rss_2.0Computer Sciences FeedSciendo RSS Feed for Computer Scienceshttps://www.sciendo.com/subject/COhttps://www.sciendo.comComputer Sciences Feedhttps://www.sciendo.com/subjectImages/Computer_Sciences.jpg700700Experiments with holographic associative memoryhttps://sciendo.com/article/10.2478/ausi-2022-0010<abstract> <title style='display:none'>Abstract</title> <p>We reiterate the theoretical basics of holographic associative memory, and conduct two experiments. During the first experiment, we teach the system many associations, while during the second experiment, we teach it only one association. In both cases, the recalling capability of the system is examined from different aspects.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/ausi-2022-00102023-02-04T00:00:00.000+00:00Computational complexity of network vulnerability analysishttps://sciendo.com/article/10.2478/ausi-2022-0012<abstract> <title style='display:none'>Abstract</title> <p>Residual closeness is recently proposed as a vulnerability measure to characterize the stability of complex networks. Residual closeness is essential in the analysis of complex networks, but costly to compute. Currently, the fastest known algorithms run in polynomial time. Motivated by the fast-growing need to compute vulnerability measures on complex networks, new algorithms for computing node and edge residual closeness are introduced in this paper. Those proposed algorithms reduce the running times to Θ(n<sup>3</sup>) and Θ (n<sup>4</sup>) on unweighted networks, respectively, where n is the number of nodes.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/ausi-2022-00122023-02-04T00:00:00.000+00:00Hourly electricity price forecast for short-and long-term, using deep neural networkshttps://sciendo.com/article/10.2478/ausi-2022-0013<abstract> <title style='display:none'>Abstract</title> <p>Despite the practical importance of accurate long-term electricity price forecast with high resolution - and the significant need for that - only small percentage of the tremendous papers on energy price forecast attempted to target this topic. Its reason can be the high volatility of electricity prices and the hidden – and often unpredictable – relations with its influencing factors.</p> <p>In our research, we performed different experiments to predicate hourly Hungarian electricity prices using deep neural networks, for short-term and long-term, too. During this work, investigations were made to compare the results of different network structures and to determine the effect of some environmental factors (meteorologic data and date/time - beside the historical electricity prices). Our results were promising, mostly for short-term forecasts - especially by using a deep neural network with one ConvLSTM encoder.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/ausi-2022-00132023-02-04T00:00:00.000+00:00Computing Laplacian energy, Laplacian-energy-like invariant and Kirchhoff index of graphshttps://sciendo.com/article/10.2478/ausi-2022-0011<abstract> <title style='display:none'>Abstract</title> <p>Let G be a simple connected graph of order n and size m. The matrix L(G)= D(G)− A(G) is called the Laplacian matrix of the graph G,where D(G) and A(G) are the degree diagonal matrix and the adjacency matrix, respectively. Let the vertex degree sequence be d1 ≥ d2 ≥··· ≥ dn and let μ1 ≥ μ2 ≥··· ≥ μ<sub>n−1</sub> &gt;μn = 0 be the eigenvalues of the Laplacian matrix of G. The graph invariants, Laplacian energy (LE), the Laplacian-energy-like invariant (LEL) and the Kirchhoff index (Kf), are defined in terms of the Laplacian eigenvalues of graph G, as <inline-formula> <alternatives> <inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="graphic/j_ausi-2022-0011_eq_001.png"/> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi>L</mml:mi><mml:mi>E</mml:mi><mml:mo>=</mml:mo><mml:msubsup><mml:mo>∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>n</mml:mi></mml:msubsup><mml:mrow><mml:mrow><mml:mo>|</mml:mo> <mml:mrow><mml:msub><mml:mrow><mml:mi>μ</mml:mi></mml:mrow><mml:mi>i</mml:mi></mml:msub><mml:mo>-</mml:mo><mml:mfrac><mml:mrow><mml:mn>2</mml:mn><mml:mi>m</mml:mi></mml:mrow><mml:mi>n</mml:mi></mml:mfrac></mml:mrow> <mml:mo>|</mml:mo></mml:mrow></mml:mrow></mml:mrow></mml:math> <tex-math>LE = \sum\nolimits_{i = 1}^n {\left| {{\mu _i} - {{2m} \over n}} \right|}</tex-math> </alternatives> </inline-formula>, <inline-formula> <alternatives> <inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="graphic/j_ausi-2022-0011_eq_002.png"/> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi>L</mml:mi><mml:mi>E</mml:mi><mml:mi>L</mml:mi><mml:mo>=</mml:mo><mml:msubsup><mml:mo>∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>n</mml:mi><mml:mo>-</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msubsup><mml:mrow><mml:msqrt><mml:mrow><mml:msub><mml:mrow><mml:mi>μ</mml:mi></mml:mrow><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:msqrt></mml:mrow></mml:mrow></mml:math> <tex-math>LEL = \sum\nolimits_{i = 1}^{n - 1} {\sqrt {{\mu _i}} }</tex-math> </alternatives> </inline-formula> and <inline-formula> <alternatives> <inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="graphic/j_ausi-2022-0011_eq_003.png"/> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi>K</mml:mi><mml:mi>f</mml:mi><mml:mo>=</mml:mo><mml:mi>n</mml:mi><mml:msubsup><mml:mo>∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>n</mml:mi><mml:mo>-</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msubsup><mml:mrow><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:msub><mml:mrow><mml:mi>μ</mml:mi></mml:mrow><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mfrac></mml:mrow></mml:mrow></mml:math> <tex-math>Kf = n\sum\nolimits_{i = 1}^{n - 1} {{1 \over {{\mu _i}}}}</tex-math> </alternatives> </inline-formula> respectively. In this paper, we obtain a new bound for the Laplacian-energy-like invariant LEL and establish the relations between Laplacian-energy-like invariant LEL and the Kirchhoff index Kf.Further,weobtain the relations between the Laplacian energy LE and Kirchhoff index Kf.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/ausi-2022-00112023-02-04T00:00:00.000+00:00A feature selection strategy using Markov clustering, for the optimization of brain tumor segmentation from MRI datahttps://sciendo.com/article/10.2478/ausi-2022-0018<abstract> <title style='display:none'>Abstract</title> <p>The automatic segmentation of medical images stands at the basis of modern medical diagnosis, therapy planning and follow-up studies after interventions. The accuracy of the segmentation is a key element in assisting the work of the physician, but the efficiency of the process is also relevant. This paper introduces a feature selection strategy that attempts to define reduced feature sets for ensemble learning methods employed in brain tumor segmentation based on MRI data such a way that the segmentation outcome hardly suffers any damage. Initially, the full set of observed and generated features are deployed in ensemble training and prediction on testing data, which provide us information on all couples of features from the full feature set. The extracted pairwise data is fed to a Markov clustering (MCL) algorithm, which uses a graph structure to characterize the relation between features. MCL produces connected subgraphs that are totally separated from each other. The largest such subgraph defines the group of features which are selected for evaluation. The proposed technique is evaluated using the high-grade and low-grade tumor records of the training dataset of the BraTS 2019 challenge, in an ensemble learning framework relying on binary decision trees. The proposed method can reduce the set of features to 30%ofits initial size without losing anything in terms of segmentation accuracy, significantly contributing to the efficiency of the segmentation process. A detailed comparison of the full set of 104 features and the reduced set of 41 features is provided, with special attention to highly discriminative and redundant features within the MRI data.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/ausi-2022-00182023-02-04T00:00:00.000+00:00A two-stage U-net approach to brain tumor segmentation from multi-spectral MRI recordshttps://sciendo.com/article/10.2478/ausi-2022-0014<abstract> <title style='display:none'>Abstract</title> <p>The automated segmentation of brain tissues and lesions represents a widely investigated research topic. The Brain Tumor Segmentation Challenges (BraTS) organized yearly since 2012 provided standard training and testing data and a unified evaluation framework to the research community, which provoked an intensification in this research field. This paper proposes a solution to the brain tumor segmentation problem, which is built upon the U-net architecture that is very popular in medical imaging. The proposed procedure involves two identical, cascaded U-net networks with 3D convolution. The first stage produces an initial segmentation of a brain volume, while the second stage applies a post-processing based on the labels provided by the first stage. In the first U-net based classification, each pixel is characterized by the four observed features (T1, T2, T1c, and FLAIR), while the second identical U-net works with four features extracted from the volumetric neighborhood of the pixels, representing the ratio of pixels with positive initial labeling within the neighborhood. Statistical accuracy indexes are employed to evaluate the initial and final segmentation of each MRI record. Tests based on BraTS 2019 training data set led to average Dice scores over 87%. The postprocessing step can increase the average Dice scores by 0.5%, it improves more those volumes whose initial segmentation was less successful.