rss_2.0Applied Computer Systems FeedSciendo RSS Feed for Applied Computer Systemshttps://sciendo.com/journal/ACSShttps://www.sciendo.comApplied Computer Systems 's Coverhttps://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/6305f646c1b7b432f7d11e0f/cover-image.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20221205T085521Z&X-Amz-SignedHeaders=host&X-Amz-Expires=604799&X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20221205%2Feu-central-1%2Fs3%2Faws4_request&X-Amz-Signature=0af86c097cba4d18890d10cdbd76da2e5844e6593b314b77efd73e3c99deab61200300A Hyper-Heuristic for the Preemptive Single Machine Scheduling Problem to Minimize the Total Weighted Tardinesshttps://sciendo.com/article/10.2478/acss-2022-0001<abstract> <title style='display:none'>Abstract</title> <p>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.</p> </abstract>ARTICLE2022-08-23T00:00:00.000+00:00Proposing a Layer to Integrate the Sub-classification of Monitoring Operations Based on AI and Big Data to Improve Efficiency of Information Technology Supervisionhttps://sciendo.com/article/10.2478/acss-2022-0005<abstract> <title style='display:none'>Abstract</title> <p>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.</p> </abstract>ARTICLE2022-08-23T00:00:00.000+00:00Urdu Sentiment Analysishttps://sciendo.com/article/10.2478/acss-2022-0004<abstract> <title style='display:none'>Abstract</title> <p>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.</p> </abstract>ARTICLE2022-08-23T00:00:00.000+00:00The Impact of Digitalisation of Higher Education: The Case of Latvia and Nordic-Baltic Regionhttps://sciendo.com/article/10.2478/acss-2022-0003<abstract> <title style='display:none'>Abstract</title> <p>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.</p> </abstract>ARTICLE2022-08-23T00:00:00.000+00:00Internet User Trackers and Where to Find Themhttps://sciendo.com/article/10.2478/acss-2022-0008<abstract> <title style='display:none'>Abstract</title> <p>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.</p> </abstract>ARTICLE2022-08-23T00:00:00.000+00:00Detection of Driver Dynamics with VGG16 Modelhttps://sciendo.com/article/10.2478/acss-2022-0009<abstract> <title style='display:none'>Abstract</title> <p>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.</p> </abstract>ARTICLE2022-08-23T00:00:00.000+00:00A Methodology and Information System for Computing and Optimization of Impellers and Vanned Diffusers Geometry Parametershttps://sciendo.com/article/10.2478/acss-2022-0007<abstract> <title style='display:none'>Abstract</title> <p>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.</p> </abstract>ARTICLE2022-08-23T00:00:00.000+00:00Incorporating Feature Selection Methods into Machine Learning-Based Covid-19 Diagnosishttps://sciendo.com/article/10.2478/acss-2022-0002<abstract> <title style='display:none'>Abstract</title> <p>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 %.</p> </abstract>ARTICLE2022-08-23T00:00:00.000+00:00mHealth and User Interaction Improvement by Personality Traits-Based Personalizationhttps://sciendo.com/article/10.2478/acss-2022-0006<abstract> <title style='display:none'>Abstract</title> <p>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.</p> </abstract>ARTICLE2022-08-23T00:00:00.000+00:00Real-Time Identification from Gait Features Using Cascade Voting Methodhttps://sciendo.com/article/10.2478/acss-2021-0020<abstract> <title style='display:none'>Abstract</title> <p>There are several biometric methods for identification. These are generally classified under two main groups as physiological and behavioural biometric methods. Recently, methods using behavioural biometric features have gained popularity. Identification made using gait pattern is also one of these methods. The present study proposes a machine learning based system performing identification in real time via gait features using a Kinect device. The data set is composed of 23 individuals’ skeleton model data obtained by the authors. From these data, 147 handcrafted features have been extracted. Deep Neural Network (DNN), Random Forest (RF), Gradient Boosting (GB), XG-Boost (XGB) and <italic>K</italic>-Nearest Neighbour (KNN) classifiers have been trained with these features. Furthermore, the output of these five machine learning models has been combined with a voting approach. The highest classification has been obtained with 97.5 % accuracy via a voting approach. The classification accuracies of the RF, DNN, XGB, GB and KNN classifiers are 95 %, 87.5 %, 85 %, 80 % and 65 %, respectively. The classification accuracy obtained via a voting approach is higher than in the previous studies. The developed system successfully performs real-time identification.