Perceptron Neural Network Based Machine Learning Approaches for Leather Defect Detection and Classification

2020 ◽  
Vol 16 (6) ◽  
pp. 421-429
Author(s):  
Praveen Kumar Moganam ◽  
Denis Ashok Sathia Seelan
Keyword(s):  
Neural Network ◽  
Defect Detection ◽  
Confusion Matrix ◽  
Texture Features ◽  
Learning Approaches ◽  
Data Set ◽  
Defect Identification ◽  
Inspection Process ◽  
Identification Approach ◽  
Occurrence Matrix

Detection of defects in a typical leather surface is a difficult task due to the complex, non-homogenous and random nature of texture pattern. This paper presents a texture analysis based leather defect identification approach using a neural network classification of defective and non-defective leather. In this work, Gray Level Co-occurrence Matrix (GLCM) is used for extracting different statistical texture features of defective and non-defective leather. Based on the labelled data set of texture features, perceptron neural network classifier is trained for defect identification. Five commonly occurring leather defects such as folding marks, grain off, growth marks, loose grain and pin holes were detected and the classification results of perceptron network are presented. Proposed method was tested for the image library of 1232 leather samples and the accuracy of classification for the defect detection using confusion matrix is found to be 94.2%. Proposed method can be implemented in the industrial environment for the automation of leather inspection process.

scholarly journals Deep Neural Network for Gender-Based Violence Detection on Twitter Messages

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 807
Author(s):  
Carlos M. Castorena ◽  
Itzel M. Abundez ◽  
Roberto Alejo ◽  
Everardo E. Granda-Gutiérrez ◽  
Eréndira Rendón ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Gender Violence ◽  
Strategic Decision ◽  
Learning Approaches ◽  
Gender Based Violence ◽  
Data Set ◽  
Violence Detection ◽  
Governmental Institutions ◽  
Gender Based

The problem of gender-based violence in Mexico has been increased considerably. Many social associations and governmental institutions have addressed this problem in different ways. In the context of computer science, some effort has been developed to deal with this problem through the use of machine learning approaches to strengthen the strategic decision making. In this work, a deep learning neural network application to identify gender-based violence on Twitter messages is presented. A total of 1,857,450 messages (generated in Mexico) were downloaded from Twitter: 61,604 of them were manually tagged by human volunteers as negative, positive or neutral messages, to serve as training and test data sets. Results presented in this paper show the effectiveness of deep neural network (about 80% of the area under the receiver operating characteristic) in detection of gender violence on Twitter messages. The main contribution of this investigation is that the data set was minimally pre-processed (as a difference versus most state-of-the-art approaches). Thus, the original messages were converted into a numerical vector in accordance to the frequency of word’s appearance and only adverbs, conjunctions and prepositions were deleted (which occur very frequently in text and we think that these words do not contribute to discriminatory messages on Twitter). Finally, this work contributes to dealing with gender violence in Mexico, which is an issue that needs to be faced immediately.

scholarly journals Texture Feature On Determining Quantity of Soil Organic Matter For Patchouli Plant Using Backpropagation Neural Network

2019 ◽  
Vol 4 (1) ◽  
pp. 1
Author(s):  
Candra Dewi ◽  
Suci Sundari ◽  
Mardji Mardji
Keyword(s):  
Neural Network ◽  
Organic Matter ◽  
Soil Organic Matter ◽  
Texture Feature ◽  
Texture Features ◽  
Training Data ◽  
Backpropagation Neural Network ◽  
Color Features ◽  
Accuracy Result ◽  
Occurrence Matrix

Patchouli (Pogostemon Cablin Bent) has higher PA (Patchouli Alcohol) and oil production if grown in soil containing 75% organic matter. One way that can be used to detect the content of organic matter is to use soil images. The problem in the use of soil images is the color of the soil that is almost similar, namely the gradation between dark brown to black. Therefore, color features are not enough to be used as input in the recognition process. For this purposes, texture features are added in this study in addition to color features. The color features are extracted using color moment and the texture features are extracted using Gray Level Co-occurrence Matrix (GLCM). These feature was then chosen to get the best combination as input in the identification process using the Backpropagation Neural Network (BPNN). The system identifies the quantity of soil organic matter into five classes, namely very low, low, medium, high, and very high. The highest accuracy result obtained was 73% and MSE value 0.5122 by using five GLCM features (Angular Second Moment, contrast, correlation, Inverse Difference Moment, and entropy). This result was obtained by using the BPNN parameter, namely learning rate values 0.5, maximum iteration values of 1000, number training data 210, and total test data 12.

