This paper addresses the problem of forecasting daily stock trends. The key consideration is to predict whether a given stock will close on uptrend tomorrow with reference to today’s closing price. We propose a forecasting model that comprises a features selection model, based on the Genetic Algorithm (GA), and Random Forest (RF) classifier. In our study, we consider four international stock indices that follow the concept of distributed lag analysis. We adopted a genetic algorithm approach to select a set of helpful features among these lags’ indices. Subsequently, we employed the Random Forest classifier, to unveil hidden relationships between stock indices and a particular stock’s trend. We tested our model by using it to predict the trends of 15 stocks. Experiments showed that our forecasting model had 80% accuracy, significantly outperforming the dummy forecast. The S&P 500 was the most useful stock index, whereas the CAC40 was the least useful in the prediction of daily stock trends. This study provides evidence of the usefulness of employing international stock indices to predict stock trends.

Purpose: The purpose of this paper is to propose a new conceptual framework for big data analytics (BDA) in the healthcare sector for the European Mediterranean region. The objective of this new conceptual framework is to improve the health conditions in a dynamic region characterized by the appearance of new diseases.Design/methodology/approach: This study presents a new conceptual framework that could be employed in the European Mediterranean healthcare sector. Practically, this study can enhance medical services, taking smart decisions based on accurate data for healthcare and, finally, reducing the medical treatment costs, thanks to data quality control.Findings: This research proposes a new conceptual framework for BDA in the healthcare sector that could be integrated in the European Mediterranean region. This framework introduces the big data quality (BDQ) module to filter and clean data that are provided from different European data sources. The BDQ module acts in a loop mode where bad data are redirected to their data source (e.g. European Centre for Disease Prevention and Control, university hospitals) to be corrected to improve the overall data quality in the proposed framework. Finally, clean data are directed to the BDA to take quick efficient decisions involving all the concerned stakeholders.Practical implications: This study proposes a new conceptual framework for executives in the healthcare sector to improve the decision-making process, decrease operational costs, enhance management performance and save human lives.Originality/value: This study focused on big data management and BDQ in the European Mediterranean healthcare sector as a broadly considered fundamental condition for the quality of medical services and conditions.

We are living in a new digital age where all corporations should move on and handle the new, challenging digital era. In this chapter, we will address the concepts of big data and artificial intelligence and their applications in the workplace. This chapter reveals the importance of the employment of big data and artificial intelligence within workplaces to enhance the decision-making process and improve overall performance. In addition, this chapter presents the processes of big data and artificial intelligence implementation in workplaces. Finally, this chapter sheds light on the challenges and recommendations that can be adopted for digital corporations seeking the employment of new innovative tools in their daily working tasks.

With a focus on the two most important cryptocurrencies, Bitcoin and Ethereum, this chapter explains the significance of blockchain in today's environment and analyzes its implementation in both networks as well as investigates how they work, describing their transactions, unique characteristics, benefits, and necessities, noting how Ethereum addressed some of Bitcoin's shortcomings with the introduction of smart contracts. They both depended on the proof of work (PoW) consensus mechanism at first, which Ethereum has moved to proof of stake (PoS) in its new upgrade as Ethereum 2.0. They examine the benefits and differences of these consensus protocols and other ones that deserve consideration and shed light on how they both have dealt with their challenges and drawbacks. Moreover, they analyze the prospects and likely improvements in blockchain technology, as they hope to illustrate the potential of blockchain along with Bitcoin and Ethereum to spark future innovation and address contemporary difficulties through their capabilities and network applications.

The field of power engineering has experienced significant advancements as a result of the integration of artificial intelligence methodologies, such as expert systems machine learning and artificial neural networks. In contemporary renewable energy systems, these approaches provide robust resources for the purposes of design, simulation, estimate, fault diagnostics, and fault-tolerant control. Over the course of recent decades, there has been rapid progress in the field of artificial intelligence technology, leading to a substantial increase in the utilization of AI applications inside modern industrial systems. This chapter provides an overview of the function of artificial intelligence in renewable energy systems. Three different types of renewable energy systems have been introduced in this discussion, including solar photovoltaic systems, wind turbine systems, and geothermal energy plants. Following the presentation of these systems, the authors proceed to critically examine the role of artificial intelligence in power prediction and defect detection within those systems.