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Applied Computer Science Volume 18, Number 4, 2022

APPLICATION OF GILLESPIE ALGORITHM FOR SIMULATING EVOLUTION OF FITNESS OF MICROBIAL POPULATION

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In this study we present simulation system based on Gillespie algorithm for generating evolutionary events in the evolution scenario of microbial population. We present Gillespie simulation system adjusted to reproducing experimental data obtained in barcoding studies – experimental techniques in microbiology allowing tracing microbial populations with very high resolution. Gillespie simulation engine is constructed by defining its state vector and rules for its modifications. In order to efficiently simulate barcoded experiment by using Gillespie algorithm we provide modification - binning cells by lineages. Different bins define components of state in the Gillespie algorithm. The elaborated simulation model captures events in microbial population growth including death, division and mutations of cells. The obtained simulation results reflect population behavior, mutation wave and mutation distribution along generations. The elaborated methodology is confronted against literature data of experimental evolution of yeast tracking clones sub-generations. Simulation model was fitted to measurements in experimental data leading to good agreement.

  • APA 7th style
Gil, J., & Polański, A. (2022). Application of gillespie algorithm for simulating evolution of fitness of microbial population. Applied Computer Science, 18(4), 5-15. https://doi.org/10.35784/acs-2022-25
  • Chicago style
Gil, Jarosław, and Andrzej Polański. "Application of gillespie algorithm for simulating evolution of fitness of microbial population." Applied Computer Science 18, no. 4 (2022): 5-15.
  • IEEE style
J. GIl, and A. Polański, "Application of gillespie algorithm for simulating evolution of fitness of microbial population," Applied Computer Science, vol. 18, no. 4, pp.5-15, 2022, doi: 10.35784/acs-2022-25.
  • Vancouver style
Gil J, Polański A. Application of gillespie algorithm for simulating evolution of fitness of microbial population. Applied Computer Science. 2022;18(4):5-15.

HOW MACHINE LEARNING ALGORITHMS ARE USED IN METEOROLOGICAL DATA CLASSIFICATION: A COMPARATIVE APPROACH BETWEEN DT, LMT, M5-MT, GRADIENT BOOSTING AND GWLM-NARX MODELS

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Rainfall prediction is one of the most challenging task faced by researchers over the years. Many machine learning and AI based algorithms have been implemented on different datasets for better prediction purposes, but there is not a single solution which perfectly predicts the rainfall. Accurate prediction still remains a question to researchers. We offer a machine learning-based comparison evaluation of rainfall models for Kashmir province. Both local geographic features and the time horizon has influence on weather forecasting. Decision trees, Logistic Model Trees (LMT), and M5 model trees are examples of predictive models based on algorithms. GWLM-NARX, Gradient Boosting, and other techniques were investigated. Weather predictors measured from three major meteorological stations in the Kashmir area of the UT of J&K, India, were utilized in the models. We compared the proposed models based on their accuracy, kappa, interpretability, and other statistics, as well as the significance of the predictors utilized. On the original dataset, the DT model delivers an accuracy of 80.12 percent, followed by the LMT and Gradient boosting models, which produce accuracy of 87.23 percent and 87.51 percent, respectively. Furthermore, when continuous data was used in the M5-MT and GWLM-NARX models, the NARX model performed better, with mean squared error (MSE) and regression value (R) predictions of 3.12 percent and 0.9899 percent in training, 0.144 percent and 0.9936 percent in validation, and 0.311 percent and 0.9988 percent in testing.

  • APA 7th style
Fayaz, S. A., Zaman, M., Butt, M. A., & Kaul, S. (2022). How machine learning algorithms are used in meteorological data classification: a comparative approach between DT, LMT, M5-MT, Gradient Boosting and GWLM-NARX models. Applied Computer Science, 18(4), 16-27. https://doi.org/10.35784/acs-2022-26
  • Chicago style
Fayaz, Sheikh Amir, Majid Zaman, Muheet Ahmed Butt, and Sameer Kaul. "How machine learning algorithms are used in meteorological data classification: a comparative approach between DT, LMT, M5-MT, Gradient Boosting and GWLM-NARX models." Applied Computer Science 18, no. 4 (2022): 16-27.
  • IEEE style
S. A. Fayaz, M. Zaman, M. A. Butt, and S. Kaul, " How machine learning algorithms are used in meteorological data classification: a comparative approach between DT, LMT, M5-MT, Gradient Boosting and GWLM-NARX models," Applied Computer Science, vol. 18, no. 4, pp.16-27, 2022, doi: 10.35784/acs-2022-26.
  • Vancouver style
Fayaz SA, Zaman M, Butt MA, Kaul S. How machine learning algorithms are used in meteorological data classification: a comparative approach between DT, LMT, M5-MT, Gradient Boosting and GWLM-NARX models. Applied Computer Science. 2022;18(4):16-27.