scholarly journals Skin Color detection Using Stepwise Neural Network and Color Mapping Co-occurrence Matrix

2014 ◽  
Vol 12 (7) ◽  
pp. 3642-3650
Author(s):  
Ghazali Osman ◽  
Muhammad Suzuri Hitam
Keyword(s):  
Neural Network ◽  
False Positive ◽  
Skin Color ◽  
Texture Features ◽  
Skin Area ◽  
Content Filtering ◽  
Color Mapping ◽  
Skin Color Detection ◽  
Color Detection ◽  
Occurrence Matrix

Skin color has been proven to be a useful and robust cue for face detection, human tracking, image content filtering, pornographic filtering, etc.  Most of skin classification researches are focused on using pixel-based method to classify skin and non-skin pixels.  This paper proposed a new technique for region-based skin color detection using texture information.  The texture information was extracted from the color mapping co-occurrence matrix (CMCM).  This technique is extension of gray level co-occurrence matrix (GLCM) which is introduced by Haralicket. al to compute second order statistical texture features.  The new color mapping matrix (CMM) between color bands have been developed for skin and non-skin area for each skin image and then, the CMCM were computed at four direction with distance, d = 1, and angle, θ = 0o, 45o, 90o, and 135o.  The thirteen Haralick’s textures have been computed and used for formulating a skin color classifiers using stepwise neural network (SNN).  The performance of each skin color classifier was measured based on true and false positive value.  Besides that, the benchmark datasets from Universidad de Chile and TDSD were also be employed to test the skin color classifiers ability.  The results shown that the skin color classifier formulated with [RGB] CMCM at direction (1, 0o) most superior as compared to other direction.  Its average of true positive and false positive are 98.38 percent and 3.67 percent, respectively.  Meanwhile, the classifier formulated with [RGB] CMCM at direction (1, 90o) is totally failed to classify skin and non-skin colors.  Meaning that, the texture features which are computed from [RGB] CMCM at direction (1, 90o) cannot represent skin and non-skin color at all.

scholarly journals Applying a Neural Network Ensemble to Intrusion Detection

2019 ◽  
Vol 9 (3) ◽  
pp. 177-188 ◽  
Author(s):  
Simone A. Ludwig
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Detection System ◽  
Confusion Matrix ◽  
Ensemble Method ◽  
Machine Learning Techniques ◽  
Neural Network Ensemble ◽  
Data Set ◽  
The Neural Network ◽  
Learning Machine

Abstract An intrusion detection system (IDS) is an important feature to employ in order to protect a system against network attacks. An IDS monitors the activity within a network of connected computers as to analyze the activity of intrusive patterns. In the event of an ‘attack’, the system has to respond appropriately. Different machine learning techniques have been applied in the past. These techniques fall either into the clustering or the classification category. In this paper, the classification method is used whereby a neural network ensemble method is employed to classify the different types of attacks. The neural network ensemble method consists of an autoencoder, a deep belief neural network, a deep neural network, and an extreme learning machine. The data used for the investigation is the NSL-KDD data set. In particular, the detection rate and false alarm rate among other measures (confusion matrix, classification accuracy, and AUC) of the implemented neural network ensemble are evaluated.

