Computer Science 2023-10-01T22:00:17+02:00 Computer Science Journal Open Journal Systems <p><img style="float: left; margin-right: 15px; margin-bottom: 5px;" src="" alt="" />The Computer Science Journal (ISSN: 1508-2806; e-ISSN: 2300-7036) is a quarterly published by the AGH University of Science and Technology, Krakow Poland since 1999.<br />We publish original papers concerning theoretical and applied computer science problems. Main areas of interest of the journal are: theoretical aspects of computer science, soft computing, HPC, cloud and distributed processing and simulation, multimedia systems and computer graphics, natural language processing.</p> <p>Please note: we don't have any article processing charges, our journal is non-profit. The journal is indexed in ESCI Web of Science and SCOPUS.</p> <p> </p> <p><strong>Web of Science Impact Factor = 0.5</strong></p> <p><strong>Scopus Quartile = 3</strong></p> <!-- <p> </p> <p>Our journal is indexed in the following services: <a href="">Google Scholar</a>, <a href="">CrossRef metadata search</a>, <a href="">Directory of Open Access Journals</a>, <a href="">Open Archives Initiative</a>, <a href="">Digital Libraries Federation</a>, <a href="">BazTech</a>, <a href="">Index Copernicus</a>, <a href="">Ulrich's Periodicals Directory</a>, <a href="">EBSCOhost Applied Sciences</a>, <a href="">DBLP</a>, <a href="">ERIH PLUS</a>, <strong><a href="">SCOPUS</a> and Emerging Sources Citation Index - part of Clarivate Web of Science</strong>.</p> <p>This is an open access journal in accordance with the <a href="">BOAI</a>definition of open access. The content of the journal is freely available according to the <a href="">Creative Commons License Attribution 4.0 International (CC BY 4.0)</a></p> <p><a href=""><strong>SUBMISSION PAGE DIRECT LINK</strong></a></p> <p>Please note:</p> <ul> <li><strong>We do not apply any Article Processing Charges. Our journal is a fully non-profit endeavour.</strong></li> <li>The journal is Open Access (also free of charges).</li> <li>First Author of the accepted paper receives one complimentary hardcopy.</li> <li>We accept PDF or DOC/DOCX manuscripts for review.</li> <li>For final typesetting we strongly prefer Latex/Bibtex. If the authors of the accepted paper are unable to prepare the paper in Latex it will be translated to Latex - for the cost of likely significant delay in publishing. </li> <li>You may use our <a href="">Overleaf template</a> to prepare your paper.</li> <li>We review Survey papers - but only if the authors cite in their paper 3 recent papers by themselves, devoted to the area of the survey. We do not accept nor review surveys authored by non-experts.</li> <li>The paper submitted to our journal is expected to be 15-20 pages long.</li> <li>You are free to publish the early version of the paper in Arxiv, Research Gate and similar websites - but the paper should be updated with the final version, after the paper is accepted and published.</li> <li>We speed up the publication process by publishing early birds versions of the paper (with DOI).</li> <li>The submitted paper should follow typical guidelines for scientific publications - see for example this <a href="">Tutorial</a> by Jennifer Widom.</li> <li>We are using <a href="">IThenticate</a> to prevent (self)plagiarism.</li> <li>You can check our position at <a href=";tip=sid&amp;clean=0">Scimago Journal &amp; Country Rank</a>.</li> </ul> --> Melanoma Skin Cancer and Nevus Mole Classification using Intensity Value Estimation with Convolutional Neural Network 2022-07-05T16:30:34+02:00 N. I. Md. Ashafuddula Rafiqul Islam <p>Melanoma skin cancer is one of the most dangerous and life-threatening cancer. Exposure to ultraviolet rays may damage the skin cell's DNA, which causes melanoma skin cancer. However, it is difficult to detect and classify melanoma and nevus mole at the immature stages. In this work, an automatic deep learning system is developed based on the intensity value estimation with a convolutional neural network model (CNN) to detect and classify melanoma and nevus mole more accurately. Since intensity levels are the most distinctive features for object or region of interest identification, the high-intensity pixel values are selected from the extracted lesion images. Incorporating those high-intensity features into the CNN improves the overall performance than the state-of-the-art methods for detecting melanoma skin cancer. To evaluate the system, we used 5-fold cross-validation. Experimental results show that a superior percentage of accuracy (92.58%), Sensitivity (93.76%), Specificity (91.56%), and Precision (90.68%) are achieved.</p> 2023-10-01T00:00:00+02:00 Copyright (c) 2023 Computer Science Hybrid Variable Neighborhood Search for Solving School Bus-Driver Problem with Resource Constraints 2022-06-22T17:03:59+02:00 Ha-Bang Ban Hong-Phuong Nguyen Dang-Hai Pham <p>The School Bus-Driver Problem with Resource Constraints (SBDP-RC) is an optimization problem with many practical applications. In the problem, the number of vehicles is prepared to pick a number of pupils, in which the total resource of all vehicles is less than a predefined value. The aim is to find a tour minimizing the sum of pupils’ waiting times. The problem is NP-hard in the general case. In many cases, reaching a feasible solution becomes an NP-hard problem. To solve the large-sized problem, a metaheuristic approach is a suitable approach. The first phase creates an initial solution by the construction heuristic based on Insertion Heuristic. After that, the post phase improves the solution by the General Variable Neighborhood Search (GVNS) with Random Neighborhood Search combined with Shaking Technique. The hybridization ensures the balance between exploitation and exploration. Therefore, the proposed algorithm can escape from local optimal solutions. The proposed metaheuristic algorithm