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Project portfolio risk (PPR) management plays an important role in promoting the smooth implementation of a project portfolio (PP). Accurate PPR prediction helps managers cope with risks timely in complicated PP environments. However, studies on accurate PPR impact degree prediction, which consists of both risk occurrence probabilities and risk impact consequences considering project interactions, are limited. This study aims to model PPR prediction and expand PPR prediction tools.

In this study, the authors build a PPR prediction model based on a genetic algorithm and back-propagation neural network (GA-BPNN) integrated with entropy-trapezoidal fuzzy numbers. Then, the authors verify the proposed model with real data and obtain PPR impact degrees.

The test results indicate that the proposed method achieves an average absolute error of 0.002 and an average prediction accuracy rate of 97.8%. The former is reduced by 0.038, while the latter is improved by 32.1% when compared with the results of the original BPNN model. Finally, the authors conduct an index sensitivity analysis for identifying critical risks to effectively control them.

This study develops a hybrid PPR prediction model that integrates a GA-BPNN with entropy-trapezoidal fuzzy numbers. The authors use this model to predict PPR impact degrees, which consist of both risk occurrence probabilities and risk impact consequences considering project interactions. The results provide insights into PPR management.

The purpose of this paper is to identify and analyse the readmission risk prediction tools reported in the literature and their benefits when it comes to healthcare organisations and management.

Readmission risk prediction is a growing topic of interest with the aim of identifying patients in particular those suffering from chronic diseases such as congestive heart failure, chronic obstructive pulmonary disease and diabetes, who are at risk of readmission. Several models have been developed with different levels of predictive ability. A structured and extensive literature search of several databases was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-analysis strategy, and this yielded a total of 48,984 records.

Forty-three articles were selected for full-text and extensive review after following the screening process and according to the eligibility criteria. About 34 unique readmission risk prediction models were identified, in which their predictive ability ranged from poor to good (c statistic 0.5–0.86). Readmission rates ranged between 3.1 and 74.1% depending on the risk category. This review shows that readmission risk prediction is a complex process and is still relatively new as a concept and poorly understood. It confirms that readmission prediction models hold significant accuracy at identifying patients at higher risk for such an event within specific context.

Since most prediction models were developed for specific populations, conditions or hospital settings, the generalisability and transferability of the predictions across wider or other contexts may be difficult to achieve. Therefore, the value of prediction models remains limited to hospital management. Future research is indicated in this regard.

This review is the first to cover readmission risk prediction tools that have been published in the literature since 2011, thereby providing an assessment of the relevance of this crucial KPI to health organisations and managers.

Integrated reporting (IR) promotes the disclosure of future-oriented information to enable financial stakeholders to make better-informed decisions. However, the downside to this type of disclosure is the risk to management of disclosing such future-oriented information. This paper aims to explore how IR preparers manage the risk of disclosing future-oriented information in companies’ integrated reports.

This study represents an exploratory interpretative thematic analysis of 33 semi-structured interviews with managers involved in IR in eight Sri Lankan companies representing various industries. The thematic analysis is informed by the research literature and prior studies on IR.

This paper provides evidence of various strategies to manage the risk associated with the disclosure of future-oriented information in integrated reports. These strategies include making non-specific predictions; increasing the accuracy of the predictions; linking performance management to disclosed targets, thus ensuring individual responsibility for target achievement; disclosing ex post explanations for not achieving previously disclosed targets; and linking disclosed targets to the company’s risk management procedures. However, these strategies can cause managers to provide conservative future-oriented information, rather than “best estimate” future-oriented information.

The study describes the strategies that managers use to mitigate the risks involved in disclosing future-oriented information. These strategies can provide support or raise concerns, for managers in deciding how to deal with such risks. Regulators tasked with investor protection, as well as stock exchanges interested in the transparency and accountability of listed companies’ activities should be aware of these strategies. Furthermore, the International Integrated Reporting Council (IIRC) should be interested in the implications of this study because some of the identified strategies could undermine the usefulness of integrated reports to stakeholders. This is a significant concern given that the IIRC envisages integrated reporting and thinking as vehicles that could align capital allocation and corporate behaviour with wider sustainable development goals.

The trend of future-oriented information moving from being used only in organisations’ internal management systems to being externally reported in integrated reports has implications for stakeholder groups interested in the reported targets. This study reveals management strategies that could affect future-oriented information reliability and reduce their usefulness for users of integrated reports.

