<abstract xmlns="http://www.w3.org/1999/xhtml">

A problem of minimizing the total weighted tardiness in the preemptive single machine scheduling for discrete manufacturing is considered. A hyper-heuristic is presented, which is composed of 24 various heuristics, to find an approximately optimal schedule whenever finding the exact solution is practically intractable. The three heuristics are based on the well-known rules, whereas the 21 heuristics are introduced first. Therefore, the hyper-heuristic selects the best heuristic schedule among 24 schedule versions, whose total weighted tardiness is minimal. Each of the 24 heuristics can solely produce a schedule which is the best one for a given scheduling problem. Despite the percentage of zero gap instances decreases as the greater number of jobs is scheduled, the average and maximal gaps decrease as well. In particular, the percentage is not less than 80 % when up to 10 jobs are scheduled. The average gap calculated over nonzero gaps does not exceed 4 % in the case of scheduling 7 jobs. When manufacturing consists of hundreds of jobs, the hyper-heuristic is made an online scheduling algorithm by applying it only to a starting part of the manufacturing process.
</abstract>

A Hyper-Heuristic for the Preemptive Single Machine Scheduling Problem to Minimize the Total Weighted Tardiness

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The aim of the study is to diagnose Covid-19 by machine learning algorithms using biochemical parameters. In addition to the aim of the study, October selection was performed using 14 different feature selection methods based on the biochemical parameters available to us. As a result of the study, the performance of the algorithms and feature selection methods was evaluated using performance evaluation criteria. The dataset used in the study consists of 100 covid-negative and 121 covid-positive data from a total of 221 patients. The dataset includes 16 biochemical parameters used for the diagnosis of Covid-19. Feature selection methods were used to reduce the number of parameters and perform the classification process. The result of the study shows that the new feature set obtained using feature selection algorithms yields very similar results to the set containing all features. Overall, 5 features obtained from 16 features by feature selection methods yielded the best performance for the K-Nearest Neighbour algorithm with the FSVFS feature selection method of 86.4 %.
</abstract>

Incorporating Feature Selection Methods into Machine Learning-Based Covid-19 Diagnosis

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For the next seven years, the digitalisation of higher education is one of the priority tasks of Latvia. An extensive review of information sources was performed, and an online survey with the technical staff of higher education institutions was conducted to evaluate the progress made towards education digitalisation in Latvia and compare these results with the countries of the Nordic-Baltic region. The paper presents the study results and identifies issues hindering the digitalisation progress, e.g., issues with the legislation, basic digital skills, and required competences for academic staff.
</abstract>

The Impact of Digitalisation of Higher Education: The Case of Latvia and Nordic-Baltic Region

<abstract xmlns="http://www.w3.org/1999/xhtml">

The world is heading towards more modernized and digitalized data and therefore a significant growth is observed in the active number of social media users with each passing day. Each post and comment can give an insight into valuable information about a certain topic or issue, a product or a brand, etc. Similarly, the process to uncover the underlying information from the opinion that a person keeps about any entity is called a sentiment analysis. The analysis can be carried out through two main approaches, i.e., either lexicon-based or machine learning algorithms. A significant amount of work in the different domains has been done in numerous languages for sentiment analysis, but minimal research has been conducted on the national language of Pakistan, which is Urdu. Twitter users who are familiar with Urdu update the tweets in two different textual formats either in Urdu Script (Nastaleeq) or in Roman Urdu. Thus, the paper is an attempt to perform the sentiment analysis on the Urdu language by extracting the tweets (Nastaleeq and Roman Urdu both) from Twitter using Tweepy API. A machine learning-based approach has been adopted for this study and the tool opted for the purpose is WEKA. The best algorithm was identified based on evaluation metrics, which comprise the number of correctly and incorrectly classified instances, accuracy, precision, and recall. SMO was found to be the most suitable machine learning algorithm for performing the sentiment analysis on Urdu (Nastaleeq) tweets, while the Roman Urdu Random Forest algorithm was identified as the best one.
</abstract>

Urdu Sentiment Analysis

<abstract xmlns="http://www.w3.org/1999/xhtml">

Intelligent monitoring of a computer network provides a clear understanding of its behaviour at various times and in various situations. It also provides relief to support teams that spend most of their time troubleshooting problems caused by hardware or software failures. This type of monitoring ensures the accuracy and efficiency of the network to meet the expectations of its users. However, to ensure intelligent monitoring, it is necessary to start by automating this process, which often leads to long and costly interventions. The success of such automation implies the establishment of predictive maintenance as a prerequisite for good preventive maintenance governance. However, even when it is practiced effectively, preventive maintenance requires a great deal of time and the mobilization of several full-time resources, especially for large IT structures. This paper gives an overview of the monitoring of a computer network and explains its process and the problems encountered. It also proposes a method based on machine learning to allow for prediction and support decision making to proactively anticipate interventions.
</abstract>

Proposing a Layer to Integrate the Sub-classification of Monitoring Operations Based on AI and Big Data to Improve Efficiency of Information Technology Supervision

<abstract xmlns="http://www.w3.org/1999/xhtml">

During COVID-19 pandemic, interest in mHealth rose dramatically. An ample literature review was carried out to discover whether personality traits could be the basis for mHealth personalization for human-computer interaction improvement. Moreover, the study of three most popular mHealth applications was conducted to determine data collected by users. The results showed that personality traits affected communication and physical activity preferences, motivation, and application usage. mHealth personalization based on personality traits could suggest enjoyable physical activities and motivational communication. mHealth applications already process enough user information to enable seamless inference of personality traits.
</abstract>

mHealth and User Interaction Improvement by Personality Traits-Based Personalization

