rss_2.0Sciendo RSS Feed for Applied Computer Systemshttps://sciendo.com/journal/ACSShttps://www.sciendo.comhttps://sciendo-parsed.s3.eu-central-1.amazonaws.com/64df442a66cae56d9e7a5fd2/cover-image.jpghttps://sciendo.com/journal/ACSS140216https://sciendo.com/article/10.2478/acss-2023-0008<abstract> <title style='display:none'>Abstract</title> <p>In this paper, we propose a new approach, based on bigraphic reactive systems (BRS), to provide a formal modelling of the architectural elements of a Multi-Viewpoints ontology (MVp ontology). We introduce a formal model in which the main elements of MVp ontology find their definition in terms of bigraphic concepts by preserving their semantics. Besides, we enrich the proposed model with reaction rules in order to handle the dynamic reactions of MVp ontology. In order to confirm the applicability of our approach, we have carried out a case study using the proposed model.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2023-00082023-08-17T00:00:00.000+00:00https://sciendo.com/article/10.2478/acss-2023-0015<abstract> <title style='display:none'>Abstract</title> <p>Many people are interested in instrumental music. They may have one piece of song, but it is a challenge to seek the song because they do not have lyrics to describe for a text-based search engine. This study leverages the Approximate Nearest Neighbours to preprocess the instrumental songs and extract the characteristics of the track in the repository using the Mel frequency cepstral coefficients (MFCC) characteristic extraction. Our method digitizes the track, extracts the track characteristics, and builds the index tree with different lengths of each MFCC and dimension number of vectors. We collected songs played with various instruments for the experiments. Our result on 100 pieces of various songs in different lengths, with a sampling rate of 16000 and a length of each MFCC of 13, gives the best results, where accuracy on the Top 1 is 36 %, Top 5 is 4 %, and Top 10 is 44 %. We expect this work to provide useful tools to develop digital music e-commerce systems.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2023-00152023-08-17T00:00:00.000+00:00https://sciendo.com/article/10.2478/acss-2023-0001<abstract> <title style='display:none'>Abstract</title> <p>A problem of partitioning large datasets of flat points is considered. Known as the centroid-based clustering problem, it is mainly addressed by the <italic>k</italic>-means algorithm and its modifications. As the <italic>k</italic>-means performance becomes poorer on large datasets, including the dataset shape stretching, the goal is to study a possibility of improving the centroid-based clustering for such cases. It is quite noticeable on non-sparse datasets that the resulting clusters produced by <italic>k</italic>-means resemble beehive honeycomb. It is natural for rectangular-shaped datasets because the hexagonal cells make efficient use of space owing to which the sum of the within-cluster squared Euclidean distances to the centroids is approximated to its minimum. Therefore, the lattices of rectangular and hexagonal clusters, consisting of stretched rectangles and regular hexagons, are suggested to be successively applied. Then the initial centroids are calculated by averaging within respective hexagons. These centroids are used as initial seeds to start the <italic>k</italic>-means algorithm. This ensures faster and more accurate convergence, where at least the expected speedup is 1.7 to 2.1 times by a 0.7 to 0.9 % accuracy gain. The lattice of rectangular clusters applied first makes rather rough but effective partition allowing to optionally run further clustering on parallel processor cores. The lattice of hexagonal clusters applied to every rectangle allows obtaining initial centroids very quickly. Such centroids are far closer to the solution than the initial centroids in the <italic>k</italic>-means++ algorithm. Another approach to the <italic>k</italic>-means update, where initial centroids are selected separately within every rectangle hexagons, can be used as well. It is faster than selecting initial centroids across all hexagons but is less accurate. The speedup is 9 to 11 times by a possible accuracy loss of 0.3 %. However, this approach may outperform the <italic>k</italic>-means algorithm. The speedup increases as both the lattices become denser and the dataset becomes larger reaching 30 to 50 times.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2023-00012023-08-17T00:00:00.000+00:00https://sciendo.com/article/10.2478/acss-2023-0014<abstract> <title style='display:none'>Abstract</title> <p>The use of smartphones and their applications is expanding rapidly, thereby increasing the demand of computational power and other hardware resources of the smartphones. On the other hand, these small devices can have limited resources of computation power, battery backup, RAM memory, and storage space due to their small size. These devices need to reconcile resource hungry applications. This research focuses on solving issues of power and efficiency of smart devices by adapting intelligently to mobile usage by profiling the user intelligently. Our designed architecture makes a smartphone smarter by intelligently utilizing its resources to increase the battery life. Our developed application makes profiles of the applications usage at different time intervals. These stored usage profiles are utilized to make intelligent resource allocation for next time interval. We implemented and evaluated the profiling scheme for different brands of android smartphone. We implemented our approach with Naive Bayes and Decision Tree for performance and compared it with conventional approach. The results show that the proposed approach based on decision trees saves 31 % CPU and 60 % of RAM usage as compared to the conventional approach.