</p> </abstract>ARTICLE2021-12-30T00:00:00.000+00:00Text Tone Determination Using Fuzzy Logichttps://sciendo.com/article/10.2478/acss-2021-0019<abstract> <title style='display:none'>Abstract</title> <p>The study proposes the text tone detection system based on sentiment dictionaries and fuzzy rules. Computer analysis of texts from different sources has been performed in emotional categories: anger, anticipation, disgust, fear, joy, sadness, surprise and trust. A synonym dictionary has been used to expand the vocabulary. To increase the accuracy and validity of sentiment analysis, the authors of the study have used coefficients that take into account different emotional loads of words of various parts of speech and the action of intensifying or softening adverbs. A quantitative value of the text tone has been obtained as a result of an aggregation of normalized data on all emotional categories by the fuzzy inference methods. It has been found that emotional words have a greater impact on the text tone value in the case of analysis of short messages. The proposed approach makes it possible to contribute to all emotional categories in the final text evaluation.</p> </abstract>ARTICLE2021-12-30T00:00:00.000+00:00Curriculum Learning for Age Estimation from Brain MRIhttps://sciendo.com/article/10.2478/acss-2021-0014<abstract> <title style='display:none'>Abstract</title> <p>Age estimation from brain MRI has proved to be considerably helpful in early diagnosis of diseases such as Alzheimer’s and Parkinson’s. In this study, curriculum learning effect on age estimation models was measured using a brain MRI dataset consisting of normal and anomaly data. Three different strategies were selected and compared using 3D Convolutional Neural Networks as the Deep Learning architecture. The strategies were as follows: (1) model training performed only on normal data, (2) model training performed on the entire dataset, (3) model training performed on normal data first and then further training on the entire dataset as per curriculum learning. The results showed that curriculum learning improved results by 20 % compared to traditional training strategies. These results suggested that in age estimation tasks datasets consisting of anomaly data could also be utilized to improve performance.</p> </abstract>ARTICLE2021-12-30T00:00:00.000+00:00Solution to On-line vs On-site Work Efficiency Analysis on the Example of Engineering System Designer Workhttps://sciendo.com/article/10.2478/acss-2021-0011<abstract> <title style='display:none'>Abstract</title> <p>Day-to-day working activities have been heavily altered by COVID-19 pandemic, forcing a transition from traditional on-site work to on-line telework across the whole world. It has become much harder to efficiently organise, guide and evaluate employee’s work. There are different factors that can influence “work from home” quality, and many of these affect such work negatively. A set of relevant methods and tools should be developed which could improve this situation. The goal of the study is to summarise related background of this problem and to propose an approach to overcoming this problem. To achieve the goal, design engineer’s work is evaluated in an appropriate environment (e.g., AutoCAD, etc.) using automated analysis and visualization of IS auditing data.</p> </abstract>ARTICLE2021-12-30T00:00:00.000+00:00Academic Performance Modelling with Machine Learning Based on Cognitive and Non-Cognitive Featureshttps://sciendo.com/article/10.2478/acss-2021-0015<abstract> <title style='display:none'>Abstract</title> <p>The academic performance of students is essential for academic progression at all levels of education. However, the availability of several cognitive and non-cognitive factors that influence students’ academic performance makes it challenging for academic authorities to use conventional analytical tools to extract hidden knowledge in educational data. Therefore, Educational Data Mining (EDM) requires computational techniques to simplify planning and determining students who might be at risk of failing or dropping