Application of adaptive neuro-fuzzy inference systems for MR image classification and tumour detection

Rajasekaran, M. Pallikonda; Meena, Ritu

doi:10.1504/ijbet.2012.047746

Cited by 8 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To maintain stability and prevent damage to electrical power transmission line devices, these faults must be detected quickly, classified and cleared within a particular time (Sharma et al , 2017). There are several methods for fault detection and classification such as wavelet transform (WT) (Balakrishnan and Sathiyasekar, 2019), artificial neural network (ANN) (Fuada et al , 2020; Upadhyay et al , 2018), fuzzy logic (Bhatnagar and Yadav, 2020), adaptive neuro-fuzzy inference System (Lirouana and Mohammed, 2021), concurrent neuro-fuzzy (Eboule et al , 2018), support vector machine (Coban and Tezcan, 2021), WT and ANN (Gowrishankar et al , 2016; Thwe and Oo, 2016), WT and fuzzy logic (Ray et al , 2016), and there are also various computational models of the ANN that have been used in transmission line system fault detection and classification, such as multi-layer perceptron neural network (MLPNN) (Okojie et al , 2021), Elman recurrent neural network (ERNN) (Aborisade et al , 2021), WT and ERNN (Zakri and Tua, 2020), and radial basis function neural network (RBFNN) (Gupta and Mahanty, 2015). In this study, an ANN was employed for its capability to detect and classify faults in power transmission line systems.…”

Section: Introductionmentioning

confidence: 99%

Shunt faults detection and classification in electrical power transmission line systems based on artificial neural networks

Assadi

Slimane

Hanene

et al. 2023

COMPEL

View full text Add to dashboard Cite

Purpose This study aims to focus on an adaptive method for fault detection and classification of fault types that trigger in three-phase transmission lines using artificial neural networks (ANNs). The proposed scheme can detect and classify several types of faults, including line-to-ground, line-to-line, double-line-to-ground, triple-line and triple-line-to-ground faults. Design/methodology/approach The fundamental components of three-phase current and voltage were used as inputs in the ANNs. An analysis of the impact of variations in the fault resistance, fault type and fault inception time was conducted to evaluate the ANNs performance. The survey compares the performance of the multi-layer perceptron neural network (MLPNN) and Elman recurrent neural network trained with the backpropagation learning technique to improve each of the three phases of the fault detection and classification process. A detailed analysis validates the choice of the ANNs architecture based on the variation in the number of hidden neurons in each step. Findings The mean square error, root mean square error, mean absolute error and linear regression are measured to improve the efficiency of the ANN models for both fault detection and classification. The results indicate that the MLPNN can detect and classify faults with a satisfactory performance. Originality/value The smart adaptive scheme is fast and accurate for fault detection and classification in a single circuit transmission line when faced with different conditions and can be useful for transmission line protection schemes.

show abstract

Section: Introductionmentioning

confidence: 99%

Shunt faults detection and classification in electrical power transmission line systems based on artificial neural networks

Assadi

Slimane

Hanene

et al. 2023

COMPEL

View full text Add to dashboard Cite

show abstract

“…Another common classifier is Adaptive Neuro Fuzzy Inference System (ANFIS) which benefits from both ANN and fuzzy logic in a single framework and overcomes their individual weaknesses and suggests more outstanding features [15,16]. ANFIS classifier can also remove inaccurate information present in the image which leads to a high interpretability and good degree of accuracy [17,18].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Brain Tumor MR Image Classification Accuracy Using Optimal Threshold, PCA and Training ANFIS with Different Repetitions

et al. 2018

View full text Add to dashboard Cite

Background: One of the leading causes of death is brain tumors. Accurate tumor classification leads to appropriate decision making and providing the most efficient treatment to the patients. This study aims to optimize brain tumor MR images classification accuracy using optimal threshold, PCA and training Adaptive Neuro Fuzzy Inference System (ANFIS) with different repetitions.Material and Methods: The procedure used in this study consists of five steps: (1) T1, T2 weighted images collection, (2) tumor separation with different threshold levels, (3) feature extraction, (4) presence and absence of feature reduction applying principal component analysis (PCA) and (5) ANFIS classification with 0, 20 and 200 training repetitions. Results: ANFIS accuracy was 40%, 80% and 97% for all features and 97%, 98.5% and 100% for the 6 selected features by PCA in 0, 20 and 200 training repetitions, respectively.Conclusion: The findings of the present study demonstrated that accuracy can be raised up to 100% by using an optimized threshold method, PCA and increasing training repetitions.

show abstract

Artificial neural networks training via bio-inspired optimisation algorithms: modelling industrial winding process, case study

2020

View full text Add to dashboard Cite

Application of adaptive neuro-fuzzy inference systems for MR image classification and tumour detection

Cited by 8 publications

References 0 publications

Shunt faults detection and classification in electrical power transmission line systems based on artificial neural networks

Shunt faults detection and classification in electrical power transmission line systems based on artificial neural networks

Optimization of Brain Tumor MR Image Classification Accuracy Using Optimal Threshold, PCA and Training ANFIS with Different Repetitions

Artificial neural networks training via bio-inspired optimisation algorithms: modelling industrial winding process, case study

Contact Info

Product

Resources

About