RETRACTED ARTICLE: Optimal feature-based multi-kernel SVM approach for thyroid disease classification

Shankar, K.; Lakshmanaprabu, S.K.; Gupta, Deepak; Maseleno, Andino; Albuquerque, Victor Hugo C. de

doi:10.1007/s11227-018-2469-4

Cited by 193 publications

(59 citation statements)

References 18 publications

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“…Including its generalizability, SVM is capable of producing high precision and a comparatively modest error rate. In its development, SVM has been successfully used to solve problems in various fields, including hyperspectral data [16], Alzheimer's disease [17], [18], thyroid disease [19]. Besides in ecology [20], including coral reef fish detection and recognition in underwater videos [21] and remote sensing [22].The ability of SVM as a classification method can be compared with other classification methods [23].…”

Section: Introductionmentioning

confidence: 99%

Using Hierarchical Likelihood Towards Support Vector Machine: Theory and Its Application

et al. 2020

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The H-likelihood method proposed by Lee and Nelder (1996) is extensively used in a wide range of data. In terms of direction, repetitive measured data within classification can be examined employing hierarchical generalized linear models (HGLMs). Whether we are concerned in multiple endpoints which are correlated, instead Multivariate Double Hierarchical Generalized Linear Models (DHGLM) can be taken into consideration. This paper addresses the implementation of this principle to vector selection and support machines. Based on the analysis with the fish morphology class Sardinella lemuru (Bali sardinella) and setting the best epsilon 0.7 cost 4 parameter reaching best performance: 0.2327401. Predictive value of fish sex was calculated 0.997319 and Region under the curve: 0.8967. At the same time, we extend the large-scale case studies for stress testing of the SVM method by using three datasets from UCI machine learning repository including the Bank marketing dataset, the car evaluation database and Human Activity Recognition Using Smartphones dataset. In a nutshell by employing SVM-DHGLM increased the accuracy, precision, recall, for feature selection and classification. Long story short, the H-likelihood provides an excellent and usable structure for statistical inference of the unobservable general deterministic model, while preserving the advantages of the original probability structure for fixed parameters. We presume that more new groups of models will be created and that the H-likelihood will be commonly used for their inferences and the application in big data and machine learning.

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Section: Introductionmentioning

confidence: 99%

Using Hierarchical Likelihood Towards Support Vector Machine: Theory and Its Application

et al. 2020

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“…Information is a computerized system for processing data entered into information or data, storing, distributing and producing new information to complete a task automatically or as a supporter of human resources in carrying out the functions of corporate operations, controlling and returning decisions within an organization [29][30][31][32]. Information is a group of interconnected component and functions to collect, process and provide output in the form of information needed to finish business duties [33][34][35][36].…”

Section: Information Systemmentioning

confidence: 99%

Boarding House Selection using SAW Method

Mukhlis¹,

Ayshwarya

Nguyen

et al. 2019

ijrte

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Determination of residence in a new area known is influenced by many factors namely location, facilities, contract systems and prices. This journal determines the criteria used in selecting a residence. These criteria were analyzed using simple additive weighting (SAW) method. The results of the analysis obtained in the form of location criteria to determine the most suitable boarding house because the existence of boarding houses in the vicinity of public facilities is highly expected for migrants in a new area. With the advancement of information technology it can answer the need to find a boarding house, and will be very helpful both from the owner and tenant side. And not only that, with this internet technology, prospective tenants can also select a boarding house that suits their wishes. Planning a system that can assist in the determination of temporary housing is needed by tenants in finding housing that fits the desired criteria. With the creation of a DSS (Decision Support System), the selection of boarding houses is expected to help resolve the problem of alternative housing. Therefore a decision support system application program is needed to determine a temporary residence or boarding house. Decision support system is a computer-based system that aims to assist decision making by utilizing data and models to solve unstructured problems.

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“…Therefore, with the help of improved MDD classification, it can accomplish more optimized classification accuracy along with best forecasting measure. As a result, different images are used to compute the above work including classification process and result in usual as well as unusual images [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Deep neural network with moth search optimization algorithm based detection and classification of diabetic retinopathy images

2020

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In recent days, progressive rate of diabetic retinopathy (DR) becomes high and it is needed to develop an automated model for effective diagnosis of DR. This paper presents a new deep neural network with moth search optimization (DNN-MSO) algorithm based detection and classification model for DR images. The presented DNN-MSO algorithm involves different processes namely preprocessing, segmentation, feature extraction and classification. Initially, the contrast level of the DR images is improved using contrast limited adaptive histogram equalization model. After that, the preprocessed images are segmented using histogram approach. Then, Inception-ResNet V2 model is applied for feature extraction. Finally, extracted feature vectors are given to the DNN-MSO based classifier model to classify the different stages of DR. An extensive series of experiments were carried out and the results are validated on Messidor DR dataset. The obtained experimental outcome stated the superior characteristics of the DNN-MSO model by attaining a maximum accuracy, sensitivity and specificity of 99.12%, 97.91% and 99.47% respectively.

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RETRACTED ARTICLE: Optimal feature-based multi-kernel SVM approach for thyroid disease classification

Cited by 193 publications

References 18 publications

Using Hierarchical Likelihood Towards Support Vector Machine: Theory and Its Application

Using Hierarchical Likelihood Towards Support Vector Machine: Theory and Its Application

Boarding House Selection using SAW Method

Deep neural network with moth search optimization algorithm based detection and classification of diabetic retinopathy images

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