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Application of machine learning in the financial sector
No full textStrojno učenje još uvijek nije dovoljno prepoznato kao kvalitetan alat za odlučivanje u financijskom sektoru. Primjena strojnog učenja može biti puno raširenija u kontekstu financija s obzirom da bi strojno učenje potencijalno moglo pridonijeti produktivnijoj i efikasnijoj upotrebi financijskih podataka radi dobivanja relevantnih izračuna i kalkulacija. Nenadziranim strojnim učenjem se mogu prepoznavati obrasci i odnosi među velikim skupovima podataka, što su inače vrlo izazovni statistički zadatci koji iziskuju resurse i vrijeme. S druge strane, nadzirano učenje može pomoći pri prognoziranju koje se temelji na statističkim podacima. Analize rizika, mehanizmi za otkrivanje prevara i prognoze mogu biti puno točnije i sofisticiranije, a u konačnici izračuni mogu biti efikasnije obavljeni uz pomoć algoritama strojnog učenja. Strojno učenje podrazumijeva da korisnik dobiva u izuzetno kratkom vremenu točne izračune koristeći napredne statističke metode koje istovremeno obuhvaćaju izuzetno velike raspone podataka. Veliki raspon podataka s druge strane pridonosi točnosti i relevantnosti modela izračuna potrebne kalkulacije jer može uzimati u izračun podatke iz vrlo različitih izvora, a rezultat je efikasno brz. Informacije za donošenje odluka obično dolaze iz opsežnih analiza gdje se koriste podaci iz raznih izvora, a informacije koje pomažu pri donošenju odluka imaju veće uporište kada se koriste različiti izvori i vrste podataka. Primjena strojnog učenja mogla bi pružiti revoluciju kada je riječ o optimizaciji poslovnih procesa u financijskim djelatnostima. No, kao i svaka druga tehnologija, potrebno je poznavati postavke funkcioniranja ovakvih tehnologija kako ne bi došlo do nedovoljno dobrog korištenja tehnologije. Potrebno je poznavati ograničenja tehnologije kako bi se s njom moglo uspješno upravljati i koristiti u poslovanju.Machine learning is still not sufficiently recognized as a quality decision-making tool in the financial sector. The application of machine learning can be much more widespread in the context of finance, considering that machine learning could potentially contribute to a more productive and efficient use of financial data to obtain relevant calculations and calculations. Unsupervised machine learning can recognize patterns and relationships among large data sets, which are otherwise very challenging statistical tasks that require resources and time. On the other hand, supervised learning can help with statistics-based forecasting. Risk analyses, fraud detection mechanisms and forecasts can be much more accurate and sophisticated, and ultimately calculations can be performed more efficiently with the help of machine learning algorithms. Machine learning means that the user gets accurate calculations in an extremely short time using advanced statistical methods that simultaneously cover extremely large ranges of data. A large range of data, on the other hand, contributes to the accuracy and relevance of the calculation model of the necessary calculation, because it can take into account data from very different sources, and the result is efficiently fast. Decision-making information usually comes from extensive analyses where data from a variety of sources are used, and decision-making information is more robust when using different sources and types of data. The application of machine learning could provide a revolution when it comes to optimizing business processes in financial industries. However, like any other technology, it is necessary to know the settings of the functioning of such technologies in order not to use the technology in an insufficiently good way. It is necessary to know the limitations of technology to successfully manage and use it in business
Application of machine learning in the financial sector
No full textStrojno učenje još uvijek nije dovoljno prepoznato kao kvalitetan alat za odlučivanje u financijskom sektoru. Primjena strojnog učenja može biti puno raširenija u kontekstu financija s obzirom da bi strojno učenje potencijalno moglo pridonijeti produktivnijoj i efikasnijoj upotrebi financijskih podataka radi dobivanja relevantnih izračuna i kalkulacija. Nenadziranim strojnim učenjem se mogu prepoznavati obrasci i odnosi među velikim skupovima podataka, što su inače vrlo izazovni statistički zadatci koji iziskuju resurse i vrijeme. S druge strane, nadzirano učenje može pomoći pri prognoziranju koje se temelji na statističkim podacima. Analize rizika, mehanizmi za otkrivanje prevara i prognoze mogu biti puno točnije i sofisticiranije, a u konačnici izračuni mogu biti efikasnije obavljeni uz pomoć algoritama strojnog učenja. Strojno učenje podrazumijeva da korisnik dobiva u izuzetno kratkom vremenu točne izračune koristeći napredne statističke metode koje istovremeno obuhvaćaju izuzetno velike raspone podataka. Veliki raspon podataka s druge strane pridonosi točnosti i relevantnosti modela