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/ausi-2022-00142023-02-04T00:00:00.000+00:00Bounds on Nirmala energy of graphshttps://sciendo.com/article/10.2478/ausi-2022-0017<abstract> <title style='display:none'>Abstract</title> <p>The Nirmala matrix of a graph and its energy have recently defined. In this paper, we establish some features of the Nirmala eigenvalues. Then we propose various bounds on the Nirmala spectral radius and energy. Moreover, we derive a bound on the Nirmala energy including graph energy and maximum vertex degree.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/ausi-2022-00172023-02-04T00:00:00.000+00:00Some new results on colour-induced signed graphshttps://sciendo.com/article/10.2478/ausi-2022-0019<abstract> <title style='display:none'>Abstract</title> <p>A signed graph is a graph in which positive or negative signs are assigned to its edges. We consider equitable colouring and Hamiltonian colouring to obtain induced signed graphs. An equitable colour-induced signed graph is a signed graph constructed from a given graph in which each edge uv receives a sign (−1)<sup>|c(v)−c(u)|</sup>,where c is an equitable colouring of vertex v. A Hamiltonian colour-induced signed graph is a signed graph obtained from a graph G in which for each edge e = uv, the signature function σ(uv)=(−1)<sup>|c(v)−c(u)|</sup>, gives a sign such that, |c(u)− c(v)| ≥ n − 1 − D(u, v) where c is a function that assigns a colour to each vertex satisfying the given condition. This paper discusses the properties and characteristics of signed graphs induced by the equitable and Hamiltonian colouring of graphs.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/ausi-2022-00192023-02-04T00:00:00.000+00:00Investigating the AlgoRythmics YouTube channel: the Comment Term Frequency Comparison social media analytics methodhttps://sciendo.com/article/10.2478/ausi-2022-0016<abstract> <title style='display:none'>Abstract</title> <p>In this paper we investigate the comments from the AlgoRythmics YouTube channel using the Comment Term Frequency Comparison social media analytics method. Comment Term Frequency Comparison can be a useful tool to understand how a social media platform, such as a Youtube channel is being discussed by users and to identify opportunities to engage with the audience. Understanding viewer opinions and reactions to a video, identifying trends and patterns in the way people are discussing a particular topic, and measuring the effectiveness of a video in achieving its intended goals is one of the most important points of view for a channel to develop. Youtube comment analytics can be a valuable tool looking to understand how the AlgoRythmics channel videos are being received by viewers and to identify opportunities for improvement. Our study focuses on the importance of user feedback based on ten algorithm visualization videos from the AlgoRythmics channel. In order to find evidence how our channel works and new ideas to improve we used the so-called comment term frequency comparison social media analytics method to investigate the main characteristics of user feedback. We analyzed the comments using both Youtube Studio Analytics and Mozdeh Big Data Analysis tool.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/ausi-2022-00162023-02-04T00:00:00.000+00:00Rendering automatic bokeh recommendation engine for photos using deep learning algorithmhttps://sciendo.com/article/10.2478/ausi-2022-0015<abstract> <title style='display:none'>Abstract</title> <p>Automatic bokeh is one of the smartphone’s essential photography effects. This effect enhances the quality of the image where the subject background gets out of focus by providing a soft (i.e., diverse) background. Most smartphones have a single rear camera that is lacking to provide which effects need to be applied to which kind of images. To do the same, smartphones depend on different software to generate the bokeh effect on images. Blur, Color-point, Zoom, Spin, Big Bokeh, Color Picker, Low-key, High-Key, and Silhouette are the popular bokeh effects. With this wide range of bokeh types available, it is difficult for the user to choose a suitable effect for their images. Deep Learning (DL) models (i.e., MobileNetV2, InceptionV3, and VGG16) are used in this work to recommend high-quality bokeh effects for images. Four thousand five hundred images are collected from online resources such as Google images, Unsplash, and Kaggle to examine the model performance. 85% accuracy has been achieved for recommending different bokeh effects using the proposed model MobileNetV2, which exceeds many of the existing models.