Image segmentation, the process of partitioning an image into meaningful regions, is a fundamental step in image processing, crucial for applications like computer vision, medical imaging, and object recognition. Imag...
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Image segmentation, the process of partitioning an image into meaningful regions, is a fundamental step in image processing, crucial for applications like computer vision, medical imaging, and object recognition. Image segmentation is an essential step of image processing that directly affects its success. Among the methods used for image segmentation, histogram-based thresholding is prevalent. Two well-known approaches to histogram-based thresholding are Otsu's and Kapur's methods in gray images that maximize the between-class variance and the entropy measure, respectively. Both techniques were introduced for bi-level thresholding. However, these techniques can be expanded to multilevel image thresholding. For this to occur, a large number of iterations are required to account for exact threshold values. To this end, various optimization techniques have been used to overcome this drawback. Recently, a new optimizationalgorithm called battleroyal Optimizer (BRO) has been published, which is shown to solve various optimization tasks effectively. In this study, BRO has been applied to yield optimum threshold values in multilevel image thresholding. Here is also demonstrated the effectiveness of BRO for image segmentation on various images from the standard publicly accessible Berkeley segmentation dataset. We compare the performance of BRO to other state-of-the-art optimization-based methods and show that it outperforms them in terms of fitness value, Peak Signal-to-Noise Ratio, Structural Similarity Index Method, Feature Similarity Index Method (FSIM), Color FSIM (FSIMc), and Standard Deviation. These results underscore the potential of BRO as a promising solution for image segmentation tasks, particularly through its effective implementation of multilevel thresholding.
Alumina-Zirconium dioxide (Al2O3-ZrO2) Ceramic Composite Material (CCM) is specifically known for its enhanced mechanical and corrosion resistance properties and is widely used as raw material for mechanical parts lik...
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Alumina-Zirconium dioxide (Al2O3-ZrO2) Ceramic Composite Material (CCM) is specifically known for its enhanced mechanical and corrosion resistance properties and is widely used as raw material for mechanical parts like, pump components, die inserts, bearings, etc. As a result, industrialists are searching for an efficient method for machining this Al2O3-ZrO2 material. In this regard, a hybrid unconventional machining process called Electrochemical Discharge Machining (ECDM) is adapted to analyze the machinability of Al2O3-ZrO2 CCM. Besides, to ensure the efficiency of the ECDM process, a magnetic field is also given to the tool electrode during this study to improve the Material Removal Rate (MRR) of the ECDM machine. The experiment is designed using Response Surface Methodology (RSM) by changing the magnitudes of input controls, namely Electrolytic Concentration (EC), Inter-electrode Gap (IEG) and Applied Voltage (AV). Moreover, a novel hybrid machine learning optimization strategy called Deep Belief Network based battleroyaloptimization (DBN-BRO) algorithm is developed to predict and optimize the ECDM process. Finally, the optimum results are perceived from 55 V AV, 22.727% EC and 40.909 mm IEG input levels. The proposed method shows less than 0.6207 Root Mean Square Error (RMSE) and tools nearly 80 iterations for optimizing the results.
Mobile ad hoc network (MANET) plays a major role in wireless devices such as defense and flooding. Despite their smart applications, MANET faces more security issues than traditional wired and wireless networks on acc...
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Mobile ad hoc network (MANET) plays a major role in wireless devices such as defense and flooding. Despite their smart applications, MANET faces more security issues than traditional wired and wireless networks on account of their distinct features, such as no central coordination, dynamic topology, temporal network life, and the nature of wireless communication. To overcome these issues, this manuscript proposes a Dual Interactive Wasserstein Generative Adversarial Network optimized with Namib Beetle optimizationalgorithm is proposed for intrusion detection and preventing attacks in MANET. By utilizing the One Way Hash Chain Function, mobile users first register with the Trusted Authority. Each mobile user sends a finger vein biometric along with their user id, latitude, and longitude for authentication verification. The packet analyzer, feature extraction, preprocessing, and classification are the four parts that make up intrusion detection. To determine if any attack patterns have been identified, the packet analyzer is examined. This is executed using a Type 2 Fuzzy Controller that deems packet header information. Anisotropic diffusion Kuwahara filtering techniques is time series is taken into consideration in the preprocessing unit. The battle royal optimization algorithm is utilized in the feature extraction unit to acquire a better collection of features for packet categorization. The classification unit classifies the packets into five categories: DoS, Probe, U2R, R2L, and Anomaly using the proposed technique. Finally, the proposed method provides 26.26%, 15.57%, 32.9% higher accuracy, 33.06%, 23.82%, and 38.84% lesser delay analysed to the existing models.
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