A defect detection method for unpatterned fabric based on multidirectional binary patterns and the gray-level co-occurrence matrix

2019 ◽  
Vol 90 (7-8) ◽  
pp. 776-796 ◽  
Author(s):  
Feng Li ◽  
Lina Yuan ◽  
Kun Zhang ◽  
Wenqing Li
Keyword(s):  
Defect Detection ◽  
Detection Threshold ◽  
Texture Feature ◽  
Texture Features ◽  
Gray Level ◽  
Detection Scheme ◽  
Second Stage ◽  
Fabric Defect ◽  
Occurrence Matrix ◽  
Different Characteristics

A new texture-feature description operator, called the multidirectional binary patterns (MDBP) operator, is proposed in this paper. The operator can extract the detailed distribution of textures in local regions by comparing the differences in the gray levels between neighboring pixels. Moreover, the texture expression ability is enhanced by focusing on the texture features in the linear neighborhood of the image in multiple directions. The MDBP operator was modified by introducing a “uniform” pattern to reduce the grayscale values in the image. Combining the “uniform” MDBP operator and the gray-level co-occurrence matrix, an unpatterned fabric-defect detection scheme is proposed, including texture-feature extraction and detection stages. In the first stage, the multidirectional texture-feature matrix of a nondefective fabric image is extracted, and then the detection threshold is determined based on the similarity between the feature matrices. In the second stage, the defect is detected with the detection threshold. The proposed method is adapted to various grayscale textile images with different characteristics and is robust to a wide variety of image-processing operations. In addition, it is invariant to grayscale changes, performs well when representing textures and detecting defects and has lower computational complexity than other methods.

scholarly journals Blister Defect Detection Based on Convolutional Neural Network for Polymer Lithium-Ion Battery

2019 ◽  
Vol 9 (6) ◽  
pp. 1085 ◽  
Author(s):  
Liyong Ma ◽  
Wei Xie ◽  
Yong Zhang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Lithium Ion Battery ◽  
Defect Detection ◽  
Confusion Matrix ◽  
Cell Sheet ◽  
Lithium Ion ◽  
Classification Performance ◽  
Field Application ◽  
Cell Sheets

To ensure the quality and reliability of polymer lithium-ion battery (PLB), automatic blister defect detection instead of manual detection is developed in the production of PLB cell sheets. A convolutional neural network (CNN) based detection method is proposed to detect blister in cell sheets employing cell sheet images. An improved architecture for dense block and a learning method based on optimization of learning rate are discussed. The proposed method was superior to other machine learning based methods when the classification performance and confusion matrix were compared in experiments. The proposed CNN method had the best defect detection performance and real-time performance for industry field application.

scholarly journals Towards Mapping Images to Text Using Deep-Learning Architectures

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1606
Author(s):  
Daniela Onita ◽  
Adriana Birlutiu ◽  
Liviu P. Dinu
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Image Features ◽  
The Other ◽  
Data Sets ◽  
Network Architectures ◽  
Learning Approaches ◽  
Data Set ◽  
Pixel Value

Images and text represent types of content that are used together for conveying a message. The process of mapping images to text can provide very useful information and can be included in many applications from the medical domain, applications for blind people, social networking, etc. In this paper, we investigate an approach for mapping images to text using a Kernel Ridge Regression model. We considered two types of features: simple RGB pixel-value features and image features extracted with deep-learning approaches. We investigated several neural network architectures for image feature extraction: VGG16, Inception V3, ResNet50, Xception. The experimental evaluation was performed on three data sets from different domains. The texts associated with images represent objective descriptions for two of the three data sets and subjective descriptions for the other data set. The experimental results show that the more complex deep-learning approaches that were used for feature extraction perform better than simple RGB pixel-value approaches. Moreover, the ResNet50 network architecture performs best in comparison to the other three deep network architectures considered for extracting image features. The model error obtained using the ResNet50 network is less by approx. 0.30 than other neural network architectures. We extracted natural language descriptors of images and we made a comparison between original and generated descriptive words. Furthermore, we investigated if there is a difference in performance between the type of text associated with the images: subjective or objective. The proposed model generated more similar descriptions to the original ones for the data set containing objective descriptions whose vocabulary is simpler, bigger and clearer.

scholarly journals Artificial Neural Network Application in the Diagnosis of Disease Conditions with Liver Ultrasound Images

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Karthik Kalyan ◽  
Binal Jakhia ◽  
Ramachandra Dattatraya Lele ◽  
Mukund Joshi ◽  
Abhay Chowdhary
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Texture Features ◽  
Speckle Noise ◽  
Training Data ◽  
Ultrasound Images ◽  
Data Set ◽  
Imaging Artifacts ◽  
Artificial Neural ◽  
Ultrasonic Images

