Application of deep learning and target visual detection in english vocabulary online teaching

2020 ◽  
Vol 39 (4) ◽  
pp. 5535-5545
Author(s):  
Jinying Cui
Keyword(s):  
Deep Learning ◽  
Word Frequency ◽  
Online Teaching ◽  
Visual Detection ◽  
Learning Algorithm ◽  
Linear Mapping ◽  
Word Formation ◽  
Future Research ◽  
Context Word ◽  
Training Time

The corpus software has many functions, such as keyword retrieval, context co-occurrence, word list generation and word frequency statistics. It can quickly and accurately provide various corpus and information, such as word-formation collocation, context, word frequency and so on. In this paper, the author analyzes the application of deep learning and target visual detection in English vocabulary online teaching. Deep learning is a kind of machine learning algorithm which includes multi-layer non-linear mapping and tries to obtain high-level abstract representation of data. By extracting features from information, the identifiable components in the image can be extracted. The results show that the application of corpus in College English vocabulary teaching can promote students’autonomous use of corpus in English vocabulary learning. The simulation experiment improves the performance of the system by choosing parameters, and the classification accuracy is more than 90%. Corpus can enable students to learn real and natural language and master natural collocation. At the same time, corpus can help students understand the semantic and pragmatic norms of words in communication and recognize the characteristics of register variants. Future research can use Map-reduce technology to accelerate the training process, save training time and test more hyperparameters.

scholarly journals Recent advances of deep learning in psychiatric disorders

2020 ◽  
Vol 3 (3) ◽  
pp. 202-213
Author(s):  
Lu Chen ◽  
Chunchao Xia ◽  
Huaiqiang Sun
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Psychiatric Disorders ◽  
Language Processing ◽  
Visual Recognition ◽  
Learning Algorithm ◽  
Future Research ◽  
Academic World ◽  
Recent Advances ◽  
Conventional Machine

ABSTRACT Deep learning (DL) is a recently proposed subset of machine learning methods that has gained extensive attention in the academic world, breaking benchmark records in areas such as visual recognition and natural language processing. Different from conventional machine learning algorithm, DL is able to learn useful representations and features directly from raw data through hierarchical nonlinear transformations. Because of its ability to detect abstract and complex patterns, DL has been used in neuroimaging studies of psychiatric disorders, which are characterized by subtle and diffuse alterations. Here, we provide a brief review of recent advances and associated challenges in neuroimaging studies of DL applied to psychiatric disorders. The results of these studies indicate that DL could be a powerful tool in assisting the diagnosis of psychiatric diseases. We conclude our review by clarifying the main promises and challenges of DL application in psychiatric disorders, and possible directions for future research.

A Broad Transfer Learning Algorithm for Classification of Bearing Faults

2020 ◽  
Author(s):  
Guokai Liu ◽  
Liang Gao ◽  
Weiming Shen ◽  
Andrew Kusiak
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Manufacturing Industry ◽  
Domain Adaptation ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Training Data ◽  
Training Time ◽  
Bearing Faults

Abstract Condition monitoring and fault diagnosis are of great interest to the manufacturing industry. Deep learning algorithms have shown promising results in equipment prognostics and health management. However, their success has been hindered by excessive training time. In addition, deep learning algorithms face the domain adaptation dilemma encountered in dynamic application environments. The emerging concept of broad learning addresses the training time and the domain adaptation issue. In this paper, a broad transfer learning algorithm is proposed for the classification of bearing faults. Data of the same frequency is used to construct one- and two-dimensional training data sets to analyze performance of the broad transfer and deep learning algorithms. A broad learning algorithm contains two main layers, an augmented feature layer and a classification layer. The broad learning algorithm with a sparse auto-encoder is employed to extract features. The optimal solution of a redefined cost function with a limited sample size to ten per class in the target domain offers the classifier of broad learning domain adaptation capability. The effectiveness of the proposed algorithm has been demonstrated on a benchmark dataset. Computational experiments have demonstrated superior efficiency and accuracy of the proposed algorithm over the deep learning algorithms tested.

scholarly journals Representation Learning for Motor Imagery Recognition with Deep Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 112
Author(s):  
Fangzhou Xu ◽  
Fenqi Rong ◽  
Yunjing Miao ◽  
Yanan Sun ◽  
Gege Dong ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Motor Imagery ◽  
Learning Algorithm ◽  
Representation Learning ◽  
Training Data ◽  
Gradient Boosting ◽  
Future Research ◽  
Deep Learning Algorithm ◽  
The Brain

This study describes a method for classifying electrocorticograms (ECoGs) based on motor imagery (MI) on the brain–computer interface (BCI) system. This method is different from the traditional feature extraction and classification method. In this paper, the proposed method employs the deep learning algorithm for extracting features and the traditional algorithm for classification. Specifically, we mainly use the convolution neural network (CNN) to extract the features from the training data and then classify those features by combing with the gradient boosting (GB) algorithm. The comprehensive study with CNN and GB algorithms will profoundly help us to obtain more feature information from brain activities, enabling us to obtain the classification results from human body actions. The performance of the proposed framework has been evaluated on the dataset I of BCI Competition III. Furthermore, the combination of deep learning and traditional algorithms provides some ideas for future research with the BCI systems.

scholarly journals Intelligent and Effective Intrusion Detection System using Machine Learning Algorithm

2020 ◽  
Vol 9 (6) ◽  
pp. 237-240
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Learning Algorithm ◽  
Detection System ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Data Set ◽  
Training Time

