor and embedding coefficient—to be used while inserting a watermark into a cover image. Using the optimal values of these parameters, a watermark image can be inserted into a cover image more efficiently. The suggested approach is evaluated in comparison with the current gold standard. The normalized cross-correlation (NCC), peak-signal-to-noise ratio (PSNR), and image fidelity (IF) are used to measure the success of the proposed approach. In addition, a statistical analysis is performed to evaluate the significance and superiority of the proposed approach. The experimental results confirm the effectiveness of the proposed approach in improving upon standard watermarking methods based on the DWT and DCT. Moreover, a set of attacks is considered to study the robustness of the proposed approach, and the results confirm the expected outcomes. It is shown by the achieved results that the proposed approach can be utilized for practical digital image watermarking, and that it significantly outperforms other digital image watermarking methods

Advanced Dipper-Throated Meta-Heuristic Optimization Algorithm for Digital Image Watermarking

Recently, piracy and copyright violations of digital content have become major concerns as computer science has advanced. In order to prevent unauthorized usage of content, digital watermarking is usually employed. This work proposes a new approach to digital image watermarking that makes use of the discrete cosine transform (DCT), discrete wavelet transform (DWT), dipper-throated optimization (DTO), and stochastic fractal search (SFS) algorithms. The proposed approach involves computing the discrete wavelet transform (DWT) on the cover image to extract its sub-components, followed by the performance of a discrete cosine transform (DCT) to convert these sub-components into the frequency domain. Finding the best scale factor for watermarking is a significant challenge in most watermarking methods. The authors used an advanced optimization algorithm, which is referred to as DTOSFS, to determine the best two parameters—namely, the scaling factor and embedding coefficient—to be used while inserting a watermark into a cover image. Using the optimal values of these parameters, a watermark image can be inserted into a cover image more efficiently. The suggested approach is evaluated in comparison with the current gold standard. The normalized cross-correlation (NCC), peak-signal-to-noise ratio (PSNR), and image fidelity (IF) are used to measure the success of the proposed approach. In addition, a statistical analysis is performed to evaluate the significance and superiority of the proposed approach. The experimental results confirm the effectiveness of the proposed approach in improving upon standard watermarking methods based on the DWT and DCT. Moreover, a set of attacks is considered to study the robustness of the proposed approach, and the results confirm the expected outcomes. It is shown by the achieved results that the proposed approach can be utilized for practical digital image watermarking, and that it significantly outperforms other digital image watermarking methods

Innovative Feature Selection Method Based on Hybrid Sine Cosine and Dipper Throated Optimization Algorithms

Introduction: In pattern recognition and data mining, feature selection is one of the most crucial tasks. To increase the efficacy of classification algorithms, it is necessary to identify the most relevant subset of features in a given domain. This means that the feature selection challenge can be seen as an optimization problem, and thus meta-heuristic techniques can be utilized to find a solution. Methodology: In this work, we propose a novel hybrid binary meta-heuristic algorithm to solve the feature selection problem by combining two algorithms: Dipper Throated Optimization (DTO) and Sine Cosine (SC) algorithm. The new algorithm is referred to as bSCWDTO. We employed the sine cosine algorithm to improve the exploration process and ensure the optimization algorithm converges quickly and accurately. Thirty datasets from the University of California Irvine (UCI) machine learning repository are used to evaluate the robustness and stability of the proposed bSCWDTO algorithm. In addition, the K-Nearest Neighbor (KNN) classifier is used to measure the selected features’ effectiveness in classification problems. Results: The achieved results demonstrate the algorithm’s superiority over ten state-of-the-art optimization methods, including the original DTO and SC, Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO), Multiverse Optimization (MVO), Satin Bowerbird Optimizer (SBO), Genetic Algorithm (GA), the hybrid of GWO and GA, and Firefly Algorithm (FA). Moreover, Wilcoxon’s rank-sum test was performed at the 0.05 significance level to study the statistical difference between the proposed method and the alternative feature selection methods. Conclusion: These results emphasized the proposed feature selection method’s significance, superiority, and statistical difference