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Tytuł:
Atrial fibrillation detection on electrocardiograms with convolutional neural networks
Detekcja migotania przedsionków na elektrokardiogramach z wykorzystaniem konwolucyjnej sieci neuronowej
Autorzy:
Kifer, Viktor
Zagorodna, Natalia
Hevko, Olena
Powiązania:
https://bibliotekanauki.pl/articles/408581.pdf
Data publikacji:
2019
Wydawca:
Politechnika Lubelska. Wydawnictwo Politechniki Lubelskiej
Tematy:
electrocardiography
machine learning
neural network
elektrokardiografia
nauczanie maszynowe
sieć neuronowa
Opis:
In this paper, we present our research which confirms the suitability of the convolutional neural network usage for the classification of single-lead ECG recordings. The proposed method was designed for classifying normal sinus rhythm, atrial fibrillation (AF), non-AF related other abnormal heart rhythms and noisy signals. The method combines manually selected features with the features learned by the deep neural network. The Physionet Challenge 2017 dataset of over 8500 ECG recordings was used for the model training and validation. The trained model reaches an average F1-score 0.71 in classifying normal sinus rhythm, AF and other rhythms respectively.
W tej pracy, przedstawiamy nasze badania, które potwierdzają przydatność zastosowania konwolucyjnych sieci neuronowych dla klasyfikacji zapisów jedno-odprowadzeniowego EKG. (tak brzmi ta nazwa). Proponowana metoda została zaprojektowana dla klasyfikowania prawidłowego rytmu zatokowego, migotania przedsionków (AF), poza-AF powiązanych z innymi nieprawidłowymi rytmami serca i zaszumionymi (głośnymi?) sygnałami. Ta metoda łączy cechy wyselekcjonowane ręcznie z cechami wyuczonymi przez głębokie sieci neuronowe. Zbiór danych Physionet Challenge 2017 zawierający ponad 8500 zapisów EKG został zastosowany dla modelu szkolenia oraz walidacji. Model nauczony (wyszkolony?) osiąga odpowiednio średni F1-wynik 0.71 w klasyfikowaniu prawidłowego rytmu zatokowego, rytmu AF oraz innych rytmów.
Źródło:
Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska; 2019, 9, 4; 69-73
2083-0157
2391-6761
Pojawia się w:
Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
ChatGPT – a tool for assisted studying or a source of misleading medical information? AI performance on Polish Medical Final Examination
ChatGPT – pomoc naukowa przyszłości czy źródło fałszywych informacji? Analiza odpowiedzi sztucznej inteligencji na przykładzie zadań Lekarskiego Egzaminu Końcowego
Autorzy:
Żmudka, Karol
Spychał, Aleksandra
Ochman, Błażej
Popowicz, Łukasz
Piłat, Patrycja
Jaroszewicz, Jerzy
Powiązania:
https://bibliotekanauki.pl/articles/29783504.pdf
Data publikacji:
2024-04-16
Wydawca:
Śląski Uniwersytet Medyczny w Katowicach
Tematy:
artificial intelligence
public health
machine learning
sztuczna inteligencja
zdrowie publiczne
nauczanie maszynowe
Opis:
INTRODUCTION: ChatGPT is a language model created by OpenAI that can engage in human-like conversations and generate text based on the input it receives. The aim of the study was to assess the overall performance of ChatGPT on the Polish Medical Final Examination (Lekarski Egzamin Końcowy – LEK) the factors influencing the percentage of correct answers. Secondly, investigate the capabilities of chatbot to provide explanations was examined. MATERIAL AND METHODS: We entered 591 questions with distractors from the LEK database into ChatGPT (version 13th February – 14th March). We compared the results with the answer key and analyzed the provided explanation for logical justification. For the correct answers we analyzed the logical consistency of the explanation, while for the incorrect answers, the ability to provide a correction was observed. Selected factors were analyzed for an influence on the chatbot’s performance. RESULTS: ChatGPT achieved impressive scores of 58.16%, 60.91% and 67.86% allowing it pass the official threshold of 56% in all instances. For the properly answered questions, more than 70% were backed by a logically coherent explanation. In the case of the wrongly answered questions the chatbot provided a seemingly correct explanation for false information in 66% of the cases. Factors such as logical construction (p < 0.05) and difficulty (p < 0.05) had an influence on the overall score, meanwhile the length (p = 0.46) and language (p = 0.14) did not. CONCLUSIONS: Although achieving a sufficient score to pass LEK, ChatGPT in many cases provides misleading information backed by a seemingly compelling explanation. The chatbot can be especially misleading for non-medical users as compared to a web search because it can provide instant compelling explanations. Thus, if used improperly, it could pose a danger to public health. This makes it a problematic recommendation for assisted studying.
