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Applied Computer Science Volume 16, Number 4, 2020

GRAPH-BASED FOG COMPUTING NETWORK MODEL

Ihor PYSMENNYI, Anatolii PETRENKO, Roman KYSLYI
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IoT networks generate numerous amounts of data that is then transferred to the cloud for processing. Transferring data cleansing and parts of calculations towards these edge-level networks improves system’s, latency, energy consumption, network bandwidth and computational resources utilization, fault tolerance and thus operational costs. On the other hand, these fog nodes are resource-constrained, have extremely distributed and heterogeneous nature, lack horizontal scalability, and, thus, the vanilla SOA approach is not applicable to them. Utilization of Software Defined Network (SDN) with task distribution capabilities advocated in this paper addresses these issues. Suggested framework may utilize various routing and data distribution algorithms allowing to build flexible system most relevant for particular use-case. Advocated architecture was evaluated in agent-based simulation environment and proved its’ feasibility and performance gains compared to conventional event-stream approach.
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  • APA 6th style
Pysmennyi, I., Petrenko, A., & Kyslyi, R. (2020). Graph-based fog computing network model. Applied Computer Science, 16(4), 5-20. doi:10.23743/acs-2020-25
  • Chicago style
Pysmennyi, Ihor, Anatolii Petrenko, and Roman Kyslyi. "Graph-Based Fog Computing Network Model." Applied Computer Science 16, no. 4 (2020): 5-20.
  • IEEE style
I. Pysmennyi, A. Petrenko, and R. Kyslyi, "Graph-based fog computing network model," Applied Computer Science, vol. 16, no. 4, pp. 5-20, 2020, doi: 10.23743/acs-2020-25.
  • Vancouver style
Pysmennyi I, Petrenko A, Kyslyi R. Graph-based fog computing network model. Applied Computer Science. 2020;16(4):5-20.

JOINT EFFECT OF FORECASTING AND LOT-SIZING METHOD ON COST MINIMIZATION OBJECTIVE OF A MANUFACTURER: A CASE STUDY

Jack OLESEN, Carl-Emil Houmoller PEDERSEN, Markus Germann KNUDSEN, Sandra TOFT, Vladimir NEDBAILO, Johan PRISAK, Izabela Ewa NIELSEN, Subrata SAHA
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Forecasting and lot-sizing problems are key for a variety of products manufactured in a plant of finite capacity. The plant manager needs to put special emphasis on the way of selecting the right forecasting methods with a higher level of accuracy and to conduct procurement planning based on specific lot-sizing methods and associated rolling horizon. The study is conducted using real case data form the Fibertex Personal Care, and has evaluated the joint influence of forecasting procedures such as ARIMA, exponential smoothing methods; and deterministic lot-sizing methods such as the Wagner-Whitin method, modified Silver-Meal heuristic to draw insights on the effect of the appropriate method selection on minimization of operational cost. The objective is to explore their joint effect on the cost minimization goal. It is found that a proficient selection process has a considerable impact on performance. The proposed method can help a manager to save substantial operational costs.
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Olesen, J., Pedersen, C.-E. H., Knudsen, M. G., Toft, S., Nedbailo, V., Prisak, J., . . . Saha, S. (2020). Joint effcet of forecasting and lot-sizing method on cost minimization objective of a manufacturer: a case study. Applied Computer Science, 16(4), 21-36. doi:10.23743/acs-2020-26
  • Chicago style
Olesen, Jack, Carl-Emil Houmoller Pedersen, Markus Germann Knudsen, Sandra Toft, Vladimir Nedbailo, Johan Prisak, Izabela Ewa Nielsen, and Subrata Saha. "Joint Effcet of Forecasting and Lot-Sizing Method on Cost Minimization Objective of a Manufacturer: A Case Study." Applied Computer Science 16, no. 4 (2020): 21-36.
  • IEEE style
J. Olesen et al., "Joint effcet of forecasting and lot-sizing method on cost minimization objective of a manufacturer: a case study," Applied Computer Science, vol. 16, no. 4, pp. 21-36, 2020, doi: 10.23743/acs-2020-26.
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Olesen J, Pedersen C-EH, Knudsen MG, Toft S, Nedbailo V, Prisak J, et al. Joint effcet of forecasting and lot-sizing method on cost minimization objective of a manufacturer: a case study. Applied Computer Science. 2020;16(4):21-36.