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/ausi-2022-00152023-02-04T00:00:00.000+00:00International Journal of Mathematics and Computer in Engineeringhttps://sciendo.com/journal/IJMCE<P><STRONG><EM>International Journal of Mathematics and Computer in Engineering</EM> (IJMCE)</STRONG> provides an international forum for rapid publication of original scientific works in the field of mathematics, computer science, nonlinear sciences, physics, and engineering. </P> <P>The journal’s scope encompasses all nonlinear dynamic phenomena associated with engineering, applied sciences, computational science, computer, mechanical science, mathematics, electrical science, physical science, and nonlinear systems. </P> <P>Review articles and original contributions are based on analytical, computational, and experimental methods. </P> <P><STRONG><EM>International Journal of Mathematics and Computer in Engineering</EM> (IJMCE)</STRONG> is dedicated to publishing the highest-quality short papers, regular papers, and expository papers. IJMCE considers only original and timely contributions containing new results in various fields of mathematics and computer engineering. </P> <P><STRONG>IJMCE</STRONG> publishes one volume and two issues per year. The scope of this journal includes but is not limited to: </P> <UL> <LI>engineering problems related to graph theory, artificial intelligence, deep learning, imaging, neural network, big data, machine learning; </LI> <LI>analysis, modelling, and control of phenomena in areas such as electrical engineering, computer science, fluid dynamics and thermal engineering, mechanics, biology, physics, applied sciences; </LI> <LI>dynamical systems related applications of computer and mathematics in physics, engineering, chemistry, economics, and social sciences; </LI> <LI>computational fluid dynamics, optimisation, fuzzy probability and heat problems; </LI> <LI>statistical computation and simulation;</LI> <LI>numerical methods for ODEs, PDEs, FDEs; </LI> <LI>analytical methods for ODEs, PDEs, FDEs; </LI> <LI>numerical analysis and algebra; </LI> <LI>mathematical analysis and its applications; </LI> <LI>soliton theory and its applications; </LI> <LI>nonlinear methods and their applications; </LI> <LI>computational geometry and topology; </LI> <LI>computational intelligence and complexity; </LI> <LI>theoretical computer science; </LI> <LI>computational physics and biology; </LI> <LI>mathematical physics.</LI></UL> <P></P> <P><STRONG>Archiving </STRONG></P> <P>Sciendo archives the contents of this journal in <A href="https://www.portico.org/">Portico</A> - digital long-term preservation service of scholarly books, journals and collections. </P> <P><STRONG>Plagiarism Policy</STRONG> </P> <P>The editorial board is participating in a growing community of <A href="https://www.crossref.org/services/similarity-check/">Similarity Check System</A>'s users in order to ensure that the content published is original and trustworthy. Similarity Check is a medium that allows for comprehensive manuscripts screening, aimed to eliminate plagiarism and provide a high standard and quality peer-review process. </P> JOURNALtruehttps://sciendo.com/journal/IJMCE2023-06-30T00:00:00.000+00:00Mechanical Vibrations Analysis in Direct Drive Using CWT with Complex Morlet Wavelethttps://sciendo.com/article/10.2478/pead-2023-0005<abstract> <title style='display:none'>Abstract</title> <p>Modern industrial process and household equipment more often use direct drives. According to European policy, Industry 4.0 and new Industry 5.0 need to undertake the effort required to ensure a sustainable, human-centric, and resilient European industry. One of the main problems of rotating machines is mechanical vibrations that can limit the lifetime of the final product or the machine in which they are applied. Therefore, analysis of vibration in electrical drives is crucial for appropriate maintenance of the machine. The present article undertakes an analysis of vibration measured at the laboratory stand with multiple dominant frequencies in the range 50–500 Hz. The fast Fourier transform (FFT) gives information about the frequency component without its time localisation. While the solution made available by the short-time Fourier transform (STFT) is able to overcome the problem of FFT, it still has limitations, particularly in terms of there being a lacuna in time and frequency localisation; accordingly, the need is felt for other methods that can give a good localisation in time and frequency. In the article, the continuous wavelet transform (CWT) was investigated, which requires selection of the wavelet function (kernel of transformation). The complex Morlet wavelet was selected with description of its central frequency and bandwidth. CWT and STFT time-frequency localisation capabilities were compared to investigate data registered from the direct-drive laboratory stand. CWT gives better frequency localisation than STFT even for the same frequency resolution. Vibration frequencies with near-locations were separated in CWT and STFT joined them into one wide pick. To ensure a good extraction of frequency features in electric drive systems, the author, based on analysing the results of the present study, recommends that CWT with complex Morlet wavelet be used instead of STFT.