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2023-00142023-08-17T00:00:00.000+00:00https://sciendo.com/article/10.2478/acss-2023-0007<abstract> <title style='display:none'>Abstract</title> <p>The advent of medical imaging significantly assisted in disease diagnosis and treatment. This study introduces to a framework for detecting several human body parts in Computerised Tomography (CT) images formatted in DICOM files. In addition, the method can highlight the bone areas inside CT images and transform 2D slices into a visual 3D model to illustrate the structure of human body parts. Firstly, we leveraged shallow convolutional Neural Networks to classify body parts and detect bone areas in each part. Then, Grad-CAM was applied to highlight the bone areas. Finally, Insight and Visualization libraries were utilized to visualize slides in 3D of a body part. As a result, the classifiers achieved 98 % in F1-score in the classification of human body parts on a CT image dataset, including 1234 slides capturing body parts from a woman for the training phase and 1245 images from a male for testing. In addition, distinguishing between bone and non-bone images can reach 97 % in F1-score on the dataset generated by setting a threshold value to reveal bone areas in CT images. Moreover, the Grad-CAM-based approach can provide clear, accurate visualizations with segmented bones in the image. Also, we successfully converted 2D slice images of a body part into a lively 3D model that provided a more intuitive view from any angle. The proposed approach is expected to provide an interesting visual tool for supporting doctors in medical image-based disease diagnosis.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2023-00072023-08-17T00:00:00.000+00:00https://sciendo.com/article/10.2478/acss-2023-0010<abstract> <title style='display:none'>Abstract</title> <p>Underwater simultaneous localization and mapping (SLAM) poses significant challenges for modern visual SLAM systems. The integration of deep learning networks within computer vision offers promising potential for addressing these difficulties. Our research draws inspiration from deep learning approaches applied to interest point detection and matching, single image depth prediction and underwater image enhancement. In response, we propose 3D-Net, a deep learning-assisted network designed to tackle these three tasks simultaneously. The network consists of three branches, each serving a distinct purpose: interest point detection, descriptor generation, and depth prediction. The interest point detector and descriptor generator can effectively serve as a front end for a classical SLAM system. The predicted depth information is akin to a virtual depth camera, opening up possibilities for various applications. We provide quantitative and qualitative evaluations to illustrate some of these potential uses. The network was trained in in several steps, using in-air datasets and followed by generated underwater datasets. Further, the network is integrated into feature-based SALM systems ORBSLAM2 and ORBSSLAM3, providing a comprehensive assessment of its effectiveness for underwater navigation.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2023-00102023-08-17T00:00:00.000+00:00https://sciendo.com/article/10.2478/acss-2023-0002<abstract> <title style='display:none'>Abstract</title> <p>The widespread use of social media all around the globe has affected the way of life in all aspects, not only for individuals but for businesses as well. Businesses share their upcoming events, reveal their products, and advertise to their potential customers, where individuals use social media to stay connected with their social circles, get updates and news from social media pages of news agencies, and update their information regarding the latest activities, businesses, economy, events, politics, trends, and about their area of interest. According to Statista, there were 4.59 billion users of social media worldwide in 2022 and expected to grow up to 5.85 billion in the year 2027. With its massive user base, social media does not only generate useful information for businesses and individuals, but at the same time, it also creates an abundance of misinformation and disinformation as well as malinformation to acquire social-political or business agendas. Individuals tend to share social media posts without checking the authenticity of the information they are sharing, which results in posts having misinformation, disinformation, or malinformation becoming viral around the world in a matter of minutes. Identifying misinformation, disinformation, and malinformation has become a prominent problem associated with social media.