In this paper, a convolved feature vector based adaptive fuzzy filter is proposed for impulse noise removal. The proposed filter follows traditional approach, i.e., detection of noisy pixels based on certain criteria followed by filtering process. In the first step, proposed noise detection mechanism initially selects a small layer of input image pixels, convolves it with a set of weighted kernels to form a convolved feature vector layer. This layer of features is then passed to fuzzy inference system, where fuzzy membership degrees and reduced set of fuzzy rules play an important part to classify the pixel as noise-free, edge or noisy. Noise-free pixels in the filtering phase remain unaffected causing maximum detail preservation whereas noisy pixels are restored using fuzzy filter. This process is carried out traditionally starting from top left corner of the noisy image to the bottom right corner with a stride rate of one for small input layer and a stride rate of two during convolution. Convolved feature vector is very helpful in finding the edge information and hidden patterns in the input image that are affected by noise. The performance of the proposed study is tested on large data set using standard performance measures and the proposed technique outperforms many existing state of the art techniques with excellent detail preservation and effective noise removal capabilities.

Image denoising and super-resolution play vital roles in imaging systems, greatly reducing the preprocessing cost of many AI techniques for object detection, segmentation, and tracking. Various advancements have been accomplished in this field, but progress is still needed. In this paper, we have proposed a novel technique named the Enhanced Learning Enriched Features (ELEF) mechanism using a deep convolutional neural network, which makes significant improvements to existing techniques. ELEF consists of two major processes: (1) Denoising, which removes the noise from images; and (2) Super-resolution, which improves the clarity and details of images. Features are learned through deep CNN and not through traditional algorithms so that we can better refine and enhance images. To effectively capture features, the network architecture adopted Dual Attention Units (DUs), which align with the Multi-Scale Residual Block (MSRB) for robust feature extraction, working sidewise with the feature-matching Selective Kernel Extraction (SKF). In addition, resolution mismatching cases are processed in detail to produce high-quality images. The effectiveness of the ELEF model is highlighted by the performance metrics, achieving a Peak Signal-to-Noise Ratio (PSNR) of 42.99 and a Structural Similarity Index (SSIM) of 0.9889, which indicates the ability to carry out the desired high-quality image restoration and enhancement.

In today's digitally connected world, safeguarding information is paramount for organizations. With the increasing complexity of digital systems and the rising number of cyber threats, information security and cybersecurity risk management has become a necessary function for today’s business. Auditing is in place to assess and mitigate risks of information security. However, the audit process can be intimidating and complex. It involves a broad spectrum of methods that analyze existing controls in a variety of information security and cybersecurity areas. To reduce risks and enhance compliance with standards, the plan has corrective actions for all controls in the checklist. Although most ISMSs can co-exist, most organizations have a single ISMS with their respective tools and processes. This setup may limit flexibility and diversity, making the overall result of audit assessments less effective. Moreover, using outdated tools and processes makes it difficult to provide answers to questions about cybersecurity management and may cause gaps in fulfilling requirements. This gap makes organizations susceptible to threats and possible non-compliance with regulations. In general, SMEs have different auditing requirements from large-scale businesses, and existing ISMSs do not support them comprehensively at the controlling level. In short, existing ISMSs are not suitable for auditing SMEs. To overcome these issues, we have developed an end-to-end system that encompasses all aspects of cybersecurity auditing within one platform: the ISO27K1 Toolkit, designed to meet user preferences. Its advanced web interface has improved automated information security auditing. The system is flexible and dynamic, supporting multiple auditing methods and the integration of predefined control checklists and corresponding mitigation actions. This paper describes the development of the ISO27K1 Toolkit, designed based on ISO 27001:2022 controls and future-proofed for compatibility with the NIST-CSF and PCI DSS standards. The ISO27K1 Toolkit automates critical security assessment tasks, improving user experience and supporting audit processes while promoting compliance, efficiency, and proactive risk management. Large-scale business organizations and SMEs can take advantage of the relational database of the ISO 27K1 Toolkit, which maintains the latest auditing and intervention data. The project was implemented among 35 SMEs and focused on information security audits and system assessments according to ISO 27001:2022. This document presents the system architecture and design, as well as the results of global testing.

The exponential growth of imaging technology has led to a surge in visual content creation, necessitating advanced image denoising algorithms. Conventional methods, which frequently rely on predefined rules and filters, are inadequate for managing intricate noise patterns while maintaining image features. In order to tackle the issue of real‐world image denoising, we investigate and integrate a new novel technique named recursive context fusion network (RCFNet) employing a deep convolutional neural network, demonstrating superior performance compared to current state‐of‐the‐art approaches. RCFNet consists of a coarse feature extraction module and a reconstruction unit, where the former provides a broad contextual understanding and the latter refines the denoising output by preserving spatial and contextual details. Deep CNN learns features instead of using conventional methods, allowing us to improve and refine images. Dual attention units (DUs), in conjunction with the multi‐scale resizing Block (MSRB) and selective kernel feature fusion (SKFF), are incorporated into the network to ensure efficient and reliable feature extraction. To demonstrate the advantages and challenges of combining many configurations into a single pipeline, we take a more detailed look at the results. By leveraging the complementary properties of these networks and computational models, we prefer to contribute to the creation of techniques that enhance image restoration while preserving crucial information, therefore encouraging further research and applications in image processing and artificial intelligence. The RCFNet achieves a high structural similarity index (SSIM) of 0.98 and a peak signal‐to‐noise ratio (PSNR) of 43.4 dB, outperforming many state‐of‐the‐art methods on two benchmark datasets (DND and SIDD) and demonstrating its superior real‐world image denoising ability.