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Tytuł:
Francesco Marconi (2020). Newsmakers: Artificial Intelligence And The Future Of Journalism
Autorzy:
Baranowski, Paweł
Powiązania:
https://bibliotekanauki.pl/articles/2042901.pdf
Data publikacji:
2021-12-28
Wydawca:
Polskie Towarzystwo Komunikacji Społecznej
Tematy:
journalism studies
technology
artificial intelligence
machine learning
Opis:
This is the review of the book by Francesco Marconi "Newsmakers: Artificial Intelligence and the Future of Journalism."
Źródło:
Central European Journal of Communication; 2021, 14, 2(29); 357-360
1899-5101
Pojawia się w:
Central European Journal of Communication
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Artificial intelligence versus natural intelligence in mineral processing
Autorzy:
Özkan, Şafak Gökhan
Powiązania:
https://bibliotekanauki.pl/articles/24148604.pdf
Data publikacji:
2023
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
NI
natural intelligence
AI
artificial intelligence
ML
machine learning
DL
deep learning
ES
expert systems
mineral processing
Opis:
This article aims to introduce the terms NI-Natural Intelligence, AI-Artificial Intelligence, ML-Machine Learning, DL-Deep Learning, ES-Expert Systems and etc. used by modern digital world to mining and mineral processing and to show the main differences between them. As well known, each scientific and technological step in mineral industry creates huge amount of raw data and there is a serious necessity to firstly classify them. Afterwards experts should find alternative solutions in order to get optimal results by using those parameters and relations between them using special simulation software platforms. Development of these simulation models for such complex operations is not only time consuming and lacks real time applicability but also requires integration of multiple software platforms, intensive process knowledge and extensive model validation. An example case study is also demonstrated and the results are discussed within the article covering the main inferences, comments and decision during NI use for the experimental parameters used in a flotation related postgraduate study and compares with possible AI use.
Źródło:
Physicochemical Problems of Mineral Processing; 2023, 59, 5; art. no. 167501
1643-1049
2084-4735
Pojawia się w:
Physicochemical Problems of Mineral Processing
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Sztuczna inteligencja w odczarowanym świecie
Artificial intelligence in the disenchanted world
Autorzy:
Koronacki, Jacek
Powiązania:
https://bibliotekanauki.pl/articles/41309731.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Instytut Filozofii i Socjologii PAN
Tematy:
artificial intelligence
strong artificial intelligence
machine learning
disenchanted world
sztuczna inteligencja
silna sztuczna inteligencja
uczenie maszynowe
świat odczarowany
Opis:
Niniejsze rozważanie jest pisane przez inżyniera. W pierwszych dwóch punktach artykułu znajdujemy narysowany kilkoma kreskami szkic metodologicznych podstaw sztucznej inteligencji (SI) i czym dziś SI jest. W dalszych punktach zasygnalizujemy kształt najbliższej przyszłości SI, umieścimy SI w kontekście kultury, odnotujemy fenomen tzw. silnej sztucznej inteligencji i zakończymy całość paroma uwagami.
This is a modest endeavour written from an engineering perspective by a nonphilosopher to set things straight if somewhat roughly: What does artificial intelligence boil down to? What are its merits and why some dangers may stem from its development in this time of confusion when, to quote Rémi Brague: “From the point of view of technology, man appears as outdated, or at least superfluous”?
