Wyniki wyszukiwania - BazTech

1

Intelligent Design of an Ultra-Thin Near-Ideal Multilayer Solar Selective Absorber Using Grey Wolf Optimization Linked to Deep Learning

Gliti Oussama, El Idrissi Mohamed Chafik, Igouzal Mohammed

Ecological Engineering & Environmental Technology

|

2024

|

Vol. 25, iss. 2

70--87

EN

This study explored the development of an optimal effective solar absorber by leveraging recent advancements in artificial intelligence and nanotechnology. A predictive computational approach for designing a multilayer metal-dielectric thin film solar selective absorber, specifically the SiO2/Cr/SiO2/Cr/SiO2/Cu structure was proposed. The adopted approach integrates the transfer matrix method (TMM) as a predictive electromagnetic tool and combines it with the swarm-based heuristic algorithm grey wolf optimization (GWO) linked to machine learning algorithms, specifically the artificial neural network (ANN). Through dynamic modeling and rigorous testing against multiple static versions, the adopted approach demonstrates exceptional predictive performance with an value of 0.999. The results obtained using this novel GWO-ANN approach reveal near-perfect broadband absorption of 0.996534 and low emission of 0.194170594 for the designed thin film structure. These outcomes represent a significant advancement in photo-to-thermal conversion efficiency, particularly for a working temperature of 500 °C and a solar concentration of 100 suns, showcasing its potential for practical applications across various fields. Additionally, the designed structure meets the stringent thermal stability requirements necessary for current Concentrated solar power (CSP) projects. This emphasizes its suitability for integration into existing CSP systems and highlights its potential to contribute to advancements in solar energy technology.

2

Predicting sea surface salinity in a tidal estuary with machine learning

Guillou Nicolas, Chapalain Georges, Petton Sébastien

Oceanologia

|

2023

|

Vol. 65 (2)

318--332

EN

As an indicator of exchanges between watersheds, rivers and coastal seas, salinity may provide valuable information about the exposure, ecological health and robustness of marine ecosystems, including especially estuaries. The temporal variations of salinity are traditionally approached with numerical models based on a physical description of hydrodynamic and hydrological processes. However, as these models require large computational resources, such an approach is, in practice, rarely considered for rapid turnaround predictions as requested by engineering and operational applications dealing with the ecological monitoring of estuaries. As an alternative efficient and rapid solution, we investigated here the potential of machine learning algorithms to mimic the non-linear complex relationships between salinity and a series of input parameters (such as tide-induced free-surface elevation, river discharges and wind velocity). Beyond regression methods, the attention was dedicated to popular machine learning approaches including MultiLayer Perceptron, Support Vector Regression and Random Forest. These algorithms were applied to six-year observations of sea surface salinity at the mouth of the Elorn estuary (bay of Brest, western Brittany, France) and compared to predictions from an advanced ecological numerical model. In spite of simple input data, machine learning algorithms reproduced the seasonal and semi-diurnal variations of sea surface salinity characterised by noticeable tide-induced modulations and low-salinity events during the winter period. Support Vector Regression provided the best estimations of surface salinity, improving especially predictions from the advanced numerical model during low-salinity events. This promotes the exploitation of machine learning algorithms as a complementary tool to process-based physical models.

3

Estimation of Suspended Sediment Load Using Artificial Neural Network in Khour Al Zubair Port, Iraq

Hassan Ayman A., Ibrahim Husham T., Al-Aboodi Ali H.

Journal of Ecological Engineering

|

2023

|

Vol. 24, nr 6

54--64

EN

The port of Khour Al-Zubair is located 60.0 km south of the city centre of Basrah; it is also located 105.0 kilometres from the northern tip of the Arabian Gulf. The main goal of this paper is to estimate the concentration of suspended deposit (SSC) in “Khour Al-Zubair” port using a Multilayer Perceptron Neural Network (MLP) based on hydraulic and local boundary parameters while also studying the effect of these parameters on estimating the SSC. Five input parameters (channel width, water depth, discharge, cross-section area, and flow velocity) are used for estimating SSC. Different input hydraulic and local boundary parameter combinations in the three sections (port center, port south, and port north) were used for creating nine models. The use of both hydraulic and local boundary parameters for SSC estimation is very important in the port area for estimating sediment loads without the need for field measurements, which require effort and time.