WSTĘP: ChatGPT jest modelem językowym stworzonym przez OpenAI, który może udzielać odpowiedzi na zapytania użytkownika, generując tekst na podstawie otrzymanych danych. Celem pracy była ocena wyników działania ChatGPT na polskim Lekarskim Egzaminie Końcowym (LEK) oraz czynników wpływających na odsetek prawidłowych odpowiedzi. Ponadto zbadano zdolność chatbota do podawania poprawnego i wnikliwego wyjaśnienia. MATERIAŁ I METODY: Wprowadzono 591 pytań z dystraktorami z bazy LEK do interfejsu ChatGPT (wersja 13 lutego – 14 marca). Porównano wyniki z kluczem odpowiedzi i przeanalizowano podane wyjaśnienia pod kątem logicznego uzasadnienia. Dla poprawnych odpowiedzi przeanalizowano spójność logiczną wyjaśnienia, natomiast w przypadku odpowiedzi błędnej obserwowano zdolność do poprawy. Wybrane czynniki zostały przeanalizowane pod kątem wpływu na zdolność chatbota do udzielenia poprawnej odpowiedzi. WYNIKI: ChatGPT osiągnął imponujące wyniki poprawnych odpowiedzi na poziomie: 58,16%, 60,91% i 67,86%, przekraczając oficjalny próg 56% w trzech ostatnich egzaminach. W przypadku poprawnie udzielonych odpowiedzi ponad 70% pytań zostało popartych logicznie spójnym wyjaśnieniem. W przypadku błędnych odpowiedzi w 66% przypadków chatbot podał pozornie poprawne wyjaśnienie dla nieprawidłowych od-powiedzi. Czynniki takie jak konstrukcja logiczna (p < 0,05) i wskaźnik trudności zadania (p < 0,05) miały wpływ na ogólną ocenę, podczas gdy liczba znaków (p = 0,46) i język (p = 0,14) takiego wpływu nie miały. WNIOSKI: Mimo iż ChatGPT osiągnął wystarczającą liczbę punktów, aby zaliczyć LEK, w wielu przypadkach podawał wprowadzające w błąd informacje poparte pozornie przekonującym wyjaśnieniem. Chatboty mogą być szczególnym zagrożeniem dla użytkownika niemającego wiedzy medycznej, ponieważ w porównaniu z wyszukiwarką internetową dają natychmiastowe, przekonujące wyjaśnienie, co może stanowić zagrożenie dla zdrowia publicznego. Z tych samych przyczyn ChatGPT powinien być ostrożnie stosowany jako pomoc naukowa.
Źródło:
Annales Academiae Medicae Silesiensis; 2024, 78; 94-103
1734-025X
Pojawia się w:
Annales Academiae Medicae Silesiensis
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An enhanced performance evaluation of workflow computing and scheduling using hybrid classification approach in the cloud environment
Autorzy:
Tharani, P.
Kalpana, A. M.
Powiązania:
https://bibliotekanauki.pl/articles/2086824.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
cloud
workflow scheduling
machine learning
CNN
AlexNet
chmura
planowanie przepływu pracy
nauczanie maszynowe
Opis:
Workflow scheduling is the major problem in cloud computing consists of a set of interdependent tasks which is used to solve the various scientific and healthcare issues. In this research work, the cloud based workflow scheduling between different tasks in medical imaging datasets using Machine Learning (ML) and Deep Learning (DL) methods (hybrid classification approach) is proposed for healthcare applications. The main objective of this research work is to develop a system which is used for both workflow computing and scheduling in order to minimize the makespan, execution cost and to segment the cancer region in the classified abnormal images. The workflow computing is performed using different Machine Learning classifiers and the workflow scheduling is carried out using Deep Learning algorithm. The conventional AlexNet Convolutional Neural Networks (CNN) architecture is modified and used for workflow scheduling between different tasks in order to improve the accuracy level. The AlexNet architecture is analyzed and tested on different cloud services Amazon Elastic Compute Cloud- EC2 and Amazon Lightsail with respect to Makespan (MS) and Execution Cost (EC).
