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Applied Computer Science Volume 19, Number 4, 2023

ENHANCING THE EFFICIENCY OF THE LEVENSHTEIN DISTANCE-BASED HEURISTIC METHOD OF ARRANGING 2D APICTORIAL ELEMENTS FOR INDUSTRIAL APPLICATIONS

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The article addresses the challenge of reconstructing 2D broken pictorial objects by automating the search for matching elements, which is particularly relevant in fields like archaeology and forensic science. The authors propose a method to match such elements and streamline the search process by detecting and filtering out low quality matches. The study delves into optimizing the search process in terms of duration and assembly quality. It examines factors like comparison window length, Levenshtein measure margin, and number of variants to check, using theoretical calculations and experiments on synthetic elements. The experimental results demonstrate enhanced method effectiveness, yielding more useful solutions and significantly reducing the complexity of element comparisons by up to 100 times in extreme cases.

  • APA 7th style
Skulimowski, S., Montusiewicz, J., & Badurowicz, M. (2023). Enhancing the efficiency of the Levenshtein distance based heuristic method of arranging 2D pictorial elements for industrial applications. Applied Computer Science, 19(4), 1–13. https://doi.org/10.35784/acs-2023-31
  • Chicago style
Skulimowski, Stanisław, Jerzy Montusiewicz, and Marcin Badurowicz.  „Enhancing the Efficiency of the Levenshtein Distance Based Heuristic Method of Arranging 2D Pictorial Elements for Industrial Applications." Applied Computer Science 19, no. 4 (2023): 1–13.
  • IEEE style
S. Skulimowski, J. Montusiewicz, and M. Badurowicz, „Enhancing the efficiency of the Levenshtein distance based heuristic method of arranging 2D pictorial elements for industrial applications,” Applied Computer Science , vol. 19, no. 4, pp. 1–13, 2023, doi: 10.35784/acs-2023-31.
  • Vancouver style
Skulimowski S, Montusiewicz J, Badurowicz M. Enhancing the efficiency of the Levenshtein distance based heuristic method of arranging 2D pictorial elements for industrial applications. Applied Computer Science. 2023;19(4):1–13.

AUTOMATIC IDENTIFICATION OF DYSPHONIAS USING MACHINE LEARNING ALGORITHMS

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Dysphonia is a prevalent symptom of some respiratory diseases that affects voice quality, even for prolonged periods. For its diagnosis, speech-language pathologists make use of different acoustic parameters to perform objective evaluations on patients and determine the type of dysphonia that affects them, such as hyperfunctional and hypofunctional dysphonia, which is important because each type requires a different treatment. In the field of artificial intelligence this problem has been addressed through the use of acoustic parameters that are used as input data to train machine learning and deep learning models. However, its purpose is usually to identify whether a patient is ill or not, making binary classifications between healthy voices and voices with dysphonia, but not between dysphonias. In this paper, harmonic-to-noise ratio, cepstral peak prominence-smoothed, zero crossing rate and the means of the Mel frequency cepstral coefficients (2-19) are used to make multiclass classification of voices with euphony, hyperfunction and hypofunction by means of six machine learning algorithms, which are: Random Forest, K nearest neighbors, Logistic regression, Decision trees, Support vector machines and Naive Bayes. In order to evaluate which of them presents a better performance to identify the three voice classes, bootstrap.632 was used. It is concluded that the best confidence interval ranges from 87% to 92%, in terms of accuracy for the K Nearest Neighbors model. Results can be implemented in the development of a complementary application for the clinical diagnosis or monitoring of a patient under the supervision of a specialist.

  • APA 7th style
Bello Rivera, M. A., Reyes García, C. A., Talavera Rojas, T. C., Quintero Flores, P. M., & Pérez Loaiza, R. E. (2023). Automatic identification of dysphonias using machine learning algorithms. Applied Computer Science, 19(4), 14–25. https://doi.org/10.35784/acs-2023-32
  • Chicago style
Bello Rivera, Miguel Angel, Carlos Alberto Reyes García, Tania Cristal Talavera Rojas, Perfecto Malaquías Quintero Flores, and Rodolfo Eleazar Pérez Loaiza.  „Automatic Identification of Dysphonias Using Machine Learning Algorithms." Applied Computer Science 19, no. 4 (2023): 14–25.
  • IEEE style
M. A. Bello Rivera, C. A. Reyes García, T. C. Talavera Rojas, P. M. Quintero Flores, and R. E. Pérez Loaiza, „Automatic identification of dysphonias using machine learning algorithms,” Applied Computer Science , vol. 19, no. 4, pp. 14–25, 2023, doi: 10.35784/acs-2023-32.
  • Vancouver style
Bello Rivera MA, Reyes García CA, Talavera Rojas TC, Quintero Flores PM, Pérez Loaiza RE. Automatic identification of dysphonias using machine learning algorithms. Applied Computer Science. 2023;19(4):14–25.

