Tuberculosis recognition &amp; - it's analysis using adaptive neuro fuzzy inference system-ANFIS

Sharma, Richa; Kochher, Rajesh

doi:10.1109/icecds.2017.8389677

Cited by 3 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As soft computing-based methods like ANFIS or ANN can substantially address the issue with reasonable accuracy, soft computing approaches can be used in this regard [ 27 ]. Moreover, these methods have been extensively used in other prominent fields like engineering, agriculture, and medical to significantly model and predict the data [ 28 , 29 , 30 ]. An extensive literature review revealed that soft computing-based approaches have widely been used in the textile industry to model and predict the properties of different materials with admirable accuracy.…”

Section: Introductionmentioning

confidence: 99%

Comparison of ANFIS and ANN modeling for predicting the water absorption behavior of polyurethane treated polyester fabric

Sarkar

Prottoy

Bari

et al. 2021

Heliyon

View full text Add to dashboard Cite

Nowadays, the polyurethane and its derivatives are highly applied as a surface modification material onto the textile substrates in different forms to enhance the functional properties of the textile materials. The primary purpose of this study is to develop prediction models to model the absorption property of the textile substrate using the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) methods. In this study, polyurethane (PU) along with acrylic binder was applied on the dyed polyester knitted fabric to develop and validate the prediction models. Through the morphological study, it was evident that the solution prepared with the polyurethane and the acrylic binder was effectively coated onto the fabric surface. The ANFIS model was constructed by considering binder (ml) and PU (%) as input parameters, whereas absorbency (%) was the only output parameter. On the other hand, the system was trained with 70% data for constructing the ANN model whereas testing and validation were done with 15% data, respectively. To train the network, feed-forward backpropagation with Levenberg–Marquardt learning algorithm was used. The coefficient of determination (R 2 ) was found to be 0.98 and 0.93 for ANFIS and ANN model, respectively. Both prediction models exhibited an excellent mean absolute error percentage (0.76% for the ANFIS model and 1.18% for the ANN model). Furthermore, an outstanding root-mean-square error (RMSE) of 0.61% and 1.28% for ANFIS and ANN models was observed. These results suggested an excellent performance of the developed models to predict the absorption property of the polyurethane and acrylic binder treated fabric. Besides, these models can be taken as a basis to develop prediction models for specific types of functional applications of the textile materials to eliminate heaps of trial and error efforts of the textile industries, which eventually be helpful in the scalable production of functional textiles.

show abstract

Section: Introductionmentioning

confidence: 99%

Comparison of ANFIS and ANN modeling for predicting the water absorption behavior of polyurethane treated polyester fabric

Sarkar

Prottoy

Bari

et al. 2021

Heliyon

View full text Add to dashboard Cite

show abstract

“…The method, named amalgam intellectual scheme, is used to model information, reasoning, and learning in vague and indeterminate neighbors. ANN is developed for further modifications, and the fuzzy system acquires the learning capability 10,11 …”

Section: Introductionmentioning

confidence: 99%

Severity detection and infection level identification of tuberculosis using deep learning

Chithra

Jagatheeswari

2020

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Tuberculosis (TB) is a highly infectious disease and is one of the major health problems all over the world. The accurate detection of TB is a major challenge faced by most of the existing methods. This work addresses these issues and developed an effective mechanism for detecting TB using deep learning. Here, the color space transformation is applied for transforming the red green and blue image to LUV space, where L stands for luminance, U and V represent chromaticity values of color images. Then, adaptive thresholding is carried out for image segmentation and various features, like coverage, density, color histogram, area, length, and texture features, are extracted to enable effective classification. After the feature extraction, the size of the features is reduced using principal component analysis. The extracted features are subjected to fractional crow search‐based deep convolutional neural network (FC‐SVNN) for the classification. Then, the image level features, like bacilli count, bacilli area, scattering coefficients and skeleton features are considered to perform severity detection using proposed adaptive fractional crow (AFC)‐deep CNN. Finally, the inflection level is determined using entropy, density and detection percentage. The proposed AFC‐Deep CNN algorithm is designed by modifying FC algorithm using self‐adaptive concept. The proposed AFC‐Deep CNN shows better performance with maximum accuracy value as 0.935.

show abstract

Investigation on the capabilities of ANFIS for the detection of Ischemic Stroke

Srinidhi

Yamini

et al. 2021

2021 IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON)

View full text Add to dashboard Cite

Tuberculosis recognition & - it's analysis using adaptive neuro fuzzy inference system-ANFIS

Cited by 3 publications

References 12 publications

Comparison of ANFIS and ANN modeling for predicting the water absorption behavior of polyurethane treated polyester fabric

Comparison of ANFIS and ANN modeling for predicting the water absorption behavior of polyurethane treated polyester fabric

Severity detection and infection level identification of tuberculosis using deep learning

Investigation on the capabilities of ANFIS for the detection of Ischemic Stroke

Contact Info

Product

Resources

About