4

Urban traffic crash analysis using deep learning techniques

Sobhana Mummaneni, Vemulapalli Nihitha, Mendu Gnana Siva Sai Venkatesh, Ginjupalli Naga Deepika, Dodda Pragathi, Subramanyam Rayanoothala Bala Venkata

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

|

2023

|

T. 13, nr 3

56--63

EN

Road accidents are concerningly increasing in Andhra Pradesh. In 2021, Andhra Pradesh experienced a 20 percent upsurge in road accidents. The state's unfortunate position of being ranked eighth in terms of fatalities, with 8,946 lives lost in 22,311 traffic accidents, underscores the urgent nature of the problem. The significant financial impact on the victims and their families stresses the necessity for effective actions to reduce road accidents.This study proposes a framework that collects accident data from regions, namely Patamata, Penamaluru, Mylavaram, Krishnalanka, Ibrahimpatnam,and Gandhinagar in Vijayawada(India)from 2019 to 2021. The dataset comprises over 12,000 records of accident data. Deep learning techniquesare applied to classify the severity of road accidents into Fatal, Grievous, and Severe Injuries. The classification procedure leverages advanced neural network models, including the Multilayer Perceptron, Long-Short Term Memory, Recurrent Neural Network, and Gated Recurrent Unit. These modelsare trained on the collected data to accurately predict the severity of road accidents. The project study to make important contributions for suggesting proactive measures and policies to reduce the severity and frequency of road accidents in Andhra Pradesh.

PL

Liczba wypadków drogowych w Andhra Pradesh niepokojąco rośnie. W 2021 r. stan Andhra Pradesh odnotował 20% wzrost liczby wypadków drogowych. Niefortunna pozycja stanu, który zajmuje ósme miejsce pod względem liczby ofiar śmiertelnych, z 8946 ofiarami śmiertelnymiw 22311 wypadkach drogowych, podkreśla pilny charakter problemu. Znaczący wymiar finansowy dla ofiari ich rodziny podkreśla konieczność podjęcia skutecznych działań w celu ograniczenia liczby wypadków drogowych. W niniejszym badaniu zaproponowano system gromadzenia danych o wypadkachz regionów Patamata, Penamaluru, Mylavaram, Krishnalanka, Ibrahimpatnam i Gandhinagar w Vijayawada (India) w latach 2019–2021. Zbiór danych obejmuje ponad 12 000 rekordów danych o wypadkach. Techniki głębokiego uczenia są stosowane do klasyfikowania wagi wypadków drogowychna śmiertelne, poważne i ciężkie obrażenia. Procedura klasyfikacji wykorzystuje zaawansowane modele sieci neuronowych, w tymwielowarstwowy perceptron, pamięć długoterminową i krótkoterminową, rekurencyjną sieć neuronową i Gated Recurrent Unit. Modele te są trenowane na zebranych danych w celu dokładnego przewidywania wagi wypadków drogowych. Projekt ma wnieść istotny wkład w sugerowanie proaktywnych środków i polityk mających na celu zmniejszenie dotkliwości i częstotliwości wypadków drogowych w Andhra Pradesh.

5

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

|

2023

|

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.

6

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

|

2023

|

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.

7

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

|

2022

|

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.