The preliminary study presented within this paper shows a comparative study of various texture features extracted from liver ultrasonic images by employing Multilayer Perceptron (MLP), a type of artificial neural network, to study the presence of disease conditions. An ultrasound (US) image shows echo-texture patterns, which defines the organ characteristics. Ultrasound images of liver disease conditions such as “fatty liver,” “cirrhosis,” and “hepatomegaly” produce distinctive echo patterns. However, various ultrasound imaging artifacts and speckle noise make these echo-texture patterns difficult to identify and often hard to distinguish visually. Here, based on the extracted features from the ultrasonic images, we employed an artificial neural network for the diagnosis of disease conditions in liver and finding of the best classifier that distinguishes between abnormal and normal conditions of the liver. Comparison of the overall performance of all the feature classifiers concluded that “mixed feature set” is the best feature set. It showed an excellent rate of accuracy for the training data set. The gray level run length matrix (GLRLM) feature shows better results when the network was tested against unknown data.

scholarly journals Automated Classification of Radiographic Positioning of Hand X-Rays Using a Deep Neural Network

2021 ◽  
pp. 229255032199701
Author(s):  
Tomas J. Saun
Keyword(s):  
Neural Network ◽  
Health Care Providers ◽  
Deep Neural Network ◽  
Confusion Matrix ◽  
Training Data ◽  
X Rays ◽  
Automated Classification ◽  
Care Providers ◽  
Image Brightness ◽  
Data Set

Background: Hand X-rays are ordered in outpatient, inpatient, and emergency settings, the results of which are often initially interpreted by non-radiology trained health care providers. There may be utility in automating upper extremity X-ray analysis to aid with rapid initial analysis. Deep neural networks have been effective in several medical imaging analysis applications. The purpose of this work was to apply a deep learning framework to automatically classify the radiographic positioning of hand X-rays. Methods: A 152-layer deep neural network was trained using the musculoskeletal radiographs data set. This data set contains 6003 hand X-rays. The data set was filtered to remove pediatric X-rays and atypical views. The X-rays were all labeled as either posteroanterior (PA), lateral, or oblique views. A subset of images was set aside for model validation and testing. Data set augmentation was performed, including horizontal and vertical flips, rotations, as well as modifications in image brightness and contrast. The model was evaluated, and performance was reported as a confusion matrix from which accuracy, precision, sensitivity, and specificity were calculated. Results: The augmented training data set consisted of 80 672 images. Their distribution was 38% PA, 35% lateral, and 27% oblique projections. When evaluated on the test data set, the model performed with overall 96.0% accuracy, 93.6% precision, 93.6% sensitivity, and 97.1% specificity. Conclusions: Radiographic positioning of hand X-rays can be effectively classified by a deep neural network. Further work will be performed on localization of abnormalities, automated assessment of standard radiographic measures and eventually on computer-aided diagnosis and management guidance of skeletal pathology.

scholarly journals Automated Detection and Classification of Diabetic Retinopathy using ConvNets

2021 ◽  
Vol 9 (10) ◽  
pp. 1360-1369
Author(s):  
Aavani B
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Confusion Matrix ◽  
Diabetic Patients ◽  
Fundus Image ◽  
Data Set ◽  
Permanent Loss ◽  
Process Computer ◽  
Degree Classification

Abstract: Diabetic retinopathy is the leading cause of blindness in diabetic patients. Screening of diabetic retinopathy using fundus image is the most effective way. As the time increases this DR leads to permanent loss of vision. At present, Diabetic retinopathy is still being treated by hand by an ophthalmologist which is a time-consuming process. Computer aided and fully automatic diagnosis of DR plays an important role in now a day. Data-set containing a collection of fundus images of different severity scale is used to analyze the fundus image of DR patients. Here the deep neural network model is trained by using this fundus image and five-degree classification task is performed. We were able to produce an sensitivity of 90%. Keywords: Confusion matrix, Deep convolutional Neural Network, Diabetic Retinopathy, Fundus image, OCT

Sign in / Sign up

Export Citation Format

Share Document