MKF-Firefly: Hybridization of Firefly and Multiple Kernel-Based Fuzzy C-Means Algorithm

Chander, Satish; Vijaya, P. A.

doi:10.17148/ijarcce.2016.5742

Cited by 9 publications

(7 citation statements)

References 10 publications

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“…P-Whale is an optimization algorithm that modifies WOA by the integration of PSO in the update rule to improve the search process, and thereby enhance the performance of the algorithm. WOA [27] is a metaheuristic algorithm that solves optimization problems [28,29] inspired by the physical behaviour of humpback whales, encircling prey, bubble-net attack mechanism and search for prey. In the position update of WOA, the update rule of PSO is adopted, and thus determines a suitable whale fusion factor for each sub-band.…”

Section: Generation Of Whale Fusion Factor Using the Proposed P-whalementioning

confidence: 99%

“…29 In P-Whale, Eqs. (29) and (31) are modified using PSO [30] to enhance the search space. The evolution process of PSO is based on particle update using the personal best and global best positions of the swarm so that the convergence becomes faster.…”

Section: Position Updatementioning

confidence: 99%

“…Substituting Eq. (37) in (29) with its distance measure substituted, the position update of P-Whale based on shrinking mechanism can be derived as follows:…”

Section: T ð þmentioning

confidence: 99%

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FWFusion: Fuzzy Whale Fusion model for MRI multimodal image fusion

Patil¹,

Shirbahadurkar

2018

Sādhanā

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Medical treatment and diagnosis require information that is taken from several modalities of images like Magnetic Resonance Imaging (MRI), Computerized Tomography and so on. The information obtained for certain ailments is often incomplete, invisible and lacking in consistent scanner performance. Hence, to overcome these issues in the image modalities, image fusion schemes are developed in the literature. This paper proposes a hybrid algorithm using fuzzy concept and a novel P-Whale algorithm, called Fuzzy Whale Fusion (FWFusion), for the fusion of MRI multimodal images. Two multimodal images from MRI (T1, T1C, T2 and FLAIR) are considered as the source images, which are fed as inputs to a wavelet transform. The transform utilized converts the images into four different bands, which are fused using two newly derived fusion factors, fuzzy fusion and whale fusion, in a weighted function. The proposed P-Whale approach combines Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO) for the effective selection of whale fusion factors. The performance of FWFusion model is compared to those of the existing strategies using Mutual Information (MI), Peak Signal-to-Noise Ratio (PSNR) and Root Mean Squared Error (RMSE), as the evaluation metrics. From the mean performance evaluation, it is observed that the proposed approach can achieve MI of 1.714, RMSE of 1.9 and PSNR of 27.9472.

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Section: Generation Of Whale Fusion Factor Using the Proposed P-whalementioning

confidence: 99%

Section: Position Updatementioning

confidence: 99%

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FWFusion: Fuzzy Whale Fusion model for MRI multimodal image fusion

Patil¹,

Shirbahadurkar

2018

Sādhanā

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“…The CSW-WLIFC algorithm performs the segmentation of the food images. The CSW-WLIFC algorithm uses various kernel functions along with the existing WLI-FC algorithm [26][27][28][29] for the segmentation. Figure 2 shows the block diagram of the segmentation process with the CSW-WLIFC algorithm.…”

Section: Segmentation Of the Food Imagementioning

confidence: 99%

“…The size of the feature vector is expressed in the equation (30). The input layer of the NN is expressed by the equation (28).…”

Section: Classificationmentioning

confidence: 99%

Fuzzy clustering and Whale-based neural network to food recognition and calorie estimation for daily dietary assessment

Emmanuel

Minija

2018

Sādhanā

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The calorie value of the food items taken by the person in everyday life needs to be monitored to reduce the risk of obesity, heart problems, and diabetes, etc. The calorie estimator in the existing models has reduced accuracy since the calorie value of each food varies with mass. This paper introduces a dietary assessment system based on the proposed Cauchy, Generalized T-Student, and Wavelet kernel based Wu-and-Li Index Fuzzy clustering (CSW-WLIFC) based segmentation and the proposed Whale Levenberg Marquardt Neural Network (WLM-NN) classifier. The proposed CSW-WLIFC based segmentation segments the image based on the existing WLI-FC algorithm. A novel CSW based kernel function is utilized in the segmentation process. Feature vectors such as color, shape, and texture are extracted from the segmented image. The Neural Network is trained with the Whale-Levenberg Marquardt (WLM) model to recognize each food item from the tray image. The proposed calorie estimator calculates the calorie value of each food item. From the simulation results, it is evident that the proposed model has the improved performance than the existing models with the values of 0.999, 0.9643, 0.9627, and 0.0184 for the segmentation accuracy, macro average accuracy, standard accuracy, mean square error, respectively.

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Applying cuckoo search based algorithm and hybrid based neural classifier for breast cancer detection using ultrasound images

Michahial¹,

Thomas²

2019

Evol. Intel.

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MKF-Firefly: Hybridization of Firefly and Multiple Kernel-Based Fuzzy C-Means Algorithm

Cited by 9 publications

References 10 publications

FWFusion: Fuzzy Whale Fusion model for MRI multimodal image fusion

FWFusion: Fuzzy Whale Fusion model for MRI multimodal image fusion

Fuzzy clustering and Whale-based neural network to food recognition and calorie estimation for daily dietary assessment

Applying cuckoo search based algorithm and hybrid based neural classifier for breast cancer detection using ultrasound images

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