8

Comparison of MLP and RBF Neural Networks in the Task of Classifying the Diameters of Water Pipes

Gvishiani Zurab, Dawidowicz Jacek

Rocznik Ochrona Środowiska

|

2022

|

Tom 24

505--519

EN

Hydraulic calculations of water distribution systems are currently performed using computer programs. In addition to the basic calculation procedure, modules responsible for evaluating the obtained calculation results are introduced more and more often into the programs. This article presents the results of research on artificial neural networks with a radial base function (RBF) and a multilayer perceptron (MLP), aimed at determining whether they can be used to model the relationship between the variables describing the computational section of the water distribution system and the diameter of the water pipe. The classification capabilities of the RBF and MLP networks were analyzed according to the number of neurons in the hidden layer of the network. A comparative analysis of RBF networks with multilayer perceptron (MLP) networks was performed. The results showed that the MLP networks have much better classification properties and are better suited for the task of assessing the selected diameters of the water pipes.

9

Cat swarm optimization algorithm tuned multilayer perceptron for stock price prediction

Chandar S. Kumar, Punjabi Hitesh

Computer Assisted Methods in Engineering and Science

|

2022

|

Vol. 29, no. 1-2 spec.

145--160

EN

Due to the nonlinear and dynamic nature of stock data, prediction is one of the mostchallenging tasks in the financial market. Nowadays, soft and bio-inspired computing algorithms are used to forecast the stock price. This article assesses the efficiency of thehybrid stock prediction model using the multilayer perceptron (MLP) and cat swarm optimization (CSO) algorithm. The CSO algorithm is a bio-inspired algorithm inspired bythe behavior traits of cats. CSO is employed to find the appropriate value of MLP parameters. Technical indicators calculated from historical data are used as input variablesfor the proposed model. The model’s performance is validated using historical data notused for training. The model’s prediction efficiency is evaluated in terms of MSE, MAPE, RMSE and MAE. The model’s results are compared with other models optimized byvarious bio-inspired algorithms presented in the literature to prove its efficiency. The empirical findings confirm that the proposed CSO-MLP prediction model provides the bestperformance compared to other models taken for analysis.

10

Artificial Intelligence Based Flood Forecasting for River Hunza at Danyor Station in Pakistan

Yaseen Muhammad Waseem, Awais Muhammad, Riaz Khuram, Rasheed Muhammad Babar, Waqar Muhammad, Rasheed Sajid

Archives of Hydro-Engineering and Environmental Mechanics

|

2022

|

Vol. 69, nr 1

59--77

EN

Floods can cause significant problems for humans and can damage the economy. Implementing a reliable flood monitoring warning system in risk areas can help to reduce the negative impacts of these natural disasters. Artificial intelligence algorithms and statistical approaches are employed by researchers to enhance flood forecasting. In this study, a dataset was created using unique features measured by sensors along the Hunza River in Pakistan over the past 31 years. The dataset was used for classification and regression problems. Two types of machine learning algorithms were tested for classification: classical algorithms (Random Forest, RF and Support Vector Classifier, SVC) and deep learning algorithms (Multi-Layer Perceptron, MLP). For the regression problem, the result of MLP and Support Vector Regression (SVR) algorithms were compared based on their mean square, root mean square and mean absolute errors. The results obtained show that the accuracy of the RF classifier is 0.99, while the accuracies of the SVC and MLP methods are 0.98; moreover, in the case of flood prediction, the SVR algorithm outperforms the MLP approach.

11

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

|

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.

12

Machine learning methods applied to sea level predictions in the upper part of a tidal estuary

Guillou Nicolas, Chapalain Georges

Oceanologia

|

2021

|

No. 63 (4)

531--544

EN

Sea levels variations in the upper part of estuary are traditionally approached by relying on refined numerical simulations with high computational cost. As an alternative efficient and rapid solution, we assessed here the performances of two types of machine learning algorithms: (i) multiple regression methods based on linear and polynomial regression functions, and (ii) an artificial neural network, the multilayer perceptron. These algorithms were applied to three-year observations of sea levels maxima during high tides in the city of Landerneau, in the upper part of the Elorn estuary (western Brittany, France). Four input variables were considered in relation to tidal and coastal surge effects on sea level: the French tidal coefficient, the atmospheric pressure, the wind velocity and the river discharge. Whereas a part of these input variables derived from large-scale models with coarse spatial resolutions, the different algorithms showed good performances in this local environment, thus being able to capture sea level temporal variations at semi-diurnal and spring-neap time scales. Predictions improved furthermore the assessment of inundation events based so far on the exploitation of observations or numerical simulations in the downstream part of the estuary. Results obtained exhibited finally the weak influences of wind and river discharges on inundation events.

