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The largest disruptive component of traffic management system is road transport. Intelligent Transportation Systems (ITS) necessitate exact traffic forecast. However, transport departments continue to struggle to choose an ITS prediction method. A user must be able to appropriately utilize estimation model information. This paper covers contemporary estimation approaches, explains their fundamental concepts and evaluates different traffic estimation approaches for the benefit of decision makers. Each method is classified as Machine Learning (ML), Artificial Intelligence (AI), Deep Learning (DL) or hybrid algorithms. Many surveys are model or datadriven. This study is the first to examine traffic prediction using a variety of algorithmic and methodological approaches to key traffic parameters. Predictive evaluation of dependent variables is done and each algorithm's traffic characteristics are examined in chronological order. The application and effectiveness of each strategy are summarized, and the issues found by analysis are addressed by the review. Hybrid Computational Intelligence (CI)-ML and DL traffic prediction systems are found to be better than previous approaches. Open questions and future possibilities for DL and hybrid traffic prediction systems are also discussed. [ABSTRACT FROM AUTHOR]

The aim of this study is to explore the influence of environmental, social, and governance (ESG) factors on business failure in Brazil by employing advanced machine learning techniques. We collected data from 235 companies and conducted principal component analysis (PCA) on 40 variables already used in the bankruptcy failure literature, resulting in the formation of seven variables that predict business failure. The results indicate that ESG factors significantly predict business failure in Brazil. This study has implications for investors, policymakers, and business leaders, offering a more precise tool for risk assessment and strategic decision-making. [ABSTRACT FROM AUTHOR]

Bitcoin and Ethereum blockchains are exploding with data. While blockchain is becoming increasingly popular, the sheer volume of data obscures important concerns about safety, and privacy. Data analysis can aid in identifying problems and recommending possible solutions, and is also essential to make blockchain useful in a wide range of industries. The paper reviews the current state of research in four key areas: security, privacy, performance and price prediction, and compiles relevant literature. Finally, it predicts future trends and challenges in this field, which will serve as a roadmap for researchers. Blockchain data analysis will increasingly rely on Machine Learning. [ABSTRACT FROM AUTHOR]