izračuna potrebne kalkulacije jer može uzimati u izračun podatke iz vrlo različitih izvora, a rezultat je efikasno brz. Informacije za donošenje odluka obično dolaze iz opsežnih analiza gdje se koriste podaci iz raznih izvora, a informacije koje pomažu pri donošenju odluka imaju veće uporište kada se koriste različiti izvori i vrste podataka. Primjena strojnog učenja mogla bi pružiti revoluciju kada je riječ o optimizaciji poslovnih procesa u financijskim djelatnostima. No, kao i svaka druga tehnologija, potrebno je poznavati postavke funkcioniranja ovakvih tehnologija kako ne bi došlo do nedovoljno dobrog korištenja tehnologije. Potrebno je poznavati ograničenja tehnologije kako bi se s njom moglo uspješno upravljati i koristiti u poslovanju.Machine learning is still not sufficiently recognized as a quality decision-making tool in the financial sector. The application of machine learning can be much more widespread in the context of finance, considering that machine learning could potentially contribute to a more productive and efficient use of financial data to obtain relevant calculations and calculations. Unsupervised machine learning can recognize patterns and relationships among large data sets, which are otherwise very challenging statistical tasks that require resources and time. On the other hand, supervised learning can help with statistics-based forecasting. Risk analyses, fraud detection mechanisms and forecasts can be much more accurate and sophisticated, and ultimately calculations can be performed more efficiently with the help of machine learning algorithms. Machine learning means that the user gets accurate calculations in an extremely short time using advanced statistical methods that simultaneously cover extremely large ranges of data. A large range of data, on the other hand, contributes to the accuracy and relevance of the calculation model of the necessary calculation, because it can take into account data from very different sources, and the result is efficiently fast. Decision-making information usually comes from extensive analyses where data from a variety of sources are used, and decision-making information is more robust when using different sources and types of data. The application of machine learning could provide a revolution when it comes to optimizing business processes in financial industries. However, like any other technology, it is necessary to know the settings of the functioning of such technologies in order not to use the technology in an insufficiently good way. It is necessary to know the limitations of technology to successfully manage and use it in business
Application of machine learning in the financial sector
No full textStrojno učenje još uvijek nije dovoljno prepoznato kao kvalitetan alat za odlučivanje u financijskom sektoru. Primjena strojnog učenja može biti puno raširenija u kontekstu financija s obzirom da bi strojno učenje potencijalno moglo pridonijeti produktivnijoj i efikasnijoj upotrebi financijskih podataka radi dobivanja relevantnih izračuna i kalkulacija. Nenadziranim strojnim učenjem se mogu prepoznavati obrasci i odnosi među velikim skupovima podataka, što su inače vrlo izazovni statistički zadatci koji iziskuju resurse i vrijeme. S druge strane, nadzirano učenje može pomoći pri prognoziranju koje se temelji na statističkim podacima. Analize rizika, mehanizmi za otkrivanje prevara i prognoze mogu biti puno točnije i sofisticiranije, a u konačnici izračuni mogu biti efikasnije obavljeni uz pomoć algoritama strojnog učenja. Strojno učenje podrazumijeva da korisnik dobiva u izuzetno kratkom vremenu točne izračune koristeći napredne statističke metode koje istovremeno obuhvaćaju izuzetno velike raspone podataka. Veliki raspon podataka s druge strane pridonosi točnosti i relevantnosti modela izračuna potrebne kalkulacije jer može uzimati u izračun podatke iz vrlo različitih izvora, a rezultat je efikasno brz. Informacije za donošenje odluka obično dolaze iz opsežnih analiza gdje se koriste podaci iz raznih izvora, a informacije koje pomažu pri donošenju odluka imaju veće uporište kada se koriste različiti izvori i vrste podataka. Primjena strojnog učenja mogla bi pružiti revoluciju kada je riječ o optimizaciji poslovnih procesa u financijskim djelatnostima. No, kao i svaka druga tehnologija, potrebno je poznavati postavke funkcioniranja ovakvih tehnologija kako ne bi došlo do nedovoljno dobrog korištenja tehnologije. Potrebno je poznavati ograničenja tehnologije kako bi se s njom moglo uspješno upravljati i koristiti u poslovanju.Machine learning is still not sufficiently recognized as a quality decision-making tool in the financial sector. The application of machine learning can be much more widespread in the context of finance, considering that machine learning could potentially contribute to a more productive and efficient use of financial data to obtain relevant calculations and calculations. Unsupervised machine learning can recognize patterns and relationships among large data sets, which