<scp>SI‐EDTL</scp>: Swarm intelligence ensemble deep transfer learning for multiple vehicle detection in<scp>UAV</scp>images

Darehnaei, Zeinab Ghasemi; Shokouhifar, Mohammad; Yazdanjouei, Hossein; Fatemi, Mohammad Jalal Rastegar

doi:10.1002/cpe.6726

Cited by 28 publications

(13 citation statements)

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“…The literature proves that hybridizing metaheuristics with other soft computing techniques is a suitable way to benefit from the advantages of the basic algorithms [33][34][35][36]. Generally, metaheuristics outperform heuristics in term of converging to a solution with higher quality.…”

Section: Our Contribution Against Existing Methodsmentioning

confidence: 99%

Designing a Renewable and Sustainable Phosphorus Fertilizer Supply Chain Network using an Ensemble Knowledge-based Heuristic-Metaheuristic Algorithm

Shokouhifar¹,

Sohrabi²,

Rabbani³

et al. 2022

Preprint

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Phosphorus (P) is the most important substance in inorganic fertilizers used in agriculture industry. In this study, a multi-product and multi-objective model is presented considering economic and environmental concerns to design a renewable and sustainable P-fertilizer supply chain management (PFSCM). To handle complexities of the proposed model, an ensemble knowledge-based three-stage heuristic-metaheuristic algorithm utilizing heuristic information available in the model, whale optimization algorithm, and variable neighborhood search (named H-WOA-VNS) is proposed. At first, a problem-dependent heuristic is designed to generate a set of near-optimal feasible solutions. These solutions are fed into a population-based whale optimization algorithm which benefits from both exploration and exploitation strategies. Finally, a single-solution metaheuristic based on variable neighborhood search is applied to further improve the quality of the solution using local search operators. The objective function of the algorithm is formulated as a weighted average function to minimize total economic cost, while increasing crop yield and P use efficiency. Experimental results over five synthetic datasets and a real case study of the P-fertilizer supply chain confirm the superiority of the proposed method against the state-of-the-art techniques. The results demonstrate that the proposed method performs well in optimizing both the economic cost and environmental issues.

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Section: Our Contribution Against Existing Methodsmentioning

confidence: 99%

Designing a Renewable and Sustainable Phosphorus Fertilizer Supply Chain Network using an Ensemble Knowledge-based Heuristic-Metaheuristic Algorithm

Shokouhifar¹,

Sohrabi²,

Rabbani³

et al. 2022

Preprint

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“…Many studies used deep learning and ensemble learning processes for classification problems 9 . The current CAD applications for Lung Cancer classifying lung nodules are very close to this paper's objective.…”

Section: Related Workmentioning

confidence: 99%

Deep learning ensemble 2D CNN approach towards the detection of lung cancer

Shah

Malik

Muhammad

et al. 2023

Sci Rep

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In recent times, deep learning has emerged as a great resource to help research in medical sciences. A lot of work has been done with the help of computer science to expose and predict different diseases in human beings. This research uses the Deep Learning algorithm Convolutional Neural Network (CNN) to detect a Lung Nodule, which can be cancerous, from different CT Scan images given to the model. For this work, an Ensemble approach has been developed to address the issue of Lung Nodule Detection. Instead of using only one Deep Learning model, we combined the performance of two or more CNNs so they could perform and predict the outcome with more accuracy. The LUNA 16 Grand challenge dataset has been utilized, which is available online on their website. The dataset consists of a CT scan with annotations that better understand the data and information about each CT scan. Deep Learning works the same way our brain neurons work; therefore, deep learning is based on Artificial Neural Networks. An extensive CT scan dataset is collected to train the deep learning model. CNNs are prepared using the data set to classify cancerous and non-cancerous images. A set of training, validation, and testing datasets is developed, which is used by our Deep Ensemble 2D CNN. Deep Ensemble 2D CNN consists of three different CNNs with different layers, kernels, and pooling techniques. Our Deep Ensemble 2D CNN gave us a great result with 95% combined accuracy, which is higher than the baseline method.

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“…Deep learning is a type of machine learning that is particularly well suited for the analysis of complex medical images, as it has the ability to automatically learn and extract features from large datasets. In addition to medical applications, deep learning is used in other applications [ 30 , 31 , 32 ]. For example, Darehnaei et al [ 30 ] presented an approach for multiple vehicle detection in UAV images using swarm intelligence ensemble deep transfer learning (SI-EDTL).…”

Section: Related Workmentioning

confidence: 99%

“…In addition to medical applications, deep learning is used in other applications [ 30 , 31 , 32 ]. For example, Darehnaei et al [ 30 ] presented an approach for multiple vehicle detection in UAV images using swarm intelligence ensemble deep transfer learning (SI-EDTL). The presented method has the potential to enhance the effectiveness of various applications, such as surveillance and disaster response.…”

Section: Related Workmentioning

confidence: 99%

Accurate Detection of Alzheimer’s Disease Using Lightweight Deep Learning Model on MRI Data

et al. 2023

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Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by cognitive impairment and aberrant protein deposition in the brain. Therefore, the early detection of AD is crucial for the development of effective treatments and interventions, as the disease is more responsive to treatment in its early stages. It is worth mentioning that deep learning techniques have been successfully applied in recent years to a wide range of medical imaging tasks, including the detection of AD. These techniques have the ability to automatically learn and extract features from large datasets, making them well suited for the analysis of complex medical images. In this paper, we propose an improved lightweight deep learning model for the accurate detection of AD from magnetic resonance imaging (MRI) images. Our proposed model achieves high detection performance without the need for deeper layers and eliminates the use of traditional methods such as feature extraction and classification by combining them all into one stage. Furthermore, our proposed method consists of only seven layers, making the system less complex than other previous deep models and less time-consuming to process. We evaluate our proposed model using a publicly available Kaggle dataset, which contains a large number of records in a small dataset size of only 36 Megabytes. Our model achieved an overall accuracy of 99.22% for binary classification and 95.93% for multi-classification tasks, which outperformed other previous models. Our study is the first to combine all methods used in the publicly available Kaggle dataset for AD detection, enabling researchers to work on a dataset with new challenges. Our findings show the effectiveness of our lightweight deep learning framework to achieve high accuracy in the classification of AD.

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SI‐EDTL: Swarm intelligence ensemble deep transfer learning for multiple vehicle detection inUAVimages

Cited by 28 publications

References 50 publications

Designing a Renewable and Sustainable Phosphorus Fertilizer Supply Chain Network using an Ensemble Knowledge-based Heuristic-Metaheuristic Algorithm

Designing a Renewable and Sustainable Phosphorus Fertilizer Supply Chain Network using an Ensemble Knowledge-based Heuristic-Metaheuristic Algorithm

Deep learning ensemble 2D CNN approach towards the detection of lung cancer

Accurate Detection of Alzheimer’s Disease Using Lightweight Deep Learning Model on MRI Data

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