DETERMINING THE DEGREE OF PLAYER ENGAGEMENT IN A COMPUTER GAME WITH ELEMENTS OF A SOCIAL CAMPAIGN USING COGNITIVE NEUROSCIENCE TECHNIQUES

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Due to the popularity of video games in various applications, including both commercial and social marketing, there is a need to assess their content in terms of player satisfaction, already at the production stage. For this purpose, the indices used in EEG tests can be used. In this publication, a formula has been created based on the player's commitment to determining which elements in the game should be improved and for which graphic emblems connected with social campaigns were more memorable and whether this was related to commitment. The survey was conducted using a 2D platform game created in Unity based on observations of 28 recipients. To evaluate the elements occurring in the game at which we obtain a higher memory for graphic characters, a corresponding pattern was created based on player involvement. The optimal Index for moving and static objects and the Index for destruction were then selected based on the feedback. Referring to the issue of graphic emblems depicting social campaigns should be placed in a place where other activities such as fighting will not be distracted, everyone will be able to reach the level where the recently placed advertisement is. This study present the developed method to determine the degree of player's engagement in particular elements in the game using the EEG and to explore the relationship between the visibility of social advertising and engagement in a 2D platform game where the player has to collect three keys and defeat the ultimate opponent. 

  • APA 7th style
Biercewicz, K., Borawski, M., Borawska, A., & Duda, J. (2022). Determining the degree of player engagement in a computer game with elements of a social campaign using cognitive neuroscience techniques. Applied Computer Science, 18(4), 28-52. https://doi.org/10.35784/acs-2022-27
  • Chicago style
Biercewicz, Konrad, Mariusz Borawski, Anna Borawska, and Jarosław Duda. "Determining the degree of player engagement in a computer game with elements of a social campaign using cognitive neuroscience techniques." Applied Computer Science 18, no. 4 (2022): 28-52.
  • IEEE style
K. Biercewicz, M. Borawski, A. Borawska, and J. Duda, "Determining the degree of player engagement in a computer game with elements of a social campaign using cognitive neuroscience techniques," Applied Computer Science, vol. 18, no. 4, pp.28-52, 2022, doi: 10.35784/acs-2022-27. 
  • Vancouver style
Biercewicz K, Borawski M, Borawska A, Duda J. Determining the degree of player engagement in a computer game with elements of a social campaign using cognitive neuroscience techniques. Applied Computer Science. 2022;18(4):28-52.

ANALYSIS OF THE POSSIBILITY OF USING THE SINGULAR VALUE DECOMPOSITION IN IMAGE COMPRESSION

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In today’s highly computerized world, data compression is a key issue to minimize the costs associated with data storage and transfer. In 2019, more than 70% of the data sent over the network were images. This paper analyses the feasibility of using the SVD algorithm in image compression and shows that it improves the efficiency of JPEG and JPEG2000 compression. Image matrices were decomposed using the SVD algorithm before compression. It has also been shown that as the image dimensions increase, the fraction of eigenvalues that must be used to reconstruct the image in good quality decreases. The study was carried out on a large and diverse set of images, more than 2500 images were examined. The results were analyzed based on criteria typical for the evaluation of numerical algorithms operating on matrices and image compression: compression ratio, size of compressed file, MSE, number of bad pixels, complexity, numerical stability, easiness of implementation. 

  • APA 7th style
Łukasik, E., & Łabuć, E. (2022) Analysis of the possibility of using the singular value decomposition in image compression. Applied Computer Science, 18(4), 53-67. https://doi.org/10.35784/acs-2022-28
  • Chicago style
Łukasik, Edyta, and Emilia Łabuć. "Analysis of the possibility of using the singular value decomposition in image compression." Applied Computer Science 18, no. 4 (2022): 53-67.
  • IEEE style
E. Łukasik, and E. Łabuć, "Analysis of the possibility of using the singular value decomposition in image compression," Applied Computer Science, vol. 18, no. 4, pp.53-67, 2022, doi: 10.35784/acs-2022-28.
  • Vancouver style
Łukasik E, Łabuć E. Analysis of the possibility of using the singular value decomposition in image compression. Applied Computer Science. 2022;18(4):53-67.

