Classification of EMG Signals Using ANFIS for the Detection of Neuromuscular Disorders

Mahapatra, Sakuntala; Mohanta, Debasis; Mohanty, Prasant; Nayak, Sanghamitra

doi:10.1007/978-981-10-3779-5_8

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…ANFIS is also popularly used to address classification problems [ 51 , 53 , 54 ]. Our research problem is also related to the detection of the network device for its upgrade or replacement via ANFIS classification in the domain of new network deployment.…”

Section: Background and Related Workmentioning

confidence: 99%

Intelligent Approach to Network Device Migration Planning towards Software-Defined IPv6 Networks

Dawadi

Rawat

Joshi

et al. 2021

Sensors

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Internet and telecom service providers worldwide are facing financial sustainability issues in migrating their existing legacy IPv4 networking system due to backward compatibility issues with the latest generation networking paradigms viz. Internet protocol version 6 (IPv6) and software-defined networking (SDN). Bench marking of existing networking devices is required to identify their status whether the existing running devices are upgradable or need replacement to make them operable with SDN and IPv6 networking so that internet and telecom service providers can properly plan their network migration to optimize capital and operational expenditures for future sustainability. In this paper, we implement “adaptive neuro fuzzy inference system (ANFIS)”, a well-known intelligent approach for network device status identification to classify whether a network device is upgradable or requires replacement. Similarly, we establish a knowledge base (KB) system to store the information of device internetwork operating system (IoS)/firmware version, its SDN, and IPv6 support with end-of-life and end-of-support. For input to ANFIS, device performance metrics such as average CPU utilization, throughput, and memory capacity are retrieved and mapped with data from KB. We run the experiment with other well-known classification methods, for example, support vector machine (SVM), fine tree, and liner regression to compare performance results with ANFIS. The comparative results show that the ANFIS-based classification approach is more accurate and optimal than other methods. For service providers with a large number of network devices, this approach assists them to properly classify the device and make a decision for the smooth transitioning to SDN-enabled IPv6 networks.

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Section: Background and Related Workmentioning

confidence: 99%

Intelligent Approach to Network Device Migration Planning towards Software-Defined IPv6 Networks

Dawadi

Rawat

Joshi

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Applications in the field of control systems can be found in [5], while [6] given detail application of control non linear systems. Applications in the field of classifications can be seen in research [7], while [8] make an detail on electromyogram (EMG) signal. Applications for the field of pattern recognitions or forecasting can be found on [9,10].…”

Section: Introductionmentioning

confidence: 99%

Implementation of Hybrid RNN-ANFIS on Forecasting Jakarta Islamic Index

Anggara,

Munandar

2023

Jambura J. Math

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RNN is a type of artificial neural network used to handle problems that require sequential data processing. ANFIS is a method that combines the advantages of fuzzy logic and artificial neural networks to create a system, so can adapt the parameters it uses according to the obtained data so that it can build an automated inference system. In this research, we make combination of RNN in ANFIS, which makes ANFIS able to accept input in the form of time series data so that ANFIS can recognize patterns contained in the time series data and its suitable for forecasting cases in the Jakarta Islamic Index. The membership functions used are three Gaussian functions. The results of the RNN-ANFIS Hybrid model training provide an interpretation that the first membership function represents the trend change indicator value, the second membership function represents the closing price change value in the last eight days, and the third membership function represents the pattern change value in the trend. The model for the Jakarta Islamic Index provides very good predictions with an MSE value of 0.001 and an MAE of 0.246.

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Forecasting the Semg Signal Using Wavelet Transform and Anfis Model

Sharma,

Sharma

2024

Proc. Natl. Acad. Sci., India, Sect. A Phys. Sci.

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Classification of EMG Signals Using ANFIS for the Detection of Neuromuscular Disorders

Cited by 5 publications

References 11 publications

Intelligent Approach to Network Device Migration Planning towards Software-Defined IPv6 Networks

Intelligent Approach to Network Device Migration Planning towards Software-Defined IPv6 Networks

Implementation of Hybrid RNN-ANFIS on Forecasting Jakarta Islamic Index

Forecasting the Semg Signal Using Wavelet Transform and Anfis Model

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