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Machine Learning is an intelligent methodology used for prediction and has shown promising results in predictive classifications. One of the critical areas in which machine learning can save lives is diabetes prediction. Diabetes is a chronic disease and one of the 10 causes of death worldwide. It is expected that the total number of diabetes will be 700 million in 2045; a 51.18% increase compared to 2019. These are alarming figures, and therefore, it becomes an emergency to provide an accurate diabetes prediction.

Health professionals and stakeholders are striving for classification models to support prognosis of diabetes and formulate strategies for prevention. The authors conduct literature review of machine models and propose an intelligent framework for diabetes prediction.

The authors provide critical analysis of machine learning models, propose and evaluate an intelligent machine learning-based architecture for diabetes prediction. The authors implement and evaluate the decision tree (DT)-based random forest (RF) and support vector machine (SVM) learning models for diabetes prediction as the mostly used approaches in the literature using our framework.

This paper provides novel intelligent diabetes mellitus prediction framework (IDMPF) using machine learning. The framework is the result of a critical examination of prediction models in the literature and their application to diabetes. The authors identify the training methodologies, models evaluation strategies, the challenges in diabetes prediction and propose solutions within the framework. The research results can be used by health professionals, stakeholders, students and researchers working in the diabetes prediction area.

Responsible management of water resources is critical owing to its effects on the environment and society. This study aims to address customer perceptions of a water utility during a severe environmental crisis that affected northern Italy and aims to deepen the understanding of the relationship between corporate social responsibility (CSR), perceived crisis response and corporate reputation.

This study draws on legitimacy theory and attribution theory, adopting a quantitative design. In detail, a moderated mediation model is used to investigate the direct effect of CSR on reputation, the mediating effect of perceived crisis response on the relationship between CSR and reputation and the moderating effect of blame attribution on the relationship between CSR and perceived crisis response. In addition, the evolution of the crisis event and its management is traced through the analysis of the water utilities’ sustainability reports published since the beginning of the crisis.

The findings show that CSR affects corporate reputation directly and via perceived crisis response. In addition, CSR improves perceived crisis response, especially when an organization is held responsible for a crisis. The analysis of the CSR report allows for understanding the evolution of CSR policies of water utilities, shifting attention from a merely informative role of sustainability disclosure to a more comprehensive approach to perfluoroalkyl substances risks in the struggle of contributing to sustainable development. Theoretical and managerial implications are also discussed.

The findings suggest some managerial implications about the usefulness of adopting CSR for crisis management and, furthermore, the importance of communicating CSR policies to all stakeholders overall – the customers of public utilities.

This paper focuses on the relationship between CSR, reputation and blame attribution. Literature on this topic is still scarce overall in the field of public utilities. Furthermore, this study is relevant because it faces one of the major European environmental crises that affected the water sector and provides helpful insights for all public utility sectors and, more generally, for environmental crisis management.

India is a fast-growing economy, that has a majority share in the global information technology industry (IT). Rapid urbanisation and modernisation in India have strained its energy sector, which is being reformed to cope. Despite being the global IT heart and having above average research output in the field of artificial intelligence (AI), India has not yet managed to leverage its benefits to the full. This study aims to address the role of AI and information management (IM) in India’s energy transition to highlight the challenges and barriers to its development and use in the energy sector.

The study, through analysis of proposed strategies, current policies, available literature and reports, discusses the role of AI and IM in the energy transition in India, highlighting the current situation and challenges.

The results show dispersed research and development incentives for IT in the Indian energy sector; however, the needed holistic top-down approach is lacking, calling for due attention in this matter. Adaptive and swift actions from policymakers towards AI and IM are warranted in India.

The ongoing transition of the Indian energy sector with the integration of smart technologies would result in increased access to big data. Extracting the maximum benefits from this would require a comprehensive AI and IM policy.

The revolution in AI and robotics must be carried out in line with sustainable development goals, to support climate action and to consider privacy issues – both areas in India must be strengthened.

The paper offers an original discussion on certain applicable solutions regarding the energy transition of AI coming from the Global South; they are based on lessons learned from the Indian case studies presented in this study.

The agricultural sector is facing pressure due to concerns about its impact on the environment. Farmers must adapt to ensure high-quality, sustainable production. This requires efficient techniques such as soilless farming. The development of agricultural innovations depends on social acceptance; thus, it is crucial to identify the factors that influence consumers' purchasing decisions. The aim of this paper is to analyse consumers' perceptions of hydroponic cultivation techniques and their willingness to pay (WTP) a premium price for hydroponic tomatoes certified as “nickel-free” and “zero-residue”.

The survey was conducted in Italy using tomatoes as a case study. Data were collected through an online questionnaire from a convenience sample of 292 respondents and were analysed using statistical analysis and a multiple linear regression model.

The results showed that WTP was influenced by frequency of purchase, familiarity with soilless technology, environmental sustainability, income and education. Consumers place a high value on the sustainability of the hydroponic production process and their perception of increased safety positively influences WTP. It is therefore recommended that marketing strategies focus on the environmental sustainability and safety of hydroponic products. In addition, it may be beneficial to implement a certification system specific to hydroponic cultivation, in addition to the existing “nickel-free” and “zero-residue” certifications.

This study introduces several novel elements: it is the first to assess the Italian consumers’ perceptions and WTP for a hydroponic product. Secondly, it assesses WTP in relation to several aspects of increasing relevance related to health claims, namely “nickel-free” and “zero-residue”.