Vol. 28, No. 2

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Now showing 1 - 5 of 13

Evaluate Database Management System Quality By Analytic Hierarchy Process (AHP) and Simple Additive Weighting (SAW) Methodolog
(Institute of Automation and Computer Science, Brno University of Technology, 2022-12-20) Al Nawaiseh, Asmaa Jameel; Albtoush, Audi; Al-Msiedeen, Ra´fat; Al Nawaiseh, Sabah Jamil
Any organization that intends to use component-based software development, like outsourcing software, must first evaluate existing components against system requirements to find the best fit among many alternatives. As a result, there should be a mechanism to help with decision-making. Our proposed methodology tries to select the best alternative among available components, using the best decision-making approach. As an integrated method for order preference, the methodology in this paper uses two well-known criterion decision-making procedures, namely Analytic Hierarchy Process (AHP) and Simple Additive Weighting (SAW). By analyzing and selecting the optimal solution among a variety of Out Sourcing (OS) modules, the new model design makes the decision-making process easier. We evaluated two software attributes and predicted which was more effective. In this case, the advantage of utilizing AHP is that it allows the developer to evaluate the structure of the OS selection problem and calculate weights for the chosen criteria. After that, the SAW technique is used to calculate the alternatives ratings for OS components. The integration strategy used in our model and the resulting preference indication, which is produced as an explicit numeric value.
An Approximate Optimization Method for Solving Stiff Ordinary Differential Equations With Combinational Mutation Strategy of Differential Evolution Algorithm
(Institute of Automation and Computer Science, Brno University of Technology, 2022-12-20) Febrianti, Werry; Sidarto, Kuntjoro Adji; Sumarti, Novriana
This paper examines the implementation of simple combination mutation of differential evolution algorithm for solving stiff ordinary differential equations. We use the weighted residual method with a series expansion to approximate the solutions of stiff ordinary differential equations. We solve the problems from an ordinary stiff differential equation for linear and nonlinear problems. Then, we also implement our method for solving stiff systems of ordinary differential equations. We find that our algorithm can approximate the exact solution of a stiff ordinary differential equation with the smallest error for each length of series that we have chosen. Thus, this approximation method, by using the optimization method of simple combination differential evolution, can be a good tool for solving stiff ordinary differential equations.
The Use of the Multi-Scale Discrete Wavelet Transform and Deep Neural Networks on ECGs for the Diagnosis of 8 Cardio-Vascular Diseases
(Institute of Automation and Computer Science, Brno University of Technology, 2022-12-20) Soumiaa, Mhamed-Amine; Elhabbari, Sara; Mansouri, Mohamed
Cardiovascular diseases (CVD) continues to be the leading cause of death worldwide, with over 17 million deaths each year. In 2015, approximately 422 million people suffered from cardiovascular disease (CVD). Reading and analyzing electrocardiograms (ECGs) can be time consuming, and the development of decision support tools based on automated systems can facilitate and speed up the diagnosis of ECGs. In this paper, we propose a 12 leads ECG signals classification using Multi-level Discrete Wavelet Transform and ResNet34 Deep Learning algorithm which classifies 8 types of cardiovascular diseases: Atrial fibrillation (AF), 1st degree atrioventricular block (AV), Left bundle branch block (LBBB), Right bundle branch block (RBBB), Premature ventricular contraction (PVC), Premature atrial contraction (PAC), ST segment depression (STD), and ST segment elevation (STE). The ECGs are preprocessed, and different features are extracted using Multi-level Discrete Wavelet Transform. The model is trained on a database of more than 6000 electrocardiograms which includes 9 types of 12-lead ECGs: a normal type and the 8 abnormal ones which correspond to the diseases mentioned above.
USTW Vs. STW: A Comparative Analysis for Exam Question Classification based on Bloom’s Taxonomy
(Institute of Automation and Computer Science, Brno University of Technology, 2022-12-20) Gani, Mohammed Osman; Ayyasamy, Ramesh Kumar; Sangodiah, Anbuselvan; Fui, Yong Tien
Bloom’s Taxonomy (BT) is widely used in educational institutions to produce high-quality exam papers to evaluate students’ knowledge at different cognitive levels. However, manual question labeling takes a long time, and not all evaluators are familiar with BT. The researchers worked to automate the exam question classification process based on BT as a solution. Enhancement in term weighting is one of the ways to increase classification accuracy while working with text data. However, all the past work on the term weighting in exam question classification focused on unsupervised term weighting (USTW) schemes. The supervised term weighting (STW) schemes showed effectiveness in text classification but were not addressed in past studies of exam question classification. As a result, this study focused on the effectiveness of STW in classifying exam questions using BT. Hence, this research performed a comparative analysis between the USTW schemes and STW for exam question classification. The STW schemes used in this study are TF-ICF, TF-IDF-ICF, and TF-IDF-ICSDF, whereas the USTW schemes used for comparison are TF-IDF, ETF-IDF, and TFPOS-IDF. This study used Support Vector Machines (SVM), Na¨ıve Bayes (NB), and Multilayer Perceptron (MLP) to train the model. Accuracy and F1 score were used in this study to evaluate the classification result. The experiment result showed that overall, the STW scheme TF-ICF outperformed all the other schemes, followed by the USTW scheme ETF-IDF. Both the ETF-IDF and TFPOS-IDF outperformed standard TFIDF. The outcome of this study indicates the future research direction where the combination of STW and USTW schemes may increase the Accuracy of BT-based exam question classification.
Mathematical Incorrectness of So-Called Higuchi‘s Fractal Dimension
(Institute of Automation and Computer Science, Brno University of Technology, 2022-12-20) Martišek, Dalibor
The so-called Higuchi’s method of fractal dimension estimation is widely used and the term Higuchi’s fractal dimension even occurs in many publications. This paper deals with this method from a mathematical point of view. Terms distance and dimension and its basic properties are explained and Higuchi’s dimension according the original source is defined. The definition of Higuchi’s dimension was compared with the mathematical definition of distance and dimension. It is shown, that the definition of Higuchi’s dimension does not satisfy axioms of distance and dimension. The so-called Higuchi’s method and Higuchi’s dimension are mathematically incorrect. Therefore, all results achieved by this method are scientifically unreliable.

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