Accuracy analysis of Japanese machine translation based on machine learning and image feature retrieval

2020 ◽  
pp. 1-12
Author(s):  
Gang Song
Keyword(s):  
Machine Learning ◽  
Machine Translation ◽  
Character Recognition ◽  
Image Data ◽  
Image Features ◽  
Image Feature ◽  
Learning Technology ◽  
Japanese Character ◽  
Knowledge Support ◽  
Translation Methods

At present, there are still many deficiencies in Chinese-Japanese machine translation methods, the processing of corpus information is not deep enough, and the translation process lacks rich language knowledge support. In particular, the recognition accuracy of Japanese characters is not high. Based on machine learning technology, this study combines image feature retrieval technology to construct a Japanese character recognition model and uses Japanese character features as the algorithm recognition object. Moreover, this study expands image features by generating a brightness enhancement function using a bilateral grid. In order to exclude the influence of the edge and contour of the image scene on the analysis of the image source, the brightness value of the HDR image is used instead of the pixel value of the image as the image data. In addition, this research designs experiments to study the translation effects of this research model. The research results show that the model proposed in this paper has certain effects and can provide theoretical references for subsequent related research.

scholarly journals Automobile tire life prediction based on image processing and machine learning technology

2021 ◽  
Vol 13 (3) ◽  
pp. 168781402110027
Author(s):  
Jianchen Zhu ◽  
Kaixin Han ◽  
Shenlong Wang
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Life Prediction ◽  
Traffic Accidents ◽  
Confusion Matrix ◽  
Small Sample ◽  
Image Feature ◽  
Engineering Practice ◽  
Learning Technology ◽  
K Nearest Neighbor

With economic growth, automobiles have become an irreplaceable means of transportation and travel. Tires are important parts of automobiles, and their wear causes a large number of traffic accidents. Therefore, predicting tire life has become one of the key factors determining vehicle safety. This paper presents a tire life prediction method based on image processing and machine learning. We first build an original image database as the initial sample. Since there are usually only a few sample image libraries in engineering practice, we propose a new image feature extraction and expression method that shows excellent performance for a small sample database. We extract the texture features of the tire image by using the gray-gradient co-occurrence matrix (GGCM) and the Gauss-Markov random field (GMRF), and classify the extracted features by using the K-nearest neighbor (KNN) classifier. We then conduct experiments and predict the wear life of automobile tires. The experimental results are estimated by using the mean average precision (MAP) and confusion matrix as evaluation criteria. Finally, we verify the effectiveness and accuracy of the proposed method for predicting tire life. The obtained results are expected to be used for real-time prediction of tire life, thereby reducing tire-related traffic accidents.

Split Process Cluster: A Distributed Computing Platform for Edge Extraction of Massive Remote Sensing Images

2014 ◽  
Vol 513-517 ◽  
pp. 2268-2272
Author(s):  
Fu Chao Cheng ◽  
Fang Miao ◽  
Wen Hui Yang
Keyword(s):  
Distributed Computing ◽  
Spatial Clustering ◽  
Image Data ◽  
Image Features ◽  
Extraction Rate ◽  
Image Feature ◽  
Edge Extraction ◽  
Computing Platform ◽  
Redundant Data ◽  
Distributed Computing Platform

In existed distributed edge extraction method based on MapReduce, the inappropriate dataset split algorithms leaded to the loss problem of image features in result. We presented a distributed computing platform called Split Process Cluster (SPC) to resolve this problem. In SPC, the images are partitioned with the resilient image pyramid model (RIP), a multi-layer and redundant data structure we presented earlier, to ensure the integrity of original image features. And SPC packages the image data to the form of Key-Value pairs, which could be processed through Hadoop, and reduces the results with density-based spatial clustering of applications with noise (DBSCAN) algorithm. Compared to traditional method, the extraction rate of image feature by using SPC has been improved, which indicates that using SPC is an efficient way to improve the extraction rate of distributed edge extraction.