13

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

|

2021

|

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.

14

Hybrid predictions of the homogenous properties’ market value with the use of ann

Anysz Hubert, Podwórna Monika, Ibadov Nabi, Lennerts Kunibert, Dikarev Kostiantyn

Archives of Civil Engineering

|

2021

|

Vol. 67, nr 1

285--301

EN

The homogenous properties – as flats are – have the set of key features that characterizes them. The area of a flat, the number of rooms and storey number where it is located, the technical state of a building, and the state of the vicinity of the blocks of flats assessed. The database comprises 222 flats with their transaction prices on the secondary estate market. The analysed flats are located in a certain quarter of Wrocław city in Poland. The database is large enough to apply machine learning for successful price predictions. Their close locations significantly lower the influence of clients’ assessments of the attractiveness of the location on the flat’s price. The hybrid approach is applied, where classifying precedes the solution of the regression problem. Dependently on the class of flats, the mean absolute percentage error achieved through the calculations presented in the article varies from 4,4 % to 7,8 %. In the classes of flats where the number of cases doesn’t allow for machine predicting, multivariate linear regression is applied. The reliable use of machine learning tools has proved that the automated valuation of homogenous types of properties can produce price predictions with the error low enough for real applications.

PL

Wycena nieruchomości jest złożonym procesem. Rzeczoznawca majątkowy musi być biegły zarówno w naukach ekonomicznych, prawnych, jak i technicznych. W praktyce często zdarzają się przypadki, w których konieczne jest poznanie zakresu wartości nieruchomości w krótkim czasie. Zautomatyzowane modele wyceny (AVM) są kwestionowane przez praktyków, ale nie oznacza to, że nie należy szukać nowych metod wyceny, innych niż te określone w Rozporządzeniu Rady Ministrów z dnia 21 września 2004 r. w sprawie wyceny nieruchomości i sporządzania operatu szacunkowego. Do określenia wartości rynkowej nieruchomości zdefiniowanej w Ustawie z dnia 21 sierpnia 1997 r o gospodarce nieruchomościami, jako „szacunkowa kwota, jaką w dniu wyceny można uzyskać za nieruchomość w transakcji sprzedaży zawieranej na warunkach rynkowych pomiędzy kupującym a sprzedającym, którzy mają stanowczy zamiar zawarcia umowy, działają z rozeznaniem i postępują rozważnie oraz nie znajdują się w sytuacji przymusowej”, najczęściej stosowaną metodą wyceny jest podejście porównawcze polegające na szacowaniu wartości na podstawie ostatnich danych sprzedaży innych podobnych nieruchomości na rynku lokalnym. Takie podejście wymaga aktywnego, rozwiniętego oraz w miarę stabilnego rynku. Rzeczoznawca majątkowy analizuje ceny transakcyjne nieruchomości, które w wystarczającym stopniu są podobne do nieruchomości wycenianej. Analiza atrybutów nieruchomości polega na badaniu nieruchomości pod względem trwałych cech, które mają znaczący wpływ na wartość, w szczególności lokalizację obiektu, jego powierzchnię, położenie w budynku, stan techniczny. W pracy przenalizowano próbkę 222 nieruchomości lokalowych, które były przedmiotem obrotu na wrocławskim rynku wtórnym. Lokalny rynek nieruchomości przyjęto jako nieruchomości lokalowe o powierzchni użytkowej z przedziału od 15 do 95 m2, w budynkach o stanie dobry lub średnim, z obrębu Grabiszyn dzielnicy Fabryczna miasta Wrocław. W pracy przyjęto dwuletni okres analizy, ze względu na w miarę stabilny rynek w okresie 2013-2014 nie uwzględniono czynnika czasu - przyjęto zerowy trend czasowy dla transakcji wolnorynkowych.

