Sürdürülebilir Kalkınma Amaçları

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• MakaleAçık Erişim2024/r/munzur/4055
  An Exploratory Study on the Application of Blockchain Technology to the Chinese Ship Auction Market

  Peng, Chen; Alatas, Bilal

  The Chinese ship trading market has undergone remarkable development, transitioning into a global exemplar of ship transactions. Nevertheless, this market continues to confront a series of challenges, encompassing low transaction rates, delayed transaction values, and market instability. Blockchain, with its secure, transparent, and tamper-resistant technological features, presents a potential solution for the ship trading market. Through its decentralized architecture, blockchain technology can reduce platform operational costs, enhancing market competitiveness. Concurrently, the utilization of asymmetric encryption technology can enhance the security of platform data, effectively addressing privacy concerns. Furthermore, through a sharing mechanism, blockchain can aid in establishing a unified credit evaluation system, thereby increasing market trust to address the absence of a credit evaluation system. Moreover, blockchain technology can be employed to construct a risk identification mechanism, fortifying the platform's regulatory model to address regulatory deficiencies.

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  AtıfWoS1·OpenAlex2

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• MakaleAçık Erişim2021Scopus SJRQ1/r/munzur/2404
  A novel approach for efficient stance detection in online social networks with metaheuristic optimization

  Can, Umit; Alatas, Bilal

  In the 19th and 20th centuries, social networks have been an important topic in a wide range of fields from sociology to education. However, with the advances in computer technology in the 21st century, significant changes have been observed in social networks, and conventional networks have evolved into online social networks. The size of these networks, along with the large amount of data they generate, has introduced new social networking problems and solutions. Social network analysis methods are used to understand social network data. Today, several methods are implemented to solve various social network analysis problems, albeit with limited success in certain problems. Thus, the researchers develop new methods or recommend solutions to improve the performance of the existing methods. In the present paper, a novel optimization method that aimed to classify social network analysis problems was proposed. The problem of stance detection, an online social network analysis problem, was first tackled as an optimization problem. Furthermore, a new hybrid metaheuristic optimization algorithm was proposed for the first time in the current study, and the algorithm was compared with various methods. The analysis of the findings obtained with accuracy, precision, recall, and F-measure classification metrics demonstrated that our method performed better than other methods.

  AtıfScopus12·WoS9·OpenAlex11

  İncele
• MakaleAçık Erişim2020Scopus SJRQ2/r/munzur/3486
  Sentiment classification within online social media using whale optimization algorithm and social impact theory based optimization

  Akyol, Sinem; Alatas, Bilal

  In recent years, social networks have become an important part of daily life and are becoming increasingly important. In this study, sentiment analysis or opinion mining, which is one of the most well-known social network analysis problems, is considered as an optimization problem for the first time. In the same way, for the first time, sentiment analysis is considered as a multi-objective problem. Whale Optimization Algorithm and Social Impact Theory based Optimization Algorithm, which are the current intelligent optimization algorithms, are adapted for the sentiment analysis problem. Furthermore, memory feature is integrated into Social Impact Theory based Optimization Algorithm in order to obtain effective results in this study. The obtained results from the proposed algorithms are also compared with thirty-three supervised learning algorithms within real IMDB, Polarity, and Amazon data sets. In order to evaluate the performance of the results; accuracy percentage, precision, recall, F-Measure, and MCC that are the five most commonly used measure in the literature are used. When the results are examined, it is observed that adapted metaheuristic optimization algorithms give more successful results in sentiment analysis problem.

  AtıfScopus35·WoS27·OpenAlex43

  İncele
• Derleme MakaleAçık Erişim2019Scopus SJRQ2/r/munzur/3671
  A new direction in social network analysis: Online social network analysis problems and applications

  Can, Umit; Alatas, B.

