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1

Automated detection of cybersecurity attacks in healthcare systems with recursive feature elimination and multilayer perceptron optimization

Firat Kilincer Ilhan, Ertam Fatih, Sengur Abdulkadir, Tan Ru-San, Rajendra Acharya U.

Biocybernetics and Biomedical Engineering

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2023

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Vol. 43, no. 1

30--41

EN

Widespread proliferation of interconnected healthcare equipment, accompanying software, operating systems, and networks in the Internet of Medical Things (IoMT) raises the risk of security compromise as the bulk of IoMT devices are not built to withstand internet attacks. In this work, we have developed a cyber-attack and anomaly detection model based on recursive feature elimination (RFE) and multilayer perceptron (MLP). The RFE approach selected optimal features using logistic regression (LR) and extreme gradient boosting regression (XGBRegressor) kernel functions. MLP parameters were adjusted by using a hyperparameter optimization and 10-fold cross-validation approach was performed for performance evaluations. The developed model was performed on various IoMT cybersecurity datasets, and attained the best accuracy rates of 99.99%, 99.94%, 98.12%, and 96.2%, using Edith Cowan University- Internet of Health Things (ECU-IoHT), Intensive Care Unit (ICU Dataset), Telemetry data, Operating systems’ data, and Network data from the testbed IoT/IIoT network (TON-IoT), and Washington University in St. Louis enhanced healthcare monitoring system (WUSTL-EHMS) datasets, respectively. The proposed method has the ability to counter cyber attacks in healthcare applications.

2

A dual-stage transformer and MLP-based network for breast ultrasound image segmentation

Lin Guidi, Chen Mingzhi, Tan Minsheng, Chen Lingna, Chen Junxi

Biocybernetics and Biomedical Engineering

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2023

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Vol. 43, no. 4

656--671

EN

Automatic segmentation of breast lesions from ultrasound images plays an important role in computer-aided breast cancer diagnosis. Many deep learning methods based on convolutional neural networks (CNNs) have been proposed for breast ultrasound image segmentation. However, breast ultrasound image segmentation is still challenging due to ambiguous lesion boundaries. We propose a novel dual-stage framework based on Transformer and Multi-layer perceptron (MLP) for the segmentation of breast lesions. We combine the Swin Transformer block with an efficient pyramid squeezed attention block in a parallel design and introduce bi-directional interactions across branches, which can efficiently extract multi-scale long-range dependencies to improve the segmentation performance and robustness of the model. Furthermore, we introduce tokenized MLP block in the MLP stage to extract global contextual information while retaining fine-grained information to segment more complex breast lesions. We have conducted extensive experiments with state-of-the-art methods on three breast ultrasound datasets, including BUSI, BUL, and MT_BUS datasets. The dice coefficient reached 0.8127 ± 0.2178, and the intersection over union reached 0.7269 ± 0.2370 on benign lesions when the Hausdorff distance was maintained at 3.75 ± 1.83. The dice coefficient of malignant lesions is improved by 3.09% for BUSI dataset. The segmentation results on the BUL and MT_BUS datasets also show that our proposed model achieves better segmentation results than other methods. Moreover, the external experiments indicate that the proposed model provides better generalization capability for breast lesion segmentation. The dual-stage scheme and the proposed Transformer module achieve the fine-grained local information and long-range dependencies to relieve the burden of radiologists.