<abstract xmlns="http://www.w3.org/1999/xhtml">

The study aims to develop an information-computing complex for computer design of a centrifugal compressor with parallel calculation of stages and optimization of the geometric parameters of the impellers and the diffusers. The paper presents a universal methodology and computerized information system of the main geometry parameter determination and optimization of the centrifugal compressor impellers and vanned diffusers. Optimization of cross-sectional areas of the input and output channels of the impeller and diffuser blade channels is held using a gradient descent method by gas flowrate quadratic integral deviation criteria. The information-computing complex is built on the algorithm proposed by the authors and implemented as a computer program with a human-machine interface. Calculation data are written in the form of numerical arrays with the possibility of interpolating data and obtaining graphical dependencies.
</abstract>

Admiral Makarov National University of Shipbuilding

A Methodology and Information System for Computing and Optimization of Impellers and Vanned Diffusers Geometry Parameters

<abstract xmlns="http://www.w3.org/1999/xhtml">

In the modern online world, users are often asked for a permission to track their actions as a permission to “allow cookies”. The gathered information could be very valuable for a potential advertiser. However, online tracking is not only a benefit for a user but also a threat to the user’s privacy. This information combined with a targeted advertisement on a mass scale has potential to alter behaviour of large groups. This study summarises previous academic work on online user tracking and anti-tracking measures. As a result, it describes the current mechanisms used to track a user, as well as some methods that can be applied to reduce tracking. The study concludes that government legislation and open dialog between Internet users and advertisers might be the only way to ensure online privacy.
</abstract>

Internet User Trackers and Where to Find Them

<abstract xmlns="http://www.w3.org/1999/xhtml">

One of the most important factors triggering the occurrence of traffic accidents is that drivers continue to drive in a tired and drowsy state. It is a great opportunity to regularly control the dynamics of the driver with transfer learning methods while driving, and to warn the driver in case of possible drowsiness and to focus their attention in order to prevent traffic accidents due to drowsiness. A classification study was carried out with the aim of detecting the drowsiness of the driver by the position of the eyelids and the presence of yawning movement using the Convolutional Neural Network (CNN) architecture. The dataset used in the study includes the face shapes of drivers of different genders and different ages while driving. Accuracy and F1-score parameters were used for experimental studies. The results achieved are 91 % accuracy for the VGG16 model and an F1-score of over 90 % for each class.
</abstract>

Detection of Driver Dynamics with VGG16 Model

AHEAD OF PRINT

Volume 28 (2023): Issue 2 (December 2023)

Volume 28 (2023): Issue 1 (June 2023)

Volume 27 (2022): Issue 2 (December 2022)

Volume 27 (2022): Issue 1 (June 2022)

Volume 26 (2021): Issue 2 (December 2021)

Volume 26 (2021): Issue 1 (May 2021)

Volume 25 (2020): Issue 2 (December 2020)

Volume 25 (2020): Issue 1 (May 2020)

Volume 24 (2019): Issue 2 (December 2019)

Volume 24 (2019): Issue 1 (May 2019)

Volume 23 (2018): Issue 2 (December 2018)

Volume 23 (2018): Issue 1 (May 2018)

Volume 22 (2017): Issue 1 (December 2017)

Volume 21 (2017): Issue 1 (May 2017)

Volume 20 (2016): Issue 1 (December 2016)

Volume 19 (2016): Issue 1 (May 2016)

Volume 18 (2015): Issue 1 (December 2015)

Volume 17 (2015): Issue 1 (May 2015)

Volume 16 (2014): Issue 1 (December 2014)

Volume 15 (2014): Issue 1 (July 2014)

Volume 14 (2013): Issue 1 (June 2013)

Volume 13 (2012): Issue 1 (October 2012)

Applied Computer Systems

 The Journal Applied Computer Systems is an open access scientific journal published biannually in May and December. The journal serves for experience generation in computer science and information technologies use in different applications and is a source of information for the industry practitioners, for academic personnel and students.  The journal publishes research papers describing new results, original ideas and applications or reports on the development and maintenance of actual systems addressing issues concerning theory, development, management and applications of advanced applied computer science.  The topics covered by the journal are:   Artificial Intelligence  Computer Applications  Data  Information Technology and Systems  Programming Technologies  Software Engineering   Besides, the reader will find papers on conception of framework for automated creation of graph visualisation systems, theoretical basis and practical usage of simulation for creating solutions for data management systems, modelling of software systems and business processes, comparison of programming languages, intelligent tutoring systems, semantic web development, applications of cloud-based services, issues of dynamic wireless networks, as well as surveys and papers focused on different other specific topics.  The aim of the publishers is to encourage the applied research by granting free publication possibilities to professionals in the software field, young scientists and doctoral students.  The authors of articles are researchers from various educational and research institutions and companies not only from different European countries, but also from Africa, North and South America, Asia and Australia.  Rejection Rate   47%   Publication History 　  Currently known as Applied Computer Systems 2012 – current, ISSN 2255-8683 (print), ISSN 2255-8691 (online).Formerly known as Scientific Journal of Riga Technical University, Series Computer Science. Applied Computer Systems 2000–2011, ISSN 1407-7493 (print).In 2012 the journal Applied Computer Systems separated from the RTU journal series Computer Science and the present volume numbering reflects the number of journals published within the series since 2000.   Archiving  Sciendo archives the contents of this journal in Portico - digital long-term preservation service of scholarly books, journals and collections.  Plagiarism Policy  The editorial board is participating in a growing community of Similarity Check System'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. 