A Deep Learning Model Based on Multi-Objective Particle Swarm Optimization for Scene Classification in Unmanned Aerial Vehicles

Rajagopal, Aghila; Joshi, Gyanendra Prasad; Ramachandran, A.; Subhalakshmi, R. T.; Khari, Manju; Jha, Sudan; Shankar, K.; You, Jinsang

doi:10.1109/access.2020.3011502

Cited by 59 publications

(27 citation statements)

References 37 publications

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“…Figure 10 performs the comparative analysis of the C‐PTRN model with existing methods in terms of precision, recall, and F score 21,22 . From the figure, it is obvious that the CA‐ResNet‐BiLSTM model has provided ineffective results with the least precision of 77.94%, recall of 89.02%, and F score of 81.47%.…”

Section: Resultsmentioning

confidence: 99%

Energy‐efficient cluster‐based unmanned aerial vehicle networks with deep learning‐based scene classification model

Pustokhina

Pustokhin

Pareek³

et al. 2021

Int J Communication

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Summary In present days, unmanned aerial vehicles (UAVs) have gained significant interest among researchers and academicians. The UAVs were found useful in diverse application areas, namely, intelligent transportation system, disaster management, surveillance, and wildlife monitoring. Clustering is a well‐known energy‐efficient technique, which elects a cluster head (CH) among other nodes. At the same time, scene classification from the high‐resolution remote sensing images captured by UAV is also a major issue in the UAV networks. In order to resolve these problems, this paper projects novel energy‐efficient cluster‐based UAV networks with deep learning (DL)‐based scene classification method. The proposed model involves a clustering with parameter tuned residual network (C‐PTRN) model, which operates on two major phases such as cluster construction and scene classification. Initially, the UAVs are clustered using the type II fuzzy logic (T2FL) technique on the basis of residual energy, distance to nearby UAVs, and UAV degree. Next, the chosen CHs transmit the captured images to the base station (BS). At the second level, a DL‐based ResNet50 technique is employed for scene classification. To tune the hyperparameters of the ResNet50 model, water wave optimization (WWO) algorithm is used. At last, kernel extreme learning machine (KELM) model is used to perform the scene classification process. In order to ensure the performance of the proposed method, a detailed set of simulations takes place under different dimensions. The obtained results ensured that the C‐PTRN model has showcased supreme outcome with the maximum precision of 95.89%, recall of 98.91%, and F score of 96.54%.

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Section: Resultsmentioning

confidence: 99%

Energy‐efficient cluster‐based unmanned aerial vehicle networks with deep learning‐based scene classification model

Pustokhina

Pustokhin

Pareek³

et al. 2021

Int J Communication

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“…The particles can go back to the best prior position according to their memory, which maintains the previous best position. PSO has been successfully used to provide solutions to optimization problems in various fields, e.g., CD in CNs [101,102], images [103], wireless sensor networks [104], etc.…”

Section: A Common Meta-heuristic Algorithmsmentioning

confidence: 99%

A Review on Community Detection in Large Complex Networks from Conventional to Deep Learning Methods: A Call for the Use of Parallel Meta-Heuristic Algorithms

et al. 2021

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Complex networks (CNs) have gained much attention in recent years due to their importance and popularity. The rapid growth in the size of CNs leads to more difficulties in the analysis of CNs tasks. Community Detection (CD) is an important multidisciplinary research area where many machine/deep learning-based methods have been applied to map CNs into a low-dimensional representation for extracting information similarity among members of CNs. Currently, Deep Learning (DL) is one of the promising methods to extract knowledge and learn information from high dimensional space and represent it in low dimensional space. However, designing an accurate and efficient DL-based CD method especially when dealing with large CNs is always an on-going research endeavor to pursue. Meta-Heuristic (MH) algorithms have shown their potentials in improving DL models in terms of solution quality and computational cost. In addition, Parallel computing is a feasible solution for building efficient DL models. The algorithmic principle of MH is parallel in nature; however, its computation framework in DL training that is reported in the literature is not really implemented in a parallel computing setup. In this paper, we present a systematic review of CD in CNs from conventional machine learning to DL methods and point out the gap of applying DL-based CD methods in large CNs. In addition, the relevant studies on DL with parallel and MH approaches are reviewed and their implications on DL models are highlighted to prospect effective solutions to overcome the challenges of DL-based CD methods. We also point out research challenges in the field of CD and suggest possible future research directions. INDEX TERMSCommunity detection; Deep learning; Complex networks; Meta-heuristic algorithms; Parallel computing.

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“…Yildiz [29] used deep learning methods to learn the texture features of video images and classify them. Rajagopal et al [30] used a convolutional neural network training method to improve the accuracy of image recognition. Patrini et al [31] combined convolutional neural networks and transfer learning methods, which performed well in the ImageNet dataset.…”

Section: Related Workmentioning

confidence: 99%

“…(2) e L-DenseNet model can effectively alleviate the problem of gradient disappearance and overfitting, enhance feature propagation, and encourage feature reuse. Literature [29] and literature [30] aim to improve the network architecture to obtain better classification performance but ignore the feature…”

Section: Experimental Comparison Of Multitexture Featuresmentioning

confidence: 99%

Virtual Reality Video Image Classification Based on Texture Features

Qin

2021

Complexity

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As one of the most widely used methods in deep learning technology, convolutional neural networks have powerful feature extraction capabilities and nonlinear data fitting capabilities. However, the convolutional neural network method still has disadvantages such as complex network model, too long training time and excessive consumption of computing resources, slow convergence speed, network overfitting, and classification accuracy that needs to be improved. Therefore, this article proposes a dense convolutional neural network classification algorithm based on texture features for images in virtual reality videos. First, the texture feature of the image is introduced as a priori information to reflect the spatial relationship between pixels and the unique characteristics of different types of ground features. Second, the grey level cooccurrence matrix (GLCM) is used to extract the grey level correlation features of the image in space. Then, Gauss Markov Random Field (GMRF) is used to establish the statistical correlation characteristics between neighbouring pixels, and the extracted GLCM-GMRF texture feature and image intensity vector are combined. Finally, based on DenseNet, an improved shallow layer dense convolutional neural network (L-DenseNet) is proposed, which can compress network parameters and improve the feature extraction ability of the network. The experimental results show that compared with the current classification method, this method can effectively suppress the influence of coherent speckle noise and obtain better classification results.

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A Deep Learning Model Based on Multi-Objective Particle Swarm Optimization for Scene Classification in Unmanned Aerial Vehicles

Abstract: the technology to automate the recommendations for big data analytic models that define data characteristics and problems).

Cited by 59 publications

References 37 publications

Energy‐efficient cluster‐based unmanned aerial vehicle networks with deep learning‐based scene classification model

Energy‐efficient cluster‐based unmanned aerial vehicle networks with deep learning‐based scene classification model

A Review on Community Detection in Large Complex Networks from Conventional to Deep Learning Methods: A Call for the Use of Parallel Meta-Heuristic Algorithms

Virtual Reality Video Image Classification Based on Texture Features

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