Data science i uczenie maszynowe / Marcin Szeliga [recenzja]

Recenzja książki Data science i uczenie maszynowe / Marcin Szeliga. – Warszawa : Wydawnictwo Naukowe PWN SA, 2017. – XXVI, [2], 371, [1] s. : il. ; 24 cm. – ISBN 978-83-01-19232-

Por\'ownanie metod detekcji zaj\k{e}to\'sci widma radiowego z wykorzystaniem uczenia federacyjnego z oraz bez w\k{e}z{\l}a centralnego

Dynamic spectrum access systems typically require information about the spectrum occupancy and thus the presence of other users in order to make a spectrum al-location decision for a new device. Simple methods of spectrum occupancy detection are often far from reliable, hence spectrum occupancy detection algorithms supported by machine learning or artificial intelligence are often and successfully used. To protect the privacy of user data and to reduce the amount of control data, an interesting approach is to use federated machine learning. This paper compares two approaches to system design using federated machine learning: with and without a central node.Comment: 4 pages, in Polish language, 3 figures, presented during conferenc

Sztuczna inteligencja w badaniu wybranych aspektów kultury

Artificial intelligence is one of the most important elements of the modern world. It already functions in the most important areas of life. It is also used in various scientific disciplines. For some time now, artificial intelligence algorithms have been used to study the world of culture. The article presents the most important methods and tools of artificial intelligence that are used in the study of various cultural phenomena, as well as examples of such studies. Cultural researches use artificial intelligence in two areas. The first one is natural language processing, while the other – the computerized examination of images (movies, photos, graphic, etc.). Artificial intelligence is part of machine learning. It uses convolutional naural networks, big data and supercomputers to analyze and visualize digital product. Artificial intelligence research into culture belongs to a new paradigm called: digital humanitiesSztuczna inteligencja jest jednym z najważniejszych elementów współczesnego świata. Funkcjonuje już ona w podstawowych dziedzinach życia. Jest także wykorzystywana w różnych dyscyplinach naukowych. Od pewnego czasu zaczyna się również stosować algorytmy sztucznej inteligencji do badania świata kultury. W artykule zostały ukazane najważniejsze metody i narzędzia sztucznej inteligencji stosowane w badaniach różnych zjawisk kultury, a także przykłady takich badań. Badacze kultury wykorzystują sztuczną inteligencję w dwóch obszarach. Pierwszym jest przetwarzanie języka naturalnego, drugim komputerowe badanie obrazów (filmów, zdjęć, grafiki itp.). Sztuczna inteligencja jest częścią uczenia maszynowego. Wykorzystuje ono konwolucyjne sieci neuronowe, duże ilości danych (big data) i superkomputery do analizy i wizualizacji cyfrowych wytworów. Badania kultury za pomocą sztucznej inteligencji są zaliczane do nowego paradygmatu zwanego humanistyką cyfrową

Future of cardiovascular diagnosis with the support of artificial intelligence

Introduction: Term Artificial inteligence was used for the first time by John McCarthy in 1956, from that time we can observe its great development, espiecially in the past decade.  Nowadays, Artificial inteligence present a great influence in every aspect of human life, also health care. In times of digitalizaton, great data bases it can enable an improvment in all aspects of healthcare system such as prevention, screening and treatment of diseases. Purpose:The main purpose of the work was to present the basic aspects related to artificial intelligence. Another important aspect of the article was to indicate the possibilities related to their use in cardiology to improve the effectiveness of doctors and make medical treatment more detailed and personalized, but also to clarify terms related do AI, such as machine learning or deep learning. Materials and methods: For the purpose of writing this article, the available literature was reviewed. Using keywords such as artificial inteligence, cardiology, machine learning, echocardiography, deep learning, data bases PubMed we ware searching for various clinical trials, meta analysis and randomized controlled trials from past 5 years. Results: According to the data published on the website of the World Health Organization (WHO), cardiovascular diseases remain the main cause of mortality worldwide.  It is the reason of the great interest in its use in cardiology. Algorithms based on artificial intelligence are also used in electrocardiography. The use of artificial intelligence can improve the estimation of cardiovascular risk. Its use in the healing process is also being investigated.  Conclusion: Artificial intelligence is used in many fields, including medicine. Its use may have a positive impact on the quality of medical care. Artificial intelligence also has numerous limitations. Due to this, it is necessary to develop and improve artificial intelligence

Applying of machine learning in the construction of a voice-controlled interface on the example of a music player

The following paper presents the results of research on the impact of machine learning in the construction of a voice-controlled interface. Two different models were used for the analysys: a feedforward neural network containing one hidden layer and a more complicated convolutional neural network. What is more, a comparison of the applied models was presented. This comparison was performed in terms of quality and the course of training