Źródło:
Filozofia i Nauka; 2020, 8, 1; 9-30
2300-4711
2545-1936
Pojawia się w:
Filozofia i Nauka
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Sztuczna inteligencja w problematyce modeli oceny ryzyka w instytucjach finansowych z perspektywy prawno-regulacyjnej
Artifical intelligence in problems of risk assessment models in financial institutions from a legal and regulatory perspective
Autorzy:
Nowakowski, Michał
Waliszewski, Krzysztof
Powiązania:
https://bibliotekanauki.pl/articles/2033957.pdf
Data publikacji:
2022-03-30
Wydawca:
Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
Tematy:
risk assessment models
artificial intelligence
bank
machine learning
Opis:
Purpose of the article / hypothesis: This article aims to verify the need to introduce additional legal and regulatory requirements in relation to the models used in banks, including, in particular, risk assessment models. At the same time, the article analyzes the need for possible introduction of sector-specific guidelines, or the need to include the above-mentioned models in the classification of high-risk artificial intelligence systems, referred to in the draft EU regulation on artificial intelligence. Methodology: The article is based on an analysis of the available literature on the subject, legal acts as well as regulations and standards developed both at the local and international level. Research results / results: The issue of the application of models in the financial sector, mainly banking, is of significant importance from the perspective of the regulator and supervisor. Quality, compliance with the regulations, but also efficiency and effective supervision may constitute the (instability) of a given financial institution, the instability of which may be a component – at least potentially – of systemic risk. Banks commonly use internal models that generally allow the calculation of capital requirements to cover specific risks in a bank’s business, such as credit risk or market risk. Internal models have been evolving for years and are undoubtedly becoming more and more accurate (they predict with a greater probability the occurrence of certain events), although they are still only certain assumptions that reality can verify, as evidenced by financial crises that have already occurred in the past as well as failures of banks considered to be stable. At the same time, the development of new technologies, in particular the so-called artificial intelligence makes institutions more and more willing to use various models, e.g. machine learning, to support these models and obtain theoretically better results. The European Union, but also other jurisdictions are considering or already introducing specific legal and regulatory solutions that are to introduce clear rules related to the use of certain artificial intelligence systems, including those used by financial institutions. As a result, institutions – already burdened with significant regulatory requirements, may soon be obliged to go through another "health path" of a legal and regulatory compliance nature.
Źródło:
Finanse i Prawo Finansowe; 2022, 1, 33; 119-141
2391-6478
2353-5601
Pojawia się w:
Finanse i Prawo Finansowe
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Rozwój sztucznej inteligencji i jej wpływ na rynek finansowy
The Development of Artificial Intelligence and its Impact on the Financial Market
Autorzy:
Tomaszek, Arkadiusz
Powiązania:
https://bibliotekanauki.pl/articles/36095177.pdf
Data publikacji:
2022
Wydawca:
Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
Tematy:
financial market
artificial intelligence
machine learning
opportunities and threats
digitization
Opis:
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.
Źródło:
Finanse i Prawo Finansowe; 2022, 2 (Numer Specjalny); 109-119
2391-6478
2353-5601
Pojawia się w:
Finanse i Prawo Finansowe
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Artificial Intelligence and Human Talent in Decision Making in the Sphere of Marketing in an Enterprise
Sztuczna inteligencja i ludzki talent w podejmowaniu decyzji z zakresu marketingu w przedsiębiorstwie
Autorzy:
Sobocińska, Magdalena
Powiązania:
https://bibliotekanauki.pl/articles/1925924.pdf
Data publikacji:
2021-04-09
Wydawca:
Uniwersytet Warszawski. Wydawnictwo Naukowe Wydziału Zarządzania
Tematy:
talent
artificial intelligence
machine learning
marketing
sztuczna inteligencja
uczenie maszynowe
Opis:
Purpose: The analysis of the content of publications concerning decision-making processes in an enterprise indicates that one of the tasks of modern management is to identify effective solutions based on the synergy of human and technological resources that support decision-making processes. This also applies to marketing, which is subject to virtualization related both to its concept and instruments, as well as marketing activities. The purpose of the paper is to show the role of artificial intelligence and human talent in decision making in the field of marketing in an enterprise. Design/methodology/approach: Critical literature review; the research procedure that is based on the review of the literature is focused on formulating the answers to the following questions: – What factors determine the effective implementation of artificial intelligence as a technology supporting decision-making processes in the sphere of marketing in enterprises? – What are the identified models of application of artificial intelligence and human talent in making decisions in enterprises? Findings: The use of the opportunities offered by artificial intelligence in supporting marketing decisions brings many benefits, but it also requires overcoming mental and cultural barriers. It should be emphasized that relying on artificial intelligence in decision-making processes does not mean eliminating people, especially the talented ones, because it is the employee who can revise the decision-making criteria or state that the algorithm on the basis of which decisions are made in the company is outdated. Research limitations/implications: Empirical verification of the proposed model would allow for identifying the role performed by talented employees and algorithms in decision-making processes in the era of development of innovative IT solutions along with determination of the hierarchy of factors stimulating these processes. Originality/value: Proposing a model of determinants and types of solutions that allow for effectively combining human resources described as talent and artificial intelligence in making decisions in the field of marketing in enterprises is the result of the considerations provided in the paper.
