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Signal-piloted processing metaheuristic optimization and wavelet decomposition based elucidation of arrhythmia for mobile healthcare

Qaisar Saeed Mian, Khan Sibghatullah I., Dallet Dominique, Tadeusiewicz Ryszard, Pławiak Paweł

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 2

681--694

EN

The next generation healthcare systems will be based on the cloud connected wireless biomedical wearables. The key performance indicators of such systems are the compression, computational efficiency, transmission and power effectiveness with precision. The electrocardiogram (ECG) signals processing based novel technique is presented for the diagnosis of arrhythmia. It employs a novel mix of the Level-Crossing Sampling (LCS), Enhanced Activity Selection (EAS) based QRS complex selection, multirate processing, Wavelet Decomposition (WD), Metaheuristic Optimization (MO), and machine learning. The MIT-BIH dataset is used for experimentation. Dataset contains 5 classes namely, ‘‘Atrial premature contraction”, ‘‘premature ventricular contraction”, ‘‘right bundle branch block”, ‘‘left bundle branch block” and ‘‘normal sinus”. For each class, 450 cardiac pulses are collected from 3 different subjects. The performance of Marine Predators Algorithm (MPA) and Artificial Butterfly Optimization Algorithm (ABOA) is investigated for features selection. The selected features sets are passed to classifiers that use machine learning for an automated diagnosis. The performance is tested by using multiple evaluation metrics while following the 10-fold cross validation (10-CV). The LCS and EAS results in a 4.04-times diminishing in the average count of collected samples. The multirate processing lead to a more than 7-times computational effectiveness over the conventional fix-rate counter parts. The respective dimension reduction ratios and classification accuracies, for the MPA and ABOA algorithms, are 29.59-times & 22.19-times and 98.38% & 98.86%.

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Multiobjective design of permanent magnet generator with dimensionality reduction in criteria space

Woźniak Piotr

Przegląd Elektrotechniczny

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2020

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R. 96, nr 7

86--91

EN

This paper addresses the problem of dimensionality reduction while preserving the characteristics of the Pareto set approximation in multiobjective optimization. The real-life engineering design problem for permanent magnet generator is considered. The Pareto front approximations with constraints, ranging from the five objectives to the set of two, are presented and compared.

PL

W artykule przedstawiono rozwiązanie problemu optymalizacji wielokryterialnej poprzez redukcję wymiarów w przestrzeni kryteriów. Rozważono właściwości zbioru Pareto w zadaniu projektowania generatora z magnesami trwałymi. Zaprezentowano i porównano aproksymacje frontu Pareto przy optymalizacji z ograniczeniami przy redukcji z pięciu dwóch kryteriów.

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A wavelet-based approach for business protocol discovery of web services from log files

Moudjari A., Kezzouli I., Talbi H., Draa A.

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2019

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Vol. 67, nr 3

535--546

EN

Recently, business protocol discovery has taken more attention in the field of web services. This activity permits a better description of the web service by giving information about its dynamics. The latter is not supported by theWSDL language which concerns only the static part. The problem is that the only information available to construct the dynamic part is the set of log files saving the runtime interaction of the web service with its clients. In this paper, a new approach based on the Discrete Wavelet Transformation (DWT) is proposed to discover the business protocol of web services. The DWT allows reducing the problem space while preserving essential information. It also overcomes the problem of noise in the log files. The proposed approach has been validated using artificially-generated log files.

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Ensemble Methods for Improving Classification of Data Produced by Latent Dirichlet Allocation

Jankowski M.

Computer Science and Mathematical Modelling

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2018

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No. 8

17--28

EN

Topic models are very popular methods of text analysis. The most popular algorithm for topic modelling is LDA (Latent Dirichlet Allocation). Recently, many new methods were proposed, that enable the usage of this model in large scale processing. One of the problem is, that a data scientist has to choose the number of topics manually. This step, requires some previous analysis. A few methods were proposed to automatize this step, but none of them works very well if LDA is used as a preprocessing for further classification. In this paper, we propose an ensemble approach which allows us to use more than one model at prediction phase, at the same time, reducing the need of finding a single best number of topics. We have also analyzed a few methods of estimating topic number.

PL

Modelowanie tematyczne, jest popularną metodą analizy tekstów. Jednym z najbardziej popularnych algorytmów modelowania tematycznego jest LDA (Latent Dirichlet Allocation) [14]. W ostatnim czasie zostało zaproponowanych wiele nowych rozszerzeń tego modelu, które pozwalają na przetwarzanie dużych ilości danych. Jednym z problemów podczas użycia algorytmu LDA jest to, że liczba tematów musi zostać wybrana przed uruchomieniem algorytmu. Ten krok, wymaga wcześniejszej analizy i zaangażowania analityka danych. Powstało kilka metod, które pozwalają automatyzować ten krok, ale żadna z nich, nie działa dobrze, gdy LDA jest użyte do redukcji wymiarów przed klasyfikacją danych. W tej pracy, proponujemy podejście oparte o ensemble wielu modeli. Taki model, unika problemu wybrania jednego, najlepszego modelu LDA. Pokażemy, że takie podejście pozwala uzyskać niższy błąd klasyfikacji. Zaproponujemy również, dwie nowe metody wyboru liczby tematów, gdy chcemy użyć tylko pojedynczego modelu.