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2023-00022023-08-17T00:00:00.000+00:00https://sciendo.com/article/10.2478/acss-2023-0017<abstract> <title style='display:none'>Abstract</title> <p>Cloud remains an active and dominant player in the field of information technology. Hence, to meet the rapidly growing requirement of computational processes and storage resources, the cloud provider deploys efficient data centres globally that comprise thousands of IT servers. Because of tremendous energy and resource utilization, a reliable cloud platform has to be necessarily optimized. Effective load balancing is a great option to overcome these issues. However, loading balancing difficulties, such as increased computational complexity, the chance of losing the client data during task rescheduling, and consuming huge memory of the host, and new VM (Virtual Machine), need appropriate optimization. Hence, the study aims to create a newly developed IG-WA (Inquisitive Genetic–Wolf Optimization) framework that meritoriously detects the optimized virtual machine in an environment. For this purpose, the system utilises the GWO (Grey Wolf Optimization) method with an evolutionary mechanism for achieving a proper compromise between exploitation and exploration, thereby accelerating the convergence and achieving optimized accuracy. Furthermore, the fitness function evaluated with an inquisitive genetic algorithm adds value to the overall efficacy. Performance evaluation brings forward the outperformance of the proposed IGWO system in terms of energy consumption, execution time and cost, makespan, CPU utilization, and memory utilization. Further, the system attains more comprehensive and better results when compared to the state of art methods.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2023-00172023-08-17T00:00:00.000+00:00https://sciendo.com/article/10.2478/acss-2023-0012<abstract> <title style='display:none'>Abstract</title> <p>Machine learning based sentiment analysis is an interdisciplinary approach in opinion mining, particularly in the field of media and communication research. In spite of their different backgrounds, researchers have collaborated to test, train and again retest the machine learning approach to collect, analyse and withdraw a meaningful insight from large datasets. This research classifies the texts of micro-blog (tweets) into positive and negative responses about a particular phenomenon. The study also demonstrates the process of compilation of corpus for review of sentiments, cleaning the body of text to make it a meaningful text, find people’s emotions about it, and interpret the findings. Till date the public sentiment after abrogation of Article 370 has not been studied, which adds the novelty to this scientific study. This study includes the dataset collection from Twitter that comprises 66.7 % of positive tweets and 34.3 % of negative tweets of the people about the abrogation of Article 370. Experimental testing reveals that the proposed methodology is much more effective than the previously proposed methodology. This study focuses on comparison of unsupervised lexicon-based models (TextBlob, AFINN, Vader Sentiment) and supervised machine learning models (KNN, SVM, Random Forest and Naïve Bayes) for sentiment analysis. This is the first study with cyber public opinion over the abrogation of Article 370. Twitter data of more than 2 lakh tweets were collected by the authors. After cleaning, 29732 tweets were selected for analysis. As per the results among supervised learning, Random Forest performs the best, whereas among unsupervised learning TextBlob achieves the highest accuracy of 99 % and 88 %, respectively. Performance parameters of the proposed supervised machine learning models also surpass the result of the recent study performed in 2023 for sentiment analysis.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2023-00122023-08-17T00:00:00.000+00:00https://sciendo.com/article/10.2478/acss-2023-0006<abstract> <title style='display:none'>Abstract</title> <p>Multimodal biometrics is the technique of using multiple modalities on a single system. This allows us to overcome the limitations of unimodal systems, such as the inability to acquire data from certain individuals or intentional fraud, while improving recognition performance. In this paper, a study of score normalization and its impact on the performance of the system is performed. The fusion of scores requires prior normalisation before applying a weighted sum fusion that separates impostor and genuine scores into a common interval with close ranges. The experiments were carried out on three biometric databases. The results show that the proposed strategy performs very encouragingly, especially in combination with Empirical Modal Decomposition (EMD). The proposed fusion system shows good performance. The best result is obtained by merging the globality online signature and fingerprint where an EER of 1.69 % is obtained by normalizing the scores according to the Min-Max method.