Cel: analiza treści publikacji z zakresu procesów podejmowania decyzji w przedsiębiorstwie wskazuje, że jednym z zadań współczesnego zarządzania jest identyfikowanie efektywnych, bazujących na synergii zasobów ludzkich i technologicznych, rozwiązań stanowiących wsparcie w procesach decyzyjnych. Dotyczy to także marketingu, który podlega wirtualizacji odnoszonej zarówno do jego koncepcji, jak i instrumentów oraz działań marketingowych. Celem artykułu jest ukazanie roli sztucznej inteligencji i ludzkiego talentu w procesach podejmowania decyzji z zakresu marketingu w przedsiębiorstwie. Metodologia: krytyczny przegląd literatury; bazujące na kwerendzie literatury postępowanie badawcze ukierunkowane zostało na sformułowanie odpowiedzi na następujące pytania: – jakie czynniki warunkują skuteczne wdrażanie sztucznej inteligencji jako technologii stanowiącej wsparcie w procesach decyzyjnych w obszarze marketingu w przedsiębiorstwie; – jakie wyróżnia się modele zastosowania sztucznej inteligencji w podejmowaniu decyzji w przedsiębiorstwie? Wyniki: wykorzystanie możliwości stwarzanych przez sztuczną inteligencję we wspieraniu decyzji marketingowych przynosi wiele korzyści, lecz wymaga przełamywania barier mentalnych i kulturowych. Należy podkreślić, że bazowanie na sztucznej inteligencji w procesach decyzyjnych nie oznacza eliminacji ludzi, w szczególności utalentowanych, ponieważ to pracownik może zrewidować kryteria decyzyjne, czy też stwierdzić, że zdezaktualizował się algorytm, w oparciu o który podejmowane były decyzje w przedsiębiorstwie. Ograniczenia/implikacje badawcze: empiryczna weryfikacja zaproponowanego modelu pozwoliłaby na identyfikację roli, którą odgrywają utalentowani pracownicy oraz algorytmy w procesach decyzyjnych w dobie rozwoju innowacyjnych rozwiązań informatycznych wraz z określeniem hierarchii czynników stymulujących te procesy. Oryginalność/wartość: efektem prowadzonych w artykule rozważań jest propozycja modelu czynników i typów rozwiązań pozwalających na efektywne łączenie zasobów ludzkich określanych jako talent i sztucznej inteligencji w podejmowaniu decyzji z zakresu marketingu w przedsiębiorstwie.
Źródło:
Problemy Zarządzania; 2021, 19, 1/2021 (91); 65-75
1644-9584
Pojawia się w:
Problemy Zarządzania
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Sztuczna inteligencja (SI) w badaniach lingwistycznych
Artificial Intelligence (AI) in Linguistic Research
Autorzy:
Sztuk, Alicja
Powiązania:
https://bibliotekanauki.pl/articles/555501.pdf
Data publikacji:
2018
Wydawca:
Uniwersytet Warszawski. Wydział Lingwistyki Stosowanej
Tematy:
artificial intelligence
machine learning
linguistic intelligence
linguistic research
intelligent tutoring system
linguistic smart software system for glottodidactics and translation intelligent
voice recognition
chatbot
terminotics
Opis:
The main purpose of the paper is both to present and to highlight the wide range of artificial intelligence appliance in linguistic research. I intend to define the so called ‘linguistic intelligence’ in the sense of machine learning, based mainly on artificial neural networks. Linguistic intelligent solutions seem to be not only up-to-date but also very promising in the area of developing and improving any intelligent linguistic tools, such as intelligent tutoring systems that are able to interact with human being, or the voice (speech) recognition systems that are able to receive, interpret (understand) and sometimes even carry out spoken commands. Finally, I intend to present the area of so called ‘terminotics’. The term refers to the meeting point of three interrelated disciplines: terminology, computational linguistics and linguistic engineering. This branch is also assisted by computer tools and new technologies based on artificial intelligence and machine learning. These (tools) are mainly designed for term extraction and corpora development but lately there are also some new possibilities to use these tools to increase the quality of terminology infrastructure as well.
Źródło:
Applied Linguistics Papers; 2018, 25/4; 159-174
2544-9354
Pojawia się w:
Applied Linguistics Papers
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Artificial Intelligence in Audit
Sztuczna inteligencja w audycie
Autorzy:
Karmańska, Anna
Powiązania:
https://bibliotekanauki.pl/articles/2158933.pdf
Data publikacji:
2022
Wydawca:
Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
Tematy:
audit
Artificial Intelligence
machine learning
audyt
sztuczna inteligencja
uczenie maszynowe
Opis:
The main objective of this paper is to identify the benefits of applying the Artificial Intelligence (AI) in the audit sector. The study employed a questionnaire for a research sample including 206 auditing and accounting practitioners and students. Data were collected via an online survey. A principal axis factor analysis with the Promax rotation was conducted to assess the underlying structure for the points of the questionnaire. The research outcomes indicate that, in the opinion of the respondents, AI adoption increases audit efficiency, and enhances client communication and service. Finally, AI can also automate time-consuming and routine tasks. The three indicated factors account for 62.223% variance. The findings reveal the advantages of AI adoption and could support managers in deploying new technology in their organizations. The research limitation concerns the fact that this study focused only on respondents from Poland.