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/pead-2023-00052023-02-01T00:00:00.000+00:00A Novel Feedback Linearisation Control of Flyback Converterhttps://sciendo.com/article/10.2478/pead-2023-0006<abstract> <title style='display:none'>Abstract</title> <p>This paper presents a novel approach for feedback linearisation in a continuous conduction mode (CCM) of the flyback converter. Due to the unstable zero dynamics, a flyback converter has highly non-linear behaviour. Flyback converters mostly use the indirect (current) control mechanism. In contrast, this paper shows a direct control of the output voltage of a flyback converter with feedback linearisation (a non-linear control method). In the designed controller, an error integrator is applied to improve the dynamic and steady-state behaviour of the controller. To design the feedback linearisation method, the state-space averaged model is determined. The converter and the proposed control are tested in a MatLab/Simulink environment, and the results are compared with other optimal controller methods. The results provide feedback about the efficiency and practical implementation of the proposed method.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/pead-2023-00062023-01-28T00:00:00.000+00:00Principles of Synthesizing Medical Datasetshttps://sciendo.com/article/10.2478/aei-2022-0019<abstract> <title style='display:none'>Abstract</title> <p>Data in many application domains provide a valuable source for analysis and data-driven decision support. On the other hand, legislative restrictions are provided, especially on personal data and patients’ data in the medical domain. In order to maximize the use of data for decision purposes and comply with legislation, sensitive data needs to be properly anonymized or synthetized. This article contributes to the area of medical records synthesis. We first introduce this topic and present it in a broader context, as well as in terms of methods used and metrics for their evaluation. Based on the related work analysis, we selected CTGAN neural network model for data synthesis and experimentally validated it on three different medical datasets. The results were evaluated both quantitatively by means of selected metrics as well as qualitatively by means of proper visualization techniques. The results showed that in most cases, the synthesized dataset is a very good approximation of the original one, with similar prediction performance.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/aei-2022-00192023-01-24T00:00:00.000+00:00Review of Recent Trends in the Detection of Hate Speech and Offensive Language on Social Mediahttps://sciendo.com/article/10.2478/aei-2022-0018<abstract> <title style='display:none'>Abstract</title> <p>In the article, we describe recent trends in the detection of hate speech and offensive language on social media. We accord from the latest studies and scientific contributions. The article describes current trends and the most used methods in connection with the detection of hate speech and offensive language. At the same time, we focus on the importance of emoticons, hashtags, and swearing in the field of social networks. We point out the topicality of the selected topic, describe the next direction of our work, and suggest possible solutions to current problems in this field of research.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/aei-2022-00182023-01-24T00:00:00.000+00:00Simulation and Laboratory Model of Small Hydropower Planthttps://sciendo.com/article/10.2478/aei-2022-0017<abstract> <title style='display:none'>Abstract</title> <p>This paper is dedicated to the description of all our research done so far in the field of simulation of small hydropower plants as well as in creating a functional laboratory model based on the proposed simulation models. Comparing to the common structures, we propose some different approaches in modelling parts of the small hydropower plants, e.g. fuzzy model of the hydraulic turbine efficiency. Moreover, this simulation model is created directly for the small hydropower plants, thus it simplifies parts, that are commonly used in other scientific papers and simulate the phenomenon connected to the big scale hydropower plants structure and physical description. As a necessary step in the process of creation of universal laboratory model of a small hydropower plant that should be used for design and tuning purposes of new approaches of controlling such systems, we discuss the research and development phase that led to the final construction of the depicted laboratory model.