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2023-00062023-08-17T00:00:00.000+00:00https://sciendo.com/article/10.2478/acss-2023-0013<abstract> <title style='display:none'>Abstract</title> <p>Sentiment analysis (SA) has been an important focus of study in the fields of computational linguistics and data analysis for a decade. Recently, promising results have been achieved when applying DNN models to sentiment analysis tasks. Long short-term memory (LSTM) models, as well as its derivatives like gated recurrent unit (GRU), are becoming increasingly popular in neural architecture used for sentiment analysis. Using these models in the feature extraction layer of a DNN results in a high dimensional feature space, despite the fact that the models can handle sequences of arbitrary length. Another problem with these models is that they weight each feature equally. Natural language processing (NLP) makes use of word embeddings created with word2vec. For many NLP jobs, deep neural networks have become the method of choice. Traditional deep networks are not dependable in storing contextual information, so dealing with sequential data like text and sound was a nightmare for such networks. This research proposes multichannel word embedding and employing stack of neural networks with lexicon-based padding and attention mechanism (MCSNNLA) method for SA. Using convolution neural network (CNN), Bi-LSTM, and the attention process in mind, this approach to sentiment analysis is described. One embedding layer, two convolution layers with max-pooling, one LSTM layer, and two fully connected (FC) layers make up the proposed technique, which is tailored for sentence-level SA. To address the shortcomings of prior SA models for product reviews, the MCSNNLA model integrates the aforementioned sentiment lexicon with deep learning technologies. The MCSNNLA model combines the strengths of emotion lexicons with those of deep learning. To begin, the reviews are processed with the sentiment lexicon in order to enhance the sentiment features. The experimental findings show that the model has the potential to greatly improve text SA performance.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2023-00132023-08-17T00:00:00.000+00:00https://sciendo.com/article/10.2478/acss-2023-0005<abstract> <title style='display:none'>Abstract</title> <p>The Coronavirus is a virus that spreads very quickly. Therefore, it has had very destructive effects in many areas worldwide. Because X-ray images are an easily accessible, fast, and inexpensive method, they are widely used worldwide to diagnose COVID-19. This study tried detecting COVID-19 from X-ray images using pre-trained VGG16, VGG19, InceptionV3, and Resnet50 CNN architectures and modified versions of these architectures. The fully connected layers of the pre-trained architectures have been reorganized in the modified CNN architectures. These architectures were trained on binary and three-class datasets, revealing their classification performance. The data set was collected from four different sources and consisted of 594 COVID-19, 1345 viral pneumonia, and 1341 normal X-ray images. Models are built using Tensorflow and Keras Libraries with Python programming language. Preprocessing was performed on the dataset by applying resizing, normalization, and one hot encoding operation. Model performances were evaluated according to many performance metrics such as recall, specificity, accuracy, precision, F1-score, confusion matrix, ROC analysis, etc., using 5-fold cross-validation. The highest classification performance was obtained in the modified VGG19 model with 99.84 % accuracy for binary classification (COVID-19 vs. Normal) and in the modified VGG16 model with 98.26 % accuracy for triple classification (COVID-19 vs. Pneumonia vs. Normal). These models have a higher accuracy rate than other studies in the literature. In addition, the number of COVID-19 X-ray images in the dataset used in this study is approximately two times higher than in other studies. Since it is obtained from different sources, it is irregular and does not have a standard. Despite this, it is noteworthy that higher classification performance was achieved than in previous studies. Modified VGG16 and VGG19 models (available at github.com/akaraci/LargeDatasetCovid19) can be used as an auxiliary tool in slight healthcare organizations’ shortage of specialists to detect COVID-19.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2023-00052023-08-17T00:00:00.000+00:00https://sciendo.com/article/10.2478/acss-2023-0011<abstract> <title style='display:none'>Abstract</title> <p>Any set of devices for controlling home appliances that link to a common network and may be controlled independently or remotely are typically referred to as smart home technology. Smart homes and home automation are not completely unknown to people anymore; smart devices and sensors are part of daily life in the 21st century. Among other benefits of home automation devices, it is possible to manage home appliances and monitor resource usage, and security. It is essential to find practical information about smart home users, and possible use cases. The current survey covers smart home usage benefits and challenges for the users. The study presents the result of the collected information from different countries, and the participants are people from a variety of age groups and occupations. The questionnaire that contains both qualitative and quantitative questions was distributed through internet channels such as blog posts and social network groups. Furthermore, to generate the survey questions we conducted a literature review to gain a better understating of the subject and the related work. The research provides a better foundation for future smart home development. As a result of this survey-based study and in addition to finding the desirable home automation features, we discovered the amount of money users are ready to spend to automate their homes. Connecting the favourite smart home features to its users and the amount of money they are ready to spend on them can provide a bigger picture for the smart home industry as a whole and particularly be beneficial for developers and start-ups.