A Preprocessed Counterpropagation Neural Network Classifier for Automated Textile Defect Classification

Rahman, Md. Mokhlesur; Habib, Tarek

doi:10.18178/jiii.4.3.209-217

Cited by 6 publications

(3 citation statements)

References 22 publications

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“…From the machine learning perception, classification is the problem of categorizing a group of elements into different classes, according to the training result of a subset of observations whose fitting class is identified [19,20]. Several researchers in the healthcare sector have intensified efforts to enhance classification in order to gain improved results when detecting or diagnosing related diseases.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Online Model for Predicting Diabetes Mellitus

Mallika¹,

Selvamuthukumaran²

2022

Intelligent Automation &Amp; Soft Computing

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Modern healthcare systems have become smart by synergizing the potentials of wireless sensors, the medical Internet of things, and big data science to provide better patient care while decreasing medical expenses. Large healthcare organizations generate and accumulate an incredible volume of data continuously. The already daunting volume of medical information has a massive amount of diagnostic features and logged details of patients for certain diseases such as diabetes. Diabetes mellitus has emerged as along-haul fatal disease across the globe and particularly in developing countries. Exact and early diagnosis of diabetes from big medical data is vital for the deterrence of disease and the selection of proper therapy. Traditional machine learning-based diagnosis systems have been initially established as offline (non-incremental) approaches that are trained with a pre-defined database before they can be applied to handle prediction problems. The major objective of the proposed work is to predict and classify diabetes mellitus by implementing a Hybrid Online Model for Early Detection of diabetes disease (HOMED) using machine learning algorithms. Our proposed online (incremental) diabetes diagnosis system exploits (i) an Adaptive Principal Component Analysis (APCA) technique for missing value imputation, data clustering, and feature selection; and (ii) an enhanced incremental support vector machine (ISVM) for classification. The efficiency of HOMED is estimated on different performance metrics such as accuracy, precision, specificity, sensitivity, positive predictive value, and negative predictive value. Experimental results on Pima Indian diabetes dataset (768 samples: 500 non diabetic and 268 diabetic patients) reveal that HOMED considerably increases the classification accuracy and decreases computational complexity with respect to the offline models. The proposed system can assist healthcare professionals as a decision support system.

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Section: Introductionmentioning

confidence: 99%

Hybrid Online Model for Predicting Diabetes Mellitus

Mallika¹,

Selvamuthukumaran²

2022

Intelligent Automation &Amp; Soft Computing

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“…Cancer classification is a machine-learning-based data mining method that is utilized to categorize the elements of a dataset into predefined classes or groups [6,7]. In the field of medicine, classification has been extended to numerous theoretical and practical applications, including breast cancer, colon cancer, heart disease, liver disease, ovarian cancer, prostate cancer, and so on.…”

Section: Introductionmentioning

confidence: 99%

An Enhancement in Cancer Classification Accuracy Using a Two-Step Feature Selection Method Based on Artificial Neural Networks with 15 Neurons

Rahman

Muniyandi

2020

Symmetry

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An artificial neural network (ANN) is a tool that can be utilized to recognize cancer effectively. Nowadays, the risk of cancer is increasing dramatically all over the world. Detecting cancer is very difficult due to a lack of data. Proper data are essential for detecting cancer accurately. Cancer classification has been carried out by many researchers, but there is still a need to improve classification accuracy. For this purpose, in this research, a two-step feature selection (FS) technique with a 15-neuron neural network (NN), which classifies cancer with high accuracy, is proposed. The FS method is utilized to reduce feature attributes, and the 15-neuron network is utilized to classify the cancer. This research utilized the benchmark Wisconsin Diagnostic Breast Cancer (WDBC) dataset to compare the proposed method with other existing techniques, showing a significant improvement of up to 99.4% in classification accuracy. The results produced in this research are more promising and significant than those in existing papers.

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“…Now, based on diff erent parameters, a lot of work has been done by many researchers to forecast knitted fabric attributes, e.g. bursting strength, pilling tendency, air permeability, thermal resistance, spirality, hand evaluation, defect classifi cation etc, and also in the woven and apparel industry [24][25][26][27][28][29][30]. An enormous amount of noise free data is required in establishing the ANN and ANFIS model, which becomes very diffi cult and time consuming to accumulate from the textile industry [22,23,31].…”

Section: Introductionmentioning

confidence: 99%

Prognosis of Dimensional Stability and Mass per Unit Area of Single Jersey Cotton Knitted Fabric with Fuzzy Inference System

Smriti¹,

Belal²,

Haque³

et al. 2019

TEK

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Endeavour has been made in this research work using experimental data for constructing a fuzzy inference model based on the Mamdani approach to prognosticate the shrinkage and mass per unit area of a single jersey cotton knitted fabric. To control the dimensional stability of the cotton knitted fabric in advance, an artifi cial intelligent system is required in the knitting industry which simulates all product and process variables and is able to give human-like decisions in advance. The most important controlling parameters of knitted fabric properties such as stitch length, yarn count and overfeed percentage in stenter were considered as input variables, and mass per unit area, lengthwise shrinkage and widthwise shrinkage as output variables. Overall, 35 experiments were conducted to construct the model, varying diff erent parameters. The applicability of the model was validated by comparing the results from 15 newly conducted experiments. The coeffi cient of determination of predicted and actual data for mass per unit area, lengthwise shrinkage and widthwise shrinkage were 0.97, 0.99 and 0.99, respectively which validates the model relatively eff ectively for an industrial application. The proposed model can assist a fabric manufacturer by taking a decision in selecting knitting and fi nishing parameters prior to producing the fabric. Moreover, it can reduce the time and energy required, and waste produced in the process by skipping the sample development step before bulk production.

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A Preprocessed Counterpropagation Neural Network Classifier for Automated Textile Defect Classification

Cited by 6 publications

References 22 publications

Hybrid Online Model for Predicting Diabetes Mellitus

Hybrid Online Model for Predicting Diabetes Mellitus

An Enhancement in Cancer Classification Accuracy Using a Two-Step Feature Selection Method Based on Artificial Neural Networks with 15 Neurons

Prognosis of Dimensional Stability and Mass per Unit Area of Single Jersey Cotton Knitted Fabric with Fuzzy Inference System

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