Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleAçık Erişim2024An 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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İncele - MakaleAçık Erişim2021Scopus SJRQ1A 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.
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İncele - MakaleAçık Erişim2020Scopus SJRQ2Sentiment 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.
AtıfScopus35WoS27OpenAlex43İncele - MakaleAçık Erişim2017Scopus SJRQ1Plant 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.
AtıfWoS188OpenAlex218İncele - MakaleAçık Erişim2017Scopus SJRQ3Automatic 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.
AtıfScopus27WoS18OpenAlex28İncele - MakaleAçık Erişim2017Scopus SJRQ2Big 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.
AtıfScopus42WoS34OpenAlex50İncele - MakaleAçık Erişim2017Performance 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.
AtıfScopus12İncele - MakaleYalnız künye2016Scopus SJRQ2Kedi sürüsü optimizasyon algoritmasıyla doğru ve anlaşılabilir nümerik sınıflandırma kurallarının otomatik keşfi
Alatas, Bilal; Akyol, Sinem
Metasezgisel algoritmalar, büyük boyutlu optimizasyon problemleri için, kabul edilebilir sürede optimuma yakın çözümler verebilen algoritmalardır. Genel amaçlı metasezgisel optimizasyon algoritmaları; biyoloji tabanlı, fizik tabanlı, sürü tabanlı, sosyal tabanlı, müzik tabanlı, spor tabanlı ve kimya tabanlı olmak üzere yedi farklı grupta değerlendirilmektedir. Sürü zekâsı tabanlı optimizasyon algoritmaları kuş, balık, kedi ve arı gibi canlı sürülerinin hareketlerinin incelenmesiyle geliştirilmiştir. Veri madenciliği, büyük ölçekli verilerden anlamlı ve faydalı bilginin keşfedilmesi işlemidir. Sınıflandırma kurallarının madenciliği üzerinde en çok çalışılan veri madenciliği problemlerinden biridir ve bu problemde veri kümelerinden kullanıcıların rahatça anlayabileceği kurallar çıkarılmaktadır. Bu çalışmada, en güncel sürü zekâsı optimizasyon algoritmalarından Kedi Sürüsü Optimizasyonu (KSO) bildiğimiz kadarıyla ilk kez nümerik ya da karışık tipte verilerden oluşan veritabanlarında sınıflandırma kurallarının otomatik keşfinde kullanılmıştır. Kuralda yer alabilecek ilgili niteliklerin doğru aralıkları için bir ön işlem kullanılmamış, kurallarla birlikte otomatik olarak bulunması da KSO tarafından sağlanmıştır. Ayrıca, kullanılan amaç fonksiyonu çok esnektir ve farklı amaçlar kolaylıkla fonksiyona entegre edilebilir. Bu amaçla, UCI veri ambarından alınan 4 adet veritabanı kullanılmış; doğru ve anlaşılabilir sınıflandırma kuralları keşfedilmiştir. Bulunan sonuçlarla Weka programından elde edilen sonuçlar karşılaştırılmıştır. KSO algoritması üzerinde herhangi bir iyileştirme yapılmadığı ve bu alanda ilk kez kullanıldığı halde, elde edilen sonuçların umut verici olduğu gözlenmiştir.
AtıfTR Dizin7
İncele - MakaleAçık Erişim2016Scopus SJRQ2Automatic 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.
AtıfWoS9OpenAlex4İncele - MakaleAçık Erişim2015Scopus SJRQ3Automatic 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.
AtıfWoS12OpenAlex18İncele - MakaleYalnız künye2012Current Social-Based Heuristic Optimization Algorithms
Alatas, Bilal; Kiziloluk, Soner
Optimization is the process of making something better. For example, in an f(x) function in optimization, it is asked for finding x values which make the values of the result minimum. Different methods have been proposed for solving optimization problems. Some of these processes have been inspired by natural processes. Ant colony optimization algorithm can be given as an example. Some of the other methods have been inspired by social events. Although the number of social based methods is limited, the most known one is tabu search algorithm. Recently, researchers have developed teaching-learning based algorithm, imperialist competitive algorithm, and parliamentary optimization algorithm. In this study, how the imperialist competitive algorithms and parliamentary optimization algorithms which are social-based algorithms are modeled and how parliamentary optimization algorithm is implemented on the problems have been mentioned.
AtıfTR Dizin2İncele - MakaleYalnız künye2012Scopus SJRQ1A novel chemistry based metaheuristic optimization method for mining of classification rules
Alatas, Bilal
When investigated carefully, chemical reactions possess efficient objects, states, process, and events that can be designed as a computational method en bloc. In this study, a novel computational method, which is robust and have less parameters than that of used in the literature, is intended to be developed inspiring from types and occurring of chemical reactions. The proposed method is named as artificial chemical reaction optimization algorithm, ACROA. In this study, one of the first applications of this method has been performed in classification rule discovery field of data mining and efficiency has been demonstrated. (C) 2012 Elsevier Ltd. All rights reserved.
AtıfWoS59OpenAlex75İncele - MakaleYalnız künye2011Scopus SJRQ1Photosynthetic algorithm approaches for bioinformatics
Alatas, Bilal
Photosynthetic algorithm (PA) is the newly biosystem-derived heuristic search algorithm that utilizes the dark reaction rules governing the transfer of carbon molecules from one substance into another in the Calvin-Benson cycle and photorespiration. There are only two works using this new algorithm in the literature, one for finite element analysis and one for N-queen problem. This paper presents PA based novel solution techniques in bioinformatics problems such as aligning multiple sequences and discovering association rules within bio-medical data. The simulation results demonstrate the applicability and potential of this algorithm. (C) 2011 Elsevier Ltd. All rights reserved.
AtıfWoS6OpenAlex7İncele - MakaleYalnız künye2011Scopus SJRQ1ACROA: Artificial Chemical Reaction Optimization Algorithm for global optimization
Alatas, Bilal
Heuristic based computational algorithms are densely being used in many different fields due to their advantages. When investigated carefully, chemical reactions possess efficient objects, states, process, and events that can be designed as a computational method en bloc. In this study, a novel computational method, which is more robust and have less parameters than that of used in the literature, is intended to be developed inspiring from types and occurring of chemical reactions. The proposed method is named as Artificial Chemical Reaction Optimization Algorithm, ACROA. Applications to multiple-sequence alignment, data mining, and benchmark functions have been performed so as to put forward the performance of developed computational method. (C) 2011 Elsevier Ltd. All rights reserved.
AtıfWoS340OpenAlex420İncele - MakaleYalnız künye2011Scopus SJRQ1Uniform Big Bang-Chaotic Big Crunch optimization
Alatas, Bilal
This study proposes methods to improve the convergence of the novel optimization method, Big Bang-Big Crunch (BB-BC). Uniform population method has been used to generate uniformly distributed random points in the Big Bang phase. Chaos has been utilized to rapidly shrink those points to a single representative point via a center of mass in the Big Crunch phase. The proposed algorithm has been named as Uniform Big Bang-Chaotic Big Crunch (UBB-CBC). The performance of the UBB-CBC optimization algorithm demonstrates superiority over the BB-BC optimization for the benchmark functions. (C) 2010 Elsevier B.V. All rights reserved.
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