scholarly journals A Convolutional Neural Network (CNN) Based Approach for the Recognition and Evaluation of Classroom Teaching Behavior

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Guang Li ◽  
Fangfang Liu ◽  
Yuping Wang ◽  
Yongde Guo ◽  
Liang Xiao ◽  
...  
Keyword(s):  
Evaluation Method ◽  
Identification Accuracy ◽  
Classroom Teaching ◽  
Support Vector ◽  
Decision Matrix ◽  
Teaching Behavior ◽  
Sample Data ◽  
Characteristic Sample ◽  
Relationship Of ◽  
Behavior Characteristic

To improve classroom teaching behavior recognition and evaluation accuracy, this paper proposes a new model based on deep learning. First, we obtain the classroom teaching behavior characteristic data through the SVM’s linear separable initial and determine the relationship of the characteristic sample data in the hyperplane. Then, we obtain the heterogeneous support vector of the online learning behavior characteristic sample data in the SVM’s hyperplane and complete the extraction of data with the help of convolutional neural networks. We then use a decision matrix to analyze the hierarchical process, determine the weight of classroom teaching behavior indicators, verify their consistency, and complete the evaluation by calculating the membership of evaluation factors. The experimental results show that the identification and evaluation method of classroom teaching behavior in this paper can effectively improve the identification accuracy of the classroom teaching behavior.

Fault Diagnosis Method of Mine Motor Based on Support Vector Machine

2019 ◽  
Vol 13 ◽  
Author(s):  
Yan Zhang ◽  
Ren Sheng
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Optimization Method ◽  
Support Vector ◽  
Optimal Parameters ◽  
Instantaneous Power ◽  
Practical Usefulness ◽  
Sample Data ◽  
Diagnosis Method ◽  
Fault Characteristic

Background: In order to improve the efficiency of fault treatment of mining motor, the method of model construction is used to construct the type of kernel function based on the principle of vector machine classification and the optimization method of parameters. Methodology: One-to-many algorithm is used to establish two kinds of support vector machine models for fault diagnosis of motor rotor of crusher. One of them is to obtain the optimal parameters C and g based on the input samples of the instantaneous power fault characteristic data of some motor rotors which have not been processed by rough sets. Patents on machine learning have also shows their practical usefulness in the selction of the feature for fault detection. Results: The results show that the instantaneous power fault feature extracted from the rotor of the crusher motor is obtained by the cross validation method of grid search k-weights (where k is 3) and the final data of the applied Gauss radial basis penalty parameter C and the nuclear parameter g are obtained. Conclusion: The model established by the optimal parameters is used to classify and diagnose the sample of instantaneous power fault characteristic measurement of motor rotor. Therefore, the classification accuracy of the sample data processed by rough set is higher.

Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images

2021 ◽  
Vol 5 (2) ◽  
Author(s):  
Alexander Knyshov ◽  
Samantha Hoang ◽  
Christiane Weirauch
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Training Image ◽  
Species Level ◽  
Identification Accuracy ◽  
Support Vector ◽  
Test Case ◽  
Learning Approaches ◽  
Lower Accuracy ◽  
Neural Network Classifiers

Abstract Automated insect identification systems have been explored for more than two decades but have only recently started to take advantage of powerful and versatile convolutional neural networks (CNNs). While typical CNN applications still require large training image datasets with hundreds of images per taxon, pretrained CNNs recently have been shown to be highly accurate, while being trained on much smaller datasets. We here evaluate the performance of CNN-based machine learning approaches in identifying three curated species-level dorsal habitus datasets for Miridae, the plant bugs. Miridae are of economic importance, but species-level identifications are challenging and typically rely on information other than dorsal habitus (e.g., host plants, locality, genitalic structures). Each dataset contained 2–6 species and 126–246 images in total, with a mean of only 32 images per species for the most difficult dataset. We find that closely related species of plant bugs can be identified with 80–90% accuracy based on their dorsal habitus alone. The pretrained CNN performed 10–20% better than a taxon expert who had access to the same dorsal habitus images. We find that feature extraction protocols (selection and combination of blocks of CNN layers) impact identification accuracy much more than the classifying mechanism (support vector machine and deep neural network classifiers). While our network has much lower accuracy on photographs of live insects (62%), overall results confirm that a pretrained CNN can be straightforwardly adapted to collection-based images for a new taxonomic group and successfully extract relevant features to classify insect species.

scholarly journals Simulating the Stress-Strain Relationship of Geomaterials by Support Vector Machine

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Hongbo Zhao ◽  
Zenghui Huang ◽  
Zhengsheng Zou
Keyword(s):  
Support Vector Machine ◽  
Constitutive Relationship ◽  
Support Vector ◽  
Nonlinear Relationship ◽  
Ann Model ◽  
Stress Strain ◽  
Svm Model ◽  
Strain Relationship ◽  
Artificial Neural Network Ann ◽  
Relationship Of

Stress-strain relationship of geomaterials is important to numerical analysis in geotechnical engineering. It is difficult to be represented by conventional constitutive model accurately. Artificial neural network (ANN) has been proposed as a more effective approach to represent this complex and nonlinear relationship, but ANN itself still has some limitations that restrict the applicability of the method. In this paper, an alternative method, support vector machine (SVM), is proposed to simulate this type of complex constitutive relationship. The SVM model can overcome the limitations of ANN model while still processing the advantages over the traditional model. The application examples show that it is an effective and accurate modeling approach for stress-strain relationship representation for geomaterials.