This study provides unique insights into the emerging area of how managers deal with the risks involved in disclosing future-oriented IR information.

Since stock return and volatility matters to investors, this study proposes to incorporate the textual sentiment of annual reports in stock price crash risk prediction.

Specific sentences gathered from management discussions and their subsequent analyses are tokenized and transformed into numeric vectors using textual mining techniques, and then the Naïve Bayes method is applied to score the sentiment, which is used as an input variable for crash risk prediction. The results are compared between a collection of predictive models, including linear regression (LR) and machine learning techniques.

The experimental results find that those predictive models that incorporate textual sentiment significantly outperform the baseline models with only accounting and market variables included. These conclusions hold when crash risk is proxied by either the negative skewness of the return distribution or down-to-up volatility (DUVOL).

It should be noted that the authors' study focuses on examining the predictive power of textual sentiment in crash risk prediction, while other dimensions of textual features such as readability and thematic contents are not considered. More analysis is needed to explore the predictive power of textual features from various dimensions, with the most recent sample data included in future studies.

The authors' study provides implications for the information value of textual data in financial analysis and risk management. It suggests that the soft information contained within annual reports may prove informative in crash risk prediction, and the incorporation of textual sentiment provides an incremental improvement in overall predictive performance.

The purpose of this paper is to investigate the prediction ability in children with ASD in the risk-involving situations and compute the impact of statistical learning (SL) in strengthening their risk knowledge. The learning index and stability with time are also calculated by comparing their performance over three consecutive weekly sessions (session 1, session 2 and session 3).

Participants were presented with a series of images, showing simple and complex risk-involving situations, using the psychophysical experimental paradigm. The stimuli in the experiment were provided with different levels of difficulty in order to keep the legacy of the prediction and SL-based experiment intact.

The first phase of experimental work showed that children with ASD accurately discriminated the risk, although performed poorly as compared to neurotypical. The attenuated response in differentiating risk levels indicates that children with ASD have a poor and underdeveloped sense of risk. The second phase investigated their capability to extract the information from repetitive patterns and calculated SL stability value in time. The learning curve shows that SL is intact and stable with time (average session r=0.74) in children with ASD.

The present work concludes that impaired action prediction could possibly be one of the factors underlying underdeveloped sense of risk in children with ASD. Their SL capability shows that risk knowledge can be strengthened in them. In future, the studies should investigate the impact of age and individual differences, by using knowledge from repetitive trials, on the learning rate and trajectories.

SL, being an integral part of different therapies, rehabilitation schemes and intervention systems, has the potential to enhance the cognitive and functional abilities of children with ASD.

Past studies have provided evidence regarding the work on the prediction ability in individuals with ASD. However, it is unclear whether the risk-involving/dangerous situations play any certain role to enhance the prediction ability in children with ASD. Also, there are limited studies predicting risk knowledge in them. Based on this, the current work has investigated the risk prediction in children with ASD.

The purpose of this paper is to develop a risk assessment method for production processes of large-size steel ship hulls.

This study uses a quantitative-probabilistic approach with involvement of clustering technique in order to analyse the database of accidents and predict the process risk. The case-based reasoning is used in here. A set of technological hazard classes as a basis for analysing the similarities between the production processes is proposed. The method has been explained using a case study on large-size shipyard.

Statistical and clustering approach ensures effective risk managing in shipbuilding process designing. Results show that by selection of adequate number of clusters in the database, the quality of predictions can be controlled.

The suggested k-means method using the Euclidean distance measure is initial approach. Testing the other distance measures and consideration of fuzzy clustering method is desirable in the future. The analysis in the case study is simplified. The use of the method according to prediction of risk related to loss of health or life among people exposed to the hazards is presented.

The risk index allows to compare the processes in terms of security, as well as provide significant information at the technology design stage of production task.

There are no studies on quantitative methods developed specifically for managing risks in shipbuilding processes. Proposed list of technological hazard classes allows to utilize database of past processes accidents in risk prediction. The clustering method of analysing the database is agile thanks to the number of clusters parameter. The case study basing on actual data from the real shipyard constitutes additional value of the paper.

