Computer Science-AGH最新文献

Hybrid Variable Neighborhood Search for Solving School Bus-Driver Problem with Resource Constraints 求解资源约束下校车司机问题的混合变量邻域搜索

Computer Science-AGH Pub Date : 2023-10-01 DOI: 10.7494/csci.2023.24.3.4367

Ha-Bang Ban, Hong-Phuong Nguyen, Dang-Hai Pham

{"title":"Hybrid Variable Neighborhood Search for Solving School Bus-Driver Problem with Resource Constraints","authors":"Ha-Bang Ban, Hong-Phuong Nguyen, Dang-Hai Pham","doi":"10.7494/csci.2023.24.3.4367","DOIUrl":"https://doi.org/10.7494/csci.2023.24.3.4367","url":null,"abstract":"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.","PeriodicalId":41917,"journal":{"name":"Computer Science-AGH","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135406613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Finding Playing Styles of Badminton Players Using Firefly Algorithm Based Clustering Algorithms 利用基于萤火虫算法的聚类算法寻找羽毛球运动员的打球风格

Computer Science-AGH Pub Date : 2023-10-01 DOI: 10.7494/csci.2023.24.3.5116

Anuradha Ariyaratne, I M T P K Ilankoon, U Samarasinghe, R M Silva

{"title":"Finding Playing Styles of Badminton Players Using Firefly Algorithm Based Clustering Algorithms","authors":"Anuradha Ariyaratne, I M T P K Ilankoon, U Samarasinghe, R M Silva","doi":"10.7494/csci.2023.24.3.5116","DOIUrl":"https://doi.org/10.7494/csci.2023.24.3.5116","url":null,"abstract":"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.","PeriodicalId":41917,"journal":{"name":"Computer Science-AGH","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135406941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Database Replication for Disconnected Operations with Quasi Real-Time Synchronization 具有准实时同步的断开连接操作的数据库复制

Computer Science-AGH Pub Date : 2023-10-01 DOI: 10.7494/csci.2023.24.3.4831

Rafal Mucha, Bartosz Balis, Costin Grigoras, Jacek Kitowski

引用次数: 0

A Survey on Multi-Objective Based Parameter Optimization for Deep Learning 基于多目标的深度学习参数优化研究综述

Computer Science-AGH Pub Date : 2023-10-01 DOI: 10.7494/csci.2023.24.3.5479

Mrittika Chakraborty, Wreetbhas Pal, Sanghamitra Bandyopadhyay, Ujjwal Maulik

{"title":"A Survey on Multi-Objective Based Parameter Optimization for Deep Learning","authors":"Mrittika Chakraborty, Wreetbhas Pal, Sanghamitra Bandyopadhyay, Ujjwal Maulik","doi":"10.7494/csci.2023.24.3.5479","DOIUrl":"https://doi.org/10.7494/csci.2023.24.3.5479","url":null,"abstract":"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 allcases. 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 twomethods are combined to provide valuable insights into the generation of predictions and analysis in multiple applications.","PeriodicalId":41917,"journal":{"name":"Computer Science-AGH","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135406617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Melanoma Skin Cancer and Nevus Mole Classification using Intensity Value Estimation with Convolutional Neural Network 基于卷积神经网络强度值估计的黑色素瘤、皮肤癌和痣分类

Computer Science-AGH Pub Date : 2023-10-01 DOI: 10.7494/csci.2023.24.3.4844

N. I. Md. Ashafuddula, Rafiqul Islam

引用次数: 1

A Nature Inspired Hybrid Partitional Clustering Method Based on Grey Wolf Optimization and JAYA Algorithm 基于灰狼优化和JAYA算法的自然启发混合分区聚类方法

Computer Science-AGH Pub Date : 2023-10-01 DOI: 10.7494/csci.2023.24.3.4962

GYANARANJAN SHIAL, Sabita Sahoo, Sibarama Panigrahi

引用次数: 0

Calculating the Centrality Values According to the Strengths of Entities Relative to their Neighbours and Designing a New Algorithm for the Solution of the Minimal Dominating Set Problem 根据实体相对于其邻居的强度计算中心性值并设计一种求解最小支配集问题的新算法

