Image segmentation using bi-level thresholds works well for straightforward scenarios; however, dealing with complex images that contain multiple objects or colors presents considerable computational difficulties. Multi-level thresholding is crucial for these situations, but it also introduces a challenging optimization problem. This paper presents an improved Reptile Search Algorithm (RSA) that includes a Gbest operator to enhance its performance. The proposed method determines optimal threshold values for both grayscale and color images, utilizing entropy-based objective functions derived from the Otsu and Kapur techniques. Experiments were carried out on 16 benchmark images, which included COVID-19 scans along with standard color and grayscale images. A thorough evaluation was conducted using metrics such as the fitness function, peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and the Friedman ranking test. The results indicate that the proposed algorithm seems to surpass existing state-of-the-art methods, demonstrating its effectiveness and robustness in multi-level thresholding tasks.

• Klíčová slova
• Image segmentation, Medical images, Multi-level threshold, Otsu method, Kapur method, Reptile search algorithm,
• MeSH
• algoritmy * MeSH
• COVID-19 * diagnostické zobrazování   virologie   MeSH
• lidé MeSH
• počítačové zpracování obrazu * metody   MeSH
• poměr signál - šum MeSH
• SARS-CoV-2 izolace a purifikace   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

The sensitive nature of the data processed by the critical infrastructures of a shared platform like the internet of things (IoT) makes it vulnerable to a wide range of security risks. These infrastructures must have robust security measures to protect the privacy of the user data transmitted to the processing systems that utilize them. However, data loss and complexities are significant issues when handling enormous data in IoT applications. This paper uses a reptile search optimization algorithm to offer attuned data protection with privacy scheme (ADP2S). This study follows the reptiles' hunting behaviours to find a vulnerability in our IoT service's security. The system activates the reptile swarm after successfully gaining access to explode ice. An attack of protection and authentication measures explodes at the breach location. The number of swarm densities and the extent to which they explore a new area are both functions of the severity of the breach. Service response and related loss prevention time verify fitness according to the service-level fitness value. The user and the service provider contribute to the authentication, which is carried out via elliptic curve cryptography and two-factor authentication. The reptile's exploration and exploitation stages are merged by sharing a similar search location across the initialized candidates. The proposed scheme leverages breach detection and protection recommendations by 11.37% and 8.04%, respectively. It reduces the data loss, estimation time, and complexity by 6.58%, 10.9%, and 11.21%, respectively.

• Klíčová slova
• Algorithms, Artificial intelligence, Big data, Internet of things, Optimization,
• Publikační typ
• časopisecké články MeSH