3

Comparison of machine learning models for predicting groundwater level, case study: Najafabad region

Zarafshan Pejman, Etezadi Hamed, Javadi Saman, Roozbahani Abbas, Hashemy S. Mehdi, Zarafshan Payam

Acta Geophysica

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2023

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Vol. 71, no. 4

1817--1830

EN

Water resources, consisting of surface water and groundwater, are considered to be among the crucial natural resources in most arid and semiarid regions. Groundwater resources as the sustainable yields can be predicted, whereas this is one of the important stages in water resource management. To this end, several models such as mathematical, statistical, empirical, and conceptual can be employed. In this paper, machine learning and deep learning methods as conceptual ones are applied for the simulations. The selected models are support vector regression (SVR), adaptive neuro-fuzzy inference system (ANFIS), and multilayer perceptron (MLP). Next, these models are optimized with the adaptive moment estimation (ADAM) optimization algorithm which results in hybrid models. The hyper-parameters of the stated models are optimized with the ADAM method. The root mean squared error (RMSE), mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R2) are used to evaluate the accuracy of the simulated groundwater level. To this end, the aquifer hydrograph is used to compare the results with observations data. So, the RMSE and R2 show that the accuracy of the machine learning and deep learning models is better than the numerical model for the given data. Moreover, the MSE is approximately the same in all three cases (ranging from 0.7113 to 0.6504). Also, the total value of R2 and RMSE for the best hybrid model is 0.9617 and 0.7313, respectively, which are obtained from the model output. The results show that all three techniques are useful tools for modeling hydrological processes in agriculture and their computational capabilities and memory are similar.

4

Neural Network Model for Control of Operating Modes of Crushing and Grinding Complex

Kalinchyk Vasyl, Meita Olexandr, Pobigaylo Vitalii, Borychenko Olena, Kalinchyk Vitalii

Rocznik Ochrona Środowiska

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2022

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Tom 24

26--40

EN

This article investigates the application of neural network models to create automated control systems for industrial processes. We reviewed and analysed works on dispatch control and evaluation of equipment operating modes and the use of artificial neural networks to solve problems of this type. It is shown that the main requirements for identification models are the accuracy of estimation and ease of algorithm implementation. It is shown that artificial neural networks meet the requirements for accuracy of classification problems, ease of execution and speed. We considered the structures of neural networks that can be used to recognise the modes of operation of technological equipment. Application of the model and structure of networks with radial basis functions and multilayer perceptrons for identifying the mode of operation of equipment under given conditions is substantiated. The input conditions for constructing neural network models of two types with a given three-layer structure are offered. The results of training neural models on the model of a multilayer perceptron and a network with radial basis functions are presented. The estimation and comparative analysis of models depending on model parameters are made. It is shown that networks with radial basis functions offer greater accuracy in solving identification problems. The structural scheme of the automated process control system with mode identification based on artificial neural networks is offered.

5

Development of a neural statistical model for the prediction of relative humidity levels in the region of Rabat-Kenitra, North West Morocco

El Azhari Kaoutar, Abdallaoui Badreddine, Dehbi Ali, Abdalloui Abdelaziz, Zineddine Hamid

Journal of Water and Land Development

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2022

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no. 54

13--20

EN

This article accounts for the development of a powerful artificial neural network (ANN) model, designed for the prediction of relative humidity levels, using other meteorological parameters such as the maximum temperature, minimum temperature, precipitation, wind speed, and intensity of solar radiation in the Rabat-Kenitra region (a coastal area where relative humidity is a real concern). The model was applied to a database containing a daily history of five meteorological parameters collected by nine stations covering this region from 1979 to mid-2014. It has been demonstrated that the best performing three-layer (input, hidden, and output) ANN mathematical model for the prediction of relative humidity in this region is the multi-layer perceptron (MLP) model. This neural model using the Levenberg-Marquard algorithm, with an architecture of [5-11-1] and the transfer functions Tansig in the hidden layer and Purelin in the output layer, was able to estimate relative humidity values that were very close to those observed. This was affirmed by a low mean squared error (MSE) and a high correlation coefficient (R), compared to the statistical indicators relating to the other models developed as part of this study.

