Cloud systems are transforming the Information Technology trade by facultative the companies to provide admission to their structure and also software products to the membership foundation. Because of the vast range within the delivered Cloud solutions, from the customer's perspective of an aspect, it's emerged as troublesome to decide whose providers they need to utilize and then what's the thought of his or her option. Especially, employing suitable metrics is vital in assessing practices. Nevertheless, to the most popular of our knowledge, there's no methodical explanation relating to metrics for estimating Cloud products and services. QoS (Quality of Service) metrics playing an important role in selecting Cloud providers and also optimizing resource utilization efficiency. While many reports have got to devote to exploitation QoS metrics, relatively not much equipment supports the remark and investigation of QoS metrics of Cloud programs. To guarantee a specialized product is published, describing metrics for assessing the QoS might be an essential necessity. So, this text suggests various QoS metrics for service vendors, especially thinking about the consumer's worry. This article provides the metrics list may stand to help the future study and also assessment within the field of Cloud service's evaluation.

Nature-inspired metaheuristic algorithms have been a topic of interest for researchers to solve optimization problems in engineering designs and real-world applications, due to their simplicity and flexibility. This paper presents a new nature-inspired search algorithm called Woodpecker Mating Algorithm (WMA) and applies it to challenging problems in structural optimization. The WMA is a population-based metaheuristic algorithm that mimics the mating behavior of woodpeckers. It was inspired by the drumming sound intensity. In WMA, the population of woodpeckers is divided into male and female groups. The female woodpeckers approach the male woodpeckers based on the intensity of their drum sound. An efficiency comparison was drawn between the WMA algorithm and other metaheuristic algorithms by employing 19 benchmark functions(including unimodal, multimodal and composite functions). Moreover, the performance of WMA is compared with 8 of the best meta-heuristic algorithms using 13 high dimensional multimodal and unimodal benchmark functions. The assessments and statistical results indicate that the WMA algorithm offers promising results and is capable of outperforming the most recent and popular algorithms proposed in the literature in most of the employed benchmark functions. Moreover, a statistically significant difference was observed compared to the other assessed algorithms. The proposed algorithm produced significant results for a non-convex, inseparable, and scalable problems.

Employee turnover prediction remains a critical issue for organizations aiming to improve talent retention and minimize recruitment costs. The ability to predict when and why employees are likely to leave enables companies to take proactive measures to reduce turnover rates. This paper presents a systematic review of 58 studies focused on applying machine learning (ML) algorithms to predict employee turnover. We analyze various ML techniques, including Random Forest, Support Vector Machine, Logistic Regression, Decision Tree, and Neural Networks, highlighting their effectiveness in predicting turnover based on employee data. The review reveals that Random Forest emerged as the most widely used technique, achieving high predictive accuracy across multiple studies. Among the features, Job Satisfaction was identified as the most critical factor in turnover prediction, appearing in a majority of studies. Additionally, large datasets (more than 10,000 samples) were predominantly used, suggesting that more comprehensive data improve model performance. This review emphasizes the potential of ML in HR analytics and provides valuable insights into the strengths and limitations of each method.