is tested on a benchmark to show the efficiency of the algorithm. The results show that the algorithm receives good feasible solutions fast. Additionally, in many cases, better solutions can be found in comparison with the previous metaheuristic algorithms. </p> 2023-10-01T00:00:00+02:00 Copyright (c) 2023 Computer Science A Survey on Multi-Objective Based Parameter Optimization for Deep Learning 2023-05-13T00:24:50+02:00 Mrittika Chakraborty Wreetbhas Pal Sanghamitra Bandyopadhyay Ujjwal Maulik <p>Deep learning models form one of the most powerful machine learning models for the extraction of important features. Most of the designs of deep neural models, i.e., the initialization of parameters, are still manually tuned. Hence, obtaining a model with high performance is exceedingly time-consuming and occasionally impossible. Optimizing the parameters of the deep networks, therefore, requires improved optimization algorithms with high convergence rates. The single objective-based optimization methods generally used are mostly time-consuming and do not guarantee optimum performance in all<br />cases. Mathematical optimization problems containing multiple objective functions that must be optimized simultaneously fall under the category of multi-objective optimization sometimes referred to as Pareto optimization. Multi-objective optimization problems form one of the alternatives yet useful options for parameter optimization. However, this domain is a bit less explored. In this survey, we focus on exploring the effectiveness of multi-objective optimization strategies for parameter optimization in conjunction with deep neural networks. The case studies used in this study focus on how the two<br />methods are combined to provide valuable insights into the generation of predictions and analysis in multiple applications.</p> 2023-10-01T00:00:00+02:00 Copyright (c) 2023 Computer Science A Nature Inspired Hybrid Partitional Clustering Method Based on Grey Wolf Optimization and JAYA Algorithm 2023-01-20T16:19:18+01:00 GYANARANJAN SHIAL Sabita Sahoo Sibarama Panigrahi <p>This paper presents a hybrid meta-heuristic algorithm using Grey Wolf optimization (GWO) and JAYA algorithm for data clustering. The idea is use exploitative capability of JAYA algorithm in the explorative phase of GWO to form compact clusters. Here, instead of using one best and one worst solution for generating offspring, three best wolfs and three worst omega wolfs of the population are used. So, the best wolfs and worst omega wolfs assist in moving the new solutions towards the best solutions and simultaneously helps in staying away from the worst solutions. This enhances the chances of reaching the near optimal solutions. The superiority of the proposed method is compared with five promising algorithms, namely GWO, Sine-Cosine Algorithm (SCA), Particle Swarm Optimization (PSO), JAYA and K-means algorithms. The result obtained from the Duncan’s multiple range test and Nemenyi hypothesis based statistical test confirms the superiority and robustness of our proposed method.</p> 2023-10-01T00:00:00+02:00 Copyright (c) 2023 Computer Science Database Replication for Disconnected Operations with Quasi Real-Time Synchronization 2022-06-01T13:42:03+02:00 Rafal Mucha Bartosz Balis Costin Grigoras Jacek Kitowski <p>Database replication is a way to improve system throughput or achieve high availability. In most cases, using an active-active replica architecture is efficient and easy to deploy. Such a system has CP properties (from the CAP theorem: Consistency, Availability and network Partition tolerance). Creating an AP (available and partition tolerant) system requires using multi-primary replication. This approach, because of many difficulties in implementation, is not widely used. However, deployment of CCDB (experiment conditions and calibration database) needs to be an AP system in two locations. This necessity became an inspiration to examine the state-of-the-art in this field and to test the available solutions. The tests performed evaluate the performance of the chosen replication tools: Bucardo and EDB Replication Server. They show that the tested tools can be successfully used for continuous synchronization of two independent database instances.</p> 2023-10-01T00:00:00+02:00 Copyright (c) 2023 Computer Science Finding Playing Styles of Badminton Players Using Firefly Algorithm Based Clustering Algorithms 2022-12-23T00:21:39+01:00 Anuradha Ariyaratne I M T P K Ilankoon U Samarasinghe R M Silva <p>Cluster analysis can be defined as applying clustering algorithms with the goal of finding hidden patterns or groupings in a dataset. Different clustering methods provide different solutions for the same dataset. Traditional clustering algorithms are popular, but handling big data sets is beyond the ability of such methods. We propose three big data clustering methods, based on the Firefly Algorithm (FA). Three different fitness functions were defined on FA using inter cluster distance, intra cluster distance, silhouette value and Calinski-Harabasz Index. The algorithms find the most appropriate cluster centers for a given data set. The algorithms were tested with four popular synthetic data sets and later applied on two badminton data sets to identify different playing styles of players based on physical characteristics. The results specify that the firefly algorithm could generate better clustering results with high accuracy. The algorithms cluster the players to find the most suitable playing strategy for a given player where expert knowledge is needed in labeling the clusters. Comparisons with a PSO based clustering algorithm (APSO) and traditional algorithms point out that the proposed firefly variants work similarly as the APSO method and surpass the performance of traditional algorithms.</p> 2023-10-01T00:00:00+02:00 Copyright (c) 2023 Computer Science