ELECTROCARDIOGRAM GENERATION SOFTWARE FOR TESTING OF PARAMETER EXTRACTION ALGORITHMS

Marcin MACIEJEWSKI, Barbara MACIEJEWSKA, Robert KARPIŃSKI, Przemysław KRAKOWSKI
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Fast and automated ECG diagnosis is of great benefit for treatment of cardiovascular and other conditions. The algorithms used to extract parameters need to be precise, robust and efficient. Appropriate training and testing methods for such algorithms need to be implemented for optimal results. This paper presents a software solution for computer ECG generation and a simplified concept of testing process. All the parameters of the resulting generated signal can be tweaked and set properly. Such software can also be beneficial for training and educational use.
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Maciejewski, M., Maciejewska, B., Karpiński, R., & Krakowski, P. (2020). Electrocardiogram generation software for testing of parameter extraction algorithms. Applied Computer Science, 16(4), 37-47. doi:10.23743/acs-2020-27
  • Chicago style
Maciejewski, Marcin, Barbara Maciejewska, Robert Karpiński, and Przemysław Krakowski. "Electrocardiogram Generation Software for Testing of Parameter Extraction Algorithms." Applied Computer Science 16, no. 4 (2020): 37-47.
  • IEEE style
M. Maciejewski, B. Maciejewska, R. Karpiński, and P. Krakowski, "Electrocardiogram generation software for testing of parameter extraction algorithms," Applied Computer Science, vol. 16, no. 4, pp. 37-47, 2020, doi: 10.23743/acs-2020-27.
  • Vancouver style
Maciejewski M, Maciejewska B, Karpiński R, Krakowski P. Electrocardiogram generation software for testing of parameter extraction algorithms. Applied Computer Science. 2020;16(4):37-47.

ARCHITECTURAL PARADIGM OF THE INTERACTIVE INTERFACE MODULE IN THE CLOUD TECHNOLOGY MODEL

Denis RATOV
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The article discusses an architectural template for building a module for organizing the work of a multiuser windowed information web-system. To solve this problem, JavaScript objects have been created: a window manager object and a window interactive interface class, which allow a web application to function when organizing cloud technologies. The software implementation is considered and the results of the practical use of the developed module are presented.
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Ratov, D. (2020). Architectural paradigm of the interactive interface module in the cloud technology model. Applied Computer Science, 16(4), 48-55. doi:10.23743/acs-2020-28
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Ratov, Denis. "Architectural Paradigm of the Interactive Interface Module in the Cloud Technology Model." Applied Computer Science 16, no. 4 (2020): 48-55.
  • IEEE style
D. Ratov, "Architectural paradigm of the interactive interface module in the cloud technology model," Applied Computer Science, vol. 16, no. 4, pp. 48-55, 2020, doi: 10.23743/acs-2020-28.
  • Vancouver style
Ratov D. Architectural paradigm of the interactive interface module in the cloud technology model. Applied Computer Science. 2020;16(4):48-55.

CLASSIFICATION OF EEG SIGNAL BY METHODS OF MACHINE LEARNING

Amina ALYAMANI, Oleh YASNIY
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Electroencephalogram (EEG) signal of two healthy subjects that was available from literature, was studied using the methods of machine learning, namely, decision trees (DT), multilayer perceptron (MLP), K-nearest neighbours (kNN), and support vector machines (SVM). Since the data were imbalanced, the appropriate balancing was performed by Kmeans clustering algorithm. The original and balanced data were classified by means of the mentioned above 4 methods. It was found, that SVM showed the best result for the both datasets in terms of accuracy. MLP and kNN produce the comparable results which are almost the same. DT accuracies are the lowest for the given dataset, with 83.82% for the original data and 61.48% for the balanced data.
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Alyamani, A., & Yasniy, O. (2020). Classification of EEG signal by methods of machine learning. Applied Computer Science, 16(4), 56-63. doi:10.23743/acs-2020-29
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Alyamani, Amina, and Oleh Yasniy. "Classification of Eeg Signal by Methods of Machine Learning." Applied Computer Science 16, no. 4 (2020): 56-63.
  • IEEE style
A. Alyamani and O. Yasniy, "Classification of EEG signal by methods of machine learning," Applied Computer Science, vol. 16, no. 4, pp. 56-63, 2020, doi: 10.23743/acs-2020-29.
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Alyamani A, Yasniy O. Classification of EEG signal by methods of machine learning. Applied Computer Science. 2020;16(4):56-63.