6

Prediction of the compressive strength of environmentally friendly concrete using artificial neural network

Kulisz Monika, Kujawska Justyna, Aubakirova Zulfiya, Zhairbaeva Gulnaz, Warowny Tomasz

Applied Computer Science

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2022

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Vol. 18, no 4

68--81

EN

The paper evaluated the possibility of using artificial neural network models for predicting the compressive strength (Fc) of concretes with the addition of recycled concrete aggregate (RCA). The artificial neural network (ANN) approaches were used for three variable processes modeling (cement content in the range of 250 to 400 kg/m3, percentage of recycled concrete aggregate from 25% to 100% and the ratios of water contents 0.45 to 0.6). The results indicate that the compressive strength of recycled concrete at 3, 7 and 28 days is strongly influenced by the cement content, %RCA and the ratios of water contents. It is found that the compressive strength at 3, 7 and 28 days decreases when increasing RCA from 25% to 100%. The obtained MLP and RBF networks are characterized by satisfactory capacity for prediction of the compressive strength of concretes with recycled concrete aggregate (RCA) addition. The results in statistical terms; correlation coefficient (R) reveals that the both ANN approaches are powerful tools for the prediction of the compressive strength.

7

Forecasting the Energy Consumption of an Industrial Enterprise Based on the Neural Network Model

Kalinchyk Vasyl, Meita Olexandr, Pobigaylo Vitalii, Kalinchyk Vitalii, Filyanin Danylo

Rocznik Ochrona Środowiska

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2021

|

Tom 23

484--492

EN

This research paper investigates the application of neural network models for forecasting in energy. The results of forecasting the weekly energy consumption of the enterprise according to the model of a multilayer perceptron at different values of neurons and training algorithms are given. The estimation and comparative analysis of models depending on model parameters is made.

8

Neural network model for enterprise energy consumption forecasting

Kalinchyk Vasyl, Meita Olexandr, Pobigaylo Vitalii, Kalinchyk Vitalii, Filyanin Danylo

Journal of New Technologies in Environmental Science

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2021

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Vol. 5, nr 2

65--71

EN

This research paper investigates the application of neural network models for forecasting in energy. The results of forecasting the weekly energy consumption of the enterprise according to the model of a multilayer perceptron at different values of neurons and training algorithms are given. The estimation and comparative analysis of models depending on model parameters is made.

9

Machine learning for predicting discharge fuctuation of a karst spring in North China

Cheng Shu, Qiao Xiaojuan, Shi Yaolin, Wang Dawei

Acta Geophysica

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2021

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Vol. 69, no. 1

257--270

EN

The quantitative analyses of karst spring discharge typically rely on physical-based models, which are inherently uncertain. To improve the understanding of the mechanism of spring discharge fuctuation and the relationship between precipitation and spring discharge, three machine learning methods were developed to reduce the predictive errors of physical-based groundwater models, simulate the discharge of Longzici spring’s karst area, and predict changes in the spring on the basis of long time series precipitation monitoring and spring water fow data from 1987 to 2018. The three machine learning methods included two artifcial neural networks (ANNs), namely multilayer perceptron (MLP) and long short-term memory–recurrent neural network (LSTM–RNN), and support vector regression (SVR). A normalization method was introduced for data preprocessing to make the three methods robust and computationally efcient. To compare and evaluate the capability of the three machine learning methods, the mean squared error (MSE), mean absolute error (MAE), and root-mean-square error (RMSE) were selected as the performance metrics for these methods. Simulations showed that MLP reduced MSE, MAE, and RMSE to 0.0010, 0.0254, and 0.0318, respectively. Meanwhile, LSTM–RNN reduced MSE to 0.0010, MAE to 0.0272, and RMSE to 0.0329. Moreover, the decrease in MSE, MAE, and RMSE was 0.0397, 0.1694, and 0.1991, respectively, for SVR. Results indicated that MLP performed slightly better than LSTM–RNN, and MLP and LSTM–RNN performed considerably better than SVR. Furthermore, ANNs were demonstrated to be prior machine learning methods for simulating and predicting karst spring discharge.

10

A deep learning model for electricity demand forecasting based on a tropical data

Adewuyi Saheed A., Aina Segun, Oluwaranti Adeniran I.