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/aei-2022-00172023-01-24T00:00:00.000+00:00Comparative Study Between Sliding Mode Controller and PI Controller for AC/DC Bridgeless Converter Under Uncertain Parametershttps://sciendo.com/article/10.2478/aei-2022-0016<abstract> <title style='display:none'>Abstract</title> <p>In this paper, a comparative study between nonlinear sliding mode controller (SMC) and standard PI controller for stabilizing bridgeless AC/DC converter under uncertain parameters is presented. This type of converters is widely used in harvesting low energy systems as in wind turbine, piezoelectric transducers and heat exchange transducers. Designing robust controllers to enhance the efficiency and accuracy of these converters has become a promising track in control engineering field. The traditional bridge rectifier is widely used in the majority types of AC/DC conventional converters to gain the rectified DC voltage from the low AC input voltage source. However, these traditional converters are not effective for the low output voltage of renewable sources due to the voltage drops across rectifier’s diodes. The proposed SMC-PI controller is used to enhance the stability and the response of these converters under uncertain parameters comparing with the standard PI controller. The proposed approach consists of both Boost and Buck-Boost converters with two controllers in order to maximize the useful output energy from the source. The graphical method has been used to obtain the limitation of the coefficients of the standard PI controller. A parameter space approach is used to find all robust stabilization PI coefficients and the stability regions. A comparative study using simulations in MATLAB is presented to ensure the effectiveness and robustness of the proposed SMC-PI controller under some external disturbances.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/aei-2022-00162023-01-24T00:00:00.000+00:00Number-Theoretic Transform with Constant Time Computation for Embedded Post-Quantum Cryptographyhttps://sciendo.com/article/10.2478/aei-2022-0020<abstract> <title style='display:none'>Abstract</title> <p>In this article, we describe the principles and advantages of using the Number-Theoretic Transform (NTT) in post-quantum cryptography. We deal with usages of NTT in post-quantum algorithms included in the competition announced by the National Institute of Standards and Technology. Attention is paid to the fast multiplication of polynomials using NTT and negacyclic convolution. We also focus on the existing implementation of NTT and its modifications to analyze the effectiveness of individual modifications. Separate attention is paid to the Constant Time implementation of NTT because the constant computation time of the transformation decreases a possibility of side channel attack. We describe measurements performed on OS Linux Ubuntu 20.04 LTS environment in Linux kernel mode, with the highest attention to the measurement executed on a microcontroller with an ARM 32-bit core. Measurements performed on microcontroller units are done using 32-bit and 16-bit arithmetic to demonstrate how to achieve constant computation time of the transformation. We present the results and analysis of measurements performed using modified implementations.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/aei-2022-00202023-01-24T00:00:00.000+00:00An Intelligent Framework for Person Identification Using Voice Recognition and Audio Data Classificationhttps://sciendo.com/article/10.2478/acss-2022-0019<abstract> <title style='display:none'>Abstract</title> <p>The paper proposes a framework to record meeting to avoid hassle of writing points of meeting. Key components of framework are “Model Trainer” and “Meeting Recorder”. In model trainer, we first clean the noise in audio, then oversample the data size and extract features from audio, in the end we train the classification model. Meeting recorder is a post-processor used for sound recognition using the trained model and converting the audio into text. Experimental results show the high accuracy and effectiveness of the proposed implementation.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2022-00192023-01-24T00:00:00.000+00:00Using a Fuzzy-Bayesian Approach for Predicting the QoS in VANEThttps://sciendo.com/article/10.2478/acss-2022-0011<abstract> <title style='display:none'>Abstract</title> <p>There are considerable obstacles in the transport sector of developing countries, including poor road conditions, poor road maintenance and congestion. The dire impacts of these challenges could be extremely damaging to both human lives and the economies of the countries involved. Intelligent Transportation Systems (ITSs) integrate modern technologies into existing transportation systems to monitor traffic. Adopting Vehicular Adhoc Network (VANET) into the road transport system is one of the most ITS developments demonstrating its benefits in reducing incidents, traffic congestion, fuel consumption, waiting times and pollution. However, this type of network is vulnerable to many problems that can affect the availability of services. This article uses a Fuzzy Bayesian approach that combines Bayesian Networks (BN) and Fuzzy Logic (FL) for predicting the risks affecting the quality of service in VANET. The implementation of this model can be used for different types of predictions in the networking field and other research areas.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2022-00112023-01-24T00:00:00.000+00:00en-us-1