Skin Color Segmentation Based on Artificial Neural Network Improved by a Modified Grasshopper Optimization Algorithm

Razmjooy, Navid; Razmjooy, Saeid; Vahedi, Zahra; Estrela, Vania V.; Oliveira, Gabriel Gomes de

doi:10.1007/978-3-030-56689-0_9

Cited by 17 publications

(6 citation statements)

References 18 publications

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“…In CNNs, the activation function is a mathematical equation that is defined at the output layers of a neuron before being sent to the next layers of the network. The activation function aims to regulate the output of a neuron, allowing the network to learn more complex patterns [28] [29]. Some commonly used activation functions are: Sigmoid, ReLU, tanh and Softmax.…”

Section: Activation Functionsmentioning

confidence: 99%

Disease Identification in Crop Plants based on Convolutional Neural Networks

Iparraguirre-Villanueva¹,

Guevara-Ponce²,

Torres-Ceclén³

et al. 2023

IJACSA

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The identification, classification and treatment of crop plant diseases are essential for agricultural production. Some of the most common diseases include root rot, powdery mildew, mosaic, leaf spot and fruit rot. Machine learning (ML) technology and convolutional neural networks (CNN) have proven to be very useful in this field. This work aims to identify and classify diseases in crop plants, from the data set obtained from Plant Village, with images of diseased plant leaves and their corresponding Tags, using CNN with transfer learning. For processing, the dataset composing of more than 87 thousand images, divided into 38 classes and 26 disease types, was used. Three CNN models (DenseNet-201, ResNet-50 and Inception-v3) were used to identify and classify the images. The results showed that the DenseNet-201 and Inception-v3 models achieved an accuracy of 98% in plant disease identification and classification, slightly higher than the ResNet-50 model, which achieved an accuracy of 97%, thus demonstrating an effective and promising approach, being able to learn relevant features from the images and classify them accurately. Overall, ML in conjunction with CNNs proved to be an effective tool for identifying and classifying diseases in crop plants. The CNN models used in this work are a very good choice for this type of tasks, since they proved to have a very high performance in classification tasks. In terms of accuracy, all three models are very accurate in image classification, with an accuracy of over 96% with large data sets.

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Section: Activation Functionsmentioning

confidence: 99%

Disease Identification in Crop Plants based on Convolutional Neural Networks

Iparraguirre-Villanueva¹,

Guevara-Ponce²,

Torres-Ceclén³

et al. 2023

IJACSA

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“…Examples of some algorithms which proposed to hybrid with GOA such as; ABC algorithm (Sharma et al 2021), Bat Algorithm (S. Yue and Zhang 2021), and Gravity Search Algorithm (GSA) (Guo et al 2020) to solve numerical problems. Other studies proposed hybrid GOA with Genetic Algorithm (GA) for optimizing the non-linear equations system (El-Shorbagy and El-Refaey 2020), with Cuckoo Search (CS) algorithm for optimizing the day-ahead scheduling of microgrid and its optimal operation (C et al 2022), with Differential Evolution algorithm (DE) for tackling the classification of colors of dyed fabrics (Li et al 2021), with Cat Swarm Optimization algorithm (CSO) for feature section problem and optimizing the multi-layer perceptron design (Bansal et al 2020), and with Artificial Neural Network (ANN) to optimize the skin color detection (Razmjooy et al 2021b). Table 1 shows the summary of the mentioned works.…”

Section: Related Workmentioning

confidence: 99%

Novel variants of grasshopper optimization algorithm to solve numerical problems and demand side management in smart grids