Applied Computer Science

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2020

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Vol. 16, no 1

5--17

EN

Electricity demand forecasting is a term used for prediction of users’ consumption on the grid ahead of actual demand. It is very important to all power stakeholders across levels. The power players employ electricity demand forecasting for sundry purposes. Moreover, the government’s policy on its market deregulation has greatly amplified its essence. Despite numerous studies on the subject using certain classical approaches, there exists an opportunity for exploration of more sophisticated methods such as the deep learning (DL) techniques. Successful researches about DL applications to computer vision, speech recognition, and acoustic computing problems are motivation. However, such researches are not sufficiently exploited for electricity demand forecasting using DL methods. In this paper, we considered specific DL techniques (LSTM, CNN, and MLP) to short-term load forecasting problems, using tropical institutional data obtained from a Transmission Company. We also test how accurate are predictions across the techniques. Our results relatively revealed models appropriateness for the problem.

11

An Efficient ANN Interference Cancelation for High Order Modulation over Rayleigh Fading Channel

Bouguerra F., Saidi L.

Journal of Telecommunications and Information Technology

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2018

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nr 4

75--80

EN

High order modulation (HOM) presents a key challenge in increasing spectrum efficiency in 4G and upcoming 5G communication systems. In this paper, two non-linear adaptive equalizer techniques based on multilayer perceptron (MLP) and radial basis function (RBF) are designed and applied on HOM to optimize its performance despite its high sensitivity to noise and channel distortions. The artiﬁcial neural network’s (ANN) adaptive equalizer architectures and learning methods are simpliﬁed to avoid more complexity and to ensure greater speed in symbol decision making. They will be compared with the following popular adaptive ﬁlters: least mean square (LMS) and recursive least squares (RLS), in terms of bit error rate (BER) and minimum square error (MSE) with 16, 64, 128, 256, 512 and 1024 quadrature amplitude modulation (QAM). By that, this work will show the advantage that the MLP equalizer has, in most cases, over RBF and traditional linear equalizers.

12

Application of artificial neural networks to analyze the emergence of soybean seeds after applying herbal treatments

Niedbała G., Piekutowska M., Rudowicz-Nawrocka J., Adamski M., Wojciechowski T., Herkowiak M., Szparaga A., Czechowska-Kosacka A.

Journal of Research and Applications in Agricultural Engineering

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2018

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Vol. 63, nr 4

145--149

EN

The aim of the following work is to indicate factors which significantly affect the emergence of selected soybean varieties after application of natural herbal extracts based on - Levisticum officinale L., Ribes nigrum L., Matricaria chamomilla L., as wet seed treatments using two methods of treatment. The research material included seeds treated for 24 hours in macerats, decoctions and infusions made from the above herb species as well as untreated seeds, seeded together with preparations in point application. Untreated seeds were used as the control group. The experiment was being conducted for 16 days in a greenhouse facility belonging to the COBORU Experimental Station for Variety Testing in Karzniczka. The assessed parameter referred to the percentage of soybean seedlings emergence ability determined based on the number of emerged plants. Indication of the importance of factors in shaping soybean emergence and considering their rank was possible due to the sensitivity analysis of the generated neural network with the MLP architecture 4:4-13-5-1:1 with two hidden layers. All analyzed factors of the experiment significantly shaped the ability of soybean emergence, with the following order: cultivar, application method, herb species from which the extract was made, form of preparation.