PREDICTION OF THE COMPRESSIVE STRENGTH OF ENVIRONMENTALLY FRIENDLY CONCRETE USING ARTIFICIAL NEURAL NETWORK

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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. 

  • APA 7th style
Kulisz, M., Kujawska, J., Aubakirova, Z., Zhairbaeva, G., & Warowny, T. (2022). Prediction of the compressive strength of environmentally friendly concrete using artificial neural network. Applied Computer Science, 18(4), 68-81. https://doi.org/10.35784/acs-2022-29
  • Chicago style
Kulisz, Monika, Justyna Kujawska, Zulfiya Aubakirova, Gulnaz Zhairbaeva, and Tomasz Warowny. "Prediction of the compressive strength of environmentally friendly concrete using artificial neural network." Applied Computer Science 18, no. 4 (2022): 68-81.
  • IEEE style
M. Kulisz, J. Kujawska, Z. Aubakirova, G. Zhairbaeva, and T. Warowny, "Prediction of the compressive strength of environmentally friendly concrete using artificial neural network," Applied Computer Science, vol. 18, no. 4, pp.68-81, 2022, doi: 10.35784/acs-2022-29.
  • Vancouver style
Kulisz M, Kujawska J, Aubakirova Z, Zhairbaeva G, Warowny T. Prediction of the compressive strength of environmentally friendly concrete using artificial neural network. Applied Computer Science. 2022;18(4):68-81.

NUMERICAL AND EXPERIMENTAL ANALYSIS OF A CENTRIFUGAL PUMP WITH DIFFERENT ROTOR GEOMETRIES

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The paper presents a comparative analysis of the operation of two variants of centrifugal pump rotors, a description of the main parameters, and the influence of the blade geometry on the performance characteristics obtained. Rotors have been designed using the arc and point method. Based on the developed 3D CAD models, the rotors were printed using the rapid prototyping method on a 3D printer in FFF (Fused Filament Fabrication) technology, in order to experimentally verify the performance, by placing them on the Armfield FM50 test stand. The analysis part of the CFD includes a fluid flow in Ansys Fluent. The process of creating a flow domain and generating a structural mesh was described, along with the definition of boundary conditions, the definition of physical conditions and the turbulence model. The distribution of pressures and velocities in the meridional sections is shown graphically. The chapter with the experimental analysis contains a description of the measuring stand and the methodology used. The results obtained made it possible to generate the characteristics, making it possible to compare the results received. The results allowed to note the influence of geometry on the behavior of the rotors during operation in the system and to indicate that the arc rotor gets a 7% higher head and 2% higher efficiency than the point method rotor, which gives the basis for its commercial use in industry.

  • APA 7th style
Semkło, Ł., & Gierz, Ł. (2022). Numerical and experimental analysis of a centrifugal pump with different rotor geometries. Applied Computer Science, 18(4), 82-95. https://doi.org/10.35784/acs-2022-30
  • Chicago style
Semkło, Łukasz, and Łukasz Gierz. "Numerical and experimental analysis of a centrifugal pump with different rotor geometries." Applied Computer Science 18, no. 4 (2022): 82-95.
  • IEEE style
Ł. Semkło, and Ł. Gierz, "Numerical and experimental analysis of a centrifugal pump with different rotor geometries," Applied Computer Science, vol. 18, no. 4, pp.82-95, 2022, doi: 10.35784/acs-2022-30.
  • Vancouver style
Semkło Ł, Gierz Ł. Numerical and experimental analysis of a centrifugal pump with different rotor geometries. Applied Computer Science. 2022;18(4):82-95.

