A Nonnegative Locally Linear KNN model for image recognition

Chen, Si‐Bao; Xu, Yu-Lan; Ding, Chris; Luo, Bin

doi:10.1016/j.patcog.2018.05.024

Cited by 19 publications

(8 citation statements)

References 29 publications

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“…The Bayes Classifier is used in medicine [43]. The K-NN technique is used for image recognition, in areas such as facial recognition [44]. Perceptron, K-NN, and the Bayes Classifier are also used in weather forecasting [45].…”

Section: Methods and Techniques For Generating Workplace Proceduresmentioning

confidence: 99%

A Model for Generating Workplace Procedures Using a CNN-SVM Architecture

Patalas‐Maliszewska

Halikowski

2019

Symmetry

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(1) Background: Improving the management and effectiveness of employees’ learning processes within manufacturing companies has attracted a high level of attention in recent years, especially within the context of Industry 4.0. Convolutional Neural Networks with a Support Vector Machine (CNN-SVM) can be applied in this business field, in order to generate workplace procedures. To overcome the problem of usefully acquiring and sharing specialist knowledge, we use CNN-SVM to examine features from video material concerning each work activity for further comparison with the instruction picture’s features. (2) Methods: This paper uses literature studies and a selected workplace procedure: repairing a solid and using a fuel boiler as the benchmark dataset, which contains 20 s of training and a test video, in order to provide a reference model of features for a workplace procedure. In this model, the method used is also known as Convolutional Neural Networks with Support Vector Machine. This method effectively determines features for the further comparison and detection of objects. (3) Results: The innovative model for generating a workplace procedure, using CNN-SVM architecture, once built, can then be used to provide a learning process to the employees of manufacturing companies. The novelty of the proposed methodology is its architecture, which combines the acquisition of specialist knowledge and formalising and recording it in a useful form for new employees in the company. Moreover, three new algorithms were created: an algorithm to match features, an algorithm to detect each activity in the workplace procedure, and an algorithm to generate an activity scenario. (4) Conclusions: The efficiency of the proposed methodology can be demonstrated on a dataset comprising a collection of workplace procedures, such as the repair of the solid fuel boiler. We also highlighted the impracticality for managers of manufacturing companies to support learning processes in a company, resulting from a lack of resources to teach new employees.

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Section: Methods and Techniques For Generating Workplace Proceduresmentioning

confidence: 99%

A Model for Generating Workplace Procedures Using a CNN-SVM Architecture

Patalas‐Maliszewska

Halikowski

2019

Symmetry

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“…We investigate its hidden neuron size across {10,20, ...100}and the optimal value is 90. Moreover, KNN is an instance-based learning method and we set 100 nearest neighbors for the classification model [45]. It is found from the figure that the EL-SDAE achieves the highest accuracy for the eight subjects among all classifiers.…”

Section: Performance Comparison With Shallow Mw Classifiersmentioning

confidence: 99%

Assessing cognitive mental workload via EEG signals and an ensemble deep learning classifier based on denoising autoencoders

Yang

Yin

Wang

et al. 2019

Computers in Biology and Medicine

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To estimate the reliability and cognitive states of operator performance in a human-machine collaborative environment, we propose a novel human mental workload (MW) recognizer based on deep learning principles and utilizing the features of the electroencephalogram (EEG). To determine personalized properties in high dimensional EEG indicators, we introduce a feature mapping layer in stacked denoising autoencoder (SDAE) that is capable of preserving the local information in EEG dynamics. The ensemble classifier is then built via the subjectspecific integrated deep learning committee, and adapts to the cognitive properties of a specific human operator and alleviates inter-subject feature variations. We validate our algorithms and the ensemble SDAE classifier with local information preservation (denoted by EL-SDAE) on an EEG database collected during the execution of complex human-machine tasks. The classification performance indicates that the EL-SDAE outperforms several classical MW estimators when its optimal network architecture has been identified.

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“…The K-nearest neighbors or KNN is a very popular method in data mining and statistics [11], [41]. It is a method that calculates distances among all training samples and every test samples [11], [41]. Then we have the nearest neighbor of all test samples.…”

Section: ) Knnmentioning

confidence: 99%

“…It is clear that KNN has simple implementation and results show that it has a great implementation performance [11], [41]. The KNN contains two major steps [11], [31], [41]: allocation of optimal samples [62] and allocation of different optimal values of k for different samples in test data [62]. To select the optimal k in KNN great efforts have been put [11], [31], [62].…”

Section: ) Knnmentioning

confidence: 99%

“…The KNN contains two major steps [11], [31], [41]: allocation of optimal samples [62] and allocation of different optimal values of k for different samples in test data [62]. To select the optimal k in KNN great efforts have been put [11], [31], [62]. For example in [31], authors indicated that the optimal value for K in all test samples could be K = √ n with n > 100 (n is the number of samples were involved in the training phase).…”

Section: ) Knnmentioning

confidence: 99%

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A Machine Learning-Based Approach for Counting Blister Cards Within Drug Packages

Bahaghighat¹,

Akbari²,

Xin

2019

IEEE Access

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Nowadays with the rapid development of technologies, machine vision has been used widely in various industries. The main applications of machine vision in industrial product lines are quality control (QC) and quality assurance (QA). The intelligent defects and anomalies recognition throughout the supply chain have come to be an integral part of quality control systems, in particular, in the food and pharmaceutical industries. In these industries, it is a legal requirement in manufacturing processes which can lead to minimizing the total number of defected products as well as maximizing the performance. In this paper, the machine vision has been utilized to monitor and control the proper packaging of drugs in pharmaceutical product lines. The main goal is counting the number of blister cards within a drug package. To tackle this problem, a new model based on object detection, feature extraction, and classification is proposed. Thanks to several strong approaches, such as the Haar cascade, HOG, ORG, Gabor wavelet, Radon transform, KNN, and SVM, and the accuracy over 88% is achieved in our experiments.

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A Nonnegative Locally Linear KNN model for image recognition

Cited by 19 publications

References 29 publications

A Model for Generating Workplace Procedures Using a CNN-SVM Architecture

A Model for Generating Workplace Procedures Using a CNN-SVM Architecture

Assessing cognitive mental workload via EEG signals and an ensemble deep learning classifier based on denoising autoencoders

A Machine Learning-Based Approach for Counting Blister Cards Within Drug Packages

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