scholarly journals Machine Learning based Identification and Classification of Field-Operation caused Solar Panel Failures observed in Electroluminescence Images

2021 ◽  
Author(s):  
Stefan Bordihn
Keyword(s):  
Machine Learning ◽  
Solar Panel ◽  
Nearest Neighbour ◽  
Analysis Method ◽  
Solar Panels ◽  
Power Losses ◽  
Field Operation ◽  
Nearest Neighbour Classifier ◽  
Failure Types

Failure or degradation effects lead to power losses in solar panels during their field operation and are identified commonly by electroluminescence imaging. Failures like potential induced degradation and light and enhanced temperature induced degradation require an identification of the electroluminescence pattern over the entire solar panel. As the manual process of analysing patterns is prone to error, we seek for an automatic detection of these failure types. We predict automatically the failure types potential induced degradation and light and enhanced temperature induced degradation by adopting the principle component analysis method in combination with a k-nearest neighbour classifier.<br>

scholarly journals Machine Learning based Identification and Classification of Field-Operation caused Solar Panel Failures observed in Electroluminescence Images

2021 ◽  
Author(s):  
Stefan Bordihn
Keyword(s):  
Machine Learning ◽  
Solar Panel ◽  
Nearest Neighbour ◽  
Analysis Method ◽  
Solar Panels ◽  
Power Losses ◽  
Field Operation ◽  
Nearest Neighbour Classifier ◽  
Failure Types

Failure or degradation effects lead to power losses in solar panels during their field operation and are identified commonly by electroluminescence imaging. Failures like potential induced degradation and light and enhanced temperature induced degradation require an identification of the electroluminescence pattern over the entire solar panel. As the manual process of analysing patterns is prone to error, we seek for an automatic detection of these failure types. We predict automatically the failure types potential induced degradation and light and enhanced temperature induced degradation by adopting the principle component analysis method in combination with a k-nearest neighbour classifier.<br>

Setting Attribute Weights for Nearest Neighbor Learning Algorithms Using C4.5

1997 ◽  
Vol 11 (03) ◽  
pp. 405-415 ◽  
Author(s):  
Charles X. Ling ◽  
John J. Parry ◽  
Handong Wang
Keyword(s):  
Machine Learning ◽  
Decision Trees ◽  
Distance Function ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Simple Approach ◽  
Nearest Neighbour ◽  
Attribute Weights ◽  
New Methods

Nearest Neighbour (NN) learning algorithms utilize a distance function to determine the classification of testing examples. The attribute weights in the distance function should be set appropriately. We study situations where a simple approach of setting attribute weights using decision trees does not work well, and design three improvements. We test these new methods thoroughly using artificially generated datasets and datasets from the machine learning repository.

scholarly journals Texture classification of fabric defects using machine learning

2020 ◽  
Vol 10 (4) ◽  
pp. 4390
Author(s):  
Yassine Ben Salem ◽  
Mohamed Naceur Abdelkrim
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Comparative Study ◽  
Texture Classification ◽  
Support Vector ◽  
Defect Classification ◽  
Analysis Method ◽  
Fabric Defect ◽  
Novel Algorithm

In this paper, a novel algorithm for automatic fabric defect classification was proposed, based on the combination of a texture analysis method and a support vector machine SVM. Three texture methods were used and compared, GLCM, LBP, and LPQ. They were combined with SVM’s classifier. The system has been tested using TILDA database. A comparative study of the performance and the running time of the three methods was carried out. The obtained results are interesting and show that LBP is the best method for recognition and classification and it proves that the SVM is a suitable classifier for such problems. We demonstrate that some defects are easier to classify than others.

Machine Learning Based Classification of Cervical Cancer Using K-Nearest Neighbour, Random Forest and Multilayer Perceptron Algorithms

2019 ◽  
Vol 16 (5) ◽  
pp. 2523-2527
Author(s):  
Shakila Basheer ◽  
S Mariyam Aysha Bivi ◽  
S Jayakumar ◽  
Arpit Rathore ◽  
Balajee Jeyakumar
Keyword(s):  
Machine Learning ◽  
Cervical Cancer ◽  
Random Forest ◽  
Multilayer Perceptron ◽  
Nearest Neighbour

scholarly journals K-NEAREST NEIGHBOUR CLASSIFIER USAGE FOR PERMISSION BASED MALWARE DETECTION IN ANDROID

2020 ◽  
Vol 4 (2) ◽  
pp. 15-27
Author(s):  
Recep Sinan ARSLAN ◽  
Ahmet Haşim Yurttakal
Keyword(s):  
Machine Learning ◽  
Learning Methods ◽  
Static And Dynamic Analysis ◽  
Machine Learning Methods ◽  
Knn Classifier ◽  
Proposed Model ◽  
Malicious Apps ◽  
Nearest Neighbour Classifier ◽  
Application Developers