A COUGH-BASED COVID-19 DETECTION SYSTEM USING PCA AND MACHINE LEARNING CLASSIFIERS

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In 2019, the whole world is facing a health emergency due to the emergence of the coronavirus (COVID-19). About 223 countries are affected by the coronavirus. Medical and health services face difficulties to manage the disease, which requires a significant amount of health system resources. Several artificial intelligence-based systems are designed to automatically detect COVID-19 for limiting the spread of the virus. Researchers have found that this virus has a major impact on voice production due to the respiratory system's dysfunction. In this paper, we investigate and analyze the effectiveness of cough analysis to accurately detect COVID-19. To do so, we performed binary classification, distinguishing positive COVID patients from healthy controls. The records are collected from the Coswara Dataset, a crowdsourcing project from the Indian Institute of Science (IIS). After data collection, we extracted the MFCC from the cough records. These acoustic features are mapped directly to the Decision Tree (DT), k-nearest neighbor (kNN) for k equals to 3, support vector machine (SVM), and deep neural network (DNN), or after a dimensionality reduction using principal component analysis (PCA), with 95 percent variance or 6 principal components. The 3NN classifier with all features has produced the best classification results. It detects COVID-19 patients with an accuracy of 97.48 percent, 96.96 percent f1-score, and 0.95 MCC. Suggesting that this method can accurately distinguish healthy controls and COVID-19 patients.

  • APA 7th style
Benmalek, E., El Mhamdi, J., Jilbab, A., & Jbari, A. (2022). A cough-based Covid-19 detection system using PCA and machine learning classifiers. Applied Computer Science, 18(4), 96-115. https://doi.org/10.35784/acs-2022-31
  • Chicago style
Benmalek, Elmehdi, Jamal El Mhamdi, Abdelilah Jilbab, and Atman Jbari. "A cough-based Covid-19 detection system using PCA and machine learning classifiers." Applied Computer Science 18, no. 4 (2022): 96-115.
  • IEEE style
E. Benmalek, J. El Mhamdi, A. Jilbab, and A. Jbari, "A cough-based Covid-19 detection system using PCA and machine learning classifiers," Applied Computer Science, vol. 18, no. 4, pp.96-115, 2022, doi: 10.35784/acs-2022-31.
  • Vancouver style
Benmalek E, El Mhamdi J, Jilbab A, Jbari A. A cough-based Covid-19 detection system using PCA and machine learning classifiers. Applied Computer Science. 2022;18(4):96-115.

IDENTIFICATION OF THE IMPACT OF THE AVAILABILITY FACTOR ON THE EFFICIENCY OF PRODUCTION PROCESSES USING THE AHP AND FUZZY AHP METHODS

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Maintenance has a key impact on the efficiency of the production processes because the efficiency of the machines determines the ability of the system to produce in accordance with the assumed schedule. The key element of the system performance assessment remains the availability of technological equipment, which directly translates into the efficiency and effectiveness of the performed production tasks. Taking into account the dynamic nature of manufacturing processes, the proper selection of machinery and equipment for the implementation of specific production tasks becomes an issue of particular importance. The purpose of this research was  to determine the impact of technical and non-technical factors on the material selection of machine tools for production tasks and to develop a method of supporting the selection of production resources using the AHP and Fuzzy AHP methods. The research was carried out in a manufacturing company from the automotive industry.

  • APA 7th style
Wittbrodt, P., Łapuńka, I., Baytikenova, G., Gola, A., & Zakimova, A. (2022). Identification of the impact of the availability factor on the efficiency of production processes using the AHP and fuzzy AHP methods. Applied Computer Science, 18(4), 116-129. https://doi.org/10.35784/acs-2022-32
  • Chicago style
Wittbrodt, Piotr, Iwona Łapuńka, Gulzhan Baytikenova, Arkadiusz Gola, and Alfiya Zakimova. "Identification of the impact of the availability factor on the efficiency of production processes using the AHP and fuzzy AHP methods." Applied Computer Science 18, no. 4 (2022): 116-129.
  • IEEE style
P. Wittbrodt, I. Łapuńka, G. Baytikenova, A. Gola, and A. Zakimova, "Identification of the impact of the availability factor on the efficiency of production processes using the AHP and fuzzy AHP methods," Applied Computer Science, vol. 18, no. 4, pp.116-129, 2022, doi: 10.35784/acs-2022-32.
  • Vancouver style
Wittbrodt P, Łapuńka I, Baytikenova G, Gola A, Zakimova A. Identification of the impact of the availability factor on the efficiency of production processes using the AHP and fuzzy AHP methods. Applied Computer Science. 2022;18(4):116-129.