COMPUTATIONAL ANALYSIS OF PEM FUEL CELL UNDER DIFFERENT OPERATING CONDITIONS

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PEM fuel cells are one of the most promising sources of electrical energy and also have interesting properties. This research is purely theoretical and based on ANSYS Fluent software. Thus, the next step of the research should be the comparison of the solutions to other models and experimental results. The PEM fuel cell can be used as an energy source in the near future in a much more common way, although there are few modifications required, such as increasing efficiency and reducing production costs.In general, a three-dimensional steady-state model of the polymer electrolyte membrane fuel cell implemented in Fluent was used to study a single channel flow inside such a PEMFC. The analysis concerns an aspect, that seems to be overlooked in this type of analysis, namely the influence of the substrate flow rate on the quality and efficiency of the chemical reaction, and thus on the value of the generated current for a given voltage. It is clearly visible that there is a rather narrow range in the amount of hydrogen fuel fed that is optimal for a given fuel cell. Such theoretical research is very useful and very much needed to design a new PEM fuel cells, utilizing Computational Fluid Dynamics (CFD) tool to statically monitor its performance for different boundary conditions.

  • APA 7th style
Sederyn, T., & Skawińska, M. (2023). Computational analysis of PEM fuel cell under different operating conditions. Applied Computer Science, 19(4), 26–38. https://doi.org/10.35784/acs-2023-33
  • Chicago style
Sederyn, Tomasz, and Małgorzata Skawińska.  „Computational Analysis of PEM Fuel Cell under Different Operating Conditions." Applied Computer Science 19, no. 4 (2023): 26–38.
  • IEEE style
T. Sederyn and M. Skawińska, „Computational analysis of PEM fuel cell under different operating conditions,” Applied Computer Science , vol. 19, no. 4, pp. 26–38, 2023, doi: 10.35784/acs-2023-33.
  • Vancouver style
Sederyn T, Skawińska M. Computational analysis of PEM fuel cell under different operating conditions. Applied Computer Science. 2023;19(4):26–38.

IMPROVING MATERIAL REQUIREMENTS PLANNING THROUGH WEB-BASED APPLICATION: A CASE STUDY THAILAND SMEs

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In the small business industry, effective cost control is of paramount importance. A well-functioning Material Requirements Planning (MRP) process plays a vital role in enabling businesses to manage their costs efficiently. This research paper explores the improvement of MRP in the context of small businesses in Thailand through the implementation of a web-based application. The objective was to enhance the MRP process by developing an MRP system using ReactJS, NodeJS, and PostgreSQL. The system was evaluated using heuristic evaluation techniques and the results indicated a positive outcome, the mean value is 0.83. The developed web based MRP system proved beneficial for small businesses, as it effectively reduced stocking costs and facilitated efficient raw material procurement. This research provides valuable insights into the implementation of web based MRP systems, enabling small businesses to optimize inventory management and enhance operational efficiency.

  • APA 7th style
Khumla, P., & Sarawan, K. (2023). Improving material requirements planning through web-based: A case study Thailand SMEs. Applied Computer Science, 19(4), 39–50. https://doi.org/10.35784/acs-2023-34
  • Chicago style
Khumla, Pornsiri, and Kamthorn Sarawan.  „Improving Material Requirements Planning through Web-Based: A Case Study Thailand SMEs." Applied Computer Science 19, no. 4 (2023): 39–50.
  • IEEE style
P. Khumla and K. Sarawan, „Improving material requirements planning through web-based: A case study Thailand SMEs,” Applied Computer Science , vol. 19, no. 4, pp. 39–50, 2023, doi: 10.35784/acs-2023-34.
  • Vancouver style
Khumla P, Sarawan K. Improving material requirements planning through web-based: A case study Thailand SMEs. Applied Computer Science. 2023;19(4):39–50.

PREDICTIVE TOOLS AS PART OF DECISSION AIDING PROCESSES AT THE AIRPORT – THE CASE OF FACEBOOK PROPHET LIBRARY

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Prophet is a quite fresh and promising open-source library for machine learning, developed by Facebook, that gains some significant interest. It could be used for predicting time series taking into account holidays and seasonality effects. Its possible applications and deficit of scientific works concerning its usage within decision processes convinced the authors to state the research question, if the Prophet library could provide reliable prediction to support decision-making processes at the airport. The case of Radawiec airport (located near Lublin, Poland) was chosen. Official measurement data (from the last 4 years) published by the Polish Government  Institute was used to train the neural network and predict daily averages of wind speed, temperature, pressure, relative humidity and rainfall totals during the day and night. It was revealed that most of the predicted data points were within the acceptance threshold, and computations were fast and highly automated. However, the authors believe that the Prophet library is not particularly useful for airport decision-making processes because the way it handles additional regressors and susceptibility to unexpected phenomena negatively affects the reliability of prediction results.

