Cyberattacks are becoming increasingly sophisticated and severe, and an organization’s protection depends on its weakest member. Although users are aware of the risks in cyberspace, most of them do not follow best practices, and there is a need for permanent structured training. The majority of previous training programs concentrated on technically educated users, but the organization is only as secure as the most vulnerable link in it. The paper presents SAWIT, a new Web tool, created with the goal of improving security awareness among employees. It is an innovative artificial intelligence framework aimed at improving the cyber security knowledge of employees by using collaborative learning and assessment within the specified knowledge transformation model.

This study explores market efficiency and behavior by integrating key theories such as the Efficient Market Hypothesis (EMH), Adaptive Market Hypothesis (AMH), Informational Efficiency and Random Walk theory. Using LSTM enhanced by optimizers like Stochastic Gradient Descent (SGD), Adam, AdaGrad, and RMSprop, we analyze market inefficiencies in the Standard and Poor’s (SPX) index over a 22-year period. Our results reveal “pockets in time” that challenge EMH predictions, particularly with the AdaGrad optimizer at a size of the hidden layer (HS) of 64. Beyond forecasting, we apply the Dominguez–Lobato (DL) and General Spectral (GS) tests as part of the Martingale Difference Hypothesis to assess statistical inefficiencies and deviations from the Random Walk model. By emphasizing “informational efficiency”, we examine how quickly new information is reflected in stock prices. We argue that market inefficiencies are transient phenomena influenced by structural shifts and information flow, challenging the notion that forecasting alone can refute EMH. Additionally, we compare LSTM with ARIMA with Exponential Smoothing, and LightGBM to highlight the strengths and limitations of these models in financial forecasting. The LSTM model excels at capturing temporal dependencies, while LightGBM demonstrates its effectiveness in detecting non-linear relationships. Our comprehensive approach offers a nuanced understanding of market dynamics and inefficiencies.

This research is devoted to the determination of hidden dependencies between the flow of particles that come from the Sun and precipitation-induced floods in the United Kingdom (UK). The analysis covers 20 flood events during the period from October 2001 to December 2019. The parameters of solar activity were used as model input data, while precipitations data in the period 10 days before and during each flood event were used as model output. The time lag of 0–9 days was taken into account in the research. Correlation analysis was conducted to determine the degree of randomness for the time series of input and output parameters. For establishing a potential causative link, machine learning classification predictive modeling was applied. Two approaches, the decision tree, and the random forest were used. We analyzed the accuracy of classification models forecast from 0 to 9 days in advance. It was found that the most important factors for flood forecasting are proton density with a time lag of 9, differential proton flux in the range of 310–580 keV, and ion temperature. Research in this paper has shown that the decision tree model is more accurate and adequate in predicting the appearance of precipitation-induced floods up to 9 days ahead with an accuracy of 91%. The results of this study confirmed that by increasing technical capabilities, using improved machine learning techniques and large data sets, it is possible to improve the understanding of the physical link between the solar wind and tropospheric weather and help improve severe weather forecasting.

The rapid advancements in artificial intelligence (AI) have presented new opportunities for enhancing efficiency and economic competitiveness across various industries, espcially in banking. Machine learning (ML), as a subset of artificial intelligence, enables systems to adapt and learn from vast datasets, revolutionizing decision-making processes, fraud detection, and customer service automation. However, these innovations also introduce new challenges, particularly in the realm of cybersecurity. Adversarial attacks, such as data poisoning and evasion attacks, represent critical threats to machine learning models, exploiting vulnerabilities to manipulate outcomes or compromise sensitive information. Furthermore, this study highlights the dual-use nature of AI tools, which can be used by malicious user. To address these challenges, the paper emphasizes the importance of developing machine learning models with key characteristics such as security, trust, resilience and robustness. These features are essential to mitigating risks and ensuring secure deployment of AI technologies in banking sectors, where the protection of financial data is paramount. The findings underscore the urgent need for enhanced cybersecurity frameworks and continuous improvements in defensive mechanisms. By exploring both opportunities and risks, this paper aims to guide the responsible integration of AI in the banking sector, paving the way for innovation while safeguarding against emerging threats.

This paper presents the process of digital transformation of banking sector aiming to emphasize the impact of applying artificial intelligence in order to enable chatbot customer service, robo advice, predictive analytics, cybersecurity, as well as credit scoring, and direct lending. The proposed framework is composed of three levels of application the artificial intelligence in digital banking. The application of artificial intelligence platforms in the banking sector is essential to meet the needs of today’s digital-first customers as well as to stay a step ahead of tomorrow’s digital banking challenges.

This paper presents the concept and the implementation of the Content and/or Knowledge Provision (CKP) service. The service is developed as a part of research within Intelligent Learning Extended Organization, an 7th Framework Program for R&D project in the area of technology-enhanced learning. The project aimed at enhancing cross-organizational Learning and Knowledge Building (LKB) practices at workplaces, leading to continuous growth of organization’s intellectual capital. By using the CKP service, employees can upload different kinds of knowledge assets into a knowledge repository, annotate them and (re-)discover relevant ones by performing semantic search over the knowledge repository. Hence, the service effectively serves as a content and knowledge-sharing facility and semantic search engine within a learning organization. Furthermore, CKP enables collaborative manipulation and annotation of knowledge assets in the repository, thus supporting all three components of intellectual capital.