Contents of Volume 32 (2022)


  • [1] Abeska Y.Y., Cavas L. (Turkey)
    Artificial neural network modelling of green synthesis of silver nanoparticles by honey , pp. 1-14

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    Abstract: Nanomaterials draw attention because of their unique physical, chemical and biological properties in areas such as catalysis, electronic, optics, medicine, solar energy conversion and water treatment. Green synthesis of silver nanoparticles has many superiorities compared to physical and chemical methods such as lowcost, nontoxicity, eco-sensitive. In this paper, experimental conditions related togreen synthesis of silver nanoparticles by honey were modelled using artificial neural network (ANN). While agitation time, agitation rate, pH, temperature, honey concentration, AgNO3 concentration were selected as input parameters, production of silver nanoparticles was used as an output parameter. According to the results, optimum hidden neuron number was found as 40 with Levenberg–Marquardt back-propagation algorithm. In this conditions, the percentages of training, validationand testing were 75, 20 and 5, respectively. After creating neural network separated input data set was applied and then experimental and ANN predicted data were compared. In conclusion, ANN can be an alternative modelling and robust approach that could help researchers in this field to estimate production of silver nanoparticles.

  • [2] Jozová Š., Uglickich E., Nagy I., Likhonina R. (CZ)
    Modeling of discrete questionnaire data with dimension reduction, pp. 15-41

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    Abstract: The paper deals with the task of modeling discrete questionnaire data with a reduced dimension of the model. The discrete model dimension is reduced using the construction of local models based on independent binomial mixtures estimated with the help of recursive Bayesian algorithms in the combination with the naive Bayes technique. The main contribution of the paper is a three-phase algorithm of the discrete model dimension reduction, which allows to model high-dimensional questionnaire data with high number of explanatory variables and their possible realizations. The proposed general solution is applied to the traffic accident questionnaire analysis, where it takes the form of the classification of the accident circumstances and prediction of the traffic accident severity using the currently measured discrete data. Results of testing the obtained model on real data and comparison with theoretical counterparts are demonstrated.

  • [3] Daqrouq K., Alkhateeb A., Ahmad W., Khalaf E., Awad M., Noeth E., Alharbey R.A., Rushdi A.M. (Pakistan, Saudi Arabia) ,
    A universal ECG signal classification system using the wavelet transform, pp. 43-54

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    Abstract: The electrocardiograph (ECG) is one of the most successful medical diagnostic tools. The ECG can show, roughly speaking, all types of heart disordersthat appear as ECG signal arrhythmias or problems with the rate or rhythm of thehuman heartbeat. In this paper, a universal ECG signal arrhythmia classificationsystem is proposed. The proposed system is based on using the wavelet transformin two of its known forms, namely, the discrete wavelet transform (DWT) andthe wavelet packet transform (WPT), or a combination thereof. The purpose ofthe research reported herein is to find out a universal classification system; in thesense of providing a capability for simultaneous classification of all types of known heart arrhythmias. Three algorithms based on the wavelet transform are tested for different wavelet levels, wavelet functions, training and testing ratios, and elapsed times. We rank these algorithms according to the elapsed times needed for their processing over the whole loop of the eight different arrhythmia classes. This ranking nominates the WPT-based algorithm to be the most superior method among the competing methods. A different ranking according to successful recognition rates assigns priority instead to the method combining the WPT and the DWT.

  • [4] Qiao F.J., Li B., Gao, M.Q., Li J.J. (China)
    ECG signal classification based on adaptive multi-channel weighted neural network, pp. 55-72

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    Abstract: The intelligent diagnosis of cardiovascular diseases is a topic of great interest. Many electrocardiogram (ECG) recognition technologies have emerged, but most of them have low recognition accuracy and poor clinical application. To improve the accuracy of ECG classification, this paper proposes a multi-channel neural network framework. Concretely, a multi-channel feature extractor is constructed by using four types of filters, which are weighted according to their importance, as measured by kurtosis. A bidirectional long short-term memory (BLSTM) network structure based on attention mechanism is constructed, and the extracted features are taken as the input of the network, and the algorithm is optimized by attention mechanism. An experiment conducted on the MIT-BIH arrhythmia database shows that the proposed algorithm obtains excellent results, with 99.20 % specificity, 99.87 % sensitivity, and 99.89 % accuracy. Therefore, the algorithm is practical and effective in the clinical diagnosis of cardiovascular diseases.