Detection of Structural Defects in Fabric Parts Using a Novel Edge Detection Method

2018 ◽  
Vol 62 (7) ◽  
pp. 1036-1043
Author(s):  
M Dhivya ◽  
M Renuka Devi
Keyword(s):  
Nearest Neighbor ◽  
Automated System ◽  
Structural Defects ◽  
K Nearest Neighbor ◽  
Detection Scheme ◽  
Detection Techniques ◽  
Production Pipeline ◽  
Edge Detection Method ◽  
Fabric Production ◽  
Performance Results

Abstract A fabric fault detection scheme is implemented in this work using frame harmonizing-based approach. Many recent research works deliver the fabric defect detection techniques. But in the textiles industry, the fast process of fabric cutting and sewing, lots of auxiliary imperfections have emerged. Particularly these defects cannot be identified easily by experts as well as an automated system. In our system, a novel frame extraction technique is used to find the defects in the fabric production pipeline. The input fabric image frame is pre-processed by transformation and filtering techniques. In this work,a novel multi-directional and multi-scale outline extraction method is proposed to extract the edge map. Contour-based features are extracted and classified by K-nearest neighbor classifier. The experimentation with real-time dataset produced the outstanding performance results when compared with state of the art methods.

Quantifying the Influence of Achievement Emotions for Student Learning in MOOCs

2020 ◽  
pp. 073563312096731
Author(s):  
Bowen Liu ◽  
Wanli Xing ◽  
Yifang Zeng ◽  
Yonghe Wu
Keyword(s):  
Random Forest ◽  
Nearest Neighbor ◽  
Online Courses ◽  
Learning Performance ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Achievement Emotions ◽  
Integrative Framework ◽  
Emotional Interaction ◽  
Performance Results

Massive Open Online Courses (MOOCs) have become a popular tool for worldwide learners. However, a lack of emotional interaction and support is an important reason for learners to abandon their learning and eventually results in poor learning performance. This study applied an integrative framework of achievement emotions to uncover their holistic influence on students’ learning by analyzing more than 400,000 forum posts from 13 MOOCs. Six machine-learning models were first built to automatically identify achievement emotions, including K-Nearest Neighbor, Logistic Regression, Naïve Bayes, Decision Tree, Random Forest, and Support Vector Machines. Results showed that Random Forest performed the best with a kappa of 0.83 and an ROC_AUC of 0.97. Then, multilevel modeling with the “Stepwise Build-up” strategy was used to quantify the effect of achievement emotions on students’ academic performance. Results showed that different achievement emotions influenced students’ learning differently. These findings allow MOOC platforms and instructors to provide relevant emotional feedback to students automatically or manually, thereby improving their learning in MOOCs.

scholarly journals Fraudulent credit card transaction detection using soft computing techniques

2021 ◽  
Vol 23 (3) ◽  
pp. 1634
Author(s):  
Aishwarya Priyadarshini ◽  
Sanhita Mishra ◽  
Debani Prasad Mishra ◽  
Surender Reddy Salkuti ◽  
Ramakanta Mohanty
Keyword(s):  
Soft Computing ◽  
Credit Card ◽  
Nearest Neighbor ◽  
Detection System ◽  
Fraud Detection ◽  
K Nearest Neighbor ◽  
Computationally Efficient ◽  
Detection Techniques ◽  
Government Organizations ◽  
Soft Computing Techniques

<p>Nowadays, fraudulent or deceitful activities associated with financial transactions, predominantly using credit cards have been increasing at an alarming rate and are one of the most prevalent activities in finance industries, corporate companies, and other government organizations. It is therefore essential to incorporate a fraud detection system that mainly consists of intelligent fraud detection techniques to keep in view the consumer and clients’ welfare alike. Numerous fraud detection procedures, techniques, and systems in literature have been implemented by employing a myriad of intelligent techniques including algorithms and frameworks to detect fraudulent and deceitful transactions. This paper initially analyses the data through exploratory data analysis and then proposes various classification models that are implemented using intelligent soft computing techniques to predictively classify fraudulent credit card transactions. Classification algorithms such as K-Nearest neighbor (K-NN), decision tree, random forest (RF), and logistic regression (LR) have been implemented to critically evaluate their performances. The proposed model is computationally efficient, light-weight and can be used for credit card fraudulent transaction detection with better accuracy.</p>

scholarly journals A Hybrid Leaf Disease Detection Scheme using Grayco-Occurance Matrix Support Vector Machine Algorithm