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 4; e137728, 1--9
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Wound image segmentation using clustering based algorithms
Autorzy:
Farmaha, Ihor
Banaś, Marian
Savchyn, Vasyl
Lukashchuk, Bohdan
Farmaha, Taras
Powiązania:
https://bibliotekanauki.pl/articles/2064381.pdf
Data publikacji:
2019
Wydawca:
STE GROUP
Tematy:
clustering
Segmentation
machine learning
neural networks
wounds
segmentacja
nauczanie maszynowe
sieci neuronowe
rany
klastrowanie
Opis:
Classic methods of measurement and analysis of the wounds on the images are very time consuming and inaccurate. Automation of this process will improve measurement accuracy and speed up the process. Research is aimed to create an algorithm based on machine learning for automated segmentation based on clustering algorithms Methods. Algorithms used: SLIC (Simple Linear Iterative Clustering), Deep Embedded Clustering (that is based on artificial neural networks and k-means). Because of insufficient amount of labeled data, classification with artificial neural networks can`t reach good results. Clustering, on the other hand is an unsupervised learning technique and doesn`t need human interaction. Combination of traditional clustering methods for image segmentation with artificial neural networks leads to combination of advantages of both of them. Preliminary step to adapt Deep Embedded Clustering to work with bio-medical images is introduced and is based on SLIC algorithm for image segmentation. Segmentation with this method, after model training, leads to better results than with traditional SLIC.
Źródło:
New Trends in Production Engineering; 2019, 2, 1; 570--578
2545-2843
Pojawia się w:
New Trends in Production Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Automatic detection and counting of platelets in microscopic image
Autorzy:
Burduk, R.
Krawczyk, B.
Powiązania:
https://bibliotekanauki.pl/articles/333065.pdf
Data publikacji:
2010
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
rozpoznawanie wzorców
nauczanie maszynowe
analiza obrazu
pattern recognition
bioinformatic
machine learning
image analysis
platelet
Opis:
In this paper we present a machine learning-based approach for detecting platelet cells in microscopic smear images. Counting how many platelets appeared in each smear image is one of the basic tasks done in many laboratories. In many cases this is still done by a human — laboratory technician. Due to very small size and often great quantity of those cells, precise estimating of the number of platelets is not a trivial task. As in all man-dependent problems the whole process is very sensitive to errors, time-consuming and its accuracy is limited by human perception. We propose alternative, fully automatic solution that is free of those drawbacks. Our idea is based on the combination of techniques driven from two fields of modern computer science: the image analysis and pattern recognition ⁄ machine learning. It not only reduces the error rate, but, what is more important, also decreases the time needed for each smear image analysis. The obtained results are very satisfying and our solution is more precise than estimation based on human perception. This will improve the quality of laboratory work and allow to save time that can be spent on other important tasks.
Źródło:
Journal of Medical Informatics & Technologies; 2010, 16; 173-178
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Implementation of a hardware trojan chip detector model using arduino microcontroller
Autorzy:
Abdulsalam, Kadeejah
Adebisi, John
Durojaiye, Victor
Powiązania:
https://bibliotekanauki.pl/articles/1956027.pdf
Data publikacji:
2021
Wydawca:
Polskie Towarzystwo Promocji Wiedzy
Tematy:
hardware trojans
chips
logic test
machine learning
microcontroller
trojan sprzętowy
test logiczny
nauczanie maszynowe
mikrokontroler
Opis:
These days, hardware devices and its associated activities are greatly impacted by threats amidst of various technologies. Hardware trojans are malicious modifications made to the circuitry of an integrated circuit, Exploiting such alterations and accessing the level of damage to devices is considered in this work. These trojans, when present in sensitive hardware system deployment, tends to have potential damage and infection to the system. This research builds a hardware trojan detector using machine learning techniques. The work uses a combination of logic testing and power side-channel analysis (SCA) coupled with machine learning for power traces. The model was trained, validated and tested using the acquired data, for 5 epochs. Preliminary logic tests were conducted on target hardware device as well as power SCA. The designed machine learning model was implemented using Arduino microcontroller and result showed that the hardware trojan detector identifies trojan chips with a reliable accuracy. The power consumption readings of the hardware characteristically start at 1035-1040mW and the power time-series data were simulated using DC power measurements mixed with additive white Gaussian noise (AWGN) with different standard deviations. The model achieves accuracy, precision and accurate recall values. Setting the threshold proba-bility for the trojan class less than 0.5 however increases the recall, which is the most important metric for overall accuracy acheivement of over 95 percent after several epochs of training.