from school due to academic performance, thus helping resolve student retention. The paper studies several cognitive and non-cognitive factors such as academic, demographic, social and behavioural and their effect on student academic performance using machine learning algorithms. Heterogenous lazy and eager machine learning classifiers, including Decision Tree (DT), <italic>K</italic>-Nearest-Neighbour (KNN), Artificial Neural Network (ANN), Logistic Regression (LR), Random Forest (RF), AdaBoost and Support Vector Machine (SVM) were adopted and training was performed based on k-fold (<italic>k</italic> = 10) and leave-one-out cross-validation. We evaluated their predictive performance using well-known evaluation metrics like Area under Curve (AUC), F-1 score, Precision, Accuracy, Kappa, Matthew’s correlation coefficient (MCC) and Recall. The study outcome shows that Student Absence Days (SAD) are the most significant predictor of students’ academic performance. In terms of prediction accuracy and AUC, the RF (Acc = 0.771, AUC = 0.903), LR (Acc = 0.779, AUC = 0.90) and ANN (Acc = 0.760, AUC = 0.895) outperformed all other algorithms (KNN (Acc = 0.638, AUC = 0.826), SVM (Acc = 0.727, AUC = 0.80), DT (Acc = 0.733, AUC = 0.876) and AdaBoost (Acc = 0.748, AUC = 0.808)), making them more suitable for predicting students’ academic performance.</p> </abstract>ARTICLE2021-12-30T00:00:00.000+00:00The Process of Data Validation and Formatting for an Event-Based Vision Dataset in Agricultural Environmentshttps://sciendo.com/article/10.2478/acss-2021-0021<abstract> <title style='display:none'>Abstract</title> <p>In this paper, we describe our team’s data processing practice for an event-based camera dataset. In addition to the event-based camera data, the Agri-EBV dataset contains data from LIDAR, RGB, depth cameras, temperature, moisture, and atmospheric pressure sensors. We describe data transfer from a platform, automatic and manual validation of data quality, conversions to multiple formats, and structuring of the final data. Accurate time offset estimation between sensors achieved in the dataset uses IMU data generated by purposeful movements of the sensor platform. Therefore, we also outline partitioning of the data and time alignment calculation during post-processing.</p> </abstract>ARTICLE2021-12-30T00:00:00.000+00:00Determining and Measuring the Amount of Region Having COVID-19 on Lung Imageshttps://sciendo.com/article/10.2478/acss-2021-0023<abstract> <title style='display:none'>Abstract</title> <p>It is important to know how much the lungs are affected in the course of the disease in patients with COVID-19. Detecting infected tissues on CT lung images not only helps diagnose the disease but also helps measure the severity of the disease. In this paper, using the hybrid artificial intelligence-based segmentation method, which we call TA-Segnet, it has been revealed how the region with COVID-19 affects the lung on 2D CT images. A hybrid convolutional neural network-based segmentation method (TA-Segnet) has been developed for this process. We use “COVID-19 CT Lung and Infection Segmentation Dataset” and “COVID-19 CT Segmentation Dataset” to evaluate TA-SegNET. At first, the tissues with COVID-19 on each lung image are determined, then the measurements obtained are evaluated according to the parameters of Accuracy, Dice, Jaccard, Mean Square Error, Mutual Information and Cross-correlation. Accuracy, Dice, Jaccard, Mean Square Error, Mutual Information and Cross-correlation values for data set-1 are 98.63 %, 0.95, 0.919, 0.139, 0.51, and 0.904, respectively. For data set-2, these parameters are 98.57 %, 0.958, 0.992, 0.0088, 0.565 and 0.8995, respectively. Second, the ratio of COVID-19 regions relative to the lung region on CT images is determined. This ratio is compared with the values in the original data set. The results obtained show that such an artificial intelligence-based method during the pandemic period will help prioritize and automate the diagnosis of COVID-19 patients.