Artificial intelligence as a coming revolution in medicine

Introduction: The development of medicine and information technology in recent decades has undoubtedly contributed to improving public health. Artificial intelligence is a technology that has great potential to revolutionize the functioning of health care around the world. Appropriate use of the development of technology can revolutionize many areas of modern medicine, however, it should not be forgotten that this technology should be subjected to appropriate standardization and legal regulation. Objective: The purpose of this study is to review the available scientific literature in order to systematize the current knowledge on the use of artificial intelligence in the process of diagnosis and treatment. Ethical aspects related to the implementation of AI for use in health care are also analyzed. Results: Artificial intelligence uses deep machine learning algorithms. It is a technology that has been known for a long time, but recently the chances of its widespread use have increased significantly, although scientists still do not fully understand the operation of AI algorithms. Currently, there are attempts to use this technology in many medical fields such as cardiology, diagnostic imaging, gastroenterology, pathomorphology, ultrasound. Artificial intelligence can also be used to improve the functioning of patient service in health care. Summary: The development of artificial intelligence algorithms creates a huge opportunity to improve the quality of diagnostic and treatment processes. The current rapid development of the technology is revolutionizing many branches of medicine, improving treatment outcomes. However, the development of this technology requires the creation of an appropriate law governing AI in medicine

Machine Learning as a method of adapting offers to the clients

Recommendation systems are class of information filter applications whose main goal is to provide personalized recommendations. The main goal of the research was to compare two ways of creating personalized recommendations. The recommendation system was built on the basis of a content-based cognitive filtering method and on the basis of a collaborative filtering method based on user ratings. The conclusions of the research show the advantages and disadvantages of both methods

PORÓWNANIE SKUTECZNOŚCI ALGORYTMÓW UCZENIA MASZYNOWEGO DLA KONSERWACJI PREDYKCYJNEJ

The consequences of failures and unscheduled maintenance are the reasons why engineers have been trying to increase the reliability of industrial equipment for years. In modern solutions, predictive maintenance is a frequently used method. It allows to forecast failures and alert about their possibility. This paper presents a summary of the machine learning algorithms that can be used in predictive maintenance and comparison of their performance. The analysis was made on the basis of data set from Microsoft Azure AI Gallery. The paper presents a comprehensive approach to the issue including feature engineering, preprocessing, dimensionality reduction techniques, as well as tuning of model parameters in order to obtain the highest possible performance. The conducted research allowed to conclude that in the analysed case , the best algorithm achieved 99.92% accuracy out of over 122 thousand test data records. In conclusion, predictive maintenance based on machine learning represents the future of machine reliability in industry.Skutki związane z awariami oraz niezaplanowaną konserwacją to powody, dla których od lat inżynierowie próbują zwiększyć niezawodność osprzętu przemysłowego. W nowoczesnych rozwiązaniach obok tradycyjnych metod stosowana jest również tzw. konserwacja predykcyjna, która pozwala przewidywać awarie i alarmować o możliwości ich powstawania. W niniejszej pracy przedstawiono zestawienie algorytmów uczenia maszynowego, które można zastosować w konserwacji predykcyjnej oraz porównanie ich skuteczności. Analizy dokonano na podstawie zbioru danych Azure AI Gallery udostępnionych przez firmę Microsoft. Praca przedstawia kompleksowe podejście do analizowanego zagadnienia uwzględniające wydobywanie cech charakterystycznych, wstępne przygotowanie danych, zastosowanie technik redukcji wymiarowości, a także dostrajanie parametrów poszczególnych modeli w celu uzyskania najwyższej możliwej skuteczności. Przeprowadzone badania pozwoliły wskazać  najlepszy  algorytm, który uzyskał dokładność na poziomie 99,92%, spośród ponad 122 tys. rekordów danych testowych. Na podstawie tego można stwierdzić, że konserwacja predykcyjna prowadzona w oparciu o uczenie maszynowe stanowi przyszłość w zakresie podniesienia niezawodności maszyn w przemyśle

ZASTOSOWANIE ROZMYTEJ MAPY KOGNITYWNEJ W PROGNOZOWANIU EFEKTYWNOŚCI PRACY WYPOŻYCZALNI ROWEROWYCH

This paper proposes application of fuzzy cognitive map with evolutionary learning algorithms to model a system for prediction of effectiveness of bike sharing systems. Fuzzy cognitive map was constructed based on historical data and next used to forecast the number of cyclists and customers of bike sharing systems on three consecutive days. The learning process was realized with the use of Individually Directional Evolutionary Algorithm IDEA and Real-Coded Genetic Algorithm RCGA. Simulation analysis of the system for prediction of effectiveness of bike sharing systems was carried out with the use of software developed in JAVA.W pracy zaproponowano zastosowanie rozmytej mapy kognitywnej wraz z ewolucyjnymi algorytmami uczenia do modelowania systemu prognozowania efektywności pracy wypożyczalni rowerowych. Na podstawie danych historycznych zbudowano rozmytą mapę kognitywną, którą następnie zastosowano do prognozowania liczby rowerzystów i klientów wypożyczalni w trzech kolejnych dniach. Proces uczenia zrealizowano z zastosowaniem indywidualnego kierunkowego algorytmu ewolucyjnego IDEA oraz algorytmu genetycznego z kodowaniem zmiennoprzecinkowym RCGA. Analizę symulacyjną systemu prognozowania efektywności pracy wypożyczalni rowerowych przeprowadzono przy pomocy oprogramowania opracowanego w technologii JAVA