Celem artykułu jest wskazanie korzyści płynących z zastosowania sztucznej inteligencji (AI) w badaniu sprawozdań finansowych. Posłużono się kwestionariuszem ankiety. Próbą badawczą objęto 206 praktyków i studentów audytu i rachunkowości. Zastosowano analizę czynnikową metodą głównych składowych z rotacją Promax. Wyniki wskazują, że w opinii respondentów zastosowanie sztucznej inteligencji zwiększa efektywność audytu. Sztuczna inteligencja usprawnia komunikację i obsługę klienta. Ponadto AI może zautomatyzować czasochłonne i rutynowe zadania. Powyższe trzy czynniki odpowiadają za 62,223% wariancji. Wyniki badania wskazują na korzyści płynące z implementacji sztucznej inteligencji w audycie i mogą wspierać menedżerów we wdrażaniu nowych technologii w ich organizacjach. Ograniczeniem badawczym jest fakt, że badanie koncentruje się na respondentach jedynie z Polski.
Źródło:
Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu; 2022, 66, 4; 87-99
1899-3192
Pojawia się w:
Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Artificial intelligence applications in project scheduling: a systematic review, bibliometric analysis, and prospects for future research
Autorzy:
Bahroun, Zied
Tanash, Moayad
Ad, Rami As
Alnajar, Mohamad
Powiązania:
https://bibliotekanauki.pl/articles/27315576.pdf
Data publikacji:
2023
Wydawca:
STE GROUP
Tematy:
artificial intelligence
machine learning
project scheduling
bibliometric analysis
network analysis
review
Opis:
The availability of digital infrastructures and the fast-paced development of accompanying revolutionary technologies have triggered an unprecedented reliance on Artificial intelligence (AI) techniques both in theory and practice. Within the AI domain, Machine Learning (ML) techniques stand out as essential facilitator largely enabling machines to possess human-like cognitive and decision making capabilities. This paper provides a focused review of the literature addressing applications of emerging ML toolsto solve various Project Scheduling Problems (PSPs). In particular, it employs bibliometric and network analysis tools along with a systematic literature review to analyze a pool of 104 papers published between 1985 and August 2021. The conducted analysis unveiled the top contributing authors, the most influential papers as well as the existing research tendencies and thematic research topics within this field of study. A noticeable growth in the number of relevant studies is seen recently with a steady increase as of the year 2018. Most of the studies adopted Artificial Neural Networks, Bayesian Network and Reinforcement Learning techniques to tackle PSPs under a stochastic environment, where these techniques are frequently hybridized with classical metaheuristics. The majority of works (57%) addressed basic Resource Constrained PSPs and only 15% are devoted to the project portfolio management problem. Furthermore, this study clearly indicates that the application of AI techniques to efficiently handle PSPs is still in its infancy stage bringing out the need for further research in this area. This work also identifies current research gaps and highlights a multitude of promising avenues for future research.
Źródło:
Management Systems in Production Engineering; 2023, 2 (31); 144--161
2299-0461
Pojawia się w:
Management Systems in Production Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Deep learning in pharmacology: opportunities and threats
Autorzy:
Kocić, Ivan
Kocić, Milan
Rusiecka, Izabela
Kocić, Adam
Kocić, Eliza
Powiązania:
https://bibliotekanauki.pl/articles/25728738.pdf
Data publikacji:
2022-09-06
Wydawca:
Gdański Uniwersytet Medyczny
Tematy:
machine learning
pharmacology
deep learning
artificial intelligence
drug research and development
Opis:
Introduction This review aims to present briefly the new horizon opened to pharmacology by the deep learning (DL) technology, but also to underline the most important threats and limitations of this method. Material and Methods We searched multiple databases for articles published before May 2021 according to the preferred reported item related to deep learning and drug research. Out of the 267 articles retrieved, we included 50 in the final review. Results DL and other different types of artificial intelligence have recently entered all spheres of science, taking an increasingly central position in the decision-making processes, also in pharmacology. Hence, there is a need for better understanding of these technologies. The basic differences between AI (artificial intelligence), DL and ML (machine learning) are explained. Additionally, the authors try to highlight the role of deep learning methods in drug research and development as well as in improving the safety of pharmacotherapy. Finally, future directions of DL in pharmacology were outlined as well as possible misuses of it. Conclusions DL is a promising and powerful tool for comprehensive analysis of big data related to all fields of pharmacology, however it has to be used carefully.
