scholarly journals Extraction and Classification of Mouth Shape Features in Oral English Teaching Based on Image Processing

2021 ◽  
Vol 38 (4) ◽  
pp. 1013-1021
Author(s):  
Qian Zhang ◽  
Liyan Xiao ◽  
Yanfang Shi
Keyword(s):  
Image Processing ◽  
English Learners ◽  
Classification Model ◽  
Shape Features ◽  
Shape Identification ◽  
English Teaching ◽  
Video Frames ◽  
Proposed Model ◽  
English Speaking

Mouth shape identification helps oral English learners discover the features of their lip movements in English speaking, and correct their pronunciation more smoothly. So far, few scholars have applied image processing to identify mouth shape features of oral English learners. Most studies consider little about environmental factors, and ignore the changing mouth shape in pronunciation. Therefore, this paper explores the extraction and classification of mouth shape features in oral English teaching based on image processing. Firstly, an extraction and classification model were established for mouth shape features in oral English teaching. Then, the mouth shape images of oral English teaching were preprocessed. After that, the authors segmented the lips in oral English video frames based on neural network, extracted the lip boundaries from the said frames, and fitted them into curves. The proposed model was proved effective through experiments.

scholarly journals Classification of Asphalt Pavement Cracks Using Laplacian Pyramid-Based Image Processing and a Hybrid Computational Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Nhat-Duc Hoang
Keyword(s):  
Image Processing ◽  
Computational Intelligence ◽  
Asphalt Pavement ◽  
Classification Model ◽  
Laplacian Pyramid ◽  
Pavement Condition ◽  
Crack Patterns ◽  
Image Processing Techniques ◽  
Processing Techniques

To improve the efficiency of the periodic surveys of the asphalt pavement condition, this study puts forward an intelligent method for automating the classification of pavement crack patterns. The new approach relies on image processing techniques and computational intelligence algorithms. The image processing techniques of Laplacian pyramid and projection integral are employed to extract numerical features from digital images. Least squares support vector machine (LSSVM) and Differential Flower Pollination (DFP) are the two computational intelligence algorithms that are employed to construct the crack classification model based on the extracted features. LSSVM is employed for data classification. In addition, the model construction phase of LSSVM requires a proper setting of the regularization and kernel function parameters. This study relies on DFP to fine-tune these two parameters of LSSVM. A dataset consisting of 500 image samples and five class labels of alligator crack, diagonal crack, longitudinal crack, no crack, and transverse crack has been collected to train and verify the established approach. The experimental results show that the Laplacian pyramid is really helpful to enhance the pavement images and reveal the crack patterns. Moreover, the hybridization of LSSVM and DFP, named as DFP-LSSVM, used with the Laplacian pyramid at the level 4 can help us to achieve the highest classification accuracy rate of 93.04%. Thus, the new hybrid approach of DFP-LSSVM is a promising tool to assist transportation agencies in the task of pavement condition surveying.

Effective Classification of Melting Curve in Real-Time PCR based on Dynamic Filter-based Convolutional Neural Network

2021 ◽  
Vol 16 ◽  
Author(s):  
Di Gai ◽  
Xuanjing Shen ◽  
Haipeng Chen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Melting Curve ◽  
Classification Model ◽  
Proposed Model ◽  
Unbalanced Dataset ◽  
Manual Selection ◽  
Low Efficiency ◽  
Dynamic Filter

Background: The effective classification of the melting curve is conducive to measure the specificity of the amplified products and the influence of invalid data on subsequent experiments is excluded. Objective: In this paper, a convolutional neural network (CNN) classification model based on dynamic filter is proposed, which can categorize the number of peaks in the melting curve image and distinguish the pollution data represented by the noise peaks. Method: The main advantage of the proposed model is that it adopts the filter which changes with the input and uses the dynamic filter to capture more information in the image, making the network learning more accurate. In addition, the residual module is used to extract the characteristics of the melting curve, and the pooling operation is replaced with an atrous convolution to prevent the loss of context information. Result: In order to train the proposed model, a novel melting curve dataset is created, which includes a balanced dataset and an unbalanced dataset. The proposed method uses six classification-based assessment criteria to compare with seven representative methods based on deep learning. Experimental results show that proposed method is not only markedly outperforms the other state-of-the-art methods in accuracy, but also has much less running time. Conclusion: It evidently proves that the proposed method is suitable for judging the specificity of amplification products according to the melting curve. Simultaneously, it overcomes the difficulties of manual selection with low efficiency and artificial bias.