2019 ◽  
Vol 8 (2S11) ◽  
pp. 300-309
Keyword(s):  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Median Filter ◽  
Noise Removal ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Detection Scheme ◽  
Leaf Disease ◽  
Fuzzy C Means Clustering ◽  
Industry Area

An Indian economy depends upon the agriculture up to 70% approximately. Hence, there is a need to Take care of agriculture and its resources. In such aspects, the plant disease and leaf disease is one of the major concerns that affect the overall processing of producing food, feed, fiber and many other favorite products by the cultivation. It is one of the reasons that disease identification and detection in plant adopts a significant job in agro industry area. Due to this reason, appropriate detection methodology consideration is to be taken here. Most of the research focused more on combining image processing and soft computing algorithms to solve this issue. With this motivation, this research utilize Median filter for noise removal in initial stage. Later, Hue-Saturation-Value is used for preprocessing. Further, Fuzzy C-Means Clustering (FCM) considered for clustering image samples at different iteration. Finally, the research considered a hybrid mechanism by combining Gray Co-Occurrence Matrix and Support Vector Machine. Further, the proposed method results better outcome in terms of efficiency as 87.43% K-nearest neighbor (KNN) classifier, Color Transform and Exponential Spider Monkey Optimization.

scholarly journals Recommender System for Term Deposit Likelihood Prediction using Cross-validated Neural Network

2021 ◽  
pp. 21-28
Author(s):  
Shawni Dutta ◽  
Samir Kumar Bandyopadhyay
Keyword(s):  
Neural Network ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Automated System ◽  
K Nearest Neighbor ◽  
Decision Tree Classifier ◽  
Proposed Model ◽  
Tree Classifier ◽  
Customer Perspective ◽  
Fold Cross Validation

For enhancing the maximized profit from bank as well as customer perspective, term deposit can accelerate finance fields. This paper focuses on likelihood of term deposit subscription taken by the customers. Bank campaign efforts and customer details are influential while considering possibilities of taking term deposit subscription. An automated system is provided in this paper that approaches towards prediction of term deposit investment possibilities in advance. Neural network along with stratified 10-fold cross-validation methodology is proposed as predictive model which is later compared with other benchmark classifiers such as k-Nearest Neighbor (k-NN), Decision tree classifier (DT), and Multi-layer perceptron classifier (MLP). Experimental study concluded that proposed model provides significant prediction results over other baseline models with an accuracy of 88.32% and MSE of 0.1168.

scholarly journals Identifikasi Kupu-Kupu Menggunakan Ekstraksi Fitur Deteksi Tepi (Edge Detection) dan Klasifikasi K-Nearest Neighbor (KNN)

2019 ◽  
Vol 5 (2) ◽  
Author(s):  
Rico Andrian ◽  
Saipul Anwar ◽  
Meizano Ardhi Muhammad ◽  
Akmal Junaidi
Keyword(s):  
Edge Detection ◽  
Nearest Neighbor ◽  
Detection Method ◽  
Butterfly Species ◽  
K Nearest Neighbor ◽  
A Value ◽  
Edge Strength ◽  
Canny Edge ◽  
Edge Detection Method

