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This paper considers the transfer of approaches from the field of to the field of to be used in decision support systems. The most relevant of the developed approaches will be roughly introduced and it will be considered how these approaches can be applied in medical informatics in the context of decision support systems. Moreover, it will be considered which of the approaches are eligible in this context. Apart from placing the approaches in the context of decision support systems, the paper thereby also provides an example for the interplay of different fields of research.

Through this paper we aimed to develop a comparison between ARIMA, Prophet, KNN and Neural Networks in terms of stock prices forecasting. After reviewing the literature, we noticed that there is a plethora of studies that address this problem of forecasting, but very few have made comparisons that include ARIMA, machine learning, but also the Prophet forecasting model developed by Facebook, which brought interesting results for certain data series. Based on methodologies validated by other authors, we compared these models in our paper and we sought to obtain promising results regarding performance evaluation. The comparison was made in-sample, the training period being 01/01/2010 - 31/07/2021, but also out-of-sample (01/08/2021 - 31/10/2021). The study was performed for Societe Generale’s stock, using daily observations. Statistical loss functions such as RMSE, MPE, MAPE, MAE, and ME were used for comparison. The results indicated an outperformance of Neural Networks, both in-sample and out-of-sample, this model being on the 1st place according to the aggregated score. It is also noteworthy that the ARIMA model was in second place in-sample, ahead of KNN, but for out of sample these two algorithms changed their positions. On the other hand, the Prophet algorithm performed the weakest, both in-sample and out of sample. Also, we must underlie that all four algorithms had a clear tendency to overestimate the price of Societe Generale, according to the results of the statistical loss functions ME and MPE. Finally, it should be noted that the results were consistent with what other authors found out, especially for the out-of-sample period, where the machine learning models performed best.

Augmented reality (AR) and machine learning (ML) are rapidly growing technologies with immense potential for transforming education. Web-based augmented reality (WebAR) provides a promising approach to delivering immersive learning experiences on mobile devices. Integrating machine learning models into WebAR applications can enable advanced interactive effects by responding to user actions, thus enhancing the educational content. However, there is a lack of effective methodologies to teach students WebAR development with integrated machine learning. This paper proposes a methodology with three main steps: (1) Integrating standard TensorFlow.js models like handpose into WebAR scenes for gestures and interactions; (2) Developing custom image classification models with Teachable Machine and exporting to TensorFlow.js; (3) Modifying WebAR applications to load and use exported custom models, displaying model outputs as augmented reality content. The proposed methodology is designed to incrementally introduce machine learning integration, build an understanding of model training and usage, and spark ideas for using machine learning to augment educational content. The methodology provides a starting point for further research into pedagogical frameworks, assessments, and empirical studies on teaching WebAR development with embedded intelligence.

Machine learning and essentialism have been connected in the past by various researchers, in order to state that the main paradigm in machine learning processes is equivalent to choosing the “essential” attributes for the machine to search for. Our goal in this paper is to show that there are connections between machine learning and essentialism, but only for some kinds of machine learning, and often not including deep learning methods. Similarity-based approaches, more connected to the overall prototype theory, spanning from psychology and linguistics, seem more suited for pattern recognition and complex deep-learning issues, while for classification problems, mostly for unsupervised learning, essentialism seems like the best choice. In order to illustrate the difference better, we will connect both paths to their sources in other disciplines and see how human psychology influences our decision in machine-learning modeling as well. This leads to a philosophically very interesting consequence: even in the setting of supervised machine learning, essences are not present in data, but in targets, which in turn means that the categories which purport to be essences are in fact human-made, and hand-coded in the targets. The success of machine learning, therefore, does not give any substantial evidence for the independent existence of essential properties. Our stance here is to state that neither the existence nor the lack of “essential” properties in machine learning can lead to metaphysical, i.e., ontological claims.

The purpose of this article. The aim of the article is to analyze selected issues related to artificial intelligence and its development, particularly its impact on the financial market, taking into account the opportunities and threats that artificial intelligence and its areas, such as machine learning or deep learning, pose to financial market participants. The research methods utilized in the study were used to evaluate the phenomenon on a macroeconomic scale. Methodology. The results of the research were based on the analysis of secondary data, such as source literature – both domestic and foreign, systems analysis of European Union legal acts, as well as the review of reports on the use of AI within the financial market. The paper is theoretical. The result of the research. The development of artificial intelligence in financial markets may provide an opportunity to gain competitive advantage, especially for financial market participants who aptly implement AI-based solutions in its initial phase. However, this entails both benefits and risks, the possible occurrence of which depends on many other factors.

