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Dökeroğlu, Tansel

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https://hdl.handle.net/20.500.12416/9481
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Dokeroglu, Tansel
Job Title
Doç. Dr.
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Yazılım Mühendisliği
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Former Staff
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WoS Researcher ID

Sustainable Development Goals

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SUSTAINABLE CITIES AND COMMUNITIES
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3

GOOD HEALTH AND WELL-BEING
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2

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9

INDUSTRY, INNOVATION AND INFRASTRUCTURE
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6

CLEAN WATER AND SANITATION
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14

LIFE BELOW WATER
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12

RESPONSIBLE CONSUMPTION AND PRODUCTION
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8

DECENT WORK AND ECONOMIC GROWTH
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NO POVERTY
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QUALITY EDUCATION
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GENDER EQUALITY
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REDUCED INEQUALITIES
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PEACE, JUSTICE AND STRONG INSTITUTIONS
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15

LIFE ON LAND
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AFFORDABLE AND CLEAN ENERGY
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CLIMATE ACTION
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ZERO HUNGER
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Scholarly Output

9

Articles

9

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203/306

Supervised MSc Theses

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WoS Citation Count

292

Scopus Citation Count

379

WoS h-index

4

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5

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WoS Citations per Publication

32.44

Scopus Citations per Publication

42.11

Open Access Source

7

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0

JournalCount
Neural Computing and Applications2
PeerJ Computer Science2
Düzce Üniversitesi Bilim ve Teknoloji Dergisi1
Ankara Science University, Researcher1
Neurocomputing1
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Now showing 1 - 9 of 9
  • Thumbnail Image
    Article
    Citation - WoS: 1
    Citation - Scopus: 3
    A New Parallel Multi-Objective Harris Hawk Algorithm for Predicting the Mortality of Covid-19 Patients
    (Peerj inc, 2023) Dokeroglu, Tansel
    Harris' Hawk Optimization (HHO) is a novel metaheuristic inspired by the collective hunting behaviors of hawks. This technique employs the flight patterns of hawks to produce (near)-optimal solutions, enhanced with feature selection, for challenging classification problems. In this study, we propose a new parallel multi-objective HHO algorithm for predicting the mortality risk of COVID-19 patients based on their symptoms. There are two objectives in this optimization problem: to reduce the number of features while increasing the accuracy of the predictions. We conduct comprehensive experiments on a recent real-world COVID-19 dataset from Kaggle. An augmented version of the COVID-19 dataset is also generated and experimentally shown to improve the quality of the solutions. Significant improvements are observed compared to existing state-of-the-art metaheuristic wrapper algorithms. We report better classification results with feature selection than when using the entire set of features. During experiments, a 98.15% prediction accuracy with a 45% reduction is achieved in the number of features. We successfully obtained new best solutions for this COVID-19 dataset.
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    Article
    Citation - WoS: 5
    Citation - Scopus: 8
    Predicting the Severity of Covid-19 Patients Using a Multi-Threaded Evolutionary Feature Selection Algorithm
    (Wiley, 2022) Kiziloz, Hakan Ezgi; Sevinc, Ender; Dokeroglu, Tansel; Deniz, Ayca
    The COVID-19 pandemic has huge effects on the global community and an extreme burden on health systems. There are more than 185 million confirmed cases and 4 million deaths as of July 2021. Besides, the exponential rise in COVID-19 cases requires a quick prediction of the patients' severity for better treatment. In this study, we propose a Multi-threaded Genetic feature selection algorithm combined with Extreme Learning Machines (MG-ELM) to predict the severity level of the COVID-19 patients. We conduct a set of experiments on a recently published real-world dataset. We reprocess the dataset via feature construction to improve the learning performance of the algorithm. Upon comprehensive experiments, we report the most impactful features and symptoms for predicting the patients' severity level. Moreover, we investigate the effects of multi-threaded implementation with statistical analysis. In order to verify the efficiency of MG-ELM, we compare our results with traditional and state-of-the-art techniques. The proposed algorithm outperforms other algorithms in terms of prediction accuracy.
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    Article
    Citation - WoS: 4
    Citation - Scopus: 6
    A New Robust Harris Hawk Optimization Algorithm for Large Quadratic Assignment Problems
    (Springer London Ltd, 2023) Dokeroglu, Tansel; Ozdemir, Yavuz Selim
    Harris Hawk optimization (HHO) is a new robust metaheuristic algorithm proposed for the solution of large intractable combinatorial optimization problems. The hawks are cooperative birds and use many intelligent hunting techniques. This study proposes new HHO algorithms for solving the well-known quadratic assignment problem (QAP). Large instances of the QAP have not been solved exactly yet. We implement HHO algorithms with robust tabu search (HHO-RTS) and introduce new operators that simulate the actions of hawks. We also developed an island parallel version of the HHO-RTS algorithm using the message passing interface. We verify the performance of our proposed algorithms on the QAPLIB benchmark library. One hundred and twenty-five of 135 problems are solved optimally, and the average deviation of all the problems is observed to be 0.020%. The HHO-RTS algorithm is a robust algorithm compared to recent studies in the literature.