are otherwise very challenging statistical tasks that require resources and time. On the other hand, supervised learning can help with statistics-based forecasting. Risk analyses, fraud detection mechanisms and forecasts can be much more accurate and sophisticated, and ultimately calculations can be performed more efficiently with the help of machine learning algorithms. Machine learning means that the user gets accurate calculations in an extremely short time using advanced statistical methods that simultaneously cover extremely large ranges of data. A large range of data, on the other hand, contributes to the accuracy and relevance of the calculation model of the necessary calculation, because it can take into account data from very different sources, and the result is efficiently fast. Decision-making information usually comes from extensive analyses where data from a variety of sources are used, and decision-making information is more robust when using different sources and types of data. The application of machine learning could provide a revolution when it comes to optimizing business processes in financial industries. However, like any other technology, it is necessary to know the settings of the functioning of such technologies in order not to use the technology in an insufficiently good way. It is necessary to know the limitations of technology to successfully manage and use it in business
DIGITALNA TRANSFORMACIJA, EKONOMSKA USPJEŠNOST I ODRŽIVOST UNUTAR EU-A
No full textBy using triple time segmentation and dual model specifications, this paper investigates the relationship between digital transformation, economic performance, and sustainability within the European Union. By employing a multi-stage methodology (PCA, cluster analysis, and fixed effects (FE) panel regression modeling) across 27 EU countries, the study confirms the complex interdependence among these three dimensions. Findings identify four heterogeneous clusters, highlighting a contradiction between digital - economic leaders and sustainability leaders, indicating a significant challenge in decoupling growth from sustainability impacts. The panel regression results confirm digitalization as a robust and statistically significant driver of economic growth. Most importantly, the positive impact of renewable energy sources on economic performance confirms their endogenous benefits.Trostrukom vremenskom segmentacijom i dvostrukim specifikacijama modela ovaj rad istražuje odnos između digitalne transformacije, ekonomske uspješnosti i održivosti unutar Europske unije. Korištenjem višestupanjskom metodologijom (PCA, klaster analiza i model panel regresije s fiksnim učincima) za 27 zemalja EU-a, studija potvrđuje složenu međuovisnost ovih triju dimenzija. Nalazi identificiraju četiri heterogena klastera, ističući kontradikciju između digitalno-ekonomskih lidera i lidera održivosti, što ukazuje na značajan izazov u odvajanju rasta od utjecaja na održivost. Rezultati panel regresije potvrđuju digitalizaciju kao robustan i statistički značajan pokretač gospodarskog rasta. Najvažnije je da pozitivan utjecaj udjela obnovljivih izvora energije na ekonomske uspješnosti potvrđuje endogene koristi obnovljivih izvora energije
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Testing the Ability of ChatGPT to Categorise Urgent and Non-Urgent Patient Conditions: Who ya gonna call?
No full textThis research explores the feasibility of utilising ChatGPT to categorise patient conditions as urgent and non-urgent. The primary objective is to assess the ChatGPT model's capacity to aid in the automation and digitalisation of healthcare processes, thereby alleviating the workload on healthcare professionals. The study employed a unique approach by presenting patient cases to the GPT and categorising the conditions based on urgency. In collaboration with an experienced hospital doctor, a set of questions was prepared and presented to a medical expert, along with the GPT model. Subsequently, the medical expert was consulted to assign urgency modalities for the same cases. The generated categorisations and the expert-assigned modalities were compared to evaluate the model's accuracy. The outcomes of this research have significant implications for healthcare management. Implementing AI to support triage processes and decisions could streamline patient care, ensuring appropriate and timely treatment allocation. By delegating specific tasks to AI, healthcare employees could focus on providing direct medical attention, leading to enhanced efficiency and improved patient outcomes. However, the results indicate that there is still uncertainty in using ChatGPT to provide medical advice. Ultimately, this study contributes to the broader exploration of AI's potential in healthcare decision-making, promoting the integration of advanced technologies to optimise medical services and enhance patient experiences
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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