scholarly journals Classification of Indramayu’s Mangoes Based on Shape Features Using Neural Network and Rule Based Programming

2017 ◽  
Vol 1 ◽  
pp. 245-248
Author(s):  
Sumarudin Sumarudin ◽  
Iryanto Iryanto ◽  
Eka Ismantohadi
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Learning Approach ◽  
Shape Features ◽  
Rule Based ◽  
General Classification ◽  
Result Show ◽  
Better Than

Object classification using image processing simplifies the process. Many approaches have been used to classify the object. In general, classification of mangoes uses image of leaves. In this research, we do a slightly different approach using image of mango itself. Here, two kinds of method are used to classify the object.  Implementations of deep learning using neural network and rule based programming are used in the process. Comparative study of the methods are presented in the article. Our result show that accuracy of deep learning approach is better than the rule based programming. The accuracy is 80% and 8% for neural network and rule based programming, respectively.

scholarly journals Reliable Classification of FAQs with Spelling Errors Using an Encoder-Decoder Neural Network in Korean

2019 ◽  
Vol 9 (22) ◽  
pp. 4758 ◽  
Author(s):  
Youngjin Jang ◽  
Harksoo Kim
Keyword(s):  
Neural Network ◽  
Domain Knowledge ◽  
Classification Model ◽  
Word Embeddings ◽  
Spelling Errors ◽  
Proposed Model ◽  
Sentence Classification ◽  
Improved Performance ◽  
Reliable Classification

To resolve lexical disagreement problems between queries and frequently asked questions (FAQs), we propose a reliable sentence classification model based on an encoder-decoder neural network. The proposed model uses three types of word embeddings; fixed word embeddings for representing domain-independent meanings of words, fined-tuned word embeddings for representing domain-specific meanings of words, and character-level word embeddings for bridging lexical gaps caused by spelling errors. It also uses class embeddings to represent domain knowledge associated with each category. In the experiments with an FAQ dataset about online banking, the proposed embedding methods contributed to an improved performance of the sentence classification. In addition, the proposed model showed better performance (with an accuracy of 0.810 in the classification of 411 categories) than that of the comparison model.

Classification of breast cancer types based on deep learning approach

2021 ◽  
Vol 10 (1) ◽  
pp. 122-132
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Classification Model ◽  
Microscopic Image ◽  
Cancer Disease ◽  
Microscopic Images ◽  
Cad System ◽  
Proposed Model ◽  
Cancer Types

Breast cancer is one of the most serious diseases that affect women, so it must be discovered in the early stages to avoid complications such as redness of the skin, pain in the armpits or breast, and discharge from a nipple, possibly containing blood. Recently, the CAD system that is based on the classification of microscopic image play a vital rule to limit cancer disease and reduce cases. Microscopic image is the currently recommended image system used to detect cancer. A computer-aided diagnosis system will help radiologists to accurately detection of cancerous cells and achieve the best result. This paper proposes a deep learning technique that exploits CAD system features and microscopic images to fight breast cancer. The proposed technique builds a classification model based on the DenseNet-161 deep learning method. The proposed model classifies the microscopic images of breast cancer into benign with four types and malignant with four types. Our proposed technique is experimentally tested and the result confirmed that a proposed technique outperforms baseline techniques.

scholarly journals An Efficient Model for TV broadcast Audio Classification through InceptionV3 and ResNet50

2020 ◽  
Vol 9 (5) ◽  
pp. 2234-2238
Keyword(s):  
Classification Model ◽  
Audio Classification ◽  
Development Environment ◽  
Data Set ◽  
Novel Approach ◽  
Proposed Model ◽  
Audio Data ◽  
Data Surveillance ◽  
Voice Data

