Comparative Analysis of AlexNet, ResNet18 and SqueezeNet with Diverse Modification and Arduous Implementation

Ullah, Asad; Elahi, Hassan; Sun, Zhaoyun; Khatoon, Amna; Ahmad, Ishfaq

doi:10.1007/s13369-021-06182-6

Cited by 45 publications

(11 citation statements)

References 29 publications

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“…Mobile application-based UAV recommendation to farmers is also being planned in the future to considerably help farmers better plan the cultivation and earn a significant profit. Finally, the proposed methodology can be integrated with different AI tools (Ullah et al 2018 , 2021 , 2022a , 2022b ; de Paula Vidal et al 2022 ) in order to improve the accuracy of such strategic decision-making processes.…”

Section: Discussionmentioning

confidence: 99%

The q-rung fuzzy LOPCOW-VIKOR model to assess the role of unmanned aerial vehicles for precision agriculture realization in the Agri-Food 4.0 era

et al. 2023

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Smart agriculture is gaining a lot of attention recently, owing to technological advancement and promotion of sustainable habits. Unmanned aerial vehicles (UAVs) play a crucial role in smart agriculture by aiding in different phases of agriculture. The contribution of UAVs to sustainable and precision agriculture is a critical and challenging issue to be taken into account, particularly for smallholder farmers in order to save time and money, and improve their agricultural skills. Thence, this study targets to propose an integrated group decision-making framework to determine the best agricultural UAV. Previous studies on UAV evaluation, (i) could not model uncertainty effectively, (ii) weights of experts are not methodically determined; (iii) importance of experts and criteria types are not considered during criteria weight calculation, and (iv) personalized ranking of UAVs is lacking along with consideration to dual weight entities. Herein, nine critical selection criteria are identified, drawing upon the relevant literature and experts’ opinions, and five extant UAVs are considered for evaluation. To circumvent the gaps, in this work, a new integrated framework is developed considering q-rung orthopair fuzzy numbers (q-ROFNs) for apt UAV selection. Specifically, methodical estimation of experts’ weights is achieved by presenting the regret measure. Further, weighted logarithmic percentage change-driven objective weighting (LOPCOW) technique is formulated for criteria weight calculation, and an algorithm for personalized ranking of UAVs is presented with visekriterijumska optimizacija i kompromisno resenje (VIKOR) approach combined with Copeland strategy. The findings show that the foremost criteria in agricultural UAV selection are “ camera ,” “ power system ,” and “ radar system ,” respectively. Further, it is inferred that the most promising UAV is the DJ AGRAS T30. Since the applicability of UAV in agriculture will get inevitable, the developed framework can be an effective decision support system for farmers, managers, policymakers, and other stakeholders.

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Section: Discussionmentioning

confidence: 99%

The q-rung fuzzy LOPCOW-VIKOR model to assess the role of unmanned aerial vehicles for precision agriculture realization in the Agri-Food 4.0 era

et al. 2023

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“…Compared to normal individual DA methods, the MDA fuse the separate DA methods together and thus can yield better performances [14]. The fire module (FM) is the core component in the N. It contains a squeeze layer (SL), which uses only 1 Â 1 kernels, followed by an expand layer (EL), which contains several 1 Â 1 and 3 Â 3 kernels [16]. The structure of FM is shown in Fig.…”