  • APA 7th style
Korga, S., Żyła, K., Józwik, J., Pytka, J., & Cybul, K. (2023). Predictive tools as part of decision-aiding processes at the airport – The case of Facebook Prophet library. Applied Computer Science, 19(4), 51–67. https://doi.org/10.35784/acs-2023-35
  • Chicago style
Korga, Sylwester, Kamil Żyła, Jerzy Józwik, Jarosław Pytka, and Kamil Cybul.  „Predictive Tools as Part of Decision-Aiding Processes at the Airport – The Case of Facebook Prophet Library." Applied Computer Science 19, no. 4 (2023): 51–67.
  • IEEE style
S. Korga, K. Żyła, J. Józwik, J. Pytka, and K. Cybul, „Predictive tools as part of decision-aiding processes at the airport – The case of Facebook Prophet library,” Applied Computer Science , vol. 19, no. 4, pp. 51–67, 2023, doi: 10.35784/acs-2023-35.
  • Vancouver style
Korga S, Żyła K, Józwik J, Pytka J, Cybul K. Predictive tools as part of decision-aiding processes at the airport – The case of Facebook Prophet library. Applied Computer Science. 2023;19(4):51–67.

IDENTIFYING THE POTENTIAL OF UNMANNED AERIAL VEHICLE ROUTING FOR EMERGENCY BLOOD DISTRIBUTION

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This study is focusing on identifying the potential of Unmanned Aerial Vehicle (UAV) routing for blood distribution in emergency requests in Sri Lanka compared to existing transportation modes. Capacitated Unmanned Aerial Vehicle Routing Problem was used as the methodology to find the optimal distribution plan between blood banks directing emergency requests. The developed UAV routing model was tested for different instances to compare the results. Finally, the proposed distribution process via UAVs was compared with the current distribution process for the objective function set up in the model and other Key Performance Indicators (KPIs) including energy consumption savings and operational cost savings. The average percentage reduction in distribution time, reduction in energy consumption costs and reduction in operating costs per day using UAVs was 58.57%, 96.35% and 61.20% respectively for the instances tested using the model, highlighting the potential of UAVs. Therefore, the deficiencies in Sri Lanka's present blood delivery system can be addressed using UAVs' potential for time, cost, and energy savings. The ability to save time through the deployment of UAVs to the fleet during emergency situations plays a crucial role in preventing the loss of human lives.

  • APA 7th style
Dewmini, J., Fernando, W. M., Nielsen, I. I., Bocewicz, G., Thibbotuwawa, A., & Banaszak, Z. (2023). Identifying the potential of unmanned aerial vehicle routing for blood distribution in emergency requests. Applied Computer Science, 19(4), 68–87. https://doi.org/10.35784/acs-2023-36
  • Chicago style
Dewmini, Janani, W Madushan Fernando, Izabela Iwa Nielsen, Grzegorz Bocewicz, Amila Thibbotuwawa, and Zbigniew Banaszak.  „Identifying the Potential of Unmanned Aerial Vehicle Routing for Blood Distribution in Emergency Requests." Applied Computer Science 19, no. 4 (2023): 68–87.
  • IEEE style
J. Dewmini, W. M. Fernando, I. I. Nielsen, G. Bocewicz, A. Thibbotuwawa, and Z. Banaszak, „Identifying the potential of unmanned aerial vehicle routing for blood distribution in emergency requests,” Applied Computer Science , vol. 19, no. 4, pp. 68–87, 2023, doi: 10.35784/acs-2023-36.
  • Vancouver style
Dewmini J, Fernando WM, Nielsen II, Bocewicz G, Thibbotuwawa A, Banaszak Z. Identifying the potential of unmanned aerial vehicle routing for blood distribution in emergency requests. Applied Computer Science. 2023;19(4):68–87.