Źródło:
European Journal of Translational and Clinical Medicine; 2022, 5, 2; 88-94
2657-3148
2657-3156
Pojawia się w:
European Journal of Translational and Clinical Medicine
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of ensemble gradient boosting decision trees to forecast stock price on WSE
Autorzy:
Dadej, Mateusz
Powiązania:
https://bibliotekanauki.pl/articles/518035.pdf
Data publikacji:
2019
Wydawca:
Uniwersytet Gdański. Wydział Ekonomiczny
Tematy:
equity investments
artificial intelligence
machine learning
algorithmic trading strategy
gradient boosting
Opis:
The main purpose of this article is to apply machine learning model based on ensemble of gradient boosted decision trees to forecast direction of share prices of Bank Handlowy S.A listed on WSE. In the introduction, the author presented the context of machine learning and its application in forecasting stock prices. Afterwards, the author describes the process of building classification model which uses XGboost framework from data preprocessing to model evaluation. The input features of the model were technical analysis indicators, like stochastic oscillators or moving averages. Output of the model was a direction of stock price after one week. The accuracy of the model based on testing dataset is 72%. The author also performed a simulation, based on the model. The simulation was made with the Monte Carlo method which stochastic process had a Laplace distribution. During interpretation, at the end, the author pointed limitations of model and algorithmic trading strategy evaluation techniques based on backtest.
Celem niniejszego artykułu jest wykorzystanie modelu z dziedziny uczenia maszynowego opartego na algorytmie zespołu wzmocnionych gradientowo drzew decyzyjnych do prognozowania kierunku zmian kursu akcji Banku Handlowego S.A. notowanego na GPW. We wstępie został przedstawiony kontekst uczenia maszynowego oraz wykorzystania go do prognozowania cen akcji. Następnie, przedstawiono proces tworzenia modelu klasyfikacyjnego wykorzystujący strukturę XGboost od etapu przetwarzania danych do jego ewaluacji. Danymi wejściowymi modelu były wskaźniki wykorzystywane w analizie technicznej, m.in. oscylatory stochastyczne oraz średnie ruchome, natomiast danymi wyjściowymi były kierunki zmian kursu na przestrzeni następnego tygodnia. Skuteczność modelu na danych testowych wyniosła 72%. Na końcu przeprowadzono symulacje portfela inwestycyjnego, podejmującego decyzje o transakcjach na podstawie wcześniej stworzonego modelu, wykorzystując metodę Monte Carlo w której dynamika procesów stochastycznych miała rozkład Laplace’a. Przy interpretacji wyników portfela inwestycyjnego wskazano ograniczenia ewaluacji modelu i strategii inwestycyjnej opartej o backtest.
Źródło:
Zeszyty Studenckie Wydziału Ekonomicznego „Nasze Studia”; 2019, 9; 265-275
1731-6707
Pojawia się w:
Zeszyty Studenckie Wydziału Ekonomicznego „Nasze Studia”
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Yet another research on GANs in cybersecurity
Autorzy:
Zimoń, Michał
Kasprzyk, Rafał
Powiązania:
https://bibliotekanauki.pl/articles/13946602.pdf
Data publikacji:
2023-02-20
Wydawca:
Akademia Sztuki Wojennej
Tematy:
cybersecurity
malware
artificial intelligence
machine learning
deep learning
generative adversarial networks
Opis:
Deep learning algorithms have achieved remarkable results in a wide range of tasks, including image classification, language translation, speech recognition, and cybersecurity. These algorithms can learn complex patterns and relationships from large amounts of data, making them highly effective for many applications. However, it is important to recognize that models built using deep learning are not fool proof and can be fooled by carefully crafted input samples. This paper presents the results of a study to explore the use of Generative Adversarial Networks (GANs) in cyber security. The results obtained confirm that GANs enable the generation of synthetic malware samples that can be used to mislead a classification model.