IF 0.5

Computer Science-AGH Pub Date : 2023-06-02 DOI: 10.53070/bbd.1295038

Şeyda Karci, F. Okumuş, A. Karcı

引用次数: 0

LBP Özellik Çıkarma ve İstatistiksel Havuzlama Tabanlı Görüntü Spam Tespit Modeli LBPÖzellikÇıkarma veïstatistiksel Havuzlama TabanlıGörüntüSpam Tespit Modeli

IF 0.5

Computer Science-AGH Pub Date : 2023-05-05 DOI: 10.53070/bbd.1268221

Aytaç Kaşoğlu, Orhan Yaman

{"title":"LBP Özellik Çıkarma ve İstatistiksel Havuzlama Tabanlı Görüntü Spam Tespit Modeli","authors":"Aytaç Kaşoğlu, Orhan Yaman","doi":"10.53070/bbd.1268221","DOIUrl":"https://doi.org/10.53070/bbd.1268221","url":null,"abstract":"Email, which stands for electronic mail, is a form of digital communication between two or more individuals. These technological instruments that facilitate communication can have a positive and negative impact on our lives due to junk e-mails, widely known as spam mail. These spam messages, which are typically delivered for commercial purposes by organizations/individuals for indirect or direct benefits, not only distract people but also consume a significant amount of system resources such as processing power, memory, and network bandwidth. In this study, a method based on LBP (Local Binary Patterns) feature extraction and statistical pooling is proposed to classify spam or raw (non-spam) images. Two datasets are used to test the proposed method. The ISH dataset is widely used in the literature and contains 1738 images. In addition to this dataset, the dataset our collect consists of 1015 images in total. Feature extraction was performed on these images. Obtained features were classified by SVM (Support Vector Machine) algorithm. In the proposed method, 98.56% and 79.01% accuracy were calculated for the ISH dataset and our collected dataset, respectively. The results obtained were compared with the studies in the literature.","PeriodicalId":41917,"journal":{"name":"Computer Science-AGH","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48272692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Parçacık Sürü Optimizasyonu Yoluyla Geliştirilen Doğrusal Bir Sınıflandırıcının Analizi 检查通过切片的智能驱动器优化增强的直接分类

IF 0.5

Computer Science-AGH Pub Date : 2023-04-15 DOI: 10.53070/bbd.1259377

F. Aydin

{"title":"Parçacık Sürü Optimizasyonu Yoluyla Geliştirilen Doğrusal Bir Sınıflandırıcının Analizi","authors":"F. Aydin","doi":"10.53070/bbd.1259377","DOIUrl":"https://doi.org/10.53070/bbd.1259377","url":null,"abstract":"Meta-heuristics are high-level approaches developed to discover a heuristic that provides a reasonable solution to many varieties of optimization problems. The classification problems contain a sort of optimization problem. Simply, the objective herein is to reduce the number of misclassified instances. In this paper, the question of whether meta-heuristic methods can be used to construct linear models or not is answered. To this end, Particle Swarm Optimization (PSO) has been engaged to address linear classification problems. The Particle Swarm Classifier (PSC) with a certain objective function has been compared with Support Vector Machine (SVM), Perceptron Learning Rule (PLR), and Logistic Regression (LR) applied to fifteen data sets. The experimental results point out that PSC can compete with the other classifiers, and it turns out to be superior to other classifiers for some binary classification problems. Furthermore, the average classification accuracies of PSC, SVM, LR, and PLR are 80.8%, 80.6%, 80.9%, and 57.7%, respectively. In order to enhance the classification performance of PSC, more advanced objective functions can be developed. Further, the classification accuracy can be boosted more by constructing tighter constraints via another meta-heuristic.","PeriodicalId":41917,"journal":{"name":"Computer Science-AGH","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46225999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ADAPTATION OF DOMAIN-SPECIFIC TRANSFORMER MODELS WITH TEXT OVERSAMPLING FOR SENTIMENT ANALYSIS OF SOCIAL MEDIA POSTS ON COVID-19 VACCINE 基于文本过采样的特定领域变压器模型用于COVID-19疫苗社交媒体帖子的情感分析

Computer Science-AGH Pub Date : 2023-03-10 DOI: 10.7494/csci.2023.24.2.4761

Anmol Bansal, Arjun Choudhry, Anubhav Sharma, Seba Susan

引用次数: 2