Źródło:
Applied Computer Science; 2021, 17, 4; 20-33
1895-3735
Pojawia się w:
Applied Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Bayesian optimization for solving high-frequency passive component design problems
Autorzy:
Baranowski, Michal
Fotyga, Grzegorz
Lamecki, Adam
Mrozowski, Michal
Powiązania:
https://bibliotekanauki.pl/articles/2173688.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
high-frequency design
machine learning
Bayesian optimization
optymalizacja bayesowska
konstrukcja o wysokiej częstotliwości
nauczanie maszynowe
Opis:
In this paper, the performance of the Bayesian Optimization (BO) technique applied to various problems of microwave engineering is studied. Bayesian optimization is a novel, non-deterministic, global optimization scheme that uses machine learning to solve complex optimization problems. However, each new optimization scheme needs to be evaluated to find its best application niche, as there is no universal technique that suits all problems. Here, BO was applied to different types of microwave and antenna engineering problems, including matching circuit design, multiband antenna and antenna array design, or microwave filter design. Since each of the presented problems has a different nature and characteristics such as different scales (i.e. number of design variables), we try to address the question about the generality of BO and identify the problem areas for which the technique is or is not recommended.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 4; art. no. e141595
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Machine learning predictive modeling of the price of cassava derivative (GARRI) in the South West Of Nigeria
Autorzy:
Olanloye, O.
Oduntan, E.
Powiązania:
https://bibliotekanauki.pl/articles/118266.pdf
Data publikacji:
2018
Wydawca:
Polskie Towarzystwo Promocji Wiedzy
Tematy:
fluctuation
prices
machine learning
predictive model
cassava derivative
fluktuacja
ceny
nauczanie maszynowe
model predykcyjny
pochodna manioku
Opis:
Fluctuation in prices of Agricultural products is inevitable in developing countries faced with economic depression and this, has brought a lot of inadequacies in the preparation of Government financial budget. Consumers and producers are poorly affected because they cannot take appropriate decision at the right time. In this study, Machine Learning(ML) predictive modeling is being implemented using the MATLAB Toolbox to predict the price of cassava derivatives (garri) in the South Western part of Nigeria. The model predicted that by the year 2020, all things being equal, the price of (1kg) of garri will be 500. This will boost the Agricultural sector and the economy of the nation.
Źródło:
Applied Computer Science; 2018, 14, 1; 53-63
1895-3735
Pojawia się w:
Applied Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Automatic speech based emotion recognition using paralinguistics features
Autorzy:
Hook, J.
Noroozi, F.
Toygar, O.
Anbarjafari, G.
Powiązania:
https://bibliotekanauki.pl/articles/200261.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
random forests
speech emotion recognition
machine learning
support vector machines
lasy
rozpoznawanie emocji mowy
nauczanie maszynowe
Opis:
Affective computing studies and develops systems capable of detecting humans affects. The search for universal well-performing features for speech-based emotion recognition is ongoing. In this paper, a?small set of features with support vector machines as the classifier is evaluated on Surrey Audio-Visual Expressed Emotion database, Berlin Database of Emotional Speech, Polish Emotional Speech database and Serbian emotional speech database. It is shown that a?set of 87 features can offer results on-par with state-of-the-art, yielding 80.21, 88.6, 75.42 and 93.41% average emotion recognition rate, respectively. In addition, an experiment is conducted to explore the significance of gender in emotion recognition using random forests. Two models, trained on the first and second database, respectively, and four speakers were used to determine the effects. It is seen that the feature set used in this work performs well for both male and female speakers, yielding approximately 27% average emotion recognition in both models. In addition, the emotions for female speakers were recognized 18% of the time in the first model and 29% in the second. A?similar effect is seen with male speakers: the first model yields 36%, the second 28% a?verage emotion recognition rate. This illustrates the relationship between the constitution of training data and emotion recognition accuracy.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2019, 67, 3; 479-488
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Terrain classification using static and dynamic texture features by uav downwash effect
Autorzy:
Carvalho, João Pedro
Fonseca, José Manuel
Mora, André Damas
Powiązania:
https://bibliotekanauki.pl/articles/384711.pdf
Data publikacji:
2019
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
image processing
texture
machine learning
terrain classificafion
neural networks
UAV
przetwarzanie obrazu
tekstura
nauczanie maszynowe
sieci neuronowe
Opis:
Knowing how to identify terrain types is especially important in the autonomous navigation, mapping, decision making and emergency landings areas. For example, an unmanned aerial vehicle (UAV) can use it to find a suitable landing position or to cooperate with other robots to navigate across an unknown region. Previous works on terrain classification from RGB images taken onboard of UAVs shown that only static pixel-based features were tested with a considerable classification error. This paper presents a computer vision algorithm capable of identifying the terrain from RGB images with improved accuracy. The algorithm complement the static image features and dynamic texture patterns produced by UAVs rotors downwash effect (visible at lower altitudes) and machine learning methods to classify the underlying terrain. The system is validated using videos acquired onboard of a UAV with a RGB camera.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2019, 13, 1; 84-93
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Discretization of data using Boolean transformations and information theory based evaluation criteria
Autorzy:
Jankowski, C.