DEVELOPMENT OF AN ONTOLOGY-BASED ADAPTIVE PERSONALIZED E-LEARNING SYSTEM

Olutayo BOYINBODE, Paul OLOTU, Kolawole AKINTOLA
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E-learning has fast become an active field of research with a lot of investments towards web-based delivery of personalized learning contents to learners. Some issues of e-learning arise from the heterogeneity and interoperability of learning content adapting to learner's styles and preferences. This has brought about the development of an ontology-based personalized learning system to solve this problem. This research developed an ontology-based personalized e-learning system that presents suitable learning contents to learners based on their learning style, preferences, background knowledge, and personal profile. 
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Boyinbode, O., Olotu, P., & Akintola, K. (2020). Development of an ontology-based adaptive personalized e-learning system. Applied Computer Science, 16(4), 64-84. doi:10.23743/acs-2020-30
  • Chicago style
Boyinbode, Olutayo, Paul Olotu, and Kolawole Akintola. "Development of an Ontology-Based Adaptive Personalized E-Learning System." Applied Computer Science 16, no. 4 (2020): 64-84.
  • IEEE style
O. Boyinbode, P. Olotu, and K. Akintola, "Development of an ontology-based adaptive personalized e-learning system," Applied Computer Science, vol. 16, no. 4, pp. 64-84, 2020, doi: 10.23743/acs-2020-30.
  • Vancouver style
Boyinbode O, Olotu P, Akintola K. Development of an ontology-based adaptive personalized e-learning system. Applied Computer Science. 2020;16(4):64-84.

COMPUTER VISION BASED ON RASPBERRY PI SYSTEM

Mohanad ABDULHAMID, Otieno ODONDI, Muaayed AL-RAWI
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The paper focused on designing and developing a Raspberry Pi based system employing a camera which is able to detect and count objects within a target area. Python was the programming language of choice for this work. This is because it is a very powerful language, and it is compatible with the Pi. Besides, it lends itself to rapid application development and there are online communities that program Raspberry Pi computer using python. The results show that the implemented system was able to detect different kinds of objects in a given image. The number of objects were also generated displayed by the system. Also the results show an average efficiency of 90.206% was determined. The system is therefore seen to be highly reliable.
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Abdulhamid, M., Odondi, O., & Al-Rawi, M. (2020). Computer vision based on Raspberry Pi system. Applied Computer Science, 16(4), 85-102. doi:10.23743/acs-2020-31
  • Chicago style
Abdulhamid, Mohanad, Otieno Odondi, and Muaayed Al-Rawi. "Computer Vision Based on Raspberry Pi System." Applied Computer Science 16, no. 4 (2020): 85-102.
  • IEEE style
M. Abdulhamid, O. Odondi, and M. Al-Rawi, "Computer vision based on Raspberry Pi system," Applied Computer Science, vol. 16, no. 4, pp. 85-102, 2020, doi: 10.23743/acs-2020-31.
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Abdulhamid M, Odondi O, Al-Rawi M. Computer vision based on Raspberry Pi system. Applied Computer Science. 2020;16(4):85-102.

ORDER VIOLATION IN MULTITHREADED APPLICATIONS AND ITS DETECTION IN STATIC CODE ANALYSIS PROCESS

Damian GIEBAS, Rafał WOJSZCZYK
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The subject presented in the paper concerns resource conflicts, which are the cause of order violation in multithreaded applications. The work focuses on developing conditions that can be implemented as a tool for allowing to detect these conflicts in the process of static code analysis. The research is based on known errors reported to developers of large applications such as Mozilla Firefox browser and MySQL relational database system. These errors could have been avoided by appropriate monitoring of the source code.
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Giebas, D., & Wojszczyk, R. (2020). Applied Computer Science, 16(4), 103-117. doi:10.23743/acs-2020-32
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Giebas, Damian, and Rafał Wojszczyk. "Order Violation in Multithreaded Applications and Its Detection in Static Code Analysis Process." Applied Computer Science 16, no. 4 (2020): 103-17.
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D. Giebas and R. Wojszczyk, "Order violation in multithreaded applications and its detection in static code analysis process," Applied Computer Science, vol. 16, no. 4, pp. 103-117, 2020, doi: 10.23743/acs-2020-32.
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Giebas D, Wojszczyk R. Order violation in multithreaded applications and its detection in static code analysis process. Applied Computer Science. 2020;16(4):103-17.