EFFICIENCY COMPARISON OF NETWORKS IN HANDWRITTEN LATIN CHARACTERS RECOGNITION WITH DIACRITICS

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The aim of the article is to analyze and compare the performance and accuracy of architectures with a different number of parameters on the example of a set of handwritten Latin characters from the Polish Handwritten Characters Database (PHCD). It is a database of handwriting scans containing letters of the Latin alphabet as well as diacritics characteristic of the Polish language. Each class in the PHCD dataset contains 6,000 scans for each character. The research was carried out on six proposed architectures and compared with the architecture from the literature. Each of the models was trained for 50 epochs, and then the accuracy of prediction was measured on a separate test set. The experiment thus constructed was repeated 20 times for each model. Accuracy, number of parameters and number of floating-point operations performed by the network were compared. The research was conducted on subsets such as uppercase letters, lowercase letters, lowercase letters with diacritics, and a subset of all available characters. The relationship between the number of parameters and the accuracy of the model was indicated. Among the examined architectures, those that significantly improved the prediction accuracy at the expense of a larger network size were selected, and a network with a similar prediction accuracy as the base one, but with twice as many model parameters was selected.

  • APA 7th style
Łukasik, E., & Flis, W. (2023). Efficiency comparison of networks in handwritten Latin characters recognition with diacritics. Applied Computer Science, 19(4), 88–102. https://doi.org/10.35784/acs-2023-37
  • Chicago style
Łukasik, Edyta, and Wiktor Flis.  „Efficiency Comparison of Networks in Handwritten Latin Characters Recognition with Diacritics." Applied Computer Science 19, no. 4 (2023): 88–102.
  • IEEE style
E. Łukasik and W. Flis, „Efficiency comparison of networks in handwritten Latin characters recognition with diacritics,” Applied Computer Science , vol. 19, no. 4, pp. 88–102, 2023, doi: 10.35784/acs-2023-37.
  • Vancouver style
Łukasik E, Flis W. Efficiency comparison of networks in handwritten Latin characters recognition with diacritics. Applied Computer Science. 2023;19(4):88–102.

THE EFFECT OF INFORMATION TECHNOLOGY AND ENTREPRENEURSHIP ON THE E-SERVICES QUALITY THAT HAVE AN IMPACT ON CUSTOMER VALUE: EVIDENCE FROM INDONESIA SMES

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The handicraft sector is one of the sectors whose sales have increased rapidly in Indonesia. The problem, however, is the unpreparedness of craft SMEs in applying information technology in their sales activities. In addition, there are also problems in understanding the ability of UKM entrepreneurship on the characteristics of online sales. The purpose of this study is to analyze the effect of entrepreneurship and information technology on the quality of e-Services and their impact on customer value in craft SMEs in Indonesia. The results of the study show that there is an influence of entrepreneurship and information technology on the quality of online services and their impact on customer value. Entrepreneurship does not have a direct effect on online service quality and customer value but the entrepreneurship variable has a significant effect on customer value through the quality of e-Services and the quality of e-Services becomes the intervening variable in this study.

  • APA 7th style
Tridalestari, F. A., & Prasetyo, H. N. (2023). The effect of information technology and entrepreneurship on the e-services quality that have an impact on customer value: Evidence from Indonesia SMEs. Applied Computer Science, 19(4), 103–120. https://doi.org/10.35784/acs-2023-38
  • Chicago style
Tridalestari, Ferra Arik, and Hanung Nindito Prasetyo.  „The Effect of Information Technology and Entrepreneurship on the E-Services Quality That Have an Impact on Customer Value: Evidence from Indonesia SMEs." Applied Computer Science 19, no. 4 (2023): 103–20.
  • IEEE style
F. A. Tridalestari and H. N. Prasetyo, „The effect of information technology and entrepreneurship on the e-services quality that have an impact on customer value: Evidence from Indonesia SMEs,” Applied Computer Science , vol. 19, no. 4, pp. 103–120, 2023, doi: 10.35784/acs-2023-38.
  • Vancouver style
Tridalestari FA, Prasetyo HN. The effect of information technology and entrepreneurship on the e-services quality that have an impact on customer value: Evidence from Indonesia SMEs. Applied Computer Science. 2023;19(4):103–20.

IMPLICATIONS OF NEURAL NETWORK AS A DECISION-MAKING TOOL IN MANAGING KAZAKHSTAN’S AGRICULTURAL ECONOMY

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This study investigates the application of Artificial Neural Networks (ANN) in forecasting agricultural yields in Kazakhstan, highlighting its implications for economic management and policy-making. Utilizing data from the Bureau of National Statistics of the Republic of Kazakhstan (2000-2023), the research develops two ANN models using the Neural Net Fitting library in MATLAB. The first model predicts the total gross yield of main agricultural crops, while the second forecasts the share of individual crops, including cereals, oilseeds, potatoes, vegetables, melons, and sugar beets. The models demonstrate high accuracy, with the total gross yield model achieving an R-squared value of 0.98 and the individual crop model showing an R value of 0.99375. These results indicate a strong predictive capability, essential for practical agricultural and economic planning. The study extends previous research by incorporating a comprehensive range of climatic and agrochemical data, enhancing the precision of yield predictions. The findings have significant implications for Kazakhstan's economy. Accurate yield predictions can optimize agricultural planning, contribute to food security, and inform policy decisions. The successful application of ANN models showcases the potential of AI and machine learning in agriculture, suggesting a pathway towards more efficient, sustainable farming practices and improved quality management systems.

