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Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic data sets on air quality (AQ) predictions has not been fully investigated. This paper aims to investigate the effects traffic data set have on the performance of machine learning (ML) predictive models in AQ prediction.

To achieve this, the authors have set up an experiment with the control data set having only the AQ data set and meteorological (Met) data set, while the experimental data set is made up of the AQ data set, Met data set and traffic data set. Several ML models (such as extra trees regressor, eXtreme gradient boosting regressor, random forest regressor, K-neighbors regressor and two others) were trained, tested and compared on these individual combinations of data sets to predict the volume of PM_2.5, PM₁₀, NO₂ and O₃ in the atmosphere at various times of the day.

The result obtained showed that various ML algorithms react differently to the traffic data set despite generally contributing to the performance improvement of all the ML algorithms considered in this study by at least 20% and an error reduction of at least 18.97%.

This research is limited in terms of the study area, and the result cannot be generalized outside of the UK as some of the inherent conditions may not be similar elsewhere. Additionally, only the ML algorithms commonly used in literature are considered in this research, therefore, leaving out a few other ML algorithms.

This study reinforces the belief that the traffic data set has a significant effect on improving the performance of air pollution ML prediction models. Hence, there is an indication that ML algorithms behave differently when trained with a form of traffic data set in the development of an AQ prediction model. This implies that developers and researchers in AQ prediction need to identify the ML algorithms that behave in their best interest before implementation.

The result of this study will enable researchers to focus more on algorithms of benefit when using traffic data sets in AQ prediction.

The sustainable development goals (SDGs) are introduced to guide achieving the sustainable goals and tackle the global problems. United Nations members may perform activities to achieve the predetermined goals and report on their SDG activities. The comprehension and commitment of several stakeholders are essential for the effective implementation of the SDGs. Countries encourage their stakeholders to perform and report their activities to meet the SDGs. The purpose of this study is to investigate the extent to which corporations’ annual reports address the SDGs to assess and comprehend their level of commitment to, priority of and integration of SDGs within their reporting structure. This research makes it easier to evaluate corporations’ sustainability performance and contributions to global sustainability goals by looking at the extent to which they address the SDGs.

In the study, it is revealed to what extent the reports meet the SDGs with the multilabel text classification approach. The SDG classification is carried out by examining the report with the help of a text analysis tool based on an enhanced version of gradient boosting. The implementation of a machine learning-based model allowed it to determine which SDGs are associated with the company’s operations without the requirement for the report’s authors to perform so. Therefore, instead of reading the texts to seek for “SDG” evidence as typically occurs in the literature, SDG proof was searched in relevant texts.

To show the feasibility of the study, the annual reports of the leading companies in Turkey are examined, and the results are interpreted. The study produced results including insights into the sustainable practices of businesses, priority SDG selection, benchmarking and business comparison, gaps and improvement opportunities identification and representation of the SDGs’ importance.

The findings of the analysis of annual reports indicate which SDGs they are concerned about. A gap in the literature can be noticed in the analysis of annual reports of companies that fall under a particular framework. In addition, it has sparked the idea of conducting research on a global scale and in a time series. With the aid of this research, decision-making procedures can be guided, and advancements toward the SDGs can be achieved.

The current study uses an advanced machine learning method and aims to investigate whether auditors perceive financial statements that are principles-based as less risky. More specifically, this study aims to explore the association between principles-based accounting standards and audit pricing and between principles-based accounting standards and the likelihood of receiving a going concern opinion.

The study uses an advanced machine-learning method to understand the role of principles-based accounting standards in predicting audit fees and going concern opinion. The study also uses multiple regression models defining audit fees and the probability of receiving going concern opinion. The analyses are complemented by additional tests such as economic significance, firm fixed effects, propensity score matching, entropy balancing, change analysis, yearly regression results and controlling for managerial risk-taking incentives and governance variables.

The paper provides empirical evidence that auditors charge less audit fees to clients whose financial statements are more principles-based. The finding suggests that auditors perceive financial statements that are principles-based less risky. The study also provides evidence that the probability of receiving a going-concern opinion reduces as firms rely more on principles-based standards. The finding further suggests that auditors discount the financial numbers supplied by the managers using rules-based standards. The study also reveals that the degree of reliance by a US firm on principles-based accounting standards has a negative impact on accounting conservatism, the risk of financial statement misstatement, accruals and the difficulty in predicting future earnings. This suggests potential mechanisms through which principles-based accounting standards influence auditors’ risk assessments.

The authors recognize the limitation of this study regarding the sample period. Prior studies compare rules vs principles-based standards by focusing on the differences between US generally accepted accounting principles (GAAP) and international financial reporting standards (IFRS) or pre- and post-IFRS adoption, which raises questions about differences in cross-country settings and institutional environment and other confounding factors such as transition costs. This study addresses these issues by comparing rules vs principles-based standards within the US GAAP setting. However, this limits the sample period to the year 2006 because the measure of the relative extent to which a US firm is reliant upon principles-based standards is available until 2006.