Lampung has the only breeding of in situ butterflies engineered in Indonesia namely Gita Persada Butterfly Park, which has approximately 211 butterfly species. Butterflies can be classified according to patterns found on the wings of a butterfly. The weakness of the human eye in distinguishing patterns on butterflies is a foundation in building butterfly identification based on pattern recognition. This study uses 6 species of butterflies: Papilio memnon, Troides helena, Papilio nephelus, Cethosia penthesilea, Papilio peranthus, and Pachliopta aristolochiae. The butterfly dataset used is 600 images. The butterfly image used is in the form of the upper wing side. The pre-processing stage uses the method of scaling, segmentation, and grayscale. The feature extraction stage uses the canny edge detection method by applying smoothing, edge strength, edge direction, non maximum suppression, and hyterisis thresholding. The classification phase uses the K-Nearest Neighbor (KNN) method with values k = 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21 and 23 obtained under the Rule of Thumb. The identification of butterfly require a classification time of 8 seconds. The highest accuracy is obtained from testing with a value of k = 5 by 80%.

Recognition of Traffic Weight Using Sobel Edge Detection Method and K-Nearest Neighbor Algorithm

10.17158/220 ◽  
2012 ◽  
Vol 18 (1) ◽  
Author(s):  
Eric John G. Emberda ◽  
Lovie Mae N. Dalagan ◽  
Christy Faith O. Baguio
Keyword(s):  
Edge Detection ◽  
Nearest Neighbor ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Software Application ◽  
K Nearest Neighbor Algorithm ◽  
Sobel Edge Detection ◽  
Edge Detection Method ◽  
The Given

This study explored the use of Sobel Edge Detection and K-Nearest Neighbor algorithm in classifying the traffic weight of a given captured image. A software application was created that accepts as input, a snapshot of a given intersection. The application could determine the traffic weight of the given snapshot, as whether it is light, moderate, or heavy by comparing it to a database of images using the K-Nearest Neighbor algorithm. The accuracy of the result was highly dependent on the training data and the quality of the snapshot. Overall, the use of Sobel Edge Detection and K-Nearest Neighbor algorithm gave significant results in recognizing the weight of a given snapshot of traffic.

scholarly journals A diagnostic genomic signal processing (GSP)-based system for automatic feature analysis and detection of COVID-19

2020 ◽  
Author(s):  
Safaa M Naeem ◽  
Mai S Mabrouk ◽  
Samir Y Marzouk ◽  
Mohamed A Eldosoky
Keyword(s):  
Nearest Neighbor ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Automated System ◽  
K Nearest Neighbor ◽  
Effective Parameters ◽  
Wuhan City ◽  
Genomic Signal Processing ◽  
Chinese Province ◽  
Genomic Signal

Abstract Coronavirus Disease 2019 (COVID-19) is a sudden viral contagion that appeared at the end of last year in Wuhan city, the Chinese province of Hubei, China. The fast spread of COVID-19 has led to a dangerous threat to worldwide health. Also in the last two decades, several viral epidemics have been listed like the severe acute respiratory syndrome coronavirus (SARS-CoV) in 2002/2003, the influenza H1N1 in 2009 and recently the Middle East respiratory syndrome coronavirus (MERS-CoV) which appeared in Saudi Arabia in 2012. In this research, an automated system is created to differentiate between the COVID-19, SARS-CoV and MERS-CoV epidemics by using their genomic sequences recorded in the NCBI GenBank in order to facilitate the diagnosis process and increase the accuracy of disease detection in less time. The selected database contains 76 genes for each epidemic. Then, some features are extracted like a discrete Fourier transform (DFT), discrete cosine transform (DCT) and the seven moment invariants to two different classifiers. These classifiers are the k-nearest neighbor (KNN) algorithm and the trainable cascade-forward back propagation neural network where they give satisfying results to compare. To evaluate the performance of classifiers, there are some effective parameters calculated. They are accuracy (ACC), F1 score, error rate and Matthews correlation coefficient (MCC) that are 100%, 100%, 0 and 1, respectively, for the KNN algorithm and 98.89%, 98.34%, 0.0111 and 0.9754, respectively, for the cascade-forward network.

scholarly journals Recommender System for Term Deposit Likelihood Prediction Using Cross-Validated Neural Network