  The use of online social networks has made significant progress in recent years as the use of the Internet has become widespread worldwide as the technological infrastructure and the use of technological products evolve. It has become more suitable to reach online social networking sites such as Facebook, Twitter, Instagram and LinkedIn via the internet and web 3.0 technologies. Thus, people have shared their views on many different topics and their emotions with other users more widely on these platforms. This means that a huge amount of data is created on platforms where millions of people connect with each other through social networks. Nevertheless, the development of computational paradigms at high speed and complexity with technological possibilities allows analysis of valuable data by means of social network analysis methods. Our goal for this paper is to present a review of novel and popular online social network analysis problems with related applications and a reference work for researchers interested in analyzing online social network data and social network problems. Unlike other individual studies we have gathered 21 online social network problems and defined them with related studies. Thus, this study is original by presenting an important source of research by explaining the problems of online social network and the studies performed in this area.

  AtıfScopus172·WoS113

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• MakaleAçık Erişim2017Scopus SJRQ1/r/munzur/4406
  Plant intelligence based metaheuristic optimization algorithms

  Akyol, Sinem; Alatas, Bilal

  Classical optimization algorithms are insufficient in large scale combinatorial problems and in nonlinear problems. Hence, metaheuristic optimization algorithms have been proposed. General purpose metaheuristic methods are evaluated in nine different groups: biology-based, physics-based, social-based, music-based, chemical-based, sport-based, mathematics-based, swarm-based, and hybrid methods which are combinations of these. Studies on plants in recent years have showed that plants exhibit intelligent behaviors. Accordingly, it is thought that plants have nervous system. In this work, all of the algorithms and applications about plant intelligence have been firstly collected and searched. Information is given about plant intelligence algorithms such as Flower Pollination Algorithm, Invasive Weed Optimization, Paddy Field Algorithm, Root Mass Optimization Algorithm, Artificial Plant Optimization Algorithm, Sapling Growing up Algorithm, Photosynthetic Algorithm, Plant Growth Optimization, Root Growth Algorithm, Strawberry Algorithm as Plant Propagation Algorithm, Runner Root Algorithm, Path Planning Algorithm, and Rooted Tree Optimization.

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  AtıfWoS188·OpenAlex218

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• MakaleAçık Erişim2017Scopus SJRQ3/r/munzur/3921
  Automatic Mining of Quantitative Association Rules with Gravitational Search Algorithm

  Can, Umit; Alatas, Bilal

  The classical optimization algorithms are not efficient in solving complex search and optimization problems. Thus, some heuristic optimization algorithms have been proposed. In this paper, exploration of association rules within numerical databases with Gravitational Search Algorithm (GSA) has been firstly performed. GSA has been designed as search method for quantitative association rules from the databases which can be regarded as search space. Furthermore, determining the minimum values of confidence and support for every database which is a hard job has been eliminated by GSA. Apart from this, the fitness function used for GSA is very flexible. According to the interested problem, some parameters can be removed from or added to the fitness function. The range values of the attributes have been automatically adjusted during the time of mining of the rules. That is why there is not any requirements for the pre-processing of the data. Attributes interaction problem has also been eliminated with the designed GSA. GSA has been tested with four real databases and promising results have been obtained. GSA seems an effective search method for complex numerical sequential patterns mining, numerical classification rules mining, and clustering rules mining tasks of data mining.

  AtıfScopus27·WoS18·OpenAlex28

  İncele
• MakaleAçık Erişim2017Scopus SJRQ2/r/munzur/3859
  Big social network data and sustainable economic development

  Can, Umit; Alatas, Bilal

  New information technologies have led to the rapid and effective growth of social networks. The amount of data produced by social networks has increased the value of the big data concept, which is one of the popular current phenomena. The immediate or unpredictable effects of a wide array of economic activities on large masses and the reactions to them can be measured by using social media platforms and big data methods. Thus, it would be extremely beneficial to analyze the harmful environmental and social impacts that are caused by unsustainable business applications. As social networks and big data are popular realms currently, their efficient use would be an important factor in sustainable economic development. Accurate analysis of people's consumption habits and economic tendencies would provide significant advantages to companies. Moreover, unknown consumption factors that affect the economic preferences of individuals can be discovered and economic efficiency can be increased. This study shows that the numerous solution opportunities that are provided by social networks and big data have become significant tools in dynamic policy creation by companies and states, in solving problems related to women's rights, the environment, and health.