The study has major public policy suggestions as it responds to the call by Jay Clayton and Mary Jo White, the former Chairs of the US Securities and Exchange Commission (SEC), to pursue high-quality, globally accepted accounting standards to ensure that investors continue to receive clear and reliable financial information globally. The study also recognizes the notable public policy implications, particularly in light of the current Chair of the International Accounting Standards Board (IASB) Andreas Barckow’s recent public statement, which emphasizes the importance of principles-based standards and their ability to address sustainability concerns, including emerging risks such as climate change.

The study has major public policy suggestions because it demonstrates the value of principles-based standards. The study responds to the call by Jay Clayton and Mary Jo White, the former Chairs of the US SEC, to pursue high-quality, globally accepted accounting standards to ensure that investors continue to receive clear and reliable financial information as business transactions and investor needs continue to evolve globally. The study also recognizes the notable public policy implications, particularly in light of the current Chair of the IASB Andreas Barckow’s recent public statement, which emphasizes the importance of principles-based standards and their ability to address sustainability concerns, including emerging risks like climate change. The study fills the gap in the literature that auditors perceive principles-based financial statements as less risky and further expands the literature by providing empirical evidence that the likelihood of receiving a going concern opinion is increasing in the degree of rules-based standards.

European Parliament adopted a new directive on gender balance in corporate boards when by 2026, companies must employ 40% of the underrepresented sex into non-executive directors or 33% among all directors. Therefore, this study aims to analyze the impact of gender diversity (GD) on board of directors and the shareholders’ structure and their impact on the likelihood of company bankruptcy during the COVID-19 pandemic.

The data sample consists of 1,351 companies for 2019 and 2020, of which 173 were large, 351 medium-sized companies and 827 small companies. Three bankruptcy indicators were tested for each company size, and extreme gradient boosting (XGBoost) and logistic regression models were developed. These models were then cross-validated by a 10-fold approach.

XGBoost models achieved area under curve (AUC) over 98%, which is 25% higher than AUC achieved by logistic regression. Prediction models with GD features performed slightly better than those without them. Furthermore, this study indicates the existence of critical mass between 30% and 50%, which decreases the probability of bankruptcy for small and medium companies. Furthermore, the representation of women in ownership structures above 50% decreases bankruptcy likelihood.

This is a pioneering study to explore GD topics by application of ensembled machine learning methods. Moreover, the study does analyze not only the GD of boards but also shareholders. A highly innovative approach is GD analysis based on company size performed in one study considering the COVID-19 pandemic perspective.

With so many placement opportunities around the students in their final or prefinal year, they start to feel the strain of the season. The students feel the need to be aware of their position and how to increase their chances of being hired. Hence, a system to guide their career is one of the needs of the day.

The job role prediction system utilizes machine learning techniques such as Naïve Bayes, K-Nearest Neighbor, Support Vector machines (SVM) and Artificial Neural Networks (ANN) to suggest a student’s job role based on their academic performance and course outcomes (CO), out of which ANN performs better. The system uses the Mepco Schlenk Engineering College curriculum, placement and students’ Assessment data sets, in which the CO and syllabus are used to determine the skills that the student has gained from their courses. The necessary skills for a job position are then extracted from the job advertisements. The system compares the student’s skills with the required skills for the job role based on the placement prediction result.

The system predicts placement possibilities with an accuracy of 93.33 and 98% precision. Also, the skill analysis for students gives the students information about their skill-set strengths and weaknesses.

For skill-set analysis, only the direct assessment of the students is considered. Indirect assessment shall also be considered for future scope.

The model is adaptable and flexible (customizable) to any type of academic institute or universities.

The research will be very much useful for the students community to bridge the gap between the academic and industrial needs.

Several works are done for career guidance for the students. However, these career guidance methodologies are designed only using the curriculum and students’ basic personal information. The proposed system will consider the students’ academic performance through direct assessment, along with their curriculum and basic personal information.

The primary purpose of this paper is to identify the so-called core authors and their publications according to pre-defined criteria and thereby direct the users to the fastest and easiest way to get a picture of the otherwise pervasive field of bankruptcy prediction models. The authors aim to present state-of-the-art bankruptcy prediction models assembled by the field's core authors and critically examine the approaches and methods adopted.

The authors conducted a literature search in November 2022 through scientific databases Scopus, ScienceDirect and the Web of Science, focussing on a publication period from 2010 to 2022. The database search query was formulated as “Bankruptcy Prediction” and “Model or Tool”. However, the authors intentionally did not specify any model or tool to make the search non-discriminatory. The authors reviewed over 7,300 articles.

This paper has addressed the research questions: (1) What are the most important publications of the core authors in terms of the target country, size of the sample, sector of the economy and specialization in SME? (2) What are the most used methods for deriving or adjusting models appearing in the articles of the core authors? (3) To what extent do the core authors include accounting-based variables, non-financial or macroeconomic indicators, in their prediction models? Despite the advantages of new-age methods, based on the information in the articles analyzed, it can be deduced that conventional methods will continue to be beneficial, mainly due to the higher degree of ease of use and the transferability of the derived model.