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    Article
    Big Data Reduction and Visualization Using the K-Means Algorithm
    (2022) Akyol, Hakan; Kızılduman, Hale Sema; Dökeroğlu, Tansel
    A huge amount of data is being produced every day in our era. In addition to high-performance processing approaches, efficiently visualizing this quantity of data (up to Terabytes) remains a major difficulty. In this study, we use the well-known clustering method K-means as a data reduction strategy that keeps the visual quality of the provided huge data as high as possible. The centroids of the dataset are used to display the distribution properties of data in a straightforward manner. Our data comes from a recent Kaggle big data set (Click Through Rate), and it is displayed using Box plots on reduced datasets, compared to the original plots. It is discovered that K-means is an effective strategy for reducing the amount of huge data in order to view the original data without sacrificing its distribution information quality
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    Article
    Citation - WoS: 38
    Citation - Scopus: 44
    Hyper-Heuristics: a Survey and Taxonomy
    (Pergamon-elsevier Science Ltd, 2024) Kucukyilmaz, Tayfun; Talbi, El-Ghazali; Dokeroglu, Tansel
    Hyper-heuristics are search techniques for selecting, generating, and sequencing (meta)-heuristics to solve challenging optimization problems. They differ from traditional (meta)-heuristics methods, which primarily employ search space-based optimization strategies. Due to the remarkable performance of hyper-heuristics in multi-objective and machine learning-based optimization, there has been an increasing interest in this field. With a fresh perspective, our work extends the current taxonomy and presents an overview of the most significant hyper-heuristic studies of the last two decades. Four categories under which we analyze hyperheuristics are selection hyper-heuristics (including machine learning techniques), low-level heuristics, target optimization problems, and parallel hyper-heuristics. Future research prospects, trends, and prospective fields of study are also explored.
  • Thumbnail Image
    Article
    Citation - WoS: 238
    Citation - Scopus: 308
    A Comprehensive Survey on Recent Metaheuristics for Feature Selection
    (Elsevier, 2022) Dokeroglu, Tansel; Deniz, Ayca; Kiziloz, Hakan Ezgi
    Feature selection has become an indispensable machine learning process for data preprocessing due to the ever-increasing sizes in actual data. There have been many solution methods proposed for feature selection since the 1970s. For the last two decades, we have witnessed the superiority of metaheuristic feature selection algorithms, and tens of new ones are being proposed every year. This survey focuses on the most outstanding recent metaheuristic feature selection algorithms of the last two decades in terms of their performance in exploration/exploitation operators, selection methods, transfer functions, fitness value evaluations, and parameter setting techniques. Current challenges of the metaheuristic feature selection algorithms and possible future research topics are examined and brought to the attention of the researchers as well.
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    Article
    Müfredat Tabanlı Ders Çizelgeleme Problemi için Yeni Bir Açgözlü Algoritma
    (2023) Batuhan,; Say, Bilge; Dokeroglu, Tansel
    Bu çalışma, iyi bilinen Müfredat Tabanlı Ders Çizelgeleme Problemini optimize etmek için yeni bir açgözlü algoritmayı açıklamaktadır. Açgözlü algoritmalar, en iyi çözümü bulmak için yürütülmesi uzun zaman alan kaba kuvvet ve evrimsel algoritmalara iyi bir alternatiftir. Birçok açgözlü algoritmanın yaptığı gibi tek bir buluşsal yöntem kullanmak yerine, aynı problem örneğine 120 yeni buluşsal yöntem tanımlıyor ve uyguluyoruz. Dersleri müsait odalara atamak için, önerilen açgözlü algoritmamız En Büyük-İlk, En Küçük-İlk, En Uygun, Önce Ortalama Ağırlık ve En Yüksek Kullanılamaz ders-ilk buluşsal yöntemlerini kullanır. İkinci Uluslararası Zaman Çizelgesi Yarışması'nın (ITC-2007) kıyaslama setinden 21 problem örneği üzerinde kapsamlı deneyler gerçekleştirilir. Önemli ölçüde azaltılmış yumuşak kısıtlama değerlerine sahip 18 problem için, önerilen açgözlü algoritma sıfır sabit kısıtlama ihlali (uygulanabilir çözümler) rapor edebilir. Önerilen algoritma, performans açısından son teknoloji ürünü açgözlü buluşsal yöntemleri geride bırakıyor.
  • Thumbnail Image
    Article
    Citation - Scopus: 3
  • Thumbnail Image
    Article
    Citation - WoS: 6
    Citation - Scopus: 7
    An Island Parallel Harris Hawks Optimization Algorithm
    (Springer London Ltd, 2022) Dokeroglu, Tansel; Sevinc, Ender
    The Harris hawk optimization (HHO) is an impressive optimization algorithm that makes use of unique mathematical approaches. This study proposes an island parallel HHO (IP-HHO) version of the algorithm for optimizing continuous multi-dimensional problems for the first time in the literature. To evaluate the performance of the IP-HHO, thirteen unimodal and multimodal benchmark problems with different dimensions (30, 100, 500, and 1000) are evaluated. The implementation of this novel algorithm took into account the investigation, exploitation, and avoidance of local optima issues effectively. Parallel computation provides a multi-swarm environment for thousands of hawks simultaneously. On all issue cases, we were able to enhance the performance of the sequential version of the HHO algorithm. As the number of processors increases, the suggested IP-HHO method enhances its performance while retaining scalability and improving its computation speed. The IP-HHO method outperforms the other state-of-the-art metaheuristic algorithms on average as the size of the dimensions grows.