Multi-objective Optimization for Green Delivery Routing Problems with Flexible Time Windows

Gülmez, Burak; Emmerich, Michael; Fan, Yingjie

doi:10.1080/08839514.2024.2325302

Cited by 5 publications

(1 citation statement)

References 66 publications

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“…Today, artificial intelligence (AI) has a wide variety of different application areas [ [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] ]. The integration of AI into agriculture has opened up new avenues for improving crop management, and one notable application is in the detection of rice diseases.…”

Section: Introductionmentioning

confidence: 99%

Advancements in rice disease detection through convolutional neural networks: A comprehensive review

Gülmez

2024

Heliyon

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Section: Introductionmentioning

confidence: 99%

Advancements in rice disease detection through convolutional neural networks: A comprehensive review

Gülmez

2024

Heliyon

Self Cite

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A new multi-objective hyperparameter optimization algorithm for COVID-19 detection from x-ray images

Gülmez

2024

Soft Comput

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The coronavirus occurred in Wuhan (China) first and it was declared a global pandemic. To detect coronavirus X-ray images can be used. Convolutional neural networks (CNNs) are used commonly to detect illness from images. There can be lots of different alternative deep CNN models or architectures. To find the best architecture, hyper-parameter optimization can be used. In this study, the problem is modeled as a multi-objective optimization (MOO) problem. Objective functions are multi-class cross entropy, error ratio, and complexity of the CNN network. For the best solutions to the objective functions, multi-objective hyper-parameter optimization is made by NSGA-III, NSGA-II, R-NSGA-II, SMS-EMOA, MOEA/D, and proposed Swarm Genetic Algorithms (SGA). SGA is a swarm-based algorithm with a cross-over process. All six algorithms are run and give Pareto optimal solution sets. When the figures obtained from the algorithms are analyzed and algorithm hypervolume values are compared, SGA outperforms the NSGA-III, NSGA-II, R-NSGA-II, SMS-EMOA, and MOEA/D algorithms. It can be concluded that SGA is better than others for multi-objective hyper-parameter optimization algorithms for COVID-19 detection from X-ray images. Also, a sensitivity analysis has been made to understand the effect of the number of the parameters of CNN on model success.

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A Comprehensive Review of Convolutional Neural Networks based Disease Detection Strategies in Potato Agriculture

Gülmez

2024

Potato Res.

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This review paper investigates the utilization of Convolutional Neural Networks (CNNs) for disease detection in potato agriculture, highlighting their pivotal role in efficiently analyzing large-scale agricultural datasets. The datasets used, preprocessing methodologies applied, specific data collection zones, and the efficacy of prominent algorithms like ResNet, VGG, and MobileNet variants for disease classification are scrutinized. Additionally, various hyperparameter optimization techniques such as grid search, random search, genetic algorithms, and Bayesian optimization are examined, and their impact on model performance is assessed. Challenges including dataset scarcity, variability in disease symptoms, and the generalization of models across diverse environmental conditions are addressed in the discussion section. Opportunities for advancing CNN-based disease detection, including the integration of multi-spectral imaging and remote sensing data, and the implementation of federated learning for collaborative model training, are explored. Future directions propose research into robust transfer learning techniques and the deployment of CNNs in real-time monitoring systems for proactive disease management in potato agriculture. Current knowledge is consolidated, research gaps are identified, and avenues for future research in CNN-based disease detection strategies to sustain potato farming effectively are proposed by this review. This study paves the way for future advancements in AI-driven disease detection, potentially revolutionizing agricultural practices and enhancing food security. Also, it aims to guide future research and development efforts in CNN-based disease detection for potato agriculture, potentially leading to improved crop management practices, increased yields, and enhanced food security.

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Multi-objective Optimization for Green Delivery Routing Problems with Flexible Time Windows

Cited by 5 publications

References 66 publications

Advancements in rice disease detection through convolutional neural networks: A comprehensive review

Advancements in rice disease detection through convolutional neural networks: A comprehensive review

A new multi-objective hyperparameter optimization algorithm for COVID-19 detection from x-ray images

A Comprehensive Review of Convolutional Neural Networks based Disease Detection Strategies in Potato Agriculture

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