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2023-00112023-08-17T00:00:00.000+00:00https://sciendo.com/article/10.2478/acss-2023-0004<abstract> <title style='display:none'>Abstract</title> <p>Besides the successful use of support software in cutting-edge medical procedures, the significance of determining a disease early signs and symptoms before its detection is a growing pressing requirement to raise the standard of medical examination and treatment. This creates favourable conditions, reduces patient inconvenience and hospital overcrowding. Before transferring patients to an appropriate doctor, healthcare staff must have the patient’s symptoms. This study leverages the PhoBERT model to assist in classifying patients with text classification tasks based on symptoms they provided in the first stages of Vietnamese hospital admission. The outcomes of PhoBERT on more than 200 000 text-based symptoms collected from Vietnamese hospitals can improve the classification performance compared to Bag of Words (BOW) with classic machine learning algorithms, and some considered deep learning architectures such as 1D-Convolutional Neural Networks and Long Short-Term Memory. The proposed method can achieve promising results to be deployed in automatic hospital admission procedures in Vietnam.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2023-00042023-08-17T00:00:00.000+00:00https://sciendo.com/article/10.2478/acss-2023-0016<abstract> <title style='display:none'>Abstract</title> <p>Due to the worldwide deficiency of medical test kits and the significant time required by radiology experts to identify the new COVID-19, it is essential to develop fast, robust, and intelligent chest X-ray (CXR) image classification system. The proposed method consists of two major components: feature extraction and classification. The Bag of image features algorithm creates visual vocabulary from two training data categories of chest X-ray images: Normal and COVID-19 patients’ datasets. The algorithm extracts salient features and descriptors from CXR images using the Speeded Up Robust Features (SURF) algorithm. Machine learning with the Clustering-Based Support Vector Machines (CB-SVMs) multiclass classifier is trained using SURF features to classify the CXR image categories. The careful collection of ground truth Normal and COVID-19 CXR datasets, provided by worldwide expert radiologists, has certainly influenced the performance of the proposed CB-SVMs classifier to preserve the generalization capabilities. The high classification accuracy of 99 % demonstrates the effectiveness of the proposed method, where the accuracy is assessed on an independent test sets.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2023-00162023-08-17T00:00:00.000+00:00https://sciendo.com/article/10.2478/acss-2023-0003<abstract> <title style='display:none'>Abstract</title> <p>In order to establish the projection properties of computer uniform designs of experiments on Sobol’s sequences, an empirical comparative statistical analysis of the homogeneity of 2D projections of the best known improved designs of experiments was carried out using the novel objective indicators of discrepancies. These designs show an incomplete solution to the problem of clustering points in low-dimensional projections graphically and numerically, which requires further research for new Sobol’s sequences without the drawback mentioned above. In the article, using the example of the first 20 improved Sobol’s sequences, a methodology for creating refined designs is proposed, which is based on the unconventional use of these already found sequences. It involves the creation of the next dimensional design based on the best homogeneity and projection properties of the previous one. The selection of sequences for creating an initial design is based on the analysis of numerical indicators of the weighted symmetrized centered discrepancy for two-dimensional projections. According to the algorithm, the combination of sequences is fixed for the found variant and a complete search of the added one-dimensional sequences is performed until the best one is detected. According to the proposed methodology, as an example, a search for more perfect variants of designs for factor spaces from two to nine dimensions was carried out. New combinations of Sobol’s sequences with better projection properties than those already known are given. Their effectiveness is confirmed by statistical calculations and graphically demonstrated box plots and histograms of the projection indicators distribution of the weighted symmetrized centred discrepancy. In addition, the numerical results of calculating the volumetric indicators of discrepancies for the created designs with different number of points are given.