Performance Analysis of Morphological Operators Based Feature Extraction and SVD, Neural Networks as Post Classifier for the Classification of Epilepsy Risk Levels

Harikumar, R.; Vijaykumar, T. N.; C, Ganesh Babu; Sreejith, M.G.

doi:10.1007/978-81-322-0997-3_1

Cited by 3 publications

(4 citation statements)

References 14 publications

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“…Although customer relationship management constitutes the main focus of corporate marketing, there is a loss of asset exclusivity in this management. In other words, as a company implements technical support activities for a customer [10], it also leads to an increase in the cost of servicing other customers because of the exclusivity of the technology equipment that locks the company into the relationship management for that customer, for example, the lack of learning effect for other customers or the switching cost due to technology specificity [11]. is risk is rarely considered in existing discussions of the problem.…”

Section: Risks In Customer Technicalmentioning

confidence: 99%

“…Petri � (pp, tt, ff, ww, kk, mm). (10) Pp in equation ( 10) is present the initial dataset, set to, expressed as the parts of criteria, given in information, given-in data, needed resources, etc., and expressed with "0," and expressed as "Φ" if all data are available in the database; tt is the initial variation combination, which can be expressed as factors such as event, measurement step, data operation, task, and so on, and is chosen to be expressed as "δ," and equation (11) then expresses the flow relationship of poetry network [20]:…”

Section: Petri Network Modelingmentioning

confidence: 99%

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Construction and Risk Analysis of Marketing System Based on Deep Neural Network

Yang

Bai

Wang

2022

Security and Communication Networks

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Enterprises needs to realize the unity of economic and social benefits. From the marketing theory, we know that the economic and social benefits form a symbiotic effect, which determines that enterprises need to pay attention to their own service quality. The current risks are in the areas of “customer relationship management” and “customer technical support,” specifically, the inability to effectively meet the needs of customers in terms of corporate infrastructure and product details, as well as the increase in costs for other customer services. The article firstly completes the construction of marketing risk early warning system, analyzes marketing risk types and formation factors, constructs indicators and mathematical modeling, then proposes the ANN combined with Petri algorithm, which is used to realize marketing risk early warning, completes sample selection and indicator quantification, sets up the early warning model, and conducts false alarm and false alarm degree analysis.

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Section: Risks In Customer Technicalmentioning

confidence: 99%

Section: Petri Network Modelingmentioning

confidence: 99%

Construction and Risk Analysis of Marketing System Based on Deep Neural Network

Yang

Bai

Wang

2022

Security and Communication Networks

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“…Sensitivity : It relates to the test's ability to identify positive results (Hand, ; Harikumar et al, ).

Sensitivity = \frac{PC}{true(PC + FA true)}

…”

Section: Performance Measuresmentioning

confidence: 99%

“…Performance Index (PI) : It is used to calculate the efficiency of the classifier (Harikumar et al, ). The Performance index defined as following equation

Performance Index = \frac{PC - MC - FA}{(TP + TN + FP + FN)}

…”

Section: Performance Measuresmentioning

confidence: 99%

Earlier detection of cancer regions from MR image features and SVM classifiers

Rajaguru

Ganesan²,

Bojan³

2016

Int J Imaging Syst Tech

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In this article, we examine the use of several segmentation algorithms for medical image classification. This work detects the cancer region from magnetic resonance (MR) images in earlier stage. This is accomplished in three stages. In first stage, four kinds of region‐based segmentation techniques are used such as K‐means clustering algorithm, expectation–maximization algorithm, partial swarm optimization algorithm, and fuzzy c‐means algorithm. In second stage, 18 texture features are extracting using gray level co‐occurrence matrix (GLCM). In stage three, classification is based on multi‐class support vector machine (SVM) classifier. Finally, the performance analysis of SVM classifier is analyzed using the four types of segmentation algorithm for a group of 200 patients (32—Glioma, 32—Meningioma, 44—Metastasis, 8—Astrocytoma, 72—Normal). The experimental results indicate that EM is an efficient segmentation method with 100% accuracy. In SVM, quadratic and RBF (σ = 0.5) kernel methods provide the highest classification accuracy compared to all other SVM kernel methods. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 196–208, 2016

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A Unique Approach to Epilepsy Classification from EEG Signals Using Dimensionality Reduction and Neural Networks

Rajaguru¹,

Prabhakar²

2016

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Characterized by recurrent and rapid seizures, epilepsy is a great threat to the livelihood of the human beings. Abnormal transient behaviour of neurons in the cortical regions of the brain leads to a seizure which characterizes epilepsy. The physical and mental activities of the patient are totally dampened with this epileptic seizure. A significant clinical tool for the study, analysis and diagnosis of the epilepsy is electroencephalogram (EEG). To detect such seizures, EEG signals aids greatly to the clinical experts and it is used as an important tool for the analysis of brain disorders, especially epilepsy. In this paper, the high dimensional EEG data are reduced to a low dimension by incorporating techniques such as Fuzzy Mutual Information (FMI), Independent Component Analysis (ICA), Linear Graph Embedding (LGE), Linear Discriminant Analysis (LDA) and Variational Bayesian Matrix Factorization (VBMF). After employing them as dimensionality reduction techniques, the Neural Networks (NN) such as Cascaded Feed Forward Neural Network (CFFNN), Time Delay Neural Network (TDNN) and Generalized Regression Neural Network (GRNN) are used as Post Classifiers for the Classification of Epilepsy Risk Levels from EEG signals. The bench mark parameters used here are Performance Index (PI), Quality Values (QV), Time Delay, Accuracy, Specificity and Sensitivity.

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Performance Analysis of Morphological Operators Based Feature Extraction and SVD, Neural Networks as Post Classifier for the Classification of Epilepsy Risk Levels

Cited by 3 publications

References 14 publications

Construction and Risk Analysis of Marketing System Based on Deep Neural Network

Construction and Risk Analysis of Marketing System Based on Deep Neural Network

Earlier detection of cancer regions from MR image features and SVM classifiers

A Unique Approach to Epilepsy Classification from EEG Signals Using Dimensionality Reduction and Neural Networks

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