A stroke is a serious, life-threatening condition that occurs when the blood supply to a part of the brain is cut off. The earlier a stroke is treated, the less damage is likely to occur. One of the methods that can lead to faster treatment is timely and accurate prediction and diagnosis. This paper aims to compare the binary integer programming-data envelopment analysis (BIP-DEA) model and the logistic regression (LR) model for diagnosing and predicting the occurrence of stroke in Iran.

In this study, two algorithms of the BIP-DEA and LR methods were introduced and key risk factors leading to stroke were extracted.

The study population consisted of 2,100 samples (patients) divided into six subsamples of different sizes. The classification table of each algorithm showed that the BIP-DEA model had more reliable results than the LR for the small data size. After running each algorithm, the BIP-DEA and LR algorithms identified eight and five factors as more effective risk factors and causes of stroke, respectively. Finally, predictive models using the important risk factors were proposed.

The main objective of this study is to provide the integrated BIP-DEA algorithm as a fast, easy and suitable tool for evaluation and prediction. In fact, the BIP-DEA algorithm can be used as an alternative tool to the LR model when the sample size is small. These algorithms can be used in various fields, including the health-care industry, to predict and prevent various diseases before the patient’s condition becomes more dangerous.

The purpose of this paper is to propose a data-driven model to predict credit risks of actors collaborating within a supply chain finance (SCF) network based on the analysis of their network attributes. This can support applying reverse factoring mechanisms in SCFs.

Based on network science, the network measures of the actors collaborating in the investigated SCF are derived through a social network analysis. Then several supervised machine learning algorithms are applied to predict the credit risks of the actors on the basis of their network level and organizational-level characteristics. For this purpose, a data set from an SCF within an automotive industry in Iran is used.

The findings of the research clearly demonstrate that considering the network attributes of the actors within the prediction models can significantly enhance the accuracy and precision of the models.

The main limitation of this research is to investigate the applicability and effectiveness of the proposed model within a single case.

The proposed model can provide a well-established basis for financial intermediaries in SCFs to make more sophisticated decisions within financial facilitation mechanisms.

This study contributes to the existing literature of credit risk evaluation by considering credit risk as a systematic risk that can be influenced by network measures of collaborating actors. To do so, the paper proposes an approach that considers network characteristics of SCFs as critical attributes to predict credit risk.

The purpose of this study to investigate the effects and possible future prediction of COVID-19. The dataset considered in this study to investigate the effects and possible future prediction of COVID-19 is constrained as follows: age, gender, systolic blood pressure, HDL-cholesterol, diabetes and its medication, does the patient suffered from heart disease or took anti-cough agent food or sensitive to cough related issues and any other chronic kidney disease, physical contact with foreign returns and social distance for the prediction of the risk of COVID-19.

This work implemented a meta-heuristic algorithm on the aforementioned dataset for possible analysis of the risk of being infected with COVID-19. The authors proposed a simple yet effective Risk Prediction through Nature Inspired Hybrid Particle Swarm Optimization and Sine Cosine Algorithm (HPSOSCA), particle swarm optimization (PSO), and sine cosine algorithm (SCA) algorithms.

The simulated results on different cases discussed in the dataset section reveal which category of individuals may happen to have the disease and of what level. The experimental results reveal that the proposed model can predict the percentage of risk with an overall accuracy of 88.63%, sensitivity (87.23%), specificity (89.02%), precision (69.49%), recall (87.23%), f_measure (77.36%) and Gmean (88.12%) with 41 and 146 true positive and negative, 18 and 6 false positive and negative cases, respectively. The proposed model provides a quite stable prediction of risk for COVID-19 on different categories of individuals.

The work for the very first time developed a novel HPSOSCA model based on PSO and SCA for the prediction of COVID-19 disease. The convergence rate of the proposed model is too high as compared to the literature. It also produces a better accuracy in a computationally efficient fashion. The obtained outputs are as follows: accuracy (88.63%), sensitivity (87.23%), specificity (89.02%), precision (69.49%), recall (87.23%), f_measure (77.36%), Gmean (88.12%), Tp (41), Tn (146), Fb (18) and Fn (06). The recommendations to reduce disease outbreaks are as follow: to control this epidemic in various regions, it is important to appropriately manage patients suspected of having the disease, immediately identify and isolate the source of infection, cut off the transmission route and prevent viral transmission from these potential patients or virus carriers.