Sürdürülebilir Kalkınma Amaçları

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• MakaleAçık Erişim2025Scopus SJRQ1/r/munzur/1090
  MobileNet- and attention-based MLP-Mixer architecture for geographical-region recognition of a marine fish (Perciformes: Carangidae) using otolith images

  Dürrani, Ömerhan; İşgüzar, Seda; İmak, Andaç; Ateşşahin, Tuncay; Cömert, Zafer; Dürrani, Syeda Zahra; Türkoğlu, Muammer

  Fishery management is crucial to sustain marine ecosystems by preventing overfishing and ensuring a fair distribution of fishing quotas. Accurately identifying the geographical origins of fish stocks is a key challenge in region-specific management strategies. Otoliths, calcified structures found in the heads of all fish species (except sharks and rays), provide insights into the life history and geographical origins of these fish. Traditional otolith analysis is time-consuming and error-prone because of manual inspection. Our study presents a novel approach using deep learning and computer vision to automate the geographical recognition of fish using otolith images. We propose a model that integrates MobileNet, which is known for its efficiency, with an advanced Mlp-Mixer that incorporates an attention mechanism to extract enhanced features. When tested on a diverse dataset of otolith images from five regions, the proposed model achieved a remarkable 96% accuracy, significantly outperforming traditional methods. This high accuracy demonstrates the potential to revolutionise fishery management by providing a fast, reliable, and automated solution for geographical region identification. In conclusion, the proposed method demonstrates the transformative potential of combining MobileNet and an attention-based Mlp-Mixer for automated fish geographic recognition using otolith images. This innovative method addresses the limitations of traditional manual inspection and paves the way for more effective and sustainable fishery management practices.

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  AtıfScopus4·OpenAlex4

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• MakaleAçık Erişim2025Scopus SJRQ1/r/munzur/645
  A novel post-processing approach for improving minimum nonlinearity in S-boxes: A case study based on 2D hyper chaotic Schaffer transformation map

  Aydın, Yılmaz; Garipcan, Ali Murat; Özkaynak, Fatih

  Substitution boxes (s-boxes), providing nonlinearity in block cipher algorithms, constitute a fundamental security layer against linear and differential cryptanalysis attacks. In this study, an innovative s-box generation method is proposed by integrating a 2D hyper-chaotic Schaffer map (SM) with biomolecular DNA-based encoding techniques. Furthermore, to enhance the security of s-box structures, a novel post-processing algorithm is developed, aiming to simultaneously optimize both the average (NLavg) and minimum (NLmin) nonlinearity levels by targeting linear components. Unlike traditional meta-heuristic methods, the proposed algorithm avoids random exploration across large search spaces, instead identifying dominant linear components through Walsh spectrum analysis and applying targeted permutation operations to suppress their effects. This strategy not only increases the NLavg value but also balances the contribution of each Boolean function to security, thereby establishing a homogeneous and consistent security structure across all input–output combinations. Comparative experimental analyses with existing methods demonstrate that the proposed approach outperforms prior techniques in terms of both cryptographic security metrics and computational efficiency. In this respect, the study presents an advanced s-box design framework, characterized by high nonlinearity and a balanced spectral distribution, suitable for integration into modern block cipher systems.

  AtıfScopus12·WoS13·OpenAlex13

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• MakaleAçık Erişim2023Scopus SJRQ1/r/munzur/1959
  Deep learning based hybrid prediction model for predicting the spread of COVID-19 in the world's most populous countries

  Utku, Anil

  COVID-19 has a disease and health phenomenon and has sociological and economic adverse effects. Accurate prediction of the spread of the epidemic will help in the planning of health management and the development of economic and sociological action plans. In the literature, there are many studies to analyse and predict the spread of COVID-19 in cities and countries. However, there is no study to predict and analyse the cross-country spread in the world's most populous countries. In this study, it was aimed to predict the spread of the COVID-19 epidemic. The motivation of this study is to reduce the workload of health workers, take preventive measures and optimize health processes by predicting the spread of the COVID-19 epidemic. A hybrid deep learning model was developed to predict and analyse COVID-19 cross-country spread and a case study was carried out for the world's most populous countries. The developed model was tested extensively using RMSE, MAE and R2. The experimental results showed that the developed model was more successful in predicting and analysis of COVID-19 cross-country spread in the world's most populous countries than LR, RF, SVM, MLP, CNN, GRU, LSTM and base CNN-GRU. In the developed model, CNN performs convolution and pooling operations to extract spatial features from the input data. GRU provides learning of long-term and non-linear relationships inferred by CNN. The developed hybrid model was more successful than the other models compared, as it enabled the effective features of the CNN and GRU models to be used together. The prediction and analysis of the cross-country spread of COVID-19 in the world's most populated countries can be presented as a novelty of this study.

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  AtıfScopus23·WoS20·OpenAlex31

  İncele
• MakaleYalnız künye2012Scopus SJRQ1/r/munzur/4683
  A 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.

  AtıfWoS59·OpenAlex75

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• MakaleYalnız künye2011Scopus SJRQ1/r/munzur/4669
  ACROA: 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.

  AtıfWoS340·OpenAlex420

  İncele
• MakaleYalnız künye2011Scopus SJRQ1/r/munzur/4696
  Photosynthetic 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.

  AtıfWoS6·OpenAlex7

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• MakaleAçık Erişim2010Scopus SJRQ1/r/munzur/3999
  A new algorithm for automatic classification of power quality events based on wavelet transform and SVM

  Eristi, Hueseyin; Demir, Yakup

  This paper presents a new approach for automatic classification of power quality events, which is based on the wavelet transform and support vector machines. In the proposed approach, an effective single feature vector representing three phase event signals is extracted after signals are applied normalization and segmentation process. The kernel and penalty parameters of the support vector machine (SVM) are determined by cross-validation. The parameter set that gives the smallest misclassification error is retained. ATP/EMTP model for six types of power system events, namely phase-to-ground fault, phase-to-phase fault, three-phase fault, load switching, capacitor switching and transformer energizing, are constructed. Both the noisy and noiseless event signals are applied to the proposed algorithm. Obtained results indicate that the proposed automatic event classification algorithm is robust and has ability to distinguish different power quality event classes easily. (C) 2009 Elsevier Ltd. All rights reserved.

  AtıfWoS78·OpenAlex106

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