PL

Celem pracy było wskazanie czynników istotnie wpływających na wschody wybranych odmian soi po zastosowaniu naturalnych ekstraktów wodnych na bazie ziół - Levisticum officinale L., Ribes nigrum L., Matricaria chamomilla L., jako zapraw nasiennych na mokro z wykorzystaniem dwóch sposobów zaprawiania. Materiał badawczy stanowiły nasiona zaprawiane przez dobę w maceratach, wywarach i naparach sporządzonych z powyższych gatunków ziół oraz nasiona niezaprawiane, wysiewane łącznie z aplikacją punktową preparatów. Za obiekt kontrolny przyjęto nasiona niezaprawiane. Eksperyment prowadzono przez 16 dni w obiekcie szklarniowych należącym do Stacji Doświadczalnej Oceny Odmian COBORU w Karzniczce. Parametrem poddanym ocenie była procentowa zdolność wschodów siewek soi określana na podstawie liczby wzeszłych roślin. Wskazanie istotności czynników w kształtowaniu zdolności wschodów soi oraz uwzględnienie ich rangi było możliwe dzięki analizie wrażliwości wytworzonej sieci neuronowej o architekturze MLP 4:4-13-5-1:1 z dwoma ukrytymi warstwami. Wszystkie analizowane czynniki doświadczenia znacząco kształtowały zdolność wschodów soi, a ich waga miała następującą kolejność: odmiana, sposób aplikacji preparatu, gatunek zioła, z którego sporządzono ekstrakt, forma preparatu.

13

Application of artificial neural networks for the prediction of quality characteristics of potato tubers - Innovator variety

Niedbała G., Piekutowska M.

Journal of Research and Applications in Agricultural Engineering

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2018

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Vol. 63, nr 4

132--138

EN

The aim of the research was to create a model for prediction of tuber dry matter on the basis of underwater weight of tubers (UWW), with the use of neural modelling methods. In order to achieve the aim of the study, data from the years 2011-2017 were collected from the production fields of an individual farm located at the border of Pomeranian and West Pomeranian Voivodeships in Słupski and Sławieński districts. The subject of the research concerned potatoes of the Innovator variety, which were grown for processing purposes - production of French fries. To build a neural model, data from September sampling as well as meteorological and fertilizer data were used. A total of 82 learning cases from the fields covered by the analyses were used, which were divided into two sets. Set 1, for the construction of the neural model consisted of 75 samples. Set 2, which consisted of 7 randomly selected samples, had a validation function and did not participate in the construction of the neural model. For proper model validation, four forecast error measures were used, i.e. relative approximation error (RAE), root mean square error (RMS), mean absolute error (MAE), mean absolute percentage error (MAPE). The model MLP 8:8-12-5-1:1 (BP100,CG31b) was based on eight inputs (meteorological data, fertilization levels) and one output (dry matter of tubers under water). The analysis resulted in a forecast error of 2.81% of MAPE. Moreover, the sensitivity analysis of the neural network showed that the mean air temperature in the period from April to September (T4-9) had the greatest influence on the dry matter of tubers.

PL

Celem pracy było wytworzenie modelu do predykcji suchej masy bulw na podstawie masy bulw pod wodą z wykorzystaniem metod modelowania neuronowego. Dla realizacji celu pracy zebrano dane pochodzące z lat 2011-2017 pochodzące z pól produkcyjnych gospodarstwa indywidualnego, zlokalizowanego przy granicy województw pomorskiego i zachodniopomorskiego w powiatach słupskim i sławieńskim. Przedmiotem badań były ziemniaki odmiany Innovator, które uprawiano na cele przetwórcze - produkcję frytek. Do budowy modelu neuronowego, wykorzystano dane pochodzące z wrześniowych próbkowań oraz dane meteorologiczne i nawozowe. Łącznie użyto 82 przypadków uczących pochodzących z pól objętych analizami, które zostały podzielone na dwa zbiory. Zbiór 1, do budowy modelu neuronowego składał się z 75 prób. Zbiór 2, który tworzyło 7 losowo wybranych prób, pełnił funkcję walidacyjną i nie uczestniczył w budowie modelu neuronowego. Dla właściwej walidacji modelu zastosowano cztery mierniki błędów prognozy, tj. globalny względny błąd aproksymacji modelu (RAE), błąd średniokwadratowy (RMS), błąd średni bezwzględny (MAE), błąd średni bezwzględny procentowy (MAPE). Wytworzony model MLP 8:8-12-5-1:1 (BP100,CG31b) bazował na ośmiu wejściach (dane meteorologiczne, poziomy nawożenia) i jednym wyjściu (sucha masa bulw pod wodą). W wyniku przeprowadzonych analiz uzyskano wynik błędu prognozy na poziomie 2.81% MAPE. Ponadto analiza wrażliwości sieci neuronowej wykazała, że największy wpływ na suchą masę bulw miała średnia temperatura powietrza w okresie od kwietnia do września (T4-9).

