Satellite images often have very narrow brightness value ranges, so it is necessary to enhance the contrast and brightness, maintain the quality of visual information, and preserve pertinent details in the images before conducting additional analysis. This is because improving the brightness and contrast of images is crucial to image processing and analysis as it makes it easier for people to identify and comprehend the images. The Incomplete Beta Function (IBF) is a popular transformation function for Image Contrast Enhancement (ICE). Nevertheless, IBF has modest efficiency in parameter selection, a small set of adjustable parameters for stretching regions with high or low gray levels, and image enhancement is almost ineffective with stretching at either end. Meta-heuristic algorithms have been utilized efficiently and effectively over the past few decades to solve complicated image processing problems. This paper presents an Augmented version of the Elk Herd Optimizer (AEHO) combined with other traditional ICE techniques to improve edge details, entropy, local contrast, and local brightness of low-contrast natural and satellite images. The AEHO method employs a multi-stage strategic procedure, where its mathematical model undergoes several enhancements before being applied to ICE to allow for further exploration and exploitation of its features. This method uses a pre-established fitness criterion for the purpose of optimizing a set of parameters to rework a well-known transformation function and an effective assessment technique as an objective standard for this purpose. In the proposed image enhancement model, contrast limited adaptive histogram equalization was first applied as a prior step to ameliorate the color intensity. Then, the optimal IBF’s parameters for ICE were adaptively determined using AEHO. After that, bilateral gamma correction was used to improve the visual quality of images without sacrificing edge details or natural color quality. The proposed AEHO-based image enhancement model is tested on natural scenes, certain standard images, and publicly available satellite images. In addition to other five techniques built on based on pre-existing meta-heuristics, the performance of the proposed method was compared against other well-known state-of-the-art image enhancement algorithms. The objective evaluation of the enhancement algorithms was achieved utilizing a variety of full-reference, no-reference, and pertinent performance evaluation norms. The experimental findings illustrated that the proposed image enhancement method can successfully outperform several other algorithms that employed the same image enhancement model as AEHO in addition to other conventional image enhancement methods included for comparison. The results on ten natural and satellite color images showed that the presented method performs better than all other comparative methods in the corresponding evaluation criteria in terms of average peak signal-to-noise ratio, average universal quality index, average structural contrast-quality index, and average values of discrete entropy results, which are more than 32.30, 94.0%, 0.98.9%, and 7.4, respectively. In a nutshell, AEHO can be an efficient method that can be used to tackle several image processing problems.

The escalating scale and psychological harm of cyberbullying across digital platforms present a critical social challenge, demanding the urgent development of highly accurate and reliable automated detection systems. Standard fine-tuned transformer models, while powerful, often fall short in capturing the nuanced, context-dependent nature of online harassment. This paper introduces a novel hybrid deep learning model called Robustly Optimized Bidirectional Encoder Representations from the Transformers with the Bidirectional Long Short-Term Memory-based Attention model (RoBERTa-BiLSTM), specifically designed to address this challenge. To maximize its effectiveness, the model was systematically optimized using the Optuna framework and rigorously benchmarked against eight state-of-the-art transformer baseline models on a large cyberbullying dataset. Our proposed model achieves state-of-the-art performance, outperforming BERT-base, RoBERTa-base, RoBERTa-large, DistilBERT, ALBERT-xxlarge, XLNet-large, ELECTRA-base, DeBERTa-v3-small with an accuracy of 94.8%, precision of 96.4%, recall of 95.3%, F1-score of 95.8%, and an AUC of 98.5%. Significantly, it demonstrates a substantial improvement in F1-score over the strongest baseline and reduces critical false negative errors by 43%, all while maintaining moderate computational efficiency. Furthermore, our efficiency analysis indicates that this superior performance is achieved with a moderate computational complexity. The results validate our hypothesis that a specialized hybrid architecture, which synergizes contextual embedding with sequential processing and attention mechanism, offers a more robust and practical solution for real-world social media applications.