</p> </abstract>ARTICLE2021-12-30T00:00:00.000+00:00Time Series Forecasting of Mobile Robot Motion Sensors Using LSTM Networkshttps://sciendo.com/article/10.2478/acss-2021-0018<abstract> <title style='display:none'>Abstract</title> <p>Deep neural networks are a tool for acquiring an approximation of the robot mathematical model without available information about its parameters. This paper compares the LSTM, stacked LSTM and phased LSTM architectures for time series forecasting. In this paper, motion sensor data from mobile robot driving episodes are used as the experimental data. From the experiment, the models show better results for short-term prediction, where the LSTM stacked model slightly outperforms the other two models. Finally, the predicted and actual trajectories of the robot are compared.</p> </abstract>ARTICLE2021-12-30T00:00:00.000+00:00Evaluation of Fingerprint Selection Algorithms for Two-Stage Plagiarism Detectionhttps://sciendo.com/article/10.2478/acss-2021-0022<abstract> <title style='display:none'>Abstract</title> <p>Generally, the process of plagiarism detection can be divided into two main stages: source retrieval and text alignment. The paper evaluates and compares effectiveness of five fingerprint selection algorithms used during the source retrieval stage: <italic>Every p-th</italic>, <italic>0 mod p</italic>, <italic>Winnowing</italic>, <italic>Frequency-biased Winnowing</italic> (<italic>FBW</italic>) and <italic>Modified FBW</italic> (<italic>MFBW</italic>). The algorithms are evaluated on a dataset containing plagiarism cases in Bachelor and Master Theses written in English in the field of computer science. The best performance is reached by <italic>0 mod p</italic>, <italic>Winnowing</italic> and <italic>MFBW</italic>. For these algorithms, reduction of fingerprint size from 100 % to about 20 % kept the effectiveness at approximately the same level. Moreover, <italic>MFBW</italic> sends overall fewer document pairs to the text alignment stage, thus also reducing the computational cost of the process. The software developed for this study is freely available at the author’s website <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://www.cs.rtu.lv/jekabsons/">http://www.cs.rtu.lv/jekabsons/</ext-link>.</p> </abstract>ARTICLE2021-12-30T00:00:00.000+00:00Adversarial Attacks and Defense Technologies on Autonomous Vehicles: A Reviewhttps://sciendo.com/article/10.2478/acss-2021-0012<abstract> <title style='display:none'>Abstract</title> <p>In recent years, various domains have been influenced by the rapid growth of machine learning. Autonomous driving is an area that has tremendously developed in parallel with the advancement of machine learning. In autonomous vehicles, various machine learning components are used such as traffic lights recognition, traffic sign recognition, limiting speed and pathfinding. For most of these components, computer vision technologies with deep learning such as object detection, semantic segmentation and image classification are used. However, these machine learning models are vulnerable to targeted tensor perturbations called adversarial attacks, which limit the performance of the applications. Therefore, implementing defense models against adversarial attacks has become an increasingly critical research area. The paper aims at summarising the latest adversarial attacks and defense models introduced in the field of autonomous driving with machine learning technologies up until mid-2021.</p> </abstract>ARTICLE2021-12-30T00:00:00.000+00:00A Cognitive Rail Track Breakage Detection System Using Artificial Neural Networkhttps://sciendo.com/article/10.2478/acss-2021-0010<abstract> <title style='display:none'>Abstract</title> <p>Rail track breakages represent broken structures consisting of rail track on the railroad. The traditional methods for detecting this problem have proven unproductive. The safe operation of rail transportation needs to be frequently monitored because of the level of trust people have in it and to ensure adequate maintenance strategy and protection of human lives and properties. This paper presents an automatic deep learning method using an improved fully Convolutional Neural Network (FCN) model based on <italic>U</italic>-Net architecture to detect and segment cracks on rail track images. An approach to evaluating the extent of damage on rail tracks is also proposed to aid efficient rail track maintenance. The model performance is evaluated using precision, recall, F1-Score, and Mean Intersection over Union (MIoU). The results obtained from the extensive analysis show <italic>U</italic>-Net capability to extract meaningful features for accurate crack detection and segmentation.</p> </abstract>ARTICLE2021-12-30T00:00:00.000+00:00en-us-1