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The management of various multimedia assets, such as photos, videos, audio files, and other rich media content, within a cloud computing environment is referred to as managing multimedia resources in the cloud. To suit the needs of applications and users, this entails the effective storage, retrieval, processing, and distribution of multimedia resources. Given the significance of work planning and managing resources in the cloud computing environment, we present a unique hybrid algorithm in this research. Many cloud-based computing systems have made extensive use of traditional scheduling techniques like ant colony optimization (ACO), first come first serve, etc. The cloud gets client tasks at a high rate, so it is important to handle resource allocation for these tasks carefully. Using the improved pelican optimization algorithm, we efficiently distribute the tasks to the virtual machines in this proposed work. The proposed hybrid algorithm (Improved POA + Improved GJO) is then used to distribute and manage the resources (Memory and CPU) as needed by the tasks. According to experimental findings, the accuracy of the proposed technique increases by 1.12%, 2.11%, and 14.2%, respectively. It shows that the proposed method has good accuracy compared with the existing HUNTER, FT-ERM, and RU-VMM approaches. [ABSTRACT FROM AUTHOR]

In Image segmentation, optimization problems have been efficiently solved by two notable swarm intelligence algorithms, Firefly Algorithm (FA) and Artificial Bee Colony (ABC). The proposed methodology presents a hybrid approach for image segmentation by integrating both the Firefly and Artificial Bee Colony (HFAABC) Algorithm is proposed for solving optimization problems. This proposed algorithm, FA investigates the search space globally to locate favorable regions of convergence and ABC is employed to perform local search. To segment the color images using by Fuzzy C-Means (FCM) method. The extracted objects are optimally selected by means of Hybrid Firefly and artificial bee colony (HFAABC). The implementation result shows the efficiency of proposed segmentation method in segmenting the images. The experimental results demonstrate the effectiveness of the proposed HFAABC algorithm and showed that it overtakes other algorithms in terms of performance measures, such as Peak Signal-to-Noise Ratio (PSNR) and Accuracy. [ABSTRACT FROM AUTHOR]

Objective: Agricultural play a major role in human life. The crop yield prediction is the needed one, because the investment and work process consume high but the yield output going low in every year. Methods: Here introduces machine learning (ML), which can be a key differentiator for obtaining real, estimated predictions for yield issues. In ML, we choose the random forest algorithm for the yield predictions. The classifier model used here includes logistic regression, naive Bayes, and random forests, of which extended random forests provide maximum accuracy Findings: Based on the dataset provided, we got the yields prediction by RF. The crop yield is different by the crops and usage of fertilizer. The fertilizer also depends upon the soil of the place. Novelty: The clustering method considers data-related environmental factors, soil factors and weather, soil fertility, and production over the past year, and recommends profitable plants that remain mature in the expected atmospheric conditions. [ABSTRACT FROM AUTHOR]