Wireless Sensor Networks are widely used nowadays to support the decision-makers in different applications by monitoring and collecting the environmental parameters in specific areas. Sensors are deployed in such areas either randomly or formally. In a high-density Wireless Sensor Network, several sensors are randomly deployed in a small area. This will make the adjacent sensors collect same data and send them to the sink, which will increase the power consumption in those sensors. Adjacent sensors are considered critical because of their effect on the network performance. In this paper, the effect of the adjacent sensors is minimized because of the above-mentioned criticality and performance influence of these sensors. The proposed mechanism is evaluated by using MATLAB simulator and is then compared with the low-energy adaptive clustering hierarchy (LEACH) protocol. Results prove that the proposed mechanism outperforms the LEACH protocol by 21 in terms of the network lifetime and by 18 in terms of the number of the transmitted packets to the cluster heads and reduces the number of the transmitted packets to the base station by approximately 3 by avoiding the duplicated packets.

Maintaining reliable and long-lasting road infrastructure requires accurate identification and management of pavement cracks, as these cracks can significantly weaken asphalt and concrete surfaces over time. Although Convolutional Neural Networks (CNNs) and meta-heuristic algorithms have proven effective in solving real-world problems, their use in low-contrast pavement crack images is worth investigating. This study proposes an automated crack detection framework that integrates three key components: (1) a new variant of a pre-trained CNN architecture, referred to as Modified EfficientNetV2 (MEfficientNetV2) for pavement crack classification; (2) a combination of opposition-based learning with White Shark Optimizer (WSO), known as Opposition WSO (OWSO), to improve the balance between exploration and exploitation; and (3) Principal Component Analysis (PCA) for efficient dimensionality reduction and feature selection. This method is validated on various publicly available asphalt crack datasets that contain low-contrast natural images. Preprocessing techniques are first applied to eliminate noise and enhance image quality. The OWSO algorithm is then integrated to optimize the classification performance of MEfficientNetV2, while PCA accelerates the learning process by retaining critical features in the thresholds of the varying components. Comparative evaluations with state-of-the-art methods demonstrate that the proposed model excels in terms of precision, robustness, and generalizability. The outcome emphasizes its ability to identify the most effective solution for crack detection in practical scenarios, where PCA-based feature selection improves computational efficiency without compromising performance. This study focuses on the potential of hybrid deep learning and bio-inspired optimization strategies to improve automated pavement maintenance systems.

Background: The clinical imperative to reduce patient ionizing radiation exposure during diagnosis and treatment planning necessitates robust, high-fidelity synthetic imaging solutions. Current cross-modal synthesis techniques, primarily based on GANs and deterministic CNNs, exhibit instability and critical errors in modeling high-contrast tissues, thereby hindering their reliability for safety-critical applications such as radiotherapy. Objectives: Our primary objective was to develop a stable, high accuracy framework for 3D Magnetic Resonance Imaging (MRI) to Computed Tomography (CT) synthesis capable of generating clinically equivalent synthetic CTs (sCTs) across multiple anatomical sites. Methods: We introduce a novel 3D Latent Diffusion Model (3DLDM) that operates in a compressed latent space, mitigating the computational burden of 3D diffusion while leveraging the stability of the denoising objective. Results: Across the Head & Neck, Thorax, and Abdomen, the 3DLDM achieved a Mean Absolute Error (MAE) of 56.44 Hounsfield Units (HU). This result demonstrates a significant 3.63% reduction in overall error compared to the strongest adversarial baseline, CycleGAN (MAE = 60.07 HU, p < 0.05), a 10.76% reduction compared to NNUNet (MAE = 67.20 HU, p < 0.01), and a 20.79% reduction compared to the transformer-based SwinUNeTr (MAE = 77.23 HU, p < 0.0001). The model also achieved the highest structural similarity (SSIM = 0.885 ± 0.031), significantly exceeding SwinUNeTr (p < 0.0001), NNUNet (p < 0.01), and Pix2Pix (p < 0.0001). Likewise, the 3D-LDM achieved the highest peak signal-to-noise ratio (PSNR = 29.73 ± 1.60 dB), with statistically significant gains over CycleGAN (p < 0.01), NNUNet (p < 0.001), and SwinUNeTr (p < 0.0001). Conclusions: This work validates a scalable, accurate approach for volumetric synthesis, positioning the 3DLDM to enable MR-only radiotherapy planning and accelerate radiation-free multi-modal imaging in the clinic.