Hybrid Weed-Particle Swarm Optimization Algorithm and CMixture for Data Publishing

doi:10.46253/j.mr.v2i3.a4

Cited by 8 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The input data and image were given as input. Here, the proposed model was compared with the Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) [11], Firefly (FF), Genetic Algorithm (GA) [12], and Crow Search (CS) algorithms.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Improved Binary Artificial Fish Swarm Algorithm for Diagnosis of Thyroid Disease

2022

View full text Add to dashboard Cite

The thyroid gland generates the thyroid hormones, which help the appropriate body's metabolism regulation. Generally, the abnormalities of thyroid function are categorized into two classifications such as small thyroid hormone production called hypothyroidism or large thyroid hormone production named hyperthyroidism. The thyroid diagnosis using suitable thyroid data interpretation is considered a very important classification problem. Till now, few contributions are performed in the automatic diagnosis of thyroid disease. The main aim of this research is to work on a novel diagnosis of thyroid approach, which follows a new working model. Here, it dealt with the integration of both the thyroid data and image. Moreover, two important working procedures are carried out such as classification as well as feature extraction. In the initial stage, two feature sorts are extracted. Image features such as gradient as well as neighborhood-based features are extracted and the principle component analysis (PCA) process is included for the feature extraction from thyroid data. Then, the process of classification is carried out in two segments. Here, the Convolutional Neural Network (CNN) is exploited to obtain the classified outcomes bypassing the image itself. At the same time, in order to obtain the data features and image as the input, the Neural Network (NN) is exploited for the classification procedure. At last, both the classified outcomes such as NN as well as CNN are integrated to raise the precise diagnosis rate. Furthermore, the main contribution of this paper is to raise the rate of accuracy, therefore this work attempts to activate the optimization model. In CNN, the convolution layer is optimally chosen and when classifying in the NN model the subjected feature must be optimal. Therefore, the necessary features are chosen optimally. For this purpose, a novel enhanced approach called Improved Binary Artificial Fish Swarm Algorithm (IBASFS) is the enhancement model of Fish Swarm Algorithm is presented in this paper. At last, the adopted model performances are evaluated with the existing models and it reveals that the enhancement of the adopted model to detect the thyroid.

show abstract

Section: Resultsmentioning

confidence: 99%

“…Eq. (11) explains the output of full connected layers, wherein, Finally, the formulation of output is stated in eq. ( 12), and it is in the structure of softmax units, wherein s O represents feature vector which is derived from the fully connected layer, s j represents actual output, and  indicates the softmax function parameter.…”

Section: Cnn Modelmentioning

confidence: 99%

Improved Binary Artificial Fish Swarm Algorithm for Diagnosis of Thyroid Disease

2022

View full text Add to dashboard Cite

show abstract

“…In this section, the adopted Hybrid GWO-CSA technique experimentation analysis regarding the image caption generation was demonstrated. Here, the proposed Hybrid GWO-CSA model was compared with the CNN, PSO-CNN [17], GWO-CNN [15], and GA-CNN [16].…”

Section: Resultsmentioning

confidence: 99%

Image Caption Generation Framework using Hybrid Grey Wolf Optimization and Crow Search Algorithm

Kamatham¹

2022

View full text Add to dashboard Cite

The image captioning process is used to generate an image textual description. To generate caption, image captioning uses both natural language processing as well as computer vision. Nevertheless, most image captioning systems present indistinct depictions about objects such as "Man", "woman", "group of people", "building" and so on. Therefore, an intelligent based image captioning technique is developed in this work. The proposed technique consists of some steps such as the formation of the sentence, generation of words, and generation of the caption. At first, the input image is fed to a Deep learning classifier named Convolutional Neural Network (CNN). Here, the main aim of the classifier is used to train the appropriate words which are associated with the image, and can simply classify related words of a given image. Furthermore, the Long-Short Term Memory (LSTM) technique is exploited to form a set of sentences with generated words. Subsequently, the Maximum Like hood (ML) function is exploited to calculate formed sentences likelihood, as well as sentences with maximum probability is exploited that is furthermore exploited to generate a visual illustration of the scene regarding the image caption. The major objective of this work is to improve CNN performance by optimally tuning its activation function and weight. Therefore, for this optimal selection, this work adopts a novel improved optimization approach named Grey Wolf Optimization (GWO) algorithm and Crow Search Algorithm (CSA), which is termed as the Hybrid GWO-CSA algorithm. At last, the adopted captioning technique performance is evaluated with other existing techniques regarding the statistical analysis.

show abstract

Trust Aware Secure Routing Model in MANET: Self-improved Particle Swarm Optimization for Optimal Route Selection

Prasath²

2022

Computer, Communication, and Signal Processing

View full text Add to dashboard Cite

Hybrid Weed-Particle Swarm Optimization Algorithm and CMixture for Data Publishing

Cited by 8 publications

References 15 publications

Improved Binary Artificial Fish Swarm Algorithm for Diagnosis of Thyroid Disease

Improved Binary Artificial Fish Swarm Algorithm for Diagnosis of Thyroid Disease

Image Caption Generation Framework using Hybrid Grey Wolf Optimization and Crow Search Algorithm

Trust Aware Secure Routing Model in MANET: Self-improved Particle Swarm Optimization for Optimal Route Selection

Contact Info

Product

Resources

About