scholarly journals Research on Image Recognition of Electrical Equipment based on Deconvolution Feature Extraction

2021 ◽  
Vol 257 ◽  
pp. 01019
Author(s):  
Zhe Li ◽  
Haifeng Su
Keyword(s):  
Machine Learning ◽  
Recognition Accuracy ◽  
Image Data ◽  
Electrical Equipment ◽  
Learning Technology ◽  
Infrared Images ◽  
Circuit Breakers ◽  
Dual Mode ◽  
Weighted Fusion ◽  
Improved Model

Based on machine learning technology and combining the operation of machine learning from the idea of neural network, this paper focuses on the classification and recognition of image data of transformers, circuit breakers and isolation switches in substations. Firstly, the image enhancement is carried out on the basis of the original image, which simulates the possible scenes in reality. Secondly, using the dual-mode a deconvolutional network to capture significant features from in-depth visible and infrared images. Furthermore, all these features are subjected to the program to conduct transfer learning and weighted fusion. The dual-mode deconvolutional network (DMDN) extracts and highlights the features of the electrical equipment. Compared to traditional model, the recognition accuracy of the improved model is reached at 99.17%.

A combined approach of convolutional neural networks and machine learning for visual fault classification in photovoltaic modules

2021 ◽  
pp. 1748006X2110203
Author(s):  
Sridharan Naveen Venkatesh ◽  
Vaithiyanathan Sugumaran
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Performance Comparison ◽  
Image Features ◽  
Image Feature ◽  
Fault Classification ◽  
Photovoltaic Modules ◽  
Accurate Performance ◽  
Deep Cnn

Fault diagnosis plays a significant role in enhancing the useful lifetime, power output, and reliability of photovoltaic modules (PVM). Visual faults such as burn marks, delamination, discoloration, glass breakage, and snail trails make detection of faults difficult under harsh environmental conditions. Various researchers have made several attempts to identify visual faults in a PVM. However, much of the previous studies were centered on the identification and analysis of limited number of faults. This article presents the use of a deep convolutional neural network (CNN) to extract image features and perform an effective classification of faults by machine learning (ML) algorithms. In contrast to the present-day work, five different fault conditions were considered in the study. The proposed solution consists of three phases, to effectively analyze various PVM defects. First, the module images are acquired using unmanned aerial vehicles (UAVs) and data augmentation is performed to generate a uniform dataset. Afterward, a pre-trained deep CNN is adopted for image feature extraction. Finally, the extracted image features are classified with the help of various ML classifiers. The final results show the effectiveness of pre-trained deep CNN and accurate performance of ML classifiers. The best-in-class ML classifier for multiple fault classification is suggested based on the performance comparison.

scholarly journals Multiple Kernel Learning approach for Medical Image Analysis

2017 ◽  
Author(s):  
Nisar Wani ◽  
Khalid Raza
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Multiple Kernel Learning ◽  
Image Data ◽  
Heterogeneous Data ◽  
Image Features ◽  
Kernel Learning ◽  
Multiple Kernel

ABSTRACTComputer aided diagnosis is gradually making its way into the domain of medical research and clinical diagnosis. With field of radiology and diagnostic imaging producing petabytes of image data. Machine learning tools, particularly kernel based algorithms seem to be an obvious choice to process and analyze this high dimensional and heterogeneous data. In this chapter, after presenting a breif description about nature of medical images, image features and basics in machine learning and kernel methods, we present the application of multiple kernel learning algorithms for medical image analysis.

Research on fuzzy English automatic recognition and human-computer interaction based on machine learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5809-5819 ◽  
Author(s):  
Yuqin Jing
Keyword(s):  
Machine Learning ◽  
Character Recognition ◽  
Model Performance ◽  
Automatic Recognition ◽  
Learning Technology ◽  
Related Research ◽  
Existing