The authors identify several gaps in the literature which this research does not address but could be the focus of future research.

The authors provide practitioners and academics with an extract from a wide range of studies, available in scientific databases, on bankruptcy prediction models or tools, resulting in a large number of records being reviewed. This research will interest shareholders, corporations, and financial institutions interested in models of financial distress prediction or bankruptcy prediction to help identify troubled firms in the early stages of distress.

Bankruptcy is a major concern for society in general, especially in today's economic environment. Therefore, being able to predict possible business failure at an early stage will give an organization time to address the issue and maybe avoid bankruptcy.

To the authors' knowledge, this is the first paper to identify the core authors in the bankruptcy prediction model and methods field. The primary value of the study is the current overview and analysis of the theoretical and practical development of knowledge in this field in the form of the construction of new models using classical or new-age methods. Also, the paper adds value by critically examining existing models and their modifications, including a discussion of the benefits of non-accounting variables usage.

Multiple topics often exist on social media platforms that compete for users' attention. To explore how users’ attention transfers in the context of multitopic competition can help us understand the development pattern of the public attention.

This study proposes the prediction model for the attention transfer behavior of social media users in the context of multitopic competition and reveals the important influencing factors of users' attention transfer. Microblogging features are selected from the dimensions of users, time, topics and competitiveness. The microblogging posts on eight topic categories from Sina Weibo, the most popular microblogging platform in China, are used for empirical analysis. A novel indicator named transfer tendency of a feature value is proposed to identify the important factors for attention transfer.

The accuracy of the prediction model based on Light GBM reaches 91%. It is found that user features are the most important for the attention transfer of microblogging users among all the features. The conditions of attention transfer in all aspects are also revealed.

The findings can help governments and enterprises understand the competition mechanism among multiple topics and improve their ability to cope with public opinions in the complex environment.

Food waste is a severe problem affecting the supply chain due to its significant adverse social and environmental effects. Even if the topic is hotly debated in the literature, there is a lack of research about the success factors influencing food waste prevention initiatives retailers undertake.

The research analyzes how several variables (i.e. product-related variables and technology-enabling variables) might impact the success of the sales of products close to the expiration date that is sold at a discounted price. Data from 390.000 products sold at a discounted price in 2020 and 2021 by a large Italian food retailer were examined with a regression analysis.

The results highlight that both product-related and technology-enabling variables influence the success of food prevention initiatives aimed at selling products close to the expiration date at a discounted price. In particular, the authors stress the importance of digital technologies in supporting food waste prevention initiatives.

The study offers several practical implications for managers in structuring a waste prevention initiative. The introduction of digital technologies, the monitoring of specific variables or the ability to find synergies with other food waste prevention initiatives are discussed to support retailers in reducing food losses.

The paper is focused on the retailer perspective, which is barely investigated due to the difficulty in finding data.

Unstructured data such as images have defied usage in property valuation for a long time. Instead, structured data in tabular format are commonly employed to estimate property prices. This study attempts to quantify the shape of land lots and uses the resultant output as an input variable for subsequent land valuation models.

Imagery data containing land lot shapes are fed into a convolutional neural network, and the shape of land lots is classified into two categories, regular and irregular-shaped. Then, the intermediate output (regularity score) is utilized in four downstream models to estimate land prices: random forest, gradient boosting, support vector machine and regression models.

Quantification of the land lot shapes and their exploitation in valuation led to an improvement in the predictive accuracy for all subsequent models.

The study findings are expected to promote the adoption of elusive price determinants such as the shape of a land lot, appearance of a house and the landscape of a neighborhood in property appraisal practices.

This study aims to identify an automated machine learning algorithm with high accuracy that sport practitioners can use to identify the specific factors for predicting Major League Baseball (MLB) attendance. Furthermore, by predicting spectators for each league (American League and National League) and division in MLB, the authors will identify the specific factors that increase accuracy, discuss them and provide implications for marketing strategies for academics and practitioners in sport.

This study used six years of daily MLB game data (2014–2019). All data were collected as predictors, such as game performance, weather and unemployment rate. Also, the attendance rate was obtained as an observation variable. The Random Forest, Lasso regression models and XGBoost were used to build the prediction model, and the analysis was conducted using Python 3.7.

The RMSE value was 0.14, and the R2 was 0.62 as a consequence of fine-tuning the tuning parameters of the XGBoost model, which had the best performance in forecasting the attendance rate. The most influential variables in the model are “Rank” of 0.247 and “Day of the week”, “Home team” and “Day/Night game” were shown as influential variables in order. The result was shown that the “Unemployment rate”, as a macroeconomic factor, has a value of 0.06 and weather factors were a total value of 0.147.

This research highlights unemployment rate as a determinant affecting MLB game attendance rates. Beyond contextual elements such as climate, the findings of this study underscore the significance of economic factors, particularly unemployment rates, necessitating further investigation into these factors to gain a more comprehensive understanding of game attendance.