An Effective Damage Evaluation Method for Crane Girder Based on SVI and LSSVM

2014 ◽  
Vol 602-605 ◽  
pp. 370-374
Author(s):  
Hong Bo Xu ◽  
Jia Yu Li
Keyword(s):  
Evaluation Method ◽  
Damage Identification ◽  
Health Assessment ◽  
Support Vector ◽  
Damage Evaluation ◽  
Damage Level ◽  
Variation Index ◽  
Stiffness Variation ◽  
Actual Damage

Health assessment of the girder is crucial to an overhead traveling crane. This paper presents an intelligent damage identification method for the girder based on stiffness variation index (SVI) and least squares support vector machine (LSSVM). In the method, the SVI indicators, which have high resolution to environmental noise, serve as the damage feature to detect damage locations. Moreover, the SVI indicators are input to the LSSVM classifier for identifying the actual damage level of the girder. A case study on girder damage identification demonstrates that the method could determine the actual conditions of the girder structure accurately.

scholarly journals Study on the Magnitude of Reservoir-Triggered Earthquake Based on Support Vector Machines

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Hai Wei ◽  
Mingming Wang ◽  
Bingyue Song ◽  
Xin Wang ◽  
Danlei Chen
Keyword(s):  
Support Vector Machines ◽  
Prediction Models ◽  
Influence Factors ◽  
Tectonic Stress ◽  
Support Vector ◽  
Fault Features ◽  
Vector Machines ◽  
Triggered Earthquake ◽  
Sample Data ◽  
Hierarchy Process

An effective approach is introduced to predict the magnitude of reservoir-triggered earthquake (RTE), based on support vector machines (SVM) and fuzzy support vector machines (FSVM) methods. The main influence factors on RTE, including lithology, rock mass integrity, fault features, tectonic stress state, and seismic activity background in reservoir area, are categorized into 11 parameters and quantified by using analytical hierarchy process (AHP). Dataset on 100 reservoirs in China, including the 48 well-documented cases of RTE, are collected and used to train and validate the prediction models established with SVM and FSVM, respectively. Through numerical tests, it is found that both the SVM and FSVM models are effective in the prediction of the magnitude of RTE with high accuracy, provided that sufficient samples are collected. While the results of FSVM which is extended from SVM by introducing a fuzzy membership to reduce the influence of noises or outliers are found to be slightly less accurate than those of SVM in the current analysis of RTE cases. The reason might be attributed to the high discreteness of the sample data in the current study.

scholarly journals KEMAMPUAN AUDIT TENURE MEMODERASI PENGARUH PENGALAMAN DAN SKEPTISISME AUDITOR DENGAN KUALITAS AUDIT

2017 ◽  
Vol 2 (1) ◽  
Author(s):  
I Gusti Ayu Nyoman Budiasih ◽  
Ketut Budiartha
Keyword(s):  
Data Analysis ◽  
Audit Quality ◽  
Total Sample ◽  
Financial Reports ◽  
Auditor Quality ◽  
Sample Data ◽  
Client Pressure ◽  
Relationship Of ◽  
The Relationship