Section: Dataset and Preprocessingmentioning

confidence: 99%

SNELM: SqueezeNet-Guided ELM for COVID-19 Recognition

Zhang¹,

Khan²,

Zhu³

et al. 2023

Computer Systems Science and Engineering

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Aim) The COVID-19 has caused 6.26 million deaths and 522.06 million confirmed cases till 17/May/2022. Chest computed tomography is a precise way to help clinicians diagnose COVID-19 patients. (Method) Two datasets are chosen for this study. The multiple-way data augmentation, including speckle noise, random translation, scaling, salt-and-pepper noise, vertical shear, Gamma correction, rotation, Gaussian noise, and horizontal shear, is harnessed to increase the size of the training set. Then, the SqueezeNet (SN) with complex bypass is used to generate SN features. Finally, the extreme learning machine (ELM) is used to serve as the classifier due to its simplicity of usage, quick learning speed, and great generalization performances. The number of hidden neurons in ELM is set to 2000. Ten runs of 10-fold cross-validation are implemented to generate impartial results. (Result) For the 296-image dataset, our SNELM model attains a sensitivity of 96.35 ± 1.50%, a specificity of 96.08 ± 1.05%, a precision of 96.10 ± 1.00%, and an accuracy of 96.22 ± 0.94%. For the 640-image dataset, the SNELM attains a sensitivity of 96.00 ± 1.25%, a specificity of 96.28 ± 1.16%, a precision of 96.28 ± 1.13%, and an accuracy of 96.14 ± 0.96%. (Conclusion) The proposed SNELM model is successful in diagnosing COVID-19. The performances of our model are higher than seven state-of-the-art COVID-19 recognition models.

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“…For this experiment, NVIDIA GPU has been used, which speeds up the processing with no need to consume and is also suitable for using GUI (graphical user interface) to enable command control parameters. GPU is also essential for processing all the above techniques [46]. e device specification on which these experiments are performed is given in Table 1 below.…”

Section: Implementing Strategymentioning

confidence: 99%

Gray-Level Image Transformation of Paved Road Cracks with Metaphorical and Computational Analysis

Ullah

Sun

Tariq

et al. 2022

Mathematical Problems in Engineering

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In today’s technology era, the field of digital image processing is growing in popularity and developing demand. When the input is given to the system, after the processing a variation is performed and the output is taken from the system. This research is about a visualized image of road pavement that was first given as an input to obtain an output using gray-level image enhancing techniques. In this research, the image enhancement is performed through different visualized enhancement images, which is the backbone of this research. Initially, it was cogitating as a piece of cake but once we start implementing the technique then we realize that it was not that easy and it needs a lot of knowledge, and the variation of research to overcome complicated problems correlate with this research. The complicated assignments were a variation of light from day to night, visibility of the cracks, homogeneous background, etc. The most complex issue is the visibility and recognition of the road cracks that are complex to classify using different algorithms in obtained images. For deep research purposes, we must identify and categorize many properties of the input images, such as intensity, color adjustment, and conversion for the sake of better image enhancement. Using MATLAB, we examine the various gray-level image using better techniques based on their computational capability. This depicts the entire outcome of road pavement images to assess the characteristics and repercussions of alteration. To more precisely check road crack detection, image enhancement techniques such as image negative, logarithmic transformations, gamma corrections, and others are applied. Every single specified technique, such as gamma and constant values, is proposed with a general mathematical implementation. Piecewise linear contrast widening subclasses are also studied to more comprehensively discover and assess road cracks. Every technique mentioned above has specific features and a unique image enhancement. Each method has a different and unique feature, so none in all the ways can be implemented for all kinds of gray-level image enhancement. Because in some cases, it will be necessary to implement gamma corrections for better enhancement, while in other areas, it will be necessary to implement logarithmic transformations for the sake of good results. The logarithmic transformation is suitable for such kind of conversion in which logarithm is taken and the image goes under process, but for the same image, the other enhancement like the negative image does not suit. A comparison will be made at the end of this study for different algorithms. We aim to compare and contrast the benefit of one technique over another, and the researcher will implement that technique for better accuracy and result. The proposed framework for image enhancement to classify the road surface cracks is beneficial in discriminating among different types. It also gives us insight into selecting the appropriate technique for the image enhancement of the road cracks according to their kinds.

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Comparative Analysis of AlexNet, ResNet18 and SqueezeNet with Diverse Modification and Arduous Implementation

Cited by 45 publications

References 29 publications

The q-rung fuzzy LOPCOW-VIKOR model to assess the role of unmanned aerial vehicles for precision agriculture realization in the Agri-Food 4.0 era

The q-rung fuzzy LOPCOW-VIKOR model to assess the role of unmanned aerial vehicles for precision agriculture realization in the Agri-Food 4.0 era

SNELM: SqueezeNet-Guided ELM for COVID-19 Recognition

Gray-Level Image Transformation of Paved Road Cracks with Metaphorical and Computational Analysis

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