15

Przekształcenie wskazania analogowego przyrządu pomiarowego na postać cyfrową

Luśtyk Dariusz

Metrologia i Probiernictwo : biuletyn Głównego Urzędu Miar

|

2020

|

nr 2 (25)

47--54

PL

W artykule przedstawiono sposób wyodrębnienia obrazu podzielni analogowego przyrządu pomiarowego uzyskanego z kamery oraz metodę przetwarzania jego wskazania na postać cyfrową z zastosowaniem perceptronu wielowarstwowego. W pracy przedstawiono wyniki skuteczności odczytu wskazań analogowych z wykorzystaniem programu implementującego proponowaną metodę.

EN

The article presents a method of extracting the image of an analog measuring instrument scale obtained from a camera, and the method of its processing into a digital form of an indication using a multilayer perceptron. The paper presents the results of the effectiveness of reading analog indication with the use of a program implementing the proposed method.

16

Solar air heater performance prediction using artificial neural network technique with relevant input variables

Ghritlahre Harish Kumar, Chandrakar Purvi, Ahmad Ashfaque

Archives of Thermodynamics

|

2020

|

Vol. 41, no 3

255--282

EN

Solar air heater (SAH) is an important device for solar energy utilization which is used for space heating, crop drying, timber seasoning etc. Its performance mainly depends on system parameters, operating parameters and meteorological parameters. Many researchers have been used these parameters to predict the performance of SAH by analytical or conventional approach and artificial neural network (ANN) technique, but performance prediction of SAH by using relevant input parameters has not been done so far. Therefore, relevant input parameters have been considered in this study. Total ten parameters were used such as mass flow rate, ambient temperature, wind speed, relative humidity, fluid inlet temperature, fluid mean temperature, plate temperature, wind direction, solar elevation and solar intensity to find out the relevant parameters for ANN prediction. Seven different neural models have been constructed using these parameters. In each model 10 to 20 neurons have been selected to find out the optimal model. The optimal neural models for ANN-I, ANN-II, ANN-III, ANN-IV, ANN-V, ANN-VI and ANN-VII were obtained as 10-17-1, 8-14-1, 6-16-1, 5- 14-1, 4-17-1, 3-16-1 and 2-14-1, respectively. It has been found that ANN-II model with 8-14-1 is the optimal model as compared to other neural models. Values of the sum of squared errors, mean relative error, and coefficient of determination were found to be 0.02138, 1.82% and 0.99387, respectively, which shows that the ANN-II developed with mass flow rate, ambient temperature, inlet and mean temperature of air, plate temperature, wind speed and direction, relative humidity, and relevant input parameters performed better. The above results show that these eight parameters are relevant for prediction.

17

The Analytical and Artificial Intelligence Methods to Investigate the Effects of Aperture Dimension Ratio on Electrical Shielding Effectiveness

Basyigit Ibrahim Bahadir, Dogan Habib

International Journal of Electronics and Telecommunications

|

2019

|

Vol. 65, No. 3

359--365

EN

This paper presents that the effect of single aperture size of metallic enclosure on electrical shielding effectiveness (ESE) at 0 – 1 GHz frequency range has been investigated by using both Robinson’s analytical formulation and artificial neural networks (ANN) methods that are multilayer perceptron (MLP) networks and a radial basis function neural network (RBFNN). All results including measurement have been compared each other in terms of aperture geometry of metallic enclosure. The geometry of single aperture varies from square to rectangular shape while the open area of aperture is fixed. It has been observed that network structure of MLP 3-40-1 in modeling with ANN modeled with fewer neurons in the sense of overlapping of faults and data and modeled accordingly. In contrast, the RBFNN 3-150-1 is the other detection that the network structure is modeled with more neurons and more. It can be seen from the same network-structured MLP and RBFNN that the MLP modeled better. In this paper, the impact of dimension of rectangular aperture on shielding performance by using RBFNN and MLP network model with ANN has been studied, as a novelty.