ABSTRAKPentingnya laporan keuangan bagi banyak pihak, menyebabkan laporan tersebut harus disajikan secara relevan dan reliabel. Kualitas audit yang baik dapat menyebabkan laporan keuangan semakin dipercaya keasliannya. Kualitas audit tergantung pada independensi dan kompetensi auditor. Independensi dan kompetensi seorang auditor tergantung pada pengalaman maupun sikap skeptis yang dimiliki auditor tersebut. Pengalaman auditor akan meningkat dikarenakan telah terbiasa dengan pekerjaannya sehingga auditor akan bekerja secara efisien dan lebih tahan terhadap tekanan klien. Sampel yang dipilih menggunakan teknik sampel jenuh dengan jumlah sampel sebanyak 85 auditor. Pengumpulan data dilakukan dengan menyebarkan kuesioner kepada auditor yang menjadi sampel penelitian. Teknik analisis datanya menggunakan analisis regresi tanpa dan dengan variabel moderasi. Berdasarkan hasil analisis data dan pembahasan yang telah dilakukan, maka dapat disimpulkan bahwa kualitas audit di KAP Provinsi Bali memiliki hubungan secara positif dengan pengalaman dan skeptisisme auditor, audit tenure tidak mampu memoderasi hubungan pengalaman auditor dengan kualitas audit auditor di KAP Provinsi Bali, dan audit tenure mampu memoderasi hubungan skeptisisme auditor dengan kualitas audit auditor di KAP Provinsi Bali.Kata Kunci: audit tenure, pengalaman , skeptisisme auditor, kualitas audit  ABSTRACTThe importance of financial statements for many, causing the report to be presented in a relevant and reliable manner. A good audit quality can lead to more authentic financial reports. The quality of the audit depends on the independence and competence of the auditor. The independence and competence of an auditor depends on the experience and skepticism of the auditor. The experience of the auditor will increase due to the familiarity with the work so that the auditor will work efficiently and more resistant to client pressure. The samples were selected using a sample saturated technique with a total sample of 85 auditors. Data collection was done by distributing questionnaires to the auditor who became the research sample. Data analysis techniques use regression analysis without and with moderation variables. Based on the results of data analysis and discussion that have been done, it can be concluded that the audit quality in KAP Bali Province has a positive relationship with the experience and skepticism of auditors, audit tenure unable to moderate the relationship of auditor experience with audit auditor quality in KAP Bali Province and audit tenure able to moderate the relationship of auditor skepticism with audit auditor quality in KAP Bali Province.Keywords: audit tenure, experience, auditor skepticism, audit quality 

scholarly journals Flexible Impedimetric Electronic Nose for High-Accurate Determination of Individual Volatile Organic Compounds by Tuning the Graphene Sensitive Properties

Chemosensors ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 360
Author(s):  
Tianqi Lu ◽  
Ammar Al-Hamry ◽  
José Mauricio Rosolen ◽  
Zheng Hu ◽  
Junfeng Hao ◽  
...  
Keyword(s):  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Electronic Nose ◽  
Accurate Determination ◽  
Identification Accuracy ◽  
Structural Defects ◽  
Support Vector ◽  
Volatile Organic ◽  
Variable Sensitivity ◽  
Relative Change

We investigated functionalized graphene materials to create highly sensitive sensors for volatile organic compounds (VOCs) such as formaldehyde, methanol, ethanol, acetone, and isopropanol. First, we prepared VOC-sensitive films consisting of mechanically exfoliated graphene (eG) and chemical graphene oxide (GO), which have different concentrations of structural defects. We deposited the films on silver interdigitated electrodes on Kapton substrate and submitted them to thermal treatment. Next, we measured the sensitive properties of the resulting sensors towards specific VOCs by impedance spectroscopy. We obtained the eG- and GO-based electronic nose composed of two eG films- and four GO film-based sensors with variable sensitivity to individual VOCs. The smallest relative change in impedance was 5% for the sensor based on eG film annealed at 180 °C toward 10 ppm formaldehyde, whereas the highest relative change was 257% for the sensor based on two-layers deposited GO film annealed at 200 °C toward 80 ppm ethanol. At 10 ppm VOC, the GO film-based sensors were sensitive enough to distinguish between individual VOCs, which implied excellent selectivity, as confirmed by Principle Component Analysis (PCA). According to a PCA-Support Vector Machine-based signal processing method, the electronic nose provided identification accuracy of 100% for individual VOCs. The proposed electronic nose can be used to detect multiple VOCs selectively because each sensor is sensitive to VOCs and has significant cross-selectivity to others.

scholarly journals Feature and Decision Level Fusion in Children Multimodal Biometrics

2020 ◽  
Vol 8 (5) ◽  
pp. 2522-2527
Keyword(s):  
Nearest Neighbor ◽  
Principal Component ◽  
Identification Accuracy ◽  
Support Vector ◽  
Biometric System ◽  
K Nearest Neighbor ◽  
Decision Level ◽  
Multimodal Biometric System ◽  
Decision Level Fusion ◽  
Level Fusion

In this paper, we design method for recognition of fingerprint and IRIS using feature level fusion and decision level fusion in Children multimodal biometric system. Initially, Histogram of Gradients (HOG), Gabour and Maximum filter response are extracted from both the domains of fingerprint and IRIS and considered for identification accuracy. The combination of feature vector of all the possible features is recommended by biometrics traits of fusion. For fusion vector the Principal Component Analysis (PCA) is used to select features. The reduced features are fed into fusion classifier of K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Navie Bayes(NB). For children multimodal biometric system the suitable combination of features and fusion classifiers is identified. The experimentation conducted on children’s fingerprint and IRIS database and results reveal that fusion combination outperforms individual. In addition the proposed model advances the unimodal biometrics system.

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