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  AtıfScopus42·WoS34·OpenAlex50

  İncele
• MakaleAçık Erişim2017/r/munzur/668
  Performance comparisons of current metaheuristic algorithms on unconstrained optimization problems

  Can, Umit; Alatas, Bilal

  Nature-inspired metaheuristic algorithms have been recognized as powerful global optimization techniques in the last few decades.Many different metaheuristic optimization algorithms have been presented and successfully applied to different types of problems. In this paper; seven of newest metaheuristic algorithms namely, Ant Lion Optimization, Dragonfly Algorithm, Grey Wolf Optimization, Moth-Flame Optimization, Multi-Verse Optimizer, Sine Cosine Algorithm, and Whale Optimization Algorithm have been tested on unconstrained benchmark optimization problems and their performances have been reported. Some of these algorithms are based on swarm while some are based on biology and mathematics. Performance analysis of these novel search and optimization algorithms satisfying equal conditions on benchmark functions for the first time has given important information about their behaviors on unimodal and multi-modal optimization problems. These algorithms have been recently proposed and many new versions of them may be proposed in future for efficient results in many different types of search and optimization problems.

  AtıfScopus12

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• MakaleAçık Erişim2016Scopus SJRQ2/r/munzur/4380
  Automatic mining of accurate and comprehensible numerical classification rules with cat swarm optimization algorithm

  Akyol, Sinem; Alatas, Bilal

  Optimization is the process of finding the best solution of a problem. There are many optimization algorithms proposed for optimization problems. The metaheuristic algorithm can give solutions close to optimum in an acceptable period of time for large-scaled optimization problems. The metaheuristic optimization algorithms are evaluated in seven different groups which are biology-based, physics-based, swarm-based, social-based, music-based, sport-based, and chemistry-based. The swarm-based optimization algorithms have been developed by observing the behaviors of creatures e.g. birds, fishes, cats, bees etc. Data mining is the process of discovery of meaningful and useful data within huge databases. Classification rules mining is one of the most commonly studied data mining problems and with methods for this problem, users can easily understand the rules extracted from databases. In this work, one of the most recent swarm based optimization algorithm, Cat Swarm Optimization (CSO), has been firstly used for classification rules mining within databases composed of numerical or mixed data types automatically. There is not any preprocess for finding true ranges for appropriate attributes of the rules, this has been automatically done by CSO. Furthermore, the used objective function is very flexible and many different objectives can easily be added to. For this purpose, four numerical databases obtained from UCI data warehouse have been used and accurate and comprehensible classification rules have been mined. The results have been compared with the results obtained from Weka program. Although CSO has not been embedded with any improvement and has firstly implemented in this research area, the obtained results seem promising.

  AtıfWoS9·OpenAlex4

  İncele
• MakaleAçık Erişim2015Scopus SJRQ3/r/munzur/4353
  Automatic Mining of Numerical Classification Rules with Parliamentary Optimization Algorithm

  Kiziloluk, Soner; Alatas, Bilal

  In recent years, classification rules mining has been one of the most important data mining tasks. In this study, one of the newest social-based metaheuristic methods, Parliamentary Optimization Algorithm (POA), is firstly used for automatically mining of comprehensible and accurate classification rules within datasets which have numerical attributes. Four different numerical datasets have been selected from UCI data warehouse and classification rules of high quality have been obtained. Furthermore, the results obtained from designed POA have been compared with the results obtained from four different popular classification rules mining algorithms used in WEKA. Although POA is very new and no applications in complex data mining problems have been performed, the results seem promising. The used objective function is very flexible and many different objectives can easily be added to. The intervals of the numerical attributes in the rules have been automatically found without any a priori process, as done in other classification rules mining algorithms, which causes the modification of datasets.

  AtıfWoS12·OpenAlex18

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