Intrusion Detection System observes the network traffic and identifies the attack and also inform the admin to corrective action. Powerful Intrusion Detection system is required for detection to various modern attack. There is need of efficient Intrusion Detection system .The focus of IDS research is the application of machine Learning and Deep Learning techniques. Projected work is combination of Deep Learning Technique in which Non Symmetric Deep Auto Encoder and Machine Learning Algorithm, Support Vector Machine Classifier is used to develop the Model. Stack power of the Non symmetric Deep Auto Encoder and Quickness with exactness of the SVM makes the Model very efficient. This Model not only improves the accuracy value but also improve recall and precision. It also cause the reduction of training time .To evaluate the performance of the Model and do the analysis the special Data set which are used are KDD CUP and NSL KDD Dataset.

scholarly journals Sensors and Artificial Intelligence Methods and Algorithms for Human–Computer Intelligent Interaction: A Systematic Mapping Study

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 20
Author(s):  
Boštjan Šumak ◽  
Saša Brdnik ◽  
Maja Pušnik
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Sensor Technology ◽  
Future Research ◽  
Systematic Mapping Study ◽  
Human Communication ◽  
Mapping Study ◽  
Systematic Mapping ◽  
Intelligent Interaction

To equip computers with human communication skills and to enable natural interaction between the computer and a human, intelligent solutions are required based on artificial intelligence (AI) methods, algorithms, and sensor technology. This study aimed at identifying and analyzing the state-of-the-art AI methods and algorithms and sensors technology in existing human–computer intelligent interaction (HCII) research to explore trends in HCII research, categorize existing evidence, and identify potential directions for future research. We conduct a systematic mapping study of the HCII body of research. Four hundred fifty-four studies published in various journals and conferences between 2010 and 2021 were identified and analyzed. Studies in the HCII and IUI fields have primarily been focused on intelligent recognition of emotion, gestures, and facial expressions using sensors technology, such as the camera, EEG, Kinect, wearable sensors, eye tracker, gyroscope, and others. Researchers most often apply deep-learning and instance-based AI methods and algorithms. The support sector machine (SVM) is the most widely used algorithm for various kinds of recognition, primarily an emotion, facial expression, and gesture. The convolutional neural network (CNN) is the often-used deep-learning algorithm for emotion recognition, facial recognition, and gesture recognition solutions.

scholarly journals Developing Ethiopian Yirgacheffe Coffee Grading Model using a Deep Learning Classifier

2020 ◽  
Vol 9 (4) ◽  
pp. 3303-3309
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Learning Algorithm ◽  
Promising Result ◽  
Noise Removal ◽  
Classification Model ◽  
Future Research ◽  
Coffee Bean ◽  
Coffee Beans ◽  
Grade 3

Coffee grading is the main procedure in producing homogenous local commercial fair system of pricing in the market and export. Grading coffee is a difficult task during the inspection, because it requires training and experience of the experts. In order to tackle grading difficulties in coffee producing industries and corporates have been employed and trained experts. Even if, those experts do not work effectively due to tiredness, costly, time consuming, inconsistency, bias and other factors. Digital image processing techniques based on automatically extracted features have been explored to classify Ethiopian coffee to corresponding quality grade labels. Samples of those coffee beans were taken from Yirgacheffe Coffee Farmers’ Cooperative Union. On average, 228 images were taken from each of three grade values or levels (grade 1, grade 2 and grade 3). The total number of images taken was 684 containing 6138 coffee beans. To extract coffee bean features and build a classification model for grading coffee, the state of art deep learning algorithm called convolutional Neural Network was used. Base on the experimental results classification accuracy obtained with testing coffee bean images for grade 1, grade 2 and grade 3 coffee beans was 99.51%, 97.56%, and 98.04%, respectively with the overall classification accuracy of 98.38%. This shows a promising result, even if, images are captured under the challenging condition without laboratory setup, such as illumination, different resolution, shadow and orientation which affects greatly the performance of the classifier and hence they are the future research direction that needs further investigations of noise removal techniques.

scholarly journals From Auto-encoders to Capsule Networks: A Survey

2021 ◽  
Vol 229 ◽  
pp. 01003
Author(s):  
Omaima El Alaoui-Elfels ◽  
Taoufiq Gadi
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Three Dimensional ◽  
Future Research ◽  
Two Dimensional ◽  
One Dimensional ◽  
New Generation ◽  
Open Issues

Convolutional Neural Networks are a very powerful Deep Learning structure used in image processing, object classification and segmentation. They are very robust in extracting features from data and largely used in several domains. Nonetheless, they require a large number of training datasets and relations between features get lost in the Max-pooling step, which can lead to a wrong classification. Capsule Networks(CapsNets) were introduced to overcome these limitations by extracting features and their pose using capsules instead of neurons. This technique shows an impressive performance in one-dimensional, two-dimensional and three-dimensional datasets as well as in sparse datasets. In this paper, we present an initial understanding of CapsNets, their concept, structure and learning algorithm. We introduce the progress made by CapsNets from their introduction in 2011 until 2020. We compare different CapsNets series architectures to demonstrate strengths and challenges. Finally, we quote different implementations of Capsule Networks and show their robustness in a variety of domains. This survey provides the state-of-theartof Capsule Networks and allows other researchers to get a clear view of this new field. Besides, we discuss the open issues and the promising directions of future research, which may lead to a new generation of CapsNets.

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