Since the early 2000s, personalized medicine (PM) has been a much-hyped field of healthcare. HIV treatment optimization tools were one of the first successful examples of PM, and have since their development been used to find tailored and optimized treatment for HIV-positive people. In this paper on a case study of the social arena of personalized HIV therapy I show how social worlds worked on both shared and distinct goals within the arena. I highlight the simultaneous centering and marginalization to which people seeking HIV therapy were subjected discursively in the social worlds. I also demonstrate that the further patients were from practitioners' daily work, the more they were reduced to their blood samples, rather than being constructed as complex and human.

Copyright of Journal for Technology in Theory & Practice / Zeitschrift für Technikfolgenabschätzung in Theorie und Praxis (TATuP) is the property of Oekom Verlag GmbH and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Cultivated Plants / Journal für Kulturpflanzen is the property of Verlag Eugen Ulmer and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper discusses various legal challenges of the “digitisation of the single market”. The question arises to which extent the current regulatory framework appears suitable to deal with the presented challenges of digitisation and where additional regulation is required. In the field of autonomous decision-making by AI, we identified the most pressing need for new regulation. While the EU (and increasingly Austria, as well) is aware of this need, regulation to date remains scarce. Though the EU legislator has already taken specific precautions for the use of algorithms in the GDPR, such regulatory approaches are missing in most other fields of law. In contrast to this, antitrust law and product liability law already appear to be well suited to meet the challenges posed by digitisation. This is especially true for product liability law, which is in principle apt to cover the specific challenges of the convergence of software and hardware in smart products. However, uncertainty about its applicability to incorporeal goods would make clarification of current product liability legislation advisable – a view shared by the European Commission. Two more fields very recently received some legislative attention due to the changing needs of a digital society: the postal sector on the one hand, and e-government on the other hand. In both fields, new legislation – tellingly in the form of (partially) directly applicable regulations – has recently been passed by the EU – a sharp contrast to the case of self-learning AI. However, while the integration of the new regulation on cross-border parcel delivery will probably not pose major challenges for domestic markets, the implementation of the Single Digital Gateway will raise serious organisational and legal challenges for national administrations (especially when taking into account the limited success of the previous related initiative on the points of single contact under the Services Directive).

An interview with mathematician Alexandra Carpentier, is presented. Topics include work with methods of mathematical statistics and machine learning on different models; and statistics and are also at home in machine learning; and Emmy Noether programs or algorithms that generate recommendations for users on websites.

This article investigates religious ideals persistent in the datafication of information society. Its nodal point is Thomas Bayes, after whom Laplace names the primal probability algorithm. It reconsiders their mathematical innovations with Laplace's providential deism and Bayes' singular theological treatise. Conceptions of divine justice one finds among probability theorists play no small part in the algorithmic data-mining and microtargeting of Cambridge Analytica. Theological traces within mathematical computation are emphasized as the vantage over large numbers shifts to weights beyond enumeration in probability theory. Collateral secularizations of predestination and theodicy emerge as probability optimizes into Bayesian prediction and machine learning. The paper revisits the semiotics and theism of Peirce and a given beyond the probable in Whitehead to recontextualize the critiques of providence by Agamben and Foucault. It reconsiders datafication problems alongside Nietzschean valuations. Religiosity likely remains encoded within the very algorithms presumed purified by technoscientific secularity or mathematical dispassion.

Copyright of Anaesthesiologie & Intensivmedizin is the property of DGAI e.V. - Deutsche Gesellschaft fur Anasthesiologie und Intensivmedizin e.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Machine Learning and Data Science have enjoyed a renaissance due to the availability of increased computational power and larger data sets. Many questions can be now asked and answered, that previously were beyond our scope. This does not translate instantly into new tools that can be used by those not skilled in the field, as many of the issues and traps still exist. In this paper, we look at some of the new tools that we have created, and some of the difficulties that still need to be taken care of during the transition from a project run by an expert, to a tool for the bench chemist.

Copyright of Deutsches Institut für Wirtschaftsforschung: DIW-Wochenbericht is the property of DIW Berlin and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)