The purpose of this paper is to explore the performance of ridge regression and the random forest model improved by genetic algorithm in predicting the Boston house price data set and conduct a comparative analysis. To achieve it, the data is divided into training set and test set according to the ratio of 70-30. The RidgeCV library is used to select the best regularization parameter for the Ridge regression model, and for the random forest model, the genetic algorithm is used to optimize the model's hyperparameters. The result shows that compared with ridge regression, the random forest model improved by genetic algorithm can perform better in the regression problem of Boston house prices.

This paper is the third in a series focused on bridging the gap between secondary and higher education. Our primary objective is to develop a robust theoretical framework for an innovative e-business model called the Undergraduate Study Programme Search System (USPSS). This system considers multiple criteria to reduce the likelihood of exam failure or the need for multiple retakes, while maximizing the chances of successful program completion. Testing of the proposed algorithm demonstrated that the Stochastic Gradient Boosted Regression Trees method outperforms the current method used in Lithuania for admitting applicants to 47 educational programs. Specifically, it is more accurate than the Probabilistic Neural Network for 25 programs, the Ensemble of Regression Trees for 24 programs, the Single Regression Tree for 18 programs, the Random Forest Regression for 16 programs, the Bayesian Additive Regression Trees for 13 programs, and the Regression by Discretization for 10 programs.

The main purpose of this research is to investigate the cash holdings behaviour on sectoral level for South African firms listed on the Johannesburg Stock Exchange (JSE). The accounting cash ratio is used to identify abnormal (excess) cash holdings for the firms listed on the JSE. This informed the panel regression analysis to identify cash holdings determinants on a sectoral level. The sample data included 255 firms of which 102 represent Financial Firms and 153 represent Non-Financial Firms for 2005 to 2019. The findings show the significant internal and external determinants of cash holdings. Comparing coefficient sizes, this research finds that financial and non-financial sectors with abnormal (excess) cash holdings exhibit higher coefficient sizes as opposed to sectors without. As a result, the higher coefficient size shows that the internal and external determinants of cash holdings have a greater effect on the cash holding levels of these sectors. The implications of the findings of this study are that each sector operates differently and that each firm within each sector has differing cash management policies and procedures. Therefore, analyzing cash holdings behaviour on an aggregated level and assuming that all sectors and firms within the collective operate the same is an erroneous assumption, as shown by this study. This research firstly contributed by introducing the use of the accounting cash ratio to indicate the presence of abnormal (excess) cash holdings. Most research focus on cash holdings of Non-Financial Firms. Therefore, the second contribution of this research is that both Non-Financial and Financial Firms with and without abnormal (excess) cash holdings were included to identify determinants of cash holdings, this was also done on a sectoral level.

COVID was initially detected in Wuhan City, Hubei Province, People's Republic of China, in late 2019, as reported by researchers. Subsequently, it rapidly disseminated to numerous nations at the beginning of 2020, ultimately manifested as a pandemic with worldwide prevalence. Regarded as one of the most severe pandemics in documented human history, this outbreak resulted in deaths and infection over a quite millions of individuals globally. Due to its airborne nature, the coronavirus can be transmitted through actions such as coughing, sneezing, talking, and similar activities. Enclosed spaces lacking sufficient airflow are more likely to facilitate the spread of air borne diseases. Wearing a face mask that can provide protection against airborne pollutants, considered as Standard Operation Procedures (SOPS) for COVID-19. It is crucial to monitor the implementation of preventive measures both within and outside the building or workplace in order to prevent the transmission of COVID-19. The main objective of this project is to develop a face mask and social distance detector. You Only Learn One Representation (YOLOR) was implemented as a most advanced end-to-end target identification approach to develop the proposed system. An online available facemask dataset was utilized. The developed system can track individuals wearing masks in real time and can also identify and highlight persons with a rectangular box if their social distance is violated. This proposed interactive framework enables constant monitoring both internally and externally, thereby enhancing the capacity to identify offenders and ensure the safety of all individuals involved.

Over the past decade, Ontario has seen a renewal in efforts to stimulate economic growth by investing in infrastructures. In this paper, we analyze the impact of public infrastructure investment on economic performance in this province. We use a multivariate dynamic time series methodological approach, based on the use of vector autoregressive models to estimate the elasticities and marginal products of six different types of public infrastructure assets on private investment, employment and output. We find that all types of public investment crowd in private investment while investment in highways, roads, and bridges crowds out employment. We also find that all types of public investment, with the exception of highways, roads and bridges, have a positive effect on output. The relatively large range of results estimated for the impact of each of the different public infrastructure types suggests that a targeted approach to the design of infrastructure investment policy is required. Infrastructure investment in transit systems and health facilities display the highest returns for output and the largest effects on employment and labor productivity. In terms of the nature of the empirical results presented here it would be important to highlight the fact that investments in health infrastructures as well as investments in education infrastructures are of great relevance. This is a pattern consistent with the mounting international evidence on the importance of human capital for long term economic performance.