In the recent advancements of applications, one of the challenging task in many gadgets are incorporated, which is based on audio classification and recognition. A set of emotion detection after post-surgical issues, classification of various voice sequence, classification of random voice data, surveillance and speaker detection audio data act as a crucial input. Most of the audio data is inherent with the environmental noise or instrumental noise. Extracting the unique features from the audio data is very important to determine the speaker effectively. Such kind of a novel idea is evaluated here. The research focus is based on classification of TV broadcast audios in which the type of audio is being class separated through a novel approach. The design evaluates, the five different categories of audio data such as advertisement, news, songs, cartoon and sports from the data collected using the TV tuner card. The proposed design associated with python as a Development environment. The audio samples are converted to images using Spectrogram and then transfer learning is applied on the pretrained models ResNet50 and Inceptionv3 to extract the deep features and to classify the audio data. Inception V3 is compared here with the ResNet50 to get greater accuracy in classification. The pre-trained models are models that was trained on the ImageNet data set for a certain task and are used here to quick train the audio classification model on training set with high accuracy. The proposed model produces accuracy of 94% for Inceptionv3 which gives greater accuracy when compared with the ResNet50 which gives 93%. accuracy.

scholarly journals Efficient Fruits Classification Using Convolutional Neural Network

2021 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
ADNAN ADNAN ABIDIN ◽  
Hamzah Hamzah ◽  
Marselina Endah
Keyword(s):  
Good Accuracy ◽  
Research Topic ◽  
Classification Model ◽  
Classification Methods ◽  
Proposed Model ◽  
Traditional Classification ◽  
Promising Solution ◽  
Learning In Computer Vision ◽  
Effective Result

Classification of fruits is a growing research topic in image processing. Various papers propose various techniques to deal with the classification of apples. However, some traditional classification methods remain drawbacks to producing an effective result with the big dataset. Inspired by deep learning in computer vision, we propose a novel learning method to construct a classification model, which can classify types of apples quickly and accurately. To conduct our experiment, we collect datasets, do preprocessing, train our model, tune parameter settings to get the highest accuracy results, then test the model using new data. Based on the experimental results, the classification model of green apples and red apples can obtain good accuracy with little loss. Therefore, the proposed model can be a promising solution to deal with apple classification.

scholarly journals Recognition of Leaves in Videos

2020 ◽  
Vol 5 (8) ◽  
pp. 1-3
Keyword(s):  
Local Binary Pattern ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Leaf Color ◽  
Speeded Up Robust Features ◽  
Video Frames ◽  
Proposed Model ◽  
Paper Work ◽  
Key Frames

The aim of this paper work is to design a user independent framework for recognizing and classifying the leaves in a video frames. This project involves classification of leaves using KNN (K- Nearest Neighbor) as a classifier. SURF (Speeded-Up Robust Features) and LBP (Local Binary Pattern) features are used for extracting Scale ,Orientation etc., In the first step our proposed model can extract most distinguish key-frames and then from extracted key-frames it detects the leaf color and recognize the different class of leaves.

scholarly journals Classification of Indoor Human Fall Events Using Deep Learning

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 328
Author(s):  
Arifa Sultana ◽  
Kaushik Deb ◽  
Pranab Kumar Dhar ◽  
Takeshi Koshiba
Keyword(s):  
Physical Therapists ◽  
Binary Classification ◽  
Human Movement ◽  
Experimental Result ◽  
Video Frame ◽  
Human Beings ◽  
Video Frames ◽  
Alarm Systems ◽  
Proposed Model

Human fall identification can play a significant role in generating sensor based alarm systems, assisting physical therapists not only to reduce after fall effects but also to save human lives. Usually, elderly people suffer from various kinds of diseases and fall action is a very frequently occurring circumstance at this time for them. In this regard, this paper represents an architecture to classify fall events from others indoor natural activities of human beings. Video frame generator is applied to extract frame from video clips. Initially, a two dimensional convolutional neural network (2DCNN) model is proposed to extract features from video frames. Afterward, gated recurrent unit (GRU) network finds the temporal dependency of human movement. Binary cross-entropy loss function is calculated to update the attributes of the network like weights, learning rate to minimize the losses. Finally, sigmoid classifier is used for binary classification to detect human fall events. Experimental result shows that the proposed model obtains an accuracy of 99%, which outperforms other state-of-the-art models.

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