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.

Accurate tumor segmentation in PET/CT imaging is essential for the diagnosis and treatment of cancer, impacting therapeutic outcomes and patient management. Our study introduces a new approach integrating a Weighted Fusion Transformer Network to enhance the segmentation of tumor volumes. This method synergizes PET and CT modalities through a Fusion FormerU-Net architecture that employs convolutional neural networks alongside transformer blocks, aiming to leverage the unique advantages of each imaging modality. We evaluated the proposed approach using a multi-institutional dataset, applying key performance metrics such as Dice Similarity Coefficient aggregate, Jaccard Index, Volume Correlation, and Average Surface Distance to assess segmentation precision. The results indicate that the CT/PET/Fusion strategy significantly improves tumor delineation, outperforming traditional segmentation methods. The main findings suggest that this integrative approach could potentially redefine the standard for tumor segmentation in clinical practice. Lastly, the proposed approach offers a promising direction for enhancing the accuracy of oncological imaging, with implications for the improvement of patient-specific treatment strategies.

The RSA (Rivest–Shamir–Adleman) asymmetric-key cryptosystem is widely used for encryptions and digital signatures. Let $(n,e)$ be the RSA public key and d be the corresponding private key (or private exponent). One of the attacks on RSA is to find the private key d using continued fractions when d is small. In this paper, we present a new technique to improve a small private exponent attack on RSA using continued fractions and multicore systems. The idea of the proposed technique is to find an interval that contains $\varphi \left(n\right),$ and then propose a method to generate different points in the interval that can be used by continued fraction and multicore systems to recover the private key, where $\varphi$ is Euler’s totient function. The practical results of three small private exponent attacks on RSA show that we extended the previous bound of the private key that is discovered by continued fractions. When n is 1024 bits, we used 20 cores to extend the bound of d by $0.016$ for de Weger, Maitra-Sarkar, and Nassr et al. attacks in average times 7.67 h, 2.7 h, and 44 min, respectively.

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.

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.