2020 ◽  
Author(s):  
Shawni Dutta ◽  
Samir Kumar Bandyopadhyay
Keyword(s):  
Neural Network ◽  
Nearest Neighbor ◽  
Mean Squared Error ◽  
Automated System ◽  
K Nearest Neighbor ◽  
Decision Tree Classifier ◽  
Squared Error ◽  
Proposed Model ◽  
Tree Classifier ◽  
Customer Perspective

For enhancing the maximized profit from bank as well as customer perspective, term deposit can accelerate finance fields. This paper focuses on likelihood of term deposit subscription taken by the customers. Bank campaign efforts and customer details are influential while considering possibilities of taking term deposit subscription. An automated system is provided in this paper that approaches towards prediction of term deposit investment possibilities in advance. Neural network(NN) along with stratified 10-fold cross-validation methodology is proposed as predictive model which is later compared with other benchmark classifiers such as k-Nearest Neighbor (k-NN), Decision tree classifier (DT), and Multi-layer perceptron classifier (MLP). Experimental study concluded that proposed model provides significant prediction results over other baseline models with an accuracy of 88.32% and Mean Squared Error (MSE) of 0.1168.

scholarly journals Applying Convolutional-GRU for Term Deposit Likelihood Prediction

2020 ◽  
Author(s):  
Shawni Dutta ◽  
Samir Bandyopadhyay
Keyword(s):  
Predictive Model ◽  
Nearest Neighbor ◽  
Automated System ◽  
K Nearest Neighbor ◽  
Decision Tree Classifier ◽  
Saving Account ◽  
Proposed Model ◽  
Tree Classifier ◽  
Customer Perspective ◽  
Gated Recurrent Unit

Banks are normally offered two kinds of deposit accounts. It consists of deposits like current/saving account and term deposits like fixed or recurring deposits. For enhancing the maximized profit from bank as well as customer perspective, term deposit can accelerate uplifting of finance fields. This paper focuses on likelihood of term deposit subscription taken by the customers. Bank campaign efforts and customer detail analysis can influence term deposit subscription chances. An automated system is approached in this paper that works towards prediction of term deposit investment possibilities in advance. This paper proposes deep learning based hybrid model that stacks Convolutional layers and Recurrent Neural Network (RNN) layers as predictive model. For RNN, Gated Recurrent Unit (GRU) is employed. The proposed predictive model is later compared with other benchmark classifiers such as k-Nearest Neighbor (k-NN), Decision tree classifier (DT), and Multi-layer perceptron classifier (MLP). Experimental study concludes that proposed model attains an accuracy of 89.59% and MSE of 0.1041 which outperform well other baseline models.

scholarly journals Adaptive Ant Colony Optimization on Mango Classification Using K-Nearest Neighbor and Support Vector Machine

2017 ◽  
Vol 3 (2) ◽  
pp. 75 ◽  
Author(s):  
Febri Liantoni ◽  
Luky Agus Hermanto
Keyword(s):  
Support Vector Machine ◽  
Edge Detection ◽  
Ant Colony Optimization ◽  
Nearest Neighbor ◽  
Optimization Method ◽  
Ant Colony ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Edge Detection Method ◽  
Mango Leaves

Abstract— Leaves recognition can use an image edge detection method. In this research, the classification of mango gadung and manalagi will be performed. In the preprocess stage edge detection method using adaptive ant colony optimization method. The use of adaptive ant colony optimization method aims to optimize the process of edge detection of a mango leaves the bone image. The application of ant colony optimization method on mango leaves classification has successfully optimized the result of edge detection of a mango leaves the bone structure. Results showed edge detection using adaptive ant colony optimization method better than Roberts and Sobel method. The result an experiment of mango leaves classification with k-nearest neighbor method get accuracy value equal to 66,25%, whereas with the method of support vector machine obtained accuracy value equal to 68,75%.Keywords— Edge Detection, Ant Colony Optimization, Classification, K-Nearest Neighbor, Support Vector Machine

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