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

Movie recommender systems are meant to give suggestions to the users based on the features they love the most. A highly performing movie recommendation will suggest movies that match the similarities with the highest degree of performance. This study conducts a systematic literature review on movie recommender systems. It highlights the filtering criteria in the recommender systems, algorithms implemented in movie recommender systems, the performance measurement criteria, the challenges in implementation, and recommendations for future research. Some of the most popular machine learning algorithms used in movie recommender systems such as K-means clustering, principal component analysis, and self-organizing maps with principal component analysis are discussed in detail. Special emphasis is given to research works performed using metaheuristic-based recommendation systems. The research aims to bring to light the advances made in developing the movie recommender systems, and what needs to be performed to reduce the current challenges in implementing the feasible solutions. The article will be helpful to researchers in the broad area of recommender systems as well as practicing data scientists involved in the implementation of such systems.

• Klíčová slova
• K-means, filtering techniques, metaheuristics, movie recommender, performance metrics,
• MeSH
• algoritmy * MeSH
• film jako téma * MeSH
• publikace MeSH
• strojové učení MeSH
• Publikační typ
• časopisecké články MeSH
• systematický přehled MeSH