Reda, D.
Mańkowski, M.
Borowik, G.
Powiązania:
https://bibliotekanauki.pl/articles/200750.pdf
Data publikacji:
2015
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
machine learning
discretization
discernibility function
logic minimization
information theory
entropy
nauczanie maszynowe
dyskretyzacja
minimalizacja funkcji logicznych
teoria informacji
entropia
Opis:
Discretization is one of the most important parts of decision table preprocessing. Transforming continuous values of attributes into discrete intervals influences further analysis using data mining methods. In particular, the accuracy of generated predictions is highly dependent on the quality of discretization. The paper contains a description of three new heuristic algorithms for discretization of numeric data, based on Boolean reasoning. Additionally, an entropy-based evaluation of discretization is introduced to compare the results of the proposed algorithms with the results of leading university software for data analysis. Considering the discretization as a data compression method, the average compression ratio achieved for databases examined in the paper is 8.02 while maintaining the consistency of databases at 100%.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2015, 63, 4; 923-932
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Identification of areas for optimising marketing communications via AI systems
Autorzy:
Bajak, Maria
Majerczyk, Paweł
Powiązania:
https://bibliotekanauki.pl/articles/27313540.pdf
Data publikacji:
2022
Wydawca:
Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
Tematy:
artificial Intelligence
marketing communication
machine learning
Big Data
new technologies
sztuczna inteligencja
komunikacja marketingowa
nauczanie maszynowe
zbiór danych
nowe technologie
Opis:
Purpose: The main objective of this article is to identify areas for optimizing marketing communication via artificial intelligence solutions. Design/methodology/approach: In order to realise the assumptions made, an analysis and evaluation of exemplary implementations of AI systems in marketing communications was carried out. For the purpose of achieving the research objective, it was decided to choose the case study method. As part of the discussion, the considerations on the use of AI undertaken in world literature were analysed, as well as the analysis of three different practical projects. Findings: AI can contribute to the optimisation and personalisation of communication with the customer. Its application generates multifaceted benefits for both sides of the market exchange. Achieving them, however, requires a good understanding of this technology and the precise setting of objectives for its implementation. Research limitations/implications: The article contains a preliminary study. In the future it is planned to conduct additional quantitative and qualitative research. Practical implications: The conclusions of the study can serve to better understand the benefits of using artificial intelligence in communication with the consumer. The results of the research can be used both in market practice and also serve as an inspiration for further studies of this topic. Originality/value: The article reveals the specifics of artificial intelligence in relation to business activities and, in particular, communication with the buyer. The research used examples from business practice.
Źródło:
Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska; 2022, 160; 25--38
1641-3466
Pojawia się w:
Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Classification and detection of skin disease based on machine learning and image processing evolutionary models
Autorzy:
Bordoloi, Dibyahash
Singh, Vijay
Kaliyaperumal, Karthikeyan
Ritonga, Mahyudin
Jawarneh, Malik
Kassanuk, Thanwamas
Quiñonez-Choquecota, Jose
Powiązania:
https://bibliotekanauki.pl/articles/38700501.pdf
Data publikacji:
2023
Wydawca:
Instytut Podstawowych Problemów Techniki PAN
Tematy:
skin disorder
machine learning
classification
image enhancement
image segmentation
disease detection
schorzenie skóry
nauczanie maszynowe
klasyfikacja
ulepszenie obrazu
segmentacja obrazów
wykrywanie choroby
Opis:
Skin disorders, a prevalent cause of illnesses, may be identified by studying their physical structure and history of the condition. Currently, skin diseases are diagnosed using invasive procedures such as clinical examination and histology. The examinations are quite effective and beneficial. This paper describes an evolutionary model for skin disease classification and detection based on machine learning and image processing. This model integrates image preprocessing, image augmentation, segmentation, and machine learning algorithms. The experimental investigation makes use of a dermatology data set. The model employs the machine learning methods: the support vector machine (SVM), the k-nearest neighbors (KNN), and random forest algorithms for image categorization and detection. This suggested methodology is beneficial for the accurate identification of skin disease using image analysis. The SVM algorithm achieved an accuracy of 98.8%. The KNN algorithm achieved a sensitivity of 91%. The specificity of KNN was 99%.