18

Use of data mining techniques to classify length of stay of emergency department patients

Sariyer Görkem, Taşar Ceren Öcal, Cepe Gizem Ersoy

Bio-Algorithms and Med-Systems

|

2019

|

Vol. 15, no. 1

art. no. 20180044

EN

Emergency departments (EDs) are the largest departments of hospitals which encounter high variety of cases as well as high level of patient volumes. Thus, an efficient classification of those patients at the time of their registration is very important for the operations planning and management. Using secondary data from the ED of an urban hospital, we examine the significance of factors while classifying patients according to their length of stay. Random Forest, Classification and Regression Tree, Logistic Regression (LR), and Multilayer Perceptron (MLP) were adopted in the data set of July 2016, and these algorithms were tested in data set of August 2016. Besides adopting and testing the algorithms on the whole data set, patients in these sets were grouped into 21 based on the similarities in their diagnoses and the algorithms were also performed in these subgroups. Performances of the classifiers were evaluated based on the sensitivity, specificity, and accuracy. It was observed that sensitivity, specificity, and accuracy values of the classifiers were similar, where LR and MLP had somehow higher values. In addition, the average performance of the classifying patients within the subgroups outperformed the classifying based on the whole data set for each of the classifiers.

19

Attribute selection for stroke prediction

Zdrodowska Małgorzata

Acta Mechanica et Automatica

|

2019

|

Vol. 13, no. 3

200--204

EN

Stroke is the third most common cause of death and the most common cause of long-term disability among adults around theworld. Therefore, stroke prediction and diagnosis is a very important issue. Data mining techniques come in handy to help determine the correlations between individual patient characterisation data, that is, extract from the medical information system the knowledge necessary to predict and treat various diseases. The study analysed the data of patients with stroke using eight known classification algorithms (J48 (C4.5), CART, PART, naive Bayes classifier, Random Forest, Supporting Vector Machine and neural networks Multilayer Perceptron), which allowed to build an exploration model given with an accuracy of over 88%. The potential features of patients, which may be factors that increase the risk of stroke, were also indicated.

20

Diagnozowanie uszkodzeń łożysk tocznych z wykorzystaniem wybranych klasyfikatorów

Piekoszewski J.

Autobusy : technika, eksploatacja, systemy transportowe

|

2018

|

R. 19, nr 12

597--601

PL

Niewielkie uszkodzenie łożysk tocznych może prowadzić do poważnej awarii urządzenia. Zatem, bardzo ważnym jest wykrycie takich defektów na ich początkowym etapie powstawania aby zapobiec dalszym uszkodzeniom. W pracy przedstawiono kilka wybranych teoretycznych narzędzi z obszaru sztucznej inteligencji zastosowanych do rozwiązania problemu diagnozowania uszkodzeń łożysk tocznych. Rozważanymi narzędziami są: algorytm k najbliższych sąsiadów, drzewo decyzyjne, maszyna wektorów podpierających, perceptron wielowarstwowy, sieć bayesowska oraz sieć neuronowa o radialnych funkcjach bazowych. Rezultaty wszystkich eksperymentów zostały otrzymane z wykorzystaniem rzeczywistych danych oraz aplikacji WEKA (ang. Waikato Environment for Knowledge Analysis) dostępnej na stronach Uniwersytetu Waikato w Nowej Zelandii.

EN

Minor roller bearing damage may lead to serious failures of the device. Thus, it is very important to detect such damage as early as possible to prevent further damage. This paper presents a selection of several theoretical tools from the field of artificial intelligence and their application in roller bearings fault classification. The considered tools are: k-nearest neighbor algorithm, decision tree, support vector machine, feed forward neural network (multilayer perceptron), Bayesian network and neural network with radial basis functions. All numerical experiments presented in the paper were performed with the use of real - world dataset and WEKA (Waikato Environment for Knowledge Analysis) software, available at the server of the University of Waikato.