scholarly journals Deep Convolutional Neural Network and Weighted Bayesian Model for Evaluation of College Foreign Language Multimedia Teaching

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Tingting Liu ◽  
Le Ning
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Teaching Quality ◽  
Colleges And Universities ◽  
Deep Convolutional Neural Network ◽  
Evaluation Index System ◽  
Assessment Model ◽  
Classification Model ◽  
Evaluation Index ◽  
Teaching Assessment

In colleges and universities, teaching quality evaluation is an integral part of the teaching management process. Many factors influence it, and the relationship between its evaluation index and instructional quality is complicated, abstract, and nonlinear. However, existing evaluation methods and models have flaws such as excessive subjectivity and randomness, difficulty determining the weight of indicators, easy over-fitting, slow convergence speed, and limited computing power, to name a few. Furthermore, the evaluation index system focuses primarily on teaching attitude, material, and methods, rarely taking into account preparation prior to teaching or the teaching situation throughout the teaching process, resulting in an incomplete evaluation. As a result, learning how to construct a model for objectively, truly, thoroughly, and accurately assessing the teaching quality of colleges and universities is beneficial not only to improving teaching quality but also to promoting scientific decision-making in education. This paper develops a teaching assessment model using a deep convolutional neural network and the weighted Naive Bayes algorithm. Based on the degree of influence of different characteristics on the assessment outcomes, a method to estimate the weight of each evaluation characteristic by employing the related probability of class attributes is proposed, and the corresponding weight is assigned for each evaluation index, resulting in a classification model ideal for teaching assessment that promotes standardization and intelligibility.

The Model of Evaluating Teaching Quality Based on BP Neural Network Algorithm

2015 ◽  
Vol 719-720 ◽  
pp. 1297-1301
Author(s):  
Lei Bai ◽  
Xiao Xin Guo
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Quality Evaluation ◽  
Hot Spot ◽  
Evaluation Model ◽  
Teaching Quality ◽  
Research Field ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Neural Network Algorithm

Teaching quality evaluation plays a key role for universities to improve its teaching quality and becomes a hot spot research field for related researchers. In this paper, we established the evaluation model of teaching quality based on BP neural network. Firstly an evaluation index system of teaching quality is designed. Then, according to the system we design the structure of BP neural network, determine the parameters and give the algorithm description. Finally, we program and verify the validity of the model in MATLAB environment. The experimental results show that the model can evaluate teaching quality practically by the evaluation index.

scholarly journals ME-Net: A Deep Convolutional Neural Network for Extracting Mangrove Using Sentinel-2A Data

2021 ◽  
Vol 13 (7) ◽  
pp. 1292
Author(s):  
Mingqiang Guo ◽  
Zhongyang Yu ◽  
Yongyang Xu ◽  
Ying Huang ◽  
Chunfeng Li
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Discrimination Index ◽  
Classification Model ◽  
Pixel Classification ◽  
Remote Sensing Imagery ◽  
Boundary Fitting ◽  
Sentinel 2A

Mangroves play an important role in many aspects of ecosystem services. Mangroves should be accurately extracted from remote sensing imagery to dynamically map and monitor the mangrove distribution area. However, popular mangrove extraction methods, such as the object-oriented method, still have some defects for remote sensing imagery, such as being low-intelligence, time-consuming, and laborious. A pixel classification model inspired by deep learning technology was proposed to solve these problems. Three modules in the proposed model were designed to improve the model performance. A multiscale context embedding module was designed to extract multiscale context information. Location information was restored by the global attention module, and the boundary of the feature map was optimized by the boundary fitting unit. Remote sensing imagery and mangrove distribution ground truth labels obtained through visual interpretation were applied to build the dataset. Then, the dataset was used to train deep convolutional neural network (CNN) for extracting the mangrove. Finally, comparative experiments were conducted to prove the potential for mangrove extraction. We selected the Sentinel-2A remote sensing data acquired on 13 April 2018 in Hainan Dongzhaigang National Nature Reserve in China to conduct a group of experiments. After processing, the data exhibited 2093 × 2214 pixels, and a mangrove extraction dataset was generated. The dataset was made from Sentinel-2A satellite, which includes five original bands, namely R, G, B, NIR, and SWIR-1, and six multispectral indices, namely normalization difference vegetation index (NDVI), modified normalized difference water index (MNDWI), forest discrimination index (FDI), wetland forest index (WFI), mangrove discrimination index (MDI), and the first principal component (PCA1). The dataset has a total of 6400 images. Experimental results based on datasets show that the overall accuracy of the trained mangrove extraction network reaches 97.48%. Our method benefits from CNN and achieves a more accurate intersection and union ratio than other machine learning and pixel classification methods by analysis. The designed model global attention module, multiscale context embedding, and boundary fitting unit are helpful for mangrove extraction.

scholarly journals A Deep Convolutional Neural Network-Based Multi-Class Image Classification for Automatic Wafer Map Failure Recognition in Semiconductor Manufacturing

2021 ◽  
Vol 11 (20) ◽  
pp. 9769
Author(s):  
Huilin Zheng ◽  
Syed Waseem Abbas Sherazi ◽  
Sang Hyeok Son ◽  
Jong Yun Lee
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Manufacturing Process ◽  
Semiconductor Manufacturing ◽  
Prediction Models ◽  
Deep Convolutional Neural Network ◽  
Classification Model ◽  
Boosted Decision Trees

Wafer maps provide engineers with important information about the root causes of failures during the semiconductor manufacturing process. Through the efficient recognition of the wafer map failure pattern type, the semiconductor manufacturing process and its product performance can be improved, as well as reducing the product cost. Therefore, this paper proposes an accurate model for the automatic recognition of wafer map failure types using a deep learning-based convolutional neural network (DCNN). For this experiment, we use WM811K, which is an open-source real-time wafer map dataset containing wafer map images of nine failure classes. Our research contents can be briefly summarized as follows. First, we use random sampling to extract 500 images from each class of the original image dataset. Then we propose a deep convolutional neural network model to generate a multi-class classification model. Lastly, we evaluate the performance of the proposed prediction model and compare it with three other popular machine learning-based models—logistic regression, random forest, and gradient boosted decision trees—and several well-known deep learning models—VGGNet, ResNet, and EfficientNet. Consequently, the comprehensive analysis showed that the performance of the proposed DCNN model outperformed those of other popular machine learning and deep learning-based prediction models.

LENS CLASSIFICATION ACCORDING TO THE TYPE OF LIGHT SPOT USING A NEURAL NETWORK

2020 ◽  
Vol 2020 (4) ◽  
pp. 4-14
Author(s):  
Vladimir Budak ◽  
Ekaterina Ilyina
Keyword(s):  
Neural Network ◽  
Light Intensity ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Beam Angle ◽  
New Model ◽  
Technical Parameters ◽  
Transfer Training ◽  
Trained Network

The article proposes the classification of lenses with different symmetrical beam angles and offers a scale as a spot-light’s palette. A collection of spotlight’s images was created and classified according to the proposed scale. The analysis of 788 pcs of existing lenses and reflectors with different LEDs and COBs carried out, and the dependence of the axial light intensity from beam angle was obtained. A transfer training of new deep convolutional neural network (CNN) based on the pre-trained GoogleNet was performed using this collection. GradCAM analysis showed that the trained network correctly identifies the features of objects. This work allows us to classify arbitrary spotlights with an accuracy of about 80 %. Thus, light designer can determine the class of spotlight and corresponding type of lens with its technical parameters using this new model based on CCN.

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