Multi kernel and dynamic fractional lion optimization algorithm for data clustering

Chander, Satish; Vijaya, P. A.; Dhyani, Praveen

doi:10.1016/j.aej.2016.12.013

Cited by 28 publications

(11 citation statements)

References 25 publications

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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%

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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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Section: Segmentation Of the Food Imagementioning

confidence: 99%

“…The size of the feature vector database D F is termed as y. The equation (27) provides the size of the feature vector database D F .…”

Section: 3b Extraction Of the Color Featuresmentioning

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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“…Considering IoT, the data is referred as an attribute value like integer values, variables, and events to indicate certain states. IoT service permits certain functions for performing effective big data classification using a predefined interface 9 . Some of the investigators are mostly attentive in detecting risk concerns by determining and assimilating big data within IoT 10 .…”

Section: Introductionmentioning

confidence: 99%

Trust and energy aware routing algorithm for Internet of Things networks

Mujeeb

Sam

Madhavi

2021

Int J Numerical Modelling

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The expansion in the Internet of Things (IoT) has led to a shift towards smart technologies. IoT focuses on integrating networks to facilitate smooth services to humans. The interface between the mobility patterns and the routing protocols is considered to increase the performance of the network. However, incorporating security in the IoT network has been a major issue that continues to nurture with increasing IoT devices. This article addresses this issue by developing a novel technique, namely energy harvesting trust aware routing algorithm (EHTARA) for initiating a trust‐based routing model in the IoT network in the presence of ambient energy sources. The cost metric is newly devised by considering energy, distance, and trust parameters for determining the best path. At the base station, big data classification is performed using the adaptive exponential‐Bat (adaptive E‐Bat) algorithm based deep belief network (DBN). The training of DBN is performed using the adaptive E‐Bat algorithm, which is the combination of adaptive concept, exponential weighted moving average (EWMA), and Bat algorithm (BA). Here, the optimization‐based map‐reduce framework helps to deal with the imbalanced data by adapting the deep learning in classification. The proposed EHTARA outperformed other methods with a maximal energy of 0.927.

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“…In addition, meta-heuristic algorithms have attracted considerable research attention for use in partitional clustering models. These include ant colony optimization (ACO) [38], artificial bee colony algorithm (ABC) [39]- [41], bat algorithm (BA) [42], black hole (BH) [43], differential evolution (DE) [44], [45], elephant algorithm (EA) [46], firefly algorithm (FA) [47], [48], genetic algorithm (GA) [49], [50], k-means based genetic algorithm (GKA) [51], [52], gravitational search algorithm (GSA) [53], [54], gray wolf optimizer (GWO) [55], lion optimization algorithm (LOA) [56], monkey algorithm (MA) [57], moth swarm algorithm (MSA) [58], particle swarm optimization (PSO) [59], [60], simulated annealing (SA) [61], [62], symbiotic organism search (SOS) [63], and whale optimization algorithm (WOA) [64]. The clustering approach has been adopted in various research fields, such as bioinformatics [65]- [67], vector quantization [68]- [71], data mining [72]- [74], geographical information systems [75], [76], pattern recognition [77]- [79], and sensor applications [80]- [82].…”

Section: Introductionmentioning

confidence: 99%

Memetic Particle Gravitation Optimization Algorithm for Solving Clustering Problems

Huang

Peng

et al. 2019

IEEE Access

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Data clustering is a well-known data analysis technique for organizing unlabeled datapoints into clusters on the basis of similarity measures. The real-world applications of data clustering include bioinformatics, vector quantization, data mining, geographical information systems, pattern recognition, image processing, and wireless sensors. The data in a cluster are similar (minimizing the intra-cluster distance) and differ from the data in other clusters (maximizing the inter-cluster distance). The cluster problem has been proven to be NP-hard, but can be solved using meta-heuristic algorithms, such as ant colony optimization, genetic algorithms, gravitational search algorithm (GSA), and particle swarm optimization (PSO). This paper proposes a memetic clustering algorithm with efficient search and fast convergence, respectively, based on PSO and GSA, called the memetic particle gravitation optimization (MPGO) algorithm. The two main mechanisms of MPGO are hybrid operation and diversity enhancement. The former involves the exchange of individuals from two subpopulations after a predefined number of function evaluations (FEs), whereas the latter involves an enhancement operator, which is similar to the crossover process of differential evolution, for enhancing the diversity of each system. Individuals from the PSO and GSA systems are selected for the exchange of solutions by using the roulette-wheel approach. The performance of the proposed algorithm was evaluated on 52 benchmark test functions, six UCI machine learning benchmarks, and image segmentation of six well-known images. A comparison with existing algorithms verified the superior performance of the proposed algorithm in terms of a fitness value, an accuracy rate, and a peak signal-to-noise ratio.

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Multi kernel and dynamic fractional lion optimization algorithm for data clustering

Cited by 28 publications

References 25 publications

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

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

Trust and energy aware routing algorithm for Internet of Things networks

Memetic Particle Gravitation Optimization Algorithm for Solving Clustering Problems

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