ABSTRACT Android application platform is making rapid progress in these days. This development has made it the target of malicious application developers. This situation provides a numerical increase in malware apps, diversity in techniques, and rise of damage. Therefore, it is very critical to detect these software and escalation the security of mobile users. Static and dynamic analysis, behaviour scrutiny, machine learning methods are used to ensure security. In this study, K-nearest Neighbourhood (KNN) classifier, one of the machine learning methods, is used. Thus, it is aimed to detect malignant mobile software successfully and quickly. The tests is conducted with dataset includes 492 malware and 697 benign applications. In the proposed algorithm, neighbour number 5 and distance metric is preferred as Minkowski. 80% of dataset randomly selected is reserved for training and 20% for testing. As a result, while 94.1% accuracy is achieved, precision 91.2%, recall 92.7% recall and f1-measure is 92.4%. The high value obtained in f1-measure shows that the proposed model is successful in detecting both malware and benevolent software. The success of using KNN algorithm in classification of malicious apps in the Android has been demonstrated.

scholarly journals Dimentionality reduction based on binary cooperative particle swarm optimization

2019 ◽  
Vol 15 (3) ◽  
pp. 1382
Author(s):  
Sharifah Sakinah Syed Ahmad ◽  
Ezzatul Farhain Azmi ◽  
Fauziah Kasmin ◽  
Zuraini Othman
Keyword(s):  
Real World ◽  
Research Work ◽  
Particle Swarm ◽  
Training Data ◽  
Nearest Neighbour ◽  
Classification Rate ◽  
Unseen Data ◽  
Real World Datasets ◽  
Nearest Neighbour Classifier

Even though there are numerous classifiers algorithms that are more complex, k-Nearest Neighbour (k-NN) is regarded as one amongst the most successful approaches to solve real-world issues. The classification process’s effectiveness relies on the training set’s data. However, when k-NN classifier is applied to a real world, various issues could arise; for instance, they are considered to be computationally expensive as the complete training set needs to be stored in the computer for classification of the unseen data. Also, intolerance of k-NN classifier towards irrelevant features can be seen. Conversely, imbalance in the training data could occur wherein considerably larger numbers of data could be seen with some classes versus other classes. Thus, selected training data are employed to improve the effectiveness of k-NN classifier when dealing with large datasets. In this research work, a substitute method is present to enhance data selection by simultaneously clubbing the feature selection as well as instances selection pertaining to k-NN classifier by employing Cooperative Binary Particle Swarm Optimisation (CBPSO). This method can also address the constraint of employing the k-nearest neighbour classifier, particularly when handling high dimensional and imbalance data. A comparison study was performed to demonstrate the performance of our approach by employing 20 real world datasets taken from the UCI Machine Learning Repository. The corresponding table of the classification rate demonstrates the algorithm’s performance. The experimental outcomes exhibit the efficacy of our proposed approach.

scholarly journals k-Nearest Neighbour Classifiers - A Tutorial

2021 ◽  
Vol 54 (6) ◽  
pp. 1-25
Author(s):  
Pádraig Cunningham ◽  
Sarah Jane Delany
Keyword(s):  
Machine Learning ◽  
Similarity Measures ◽  
Machine Learning Techniques ◽  
Nearest Neighbour ◽  
Computational Power ◽  
Learning Techniques ◽  
Nearest Neighbours ◽  
Technical Report ◽  
Nearest Neighbour Classifier ◽  
Similarity Distance

Perhaps the most straightforward classifier in the arsenal or Machine Learning techniques is the Nearest Neighbour Classifier—classification is achieved by identifying the nearest neighbours to a query example and using those neighbours to determine the class of the query. This approach to classification is of particular importance, because issues of poor runtime performance is not such a problem these days with the computational power that is available. This article presents an overview of techniques for Nearest Neighbour classification focusing on: mechanisms for assessing similarity (distance), computational issues in identifying nearest neighbours, and mechanisms for reducing the dimension of the data. This article is the second edition of a paper previously published as a technical report [16]. Sections on similarity measures for time-series, retrieval speedup, and intrinsic dimensionality have been added. An Appendix is included, providing access to Python code for the key methods.

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