14

Comparative Application of Radial Basis Function and Multilayer Perceptron Neural Networks to Predict Traffic Noise Pollution in Tehran Roads

Mansourkhaki A., Berangi M., Haghiri M.

Journal of Ecological Engineering

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2018

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Vol. 19, nr 1

113--121

EN

Noise pollution is a level of environmental noise which is considered as a disturbing and annoying phenomenon for human and wildlife. It is one of the environmental problems which has not been considered as harmful as the air and water pollution. Compared with other pollutants, the attempts to control noise pollution have largely been unsuccessful due to the inadequate knowledge of its effects on humans, as well as the lack of clear standards in previous years. However, with an increase of traveling vehicles, the adverse impact of increasing noise pollution on human health is progressively emerging. Hence, investigators all around the world are seeking to find new approaches for predicting, estimating and controlling this problem and various models have been proposed. Recently, developing learning algorithms such as neural network has led to novel solutions for this challenge. These algorithms provide intelligent performance based on the situations and input data, enabling to obtain the best result for predicting noise level. In this study, two types of neural networks – multilayer perceptron and radial basis function – were developed for predicting equivalent continuous sound level (LAeq) by measuring the traffic volume, average speed and percentage of heavy vehicles in some roads in west and northwest of Tehran. Then, their prediction results were compared based on the coefficient of determination (R2) and the Mean Squared Error (MSE). Although both networks are of high accuracy in prediction of noise level, multilayer perceptron neural network based on selected criteria had a better performance.

15

Using artificial neural network (ANN) for prediction of sediment loads, application to the Mellah catchment, northeast Algeria

Bouzeria H., Ghenim A. N., Khanchoul K.

Journal of Water and Land Development

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2017

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no. 33

47--55

EN

In this study, we present the performances of the best training algorithm in Multilayer Perceptron (MLP) neural networks for prediction of suspended sediment discharges in Mellah catchment. Time series data of daily suspended sediment discharge and water discharge from the gauging station of Bouchegouf were used for training and testing the networks. A number of statistical parameters, i.e. root mean square error (RMSE), mean absolute error (MAE), coefficient of efficiency (CE) and coefficient of determination (R2) were used for performance evaluation of the model. The model produced satisfactory results and showed a very good agreement between the predicted and observed data. The results also showed that the performance of the MLP model was capable to capture the exact pattern of the sediment discharge data in the Mellah catchment.

PL

W niniejszej pracy przedstawiono działanie najlepszego algorytmu sieci neuronowych z użyciem wielowarstwowego perceptronu do przewidywania odpływu zawiesiny ze zlewni rzeki Mellah. Do treningu i testowania sieci użyto serii czasowych dobowego odpływu zawiesiny i odpływu wody z profilu wodowskazowego Bouchegouf. Do oceny działania modelu wykorzystano szereg parametrów statystycznych, takich jak pierwiastek ze średniego błędu kwadratowego, średni błąd bezwzględny, współczynnik wydajności i współczynnik determinacji. Model dawał zadowalające wyniki i wykazywał bardzo dobrą zgodność między obserwowanymi i przewidywanymi danymi. Wyniki świadczą także, że model jest w stanie wychwycić szczegółowy wzorzec odpływu zawiesiny ze zlewni rzeki Mellah.