  • APA 7th style
Kulisz, M., Duisenbekova, A., Kujawska, J., Kaldybayeva, D., Issayeva, B., Lichograj, P., & Cel, W. (2024). Implications of neural network as a decision-making tool in managing Kazakhstan’s agricultural economy. Applied Computer Science, 19(4), 121–135. https://doi.org/10.35784/acs-2023-39
  • Chicago style
Kulisz, Monika, Aigerim Duisenbekova, Justyna Kujawska, Danira Kaldybayeva, Bibigul Issayeva, Piotr Lichograj, and Wojciech Cel.  „Implications of Neural Network as a Decision-Making Tool in Managing Kazakhstan’s Agricultural Economy." Applied Computer Science 19, no. 4 ( 2024): 121–35.
  • IEEE style
M. Kulisz et al., „Implications of neural network as a decision-making tool in managing Kazakhstan”s agricultural economy,” Applied Computer Science , vol. 19, no. 4, pp. 121–135, 2024, doi: 10.35784/acs-2023-39.
  • Vancouver style
Korga S, Żyła K, Józwik J, Pytka J, Cybul K. Predictive tools as part of decision-aiding processes at the airport – The case of Facebook Prophet library. Applied Computer Science. 2023;19(4):51–67.

COMPARISON OF SELECTED CLASSIFICATION METHODS BASED ON MACHINE LEARNING AS A DIAGNOSTIC TOOL FOR KNEE JOINT CARTILAGE DAMAGE BASED ON GENERATED VIBROACOUSTIC PROCESSES

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Osteoarthritis is one of the most common cause of disability among elderly. It can affect every joint in human body, however, it is most prevalent in hip, knee, and hand joints. Early diagnosis of cartilage lesions is essential for fast and accurate treatment, which can prolong joint function. Available diagnostic methods include conventional X-ray, ultrasound and magnetic resonance imaging. However, those diagnostic modalities are not suitable for screening purposes. Vibroarthrography is proposed in literature as a screening method for cartilage lesions. However, exact method of signal acquisition as well as classification method is still not well established in literature. In this study, 84 patients were assessed, of whom 40 were in the control group and 44 in the study group. Cartilage status in the study group was evaluated during surgical treatment. Multilayer perceptron - MLP, radial basis function - RBF, support vector method - SVM and naive classifier – NBC were introduced in this study as classification protocols. Highest accuracy (0.893) was found when MLP was introduced, also RBF classification showed high sensitivity (0.822) and specificity (0.821). On the other hand, NBC showed lowest diagnostic accuracy reaching 0.702. In conclusion vibroarthrography presents a promising diagnostic modality for cartilage evaluation in clinical setting with the use of MLP and RBF classification methods.

  • APA 7th style
Karpiński, R., Krakowski, P., Jonak, J., Machrowska, A., & Maciejewski, M. (2023). Comparison of selected classification methods based on machine learning as a diagnostic tool for knee joint cartilage damage based on generated vibroacoustic processes. Applied Computer Science, 19(4), 136–150. https://doi.org/10.35784/acs-2023-40
  • Chicago style
Karpiński, Robert, Przemysław Krakowski, Józef Jonak, Anna Machrowska, and Marcin Maciejewski.  „Comparison of Selected Classification Methods Based on Machine Learning as a Diagnostic Tool for Knee Joint Cartilage Damage Based on Generated Vibroacoustic Processes." Applied Computer Science 19, no. 4 (2023): 136–50.
  • IEEE style
R. Karpiński, P. Krakowski, J. Jonak, A. Machrowska, and M. Maciejewski, „Comparison of selected classification methods based on machine learning as a diagnostic tool for knee joint cartilage damage based on generated vibroacoustic processes,” Applied Computer Science , vol. 19, no. 4, pp. 136–150, 2023, doi: 10.35784/acs-2023-40.
  • Vancouver style
Karpiński R, Krakowski P, Jonak J, Machrowska A, Maciejewski M. Comparison of selected classification methods based on machine learning as a diagnostic tool for knee joint cartilage damage based on generated vibroacoustic processes. Applied Computer Science. 2023;19(4):136–50.