Źródło:
Cybersecurity and Law; 2023, 9, 1; 61-72
2658-1493
Pojawia się w:
Cybersecurity and Law
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Artificial intelligence and future crime in the context of computer forensics
Autorzy:
Olber, Paweł
Powiązania:
https://bibliotekanauki.pl/articles/45491957.pdf
Data publikacji:
2023-07-25
Wydawca:
Akademia Policji w Szczytnie
Tematy:
machine learning
computer forensics
digital evidence
crime
forensic science
artificial intelligence
Opis:
The aim of this article is to discuss the role, tasks and challenges of computer forensics in the context of the development of AI-enabled crime. The issues described in the article refer to potential future threats that have been identifi ed as the most troublesome for society. The considerations in the article are preceded by a critical analysis of the research that has been conducted in the fi eld of artifi cial intelligence and computer forensics so far. The literature analysis allows the claim that the future of computer forensics is automation based on machine learning algorithms. It has also been concluded that the development of artifi cial intelligence will defi ne new areas of computer forensics that take into account the analysis of neural network models and learning datasets.
Źródło:
Przegląd Policyjny; 2023, 149(1); 342-358
0867-5708
Pojawia się w:
Przegląd Policyjny
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Applying Machine Learning to Software Fault Prediction
Autorzy:
Wójcicki, B.
Dabrowski, R.
Powiązania:
https://bibliotekanauki.pl/articles/384105.pdf
Data publikacji:
2018
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
classifier
fault prediction
machine learning
metric
Naïve Bayes
Python
quality
software intelligence
Opis:
Introduction: Software engineering continuously suffers from inadequate software testing. The automated prediction of possibly faulty fragments of source code allows developers to focus development efforts on fault-prone fragments first. Fault prediction has been a topic of many studies concentrating on C/C++ and Java programs, with little focus on such programming languages as Python. Objectives: In this study the authors want to verify whether the type of approach used in former fault prediction studies can be applied to Python. More precisely, the primary objective is conducting preliminary research using simple methods that would support (or contradict) the expectation that predicting faults in Python programs is also feasible. The secondary objective is establishing grounds for more thorough future research and publications, provided promising results are obtained during the preliminary research. Methods: It has been demonstrated that using machine learning techniques, it is possible to predict faults for C/C++ and Java projects with recall 0.71 and false positive rate 0.25. A similar approach was applied in order to find out if promising results can be obtained for Python projects. The working hypothesis is that choosing Python as a programming language does not significantly alter those results. A preliminary study is conducted and a basic machine learning technique is applied to a few sample Python projects. If these efforts succeed, it will indicate that the selected approach is worth pursuing as it is possible to obtain for Python results similar to the ones obtained for C/C++ and Java. However, if these efforts fail, it will indicate that the selected approach was not appropriate for the selected group of Python projects. Results: The research demonstrates experimental evidence that fault-prediction methods similar to those developed for C/C++ and Java programs can be successfully applied to Python programs, achieving recall up to 0.64 with false positive rate 0.23 (mean recall 0.53 with false positive rate 0.24). This indicates that more thorough research in this area is worth conducting. Conclusion: Having obtained promising results using this simple approach, the authors conclude that the research on predicting faults in Python programs using machine learning techniques is worth conducting, natural ways to enhance the future research being: using more sophisticated machine learning techniques, using additional Python-specific features and extended data sets.
Źródło:
e-Informatica Software Engineering Journal; 2018, 12, 1; 199-216
1897-7979
Pojawia się w:
e-Informatica Software Engineering Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A survey of big data classification strategies
Autorzy:
Banchhor, Chitrakant
Srinivasu, N.
Powiązania:
https://bibliotekanauki.pl/articles/2050171.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
big data
data mining
MapReduce
classification
machine learning
evolutionary intelligence
deep learning
Opis:
Big data plays nowadays a major role in finance, industry, medicine, and various other fields. In this survey, 50 research papers are reviewed regarding different big data classification techniques presented and/or used in the respective studies. The classification techniques are categorized into machine learning, evolutionary intelligence, fuzzy-based approaches, deep learning and so on. The research gaps and the challenges of the big data classification, faced by the existing techniques are also listed and described, which should help the researchers in enhancing the effectiveness of their future works. The research papers are analyzed for different techniques with respect to software tools, datasets used, publication year, classification techniques, and the performance metrics. It can be concluded from the here presented survey that the most frequently used big data classification methods are based on the machine learning techniques and the apparently most commonly used dataset for big data classification is the UCI repository dataset. The most frequently used performance metrics are accuracy and execution time.
Źródło:
Control and Cybernetics; 2020, 49, 4; 447-469
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
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