Źródło:
Computer Assisted Methods in Engineering and Science; 2023, 30, 2; 247-256
2299-3649
Pojawia się w:
Computer Assisted Methods in Engineering and Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Classifiers accuracy improvement based on missing data imputation
Autorzy:
Jordanov, I.
Petrov, N.
Petrozziello, A.
Powiązania:
https://bibliotekanauki.pl/articles/91626.pdf
Data publikacji:
2018
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
machine learning
missing data
model-based imputation
neural networks
random forests
support vector machine
radar signal classification
nauczanie maszynowe
brakujące dane
sieci neuronowe
maszyna wektorów nośnych
klasyfikacja sygnałów radarowych
Opis:
In this paper we investigate further and extend our previous work on radar signal identification and classification based on a data set which comprises continuous, discrete and categorical data that represent radar pulse train characteristics such as signal frequencies, pulse repetition, type of modulation, intervals, scan period, scanning type, etc. As the most of the real world datasets, it also contains high percentage of missing values and to deal with this problem we investigate three imputation techniques: Multiple Imputation (MI); K-Nearest Neighbour Imputation (KNNI); and Bagged Tree Imputation (BTI). We apply these methods to data samples with up to 60% missingness, this way doubling the number of instances with complete values in the resulting dataset. The imputation models performance is assessed with Wilcoxon’s test for statistical significance and Cohen’s effect size metrics. To solve the classification task, we employ three intelligent approaches: Neural Networks (NN); Support Vector Machines (SVM); and Random Forests (RF). Subsequently, we critically analyse which imputation method influences most the classifiers’ performance, using a multiclass classification accuracy metric, based on the area under the ROC curves. We consider two superclasses (‘military’ and ‘civil’), each containing several ‘subclasses’, and introduce and propose two new metrics: inner class accuracy (IA); and outer class accuracy (OA), in addition to the overall classification accuracy (OCA) metric. We conclude that they can be used as complementary to the OCA when choosing the best classifier for the problem at hand.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2018, 8, 1; 31-48
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Machine learning-based business rule engine data transformation over high-speed networks
Autorzy:
Neelima, Kenpi
Vasundra, S.
Powiązania:
https://bibliotekanauki.pl/articles/38700094.pdf
Data publikacji:
2023
Wydawca:
Instytut Podstawowych Problemów Techniki PAN
Tematy:
CRISP-DM
data mining algorithms
business rule
prediction
classification
machine learning
deep learning
AI design
algorytmy eksploracji danych
reguła biznesowa
prognoza
klasyfikacja
nauczanie maszynowe
uczenie głębokie
projekt Sztucznej Inteligencji
Opis:
Raw data processing is a key business operation. Business-specific rules determine howthe raw data should be transformed into business-required formats. When source datacontinuously changes its formats and has keying errors and invalid data, then the effectiveness of the data transformation is a big challenge. The conventional data extraction andtransformation technique produces a delay in handling such data because of continuousfluctuations in data formats and requires continuous development of a business rule engine.The best business rule engines require near real-time detection of business rule and datatransformation mechanisms utilizing machine learning classification models. Since data iscombined from numerous sources and older systems, it is challenging to categorize andcluster the data and apply suitable business rules to turn raw data into the business-required format. This paper proposes a methodology for designing ensemble machine learning techniques and approaches for classifying and segmenting registered numbersof registered title records to choose the most suitable business rule that can convert theregistered number into the format the business expects, allowing businesses to provide customers with the most recent data in less time. This study evaluates the suggested modelby gathering sample data and analyzing classification machine learning (ML) models todetermine the relevant business rule. Experimentation employed Python, R, SQL storedprocedures, Impala scripts, and Datameer tools.
Źródło:
Computer Assisted Methods in Engineering and Science; 2023, 30, 1; 55-71
2299-3649
Pojawia się w:
Computer Assisted Methods in Engineering and Science
Dostawca treści:
Biblioteka Nauki
Artykuł
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