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2023-00032023-08-17T00:00:00.000+00:00https://sciendo.com/article/10.2478/acss-2023-0009<abstract> <title style='display:none'>Abstract</title> <p>Plant diseases are a primary hazard to the productiveness of crops, which impacts food protection and decreases the profitability of farmers. Consequently, identification of plant diseases becomes a crucial task. By taking the right nurturing measures to remediate these diseases in the early stages can drastically help in fending off the reduction in productivity/profit. Providing an intelligent and automated solution becomes a necessity. This can be achieved with the help of machine learning techniques. It involves a number of steps like image acquisition, image pre-processing using filtering and contrast enhancement techniques. Image segmentation, which is a crucial part in disease detection system, is done by applying genetic algorithm and the colour, texture features extracted using a local binary pattern. The novelty of this approach is applying the genetic algorithm for image segmentation and combining a set of propositions from all the learning classifiers with an ensemble method and calculating the results. This obeys the optimistic features of all the learning classifiers. System accuracy is evaluated using precision, recall, and accuracy measures. After analysing the results, it clearly shows that the ensemble models deliver very good accuracy of over 92 % as compared to an individual SVM, Naïve Bayes, and KNN classifiers.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2023-00092023-08-17T00:00:00.000+00:00https://sciendo.com/article/10.2478/acss-2022-0014<abstract> <title style='display:none'>Abstract</title> <p>Estimation of work efficiency is one of the important tasks in company management and analysis of workers’ activities. The environment for determining the efficiency of workers is the use of one or more IS of any kind, within the framework of which the worker’s duties are accomplished. This process can be performed both from home (isolated from the work environment and co-workers) and in the office on-site. Another aspect is related to the fact that a worker can work both with only one IS, or with multiple specialised systems in parallel, which requires the simultaneous integration of several data sources and the parallel reading and analysis of information relevant to worker activities. The task of work efficiency estimation is especially complicated if the company’s activity domain is narrowly specific. In this case, solving such a task of working hour accounting and efficiency estimation is not a trivial accumulation of statistical data, but requires a dedicated method. The work of an engineering system designer is one of the examples of such specific activities. Therefore, exactly this domain was chosen as an application case of the method and metrics proposed in the paper.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2022-00142023-01-24T00:00:00.000+00:00https://sciendo.com/article/10.2478/acss-2022-0010<abstract> <title style='display:none'>Abstract</title> <p>A method is suggested to find all local minima and the global minimum of an unknown two-variable function bounded on a given rectangle regardless of the rectangle area. The method has eight inputs: five inputs defined straightforwardly and three inputs, which are adjustable. The endpoints of the initial intervals constituting the rectangle and a formula for evaluating the two-variable function at any point of this rectangle are the straightforward inputs. The three adjustable inputs are a tolerance with the minimal and maximal numbers of subintervals along each dimension. The tolerance is the secondary adjustable input. Having broken the initial rectangle into a set of subrectangles, the nine-point iterated rectangle dichotomy “gropes” around every local minimum by successively cutting off 75 % of the subrectangle area or dividing the subrectangle in four. A range of subrectangle sets defined by the minimal and maximal numbers of subintervals along each dimension is covered by running the nine-point rectangle dichotomy on every set of subrectangles. As a set of values of currently found local minima points changes no more than by the tolerance, the set of local minimum points and the respective set of minimum values of the surface are returned. The presented approach is applicable to whichever task of finding local extrema is. If primarily the purpose is to find all local maxima or the global maximum of the two-variable function, the presented approach is applied to the function taken with the negative sign. The presented approach is a significant and important contribution to the field of numerical estimation and approximate analysis. Although the method does not assure obtaining all local minima (or maxima) for any two-variable function, setting appropriate minimal and maximal numbers of subintervals makes missing some minima (or maxima) very unlikely.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2022-00102023-01-24T00:00:00.000+00:00https://sciendo.com/article/10.2478/acss-2022-0017<abstract> <title style='display:none'>Abstract</title> <p>Nowadays, vehicular networks attract car manufacturers, network researchers, and governments as well. They represent one of the building blocks, for the intelligent transportation systems. Our task is to study the employment of SDN advantages to facilitate and improve the performance of vehicular ad-hoc networks. The goal of the research is to evaluate AODV routing protocol performance improved with SDN technology applied on VANET network in specified environment of a city. We have evaluated three parameters: packet delivery ratio, end-to-end delay and throughput using SUMO and NS3 simulators. The implemented evaluation protocol shows the importance of the adopted approach.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/acss-2022-00172023-01-24T00:00:00.000+00:00en-us-1