et al. 2023

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The grasshopper optimization algorithm (GOA), which is one of the recent metaheuristic optimization algorithms, mimics the natural movements of grasshoppers in swarms seeking food sources. Some deficiencies have existed in the original GOA such as slow convergence speed, and the original GOA may get quickly stuck into local solutions facing some complex. For tackling these drawbacks of the original GOA, enhanced versions of GOA have been proposed to deal with the optimization problems more effectively. In the current study, two strategies have been integrated into GOA: the grouping mechanism of non-linear ‘c’ parameters and the mutation mechanism. Moreover, two different groups of non-linear ‘c’ parameters have been suggested in the grouping mechanism. Incorporating the grouping mechanism into GOA can update the grasshoppers’ positions within a limited local area, whereas the diversity of agents can be improved by integrating the mutation mechanism. Eight Novel-Variants GOA (NVGOAs) are proposed to address the deficiencies of the original GOA. Where two variants NVGOA1_1 and NVGOA2_1 represent the impact of each proposed group of ‘c’ parameters. Another two variants NVGOA3 and NVGOA4 represent the impact of the mutation mechanism with two different values of probability. Moreover, four variants: NVGOA1_2, NVGOA1_3, NVGOA2_2, and NVGOA2_3 represent the combination of the two proposed mechanisms. First, the comparison between the performance of the proposed variants and the original GOA has been conducted. Then, for validation of the efficiency of the proposed NVGOAs, the performance of the best-recorded NVGOA variants has been tested against the 29 CEC-2017 benchmark functions and compared with six state-of-the-art optimization algorithms based on the mean and the standard deviation metrics. Moreover, the Wilcoxon Signed-Rank test has been employed to exhibit the efficiency of the proposed variants. As well comparative analysis with previous enhancements of GOA has been conducted against the best-recorded NVGOA variants. Also, conducting a dimension study between the best-recorded chaotic previous variants against the best-recorded proposed NVGOA variants has revealed the superiority of NVGOAs. The results of all these analyses demonstrated the success and efficiency of the proposed NVGOA variants to solve numerical optimization problems. Concerning demand side management in smart grids, the proposed NVGOA variants have been applied to schedule the loads in three areas: residential, commercial, and industrial to decrease the daily operating costs and peak demand. The results show that the peak demand is reduced by 23.9%, 17.6%, and 9.2% in residential areas, commercial areas, and industrial areas respectively. Also, the operating cost decreased by 7.25%, 9.2%, and 18.89% in residential, commercial, and industrial areas, respectively. Finally, the overall results show that the proposed NVGOA algorithms are effective solutions to address the flaws of the original version of GOA and can get high-quality solutions for different optimization problems.

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“…CNN is a type of deep learning structure that has a grid pattern and is employed for processing data, such as images. CNN is employed for adaptively and automatically learning spatial hierarchies of features via a backpropagation process using various building blocks [50][51][52][53]. This neuron-based network that has a gridlike topology, automatically extracts high-level features from raw input data whereas, their corresponding spatial information can be preserved.…”

Section: Proposed Convolutional Neural Network Architecturementioning

confidence: 99%

Nerve optic segmentation in CT images using a deep learning model and a texture descriptor

Ranjbarzadeh

Dorosti

Ghoushchi

et al. 2022

Complex Intell. Syst.

Self Cite

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The increased intracranial pressure (ICP) can be described as an increase in pressure around the brain and can lead to serious health problems. The assessment of ultrasound images is commonly conducted by skilled experts which is a time-consuming approach, but advanced computer-aided diagnosis (CAD) systems can assist the physician to decrease the time of ICP diagnosis. The accurate detection of the nerve optic regions, with drawing a precise slope line behind the eyeball and calculating the diameter of nerve optic, are the main aims of this research. First, the Fuzzy C-mean (FCM) clustering is employed for segmenting the input CT screening images into the different parts. Second, a histogram equalization approach is used for region-based image quality enhancement. Then, the Local Directional Number method (LDN) is used for representing some key information in a new image. Finally, a cascade Convolutional Neural Network (CNN) is employed for nerve optic segmentation by two distinct input images. Comprehensive experiments on the CT screening dataset [The Cancer Imaging Archive (TCIA)] consisting of 1600 images show the competitive results of inaccurate extraction of the brain features. Also, the indexes such as Dice, Specificity, and Precision for the proposed approach are reported 87.7%, 91.3%, and 90.1%, respectively. The final classification results show that the proposed approach effectively and accurately detects the nerve optic and its diameter in comparison with the other methods. Therefore, this method can be used for early diagnose of ICP and preventing the occurrence of serious health problems in patients.

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Skin Color Segmentation Based on Artificial Neural Network Improved by a Modified Grasshopper Optimization Algorithm

Cited by 17 publications

References 18 publications

Disease Identification in Crop Plants based on Convolutional Neural Networks

Disease Identification in Crop Plants based on Convolutional Neural Networks

Novel variants of grasshopper optimization algorithm to solve numerical problems and demand side management in smart grids

Nerve optic segmentation in CT images using a deep learning model and a texture descriptor

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