16

Zastosowanie wielołączowego systemu transmisji danych Link 11 w wyrobach PIT-RADWAR S.A.

Dróżdż H., Sędek E.

Elektronika : konstrukcje, technologie, zastosowania

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2017

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Vol. 58, nr 6

47--51

PL

Artykuł prezentuje wielołączowy system transmisji danych Link 11 (NATO CONFIDENTIAL) jako przykład systemu wspomagającego świadomość sytuacyjną na polu walki wykorzystywanego w systemach taktycznych państw sojuszniczych, szczególnie w Marynarce Wojennej. Jednocześnie artykuł omawia technologię i technikę przetwarzania informacji niejawnych w tym systemie. Ponadto zaprezentowane zostały przykłady zastosowania różnych wariantów rozwiązań systemu Link 11 i LINK 11B w aktualnych wyrobach PIT-RADWAR S.A.

EN

The paper presents multilink system data transmission Link 11 (NATO CONFIDENTIAL) as example the SADL Systems (Situational Awareness Data Link Systems), which is used in the tactical Systems of allied countries, especially in Navy. The paper discusses the technology and processing technique of the classified information in this system. Additionaly paper presents the examples of different solutions of the Link 11 and Link 11B systems in current products of PIT-RADWAR S.A.

17

Ocena przydatności wybranych metod sztucznej inteligencji w prognozach przemieszczeń spowodowanych odwodnieniem górotworu na terenach górniczych

Witkowski W. T.

Przegląd Górniczy

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2016

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T. 72, nr 2

88--94

PL

W publikacji przedstawiono problem osiadań powierzchni spowodowanych przez odwodnienie górotworu, obserwowanych na terenach górniczych. Przedstawiono możliwość prognozowania tych ruchów z wykorzystaniem narzędzi sztucznej inteligencji. Omówiono dwie metody obliczeniowe: wielowarstwową sieć perceptronową oraz metodę wektorów podtrzymujących. Proces uczenia sieci wykonano na zestawie danych reprezentujących jeden z polskich terenów górniczych. Uzyskane wyniki zaprezentowano w postaci wykresów korelacyjnych danych prognozowanych przez sieci oraz oczekiwanych odpowiedzi (dane wysokościowe). Weryfikację poprawności wytrenowania sieci przeprowadzono na próbce danych nieuczestniczących we wcześniejszej procedurze obliczeniowej. Zaprezentowano najlepsze rezultaty z procesu uczenia sieci MLP oraz SVM. W podsumowaniu wskazano możliwości dalszego rozwoju badań w zakresie wykorzystania sztucznej inteligencji w zagadnieniu osiadań odwodnieniowych obserwowanych na terenach górniczych.

EN

This paper presents a phenomenon of surface subsidence caused by dewatering of rock mass observed in mining areas. The possibility of forecasting these movements by the use of artificial intelligence tools was presented, and two calculation methods discussed: Multilayer Perceptron Network (MLP) and the Support Vectors Machines (SVM). The teaching process of the network was performed on the basis of a data set, representing one of the Polish mining areas. Obtained results were presented in the form of correlation graphs of data forecasted by neural networks and expected responses (elevation data). Verification of network training correctness was conducted on a sample of data not involved in the earlier calculation procedure. The best results of the learning process of MLP and SVM networks were presented. The summary indicated the possibility of further development of research in terms of using artificial intelligence in the issue of drainage subsidence observed in mining areas.

18

Porównanie skuteczności wybranych klasyﬁkatorów neuronowych w identyﬁkacji grup macerałów węgla kamiennego oraz materii nieorganicznej

Skiba M., Młynarczuk M.

Prace Instytutu Mechaniki Górotworu PAN

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2016

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T. 18, nr 4

133--138

PL

Jednym z podstawowych zastosowań sztucznych sieci neuronowych jest rozpoznawanie i klasyfikacja wzorców. W ramach pracy przeprowadzono automatyczną identyfikację grup macerałów oraz materii nieorganicznej za pomocą trzech klasyfikatorów neuronowych: dwuwarstwowej sieci jednokierunkowej (Multi-Layer Perceptron, MLP), sieci o radialnych funkcjach bazowych (Radial Basis Function, RBF) oraz samoorganizującej mapy Kohonena (Self- -Organizing Maps, SOM). Do analiz wykorzystano zbiór 3000 mikroskopowych zdjęć próbek węgla kamiennego. Każde z nich opisano 12 – wymiarowym wektorem cech. Dla każdej z rozpatrywanych sieci dokonano 100 – krotnego powtórzenia losowego wyboru ciągu uczącego, treningu sieci oraz rozpoznania badanych obiektów. Analizy wykazały wysoką skuteczność zastosowanych klasyfikatorów neuronowych w identyfikacji grup macerałów oraz materii nieorganicznej. Najlepsze rezultaty, na poziomie przekraczającym 98% poprawnych rozpoznań, uzyskano dla klasyfikatorów bazujących na uczeniu nadzorowanym (MLP oraz RBF). Nieznacznie niższą skuteczność rozpoznań otrzymano w przypadku sieci SOM – 95,9% klasyfikacji zgodnych z decyzjami obserwatora.

EN

One of the main applications of artificial neural networks is the recognition and classification of different patterns. In the framework of the work an automatic identification of maceral groups and inorganic matter was carried. Three neural classifiers were used: a Multi-Layer Perceptron (MLP), a network of Radial Basis Function (RBF) and Kohonen Self-Organizing Maps (SOM). For the purposes of the analysis a collection of 3,000 images of microscopic samples of coal was used. Each image was described by 12-dimensional feature vector. For each network were carried out: a hundredfold draw of learning set, the network training and classification of objects under study. The analyses have shown high effectiveness of the neural classifi ers used to identify maceral groups and inorganic matter. The best results were obtained for the classifiers based on supervised learning (MLP and RBF). They were at a level exceeding 98% of correct diagnoses. Slightly lower efficiency of diagnosis was obtained in the case of SOM network – 95.9% of classification compatible with the observer decisions.

19

Offline handwritten pre-segmented character recognition of Gurmukhi script

Kumar M., Jindal M. K., Sharma R. K., Jindal S. J.

Machine Graphics and Vision

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2016

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Vol. 25, No. 1/4

45--55

EN

In this paper, we have proposed a feature extraction technique for recognition of segmented handwritten characters of Gurmukhi script. The experiments have been performed with 7000 specimens of segmented offline handwritten Gurmukhi characters collected from 200 different writers. We have considered the set of 35 basic characters of the Gurmukhi script and have proposed the feature extraction technique based on boundary extents of the character image. PCA based feature selection technique has also been implemented in this work to reduce the dimension of data. We have used k-NN, SVM and MLP classifiers. SVM has been used with four different kernels. In this work, we have achieved maximum recognition accuracy of 93.8% for the 35-class problem when SVM with RBF kernel and 5-fold cross validation technique were employed.

20

Comparative evaluation of the different data mining techniques used for the medical database

Kasperczuk A., Dardzińska A.

Acta Mechanica et Automatica

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2016

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Vol. 10, no. 3

233--238

EN

Data mining is the upcoming research area to solve various problems. Classification and finding association are two main steps in the field of data mining. In this paper, we use three classification algorithms: J48 (an open source Java implementation of C4.5 algorithm), Multilayer Perceptron - MLP (a modification of the standard linear perceptron) and Naïve Bayes (based on Bayes rule and a set of conditional independence assumptions) of the Weka interface. These classifiers have been used to choose the best algorithm based on the conditions of the voice disorders database. To find association rules over transactional medical database first we use apriori algorithm for frequent item set mining. These two initial steps of analysis will help to create the medical knowledgebase. The ultimate goal is to build a model, which can improve the way to read and interpret the existing data in medical database and future data as well.