A Novel Subset Feature Selection Framework for Increasing the Classification Performance of SONAR Targets

Potharaju, Sai Prasad; Sreedevi, M.

doi:10.1016/j.procs.2017.12.115

Cited by 7 publications

(4 citation statements)

References 11 publications

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“…Feature selection methods provide a way to minimize computational time, boost predictive efficiency, and deeper knowledge of machine learning data [16][17][18].…”

Section: Feature Selection Using Improved Step Adjustment Based Glowworm Swarm Optimization Algorithmmentioning

confidence: 99%

Heart Disease Prediction Using Glowworm Swarm Optimization and Support Vector Machine Classifier

Sreedevi

2022

International Journal of Biology and Biomedical Engineering

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At present, health disorder is growing day by way of the day due to existence lifestyle, hereditary. Particularly, heart disease has ended up greater frequent these days. Heart disorder prognosis technique is very quintessential and integral trouble for the patient's health. Besides, it will help out to limit disorder to a larger distinctive level. The role of using strategy like machine learning and algorithm such as heart disease diagnosis using Data Mining(DM) techniques is very significant. In the previous system, the Fuzzy Extreme Learning Machine (FELM) was proposed to predict heart disease, ensuring an accurate and timely diagnosis. However, it only achieves 87.14 % of accuracy. To improve the classification accuracy, the proposed system designed an Improved Step Adjustment based Glowworm Swarm Optimization Algorithm with Weighted Feature based Support Vector Machine (ISAGSO-WFSVM) for Heart disease diagnosis. This proposed venture utilizes the dataset of heart disease for input. Using the Improved Step Adjustment based Glowworm Swarm Optimization Algorithm (ISAGSO) to enhance the true positive rate, optimal features are then selected. Finally, with the aid of the Weighted Feature based Support Vector Machine (WFSVM) classifier, classification is carried out relying selected features. In the proposed method, better performance obtained and that is validated through the experimental results in terms of precision, accuracy, recall and f-measures

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“…Feature selection methods provide a way to minimize computational time, boost predictive efficiency, and deeper knowledge of machine learning data [16][17][18].…”

Section: Feature Selection Using Improved Step Adjustment Based Glowworm Swarm Optimization Algorithmmentioning

confidence: 99%

Heart Disease Prediction Using Glowworm Swarm Optimization and Support Vector Machine Classifier

Sreedevi

2022

International Journal of Biology and Biomedical Engineering

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“…The future scope is this is a comparative study not much is to be improved. In [7], A method was proposed to predict lung cancer from C.T. scan images by using Convolutional Neural Networks.…”

Section: Literature Surveymentioning

confidence: 99%

Mineral Rock Classification Using Convolutional Neural Network

Amiripalli

Rao

Behara

et al. 2021

Recent Trends in Intensive Computing

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The main aim of the research is to build a model that can effectively predict the type of mineral rocks. Rocks can be predicted by observing it is colour, shape and chemical composition. On-site technicians need to apply different techniques on rock sample in order to predict rock type. Technicians need to apply different techniques on rock samples, so it is a time-consuming process, and sometimes the predictions may be accurate, and sometimes predictions may be false. When predictions are false, it might show a negative impact in several ways for workers and organization as well. We considered an image dataset of rock types, namely Biotite, Bornite, Chrysocolla, Malachite, Muscovite, Pyrite, and Quartz. We applied CNN (Convolutional Neural Network) Algorithm to get a better prediction of different mineral rocks. Nowadays, CNN is mainly used for image classification and image recognition tasks.

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“…Because GPCR is the largest super family among all the receptors in cell biology and has much functionality such as cell signaling, drug targeting etc. We know that it is an emerging area of research so many researchers have implemented varieties of algorithms upon it like support vector machine, naive Bayes, neural network, fuzzy c-means [21][22][23][24][25][26][27][28][29][30] etc. Therefore we have concentrated our experiment on GPCR along with membrane cholesterol which is an innovative idea.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Plasma Membrane Cholesterol from 7-Transmembrane Receptor Using Hybrid Machine Learning Algorithm

Nayak

Tripathy²,

Mohapatra

et al. 2022

Int. j. electr. comput. eng. syst. (Online)

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The researches have been made on G-protein coupled receptors (GPCRs) over the long-ago decades. GPCR is also named as 7-transmembrane (7TM) receptor. According to biological prospective GPCRs consist of large protein family with respective subfamilies and are mediated by different physiological phenomena like taste, smell, vision etc. The main functionality of these 7TM receptors is signal transduction among various cells. In human genome, cell membrane plays significant role. All cells are made up of trillion of cells and have dissimilar functionality. Cell membrane composed of different components. GPCRs are reported to be modulated by membrane cholesterol by interacting with cholesterol recognition amino acid consensus (L/V-X (1-5)-Y-X (1-5)-R/K) (CRAC) or reverse orientation of CRAC (R/K-X (1-5)-Y-X (1-5)-L/V) (CARC) motifs present in the TM helices. Among all, cholesterol is one who is regulated by membrane proteins. Here we took GPCR as membrane proteins and this protein modulates membrane cholesterol. According to cell biology, GPCR regulates a wide diversity of vital cellular processes and are targeted by a huge fraction of approved drugs. In this paper we have concentrated our investigation on membrane protein with membrane cholesterol. A hybrid algorithm consisting of spectral clustering and support vector machine is proposed for prediction of membrane cholesterol with GPCR. Spectral clustering uses graph nodes for calculating the cluster points and also it considers other concept such as similarity matrix, low-dimensional space for projecting the data points and upon this parameter at last construct the cluster centre. Supervised learning method is used for solving regression and classification problems. From the analysis we found that our result shows better prediction accuracy in terms of time complexity when compared with two existing models such as fuzzy c-means (FCM) and rough set with FCM model

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A Novel Subset Feature Selection Framework for Increasing the Classification Performance of SONAR Targets

Cited by 7 publications

References 11 publications

Heart Disease Prediction Using Glowworm Swarm Optimization and Support Vector Machine Classifier

Heart Disease Prediction Using Glowworm Swarm Optimization and Support Vector Machine Classifier

Mineral Rock Classification Using Convolutional Neural Network

Prediction of Plasma Membrane Cholesterol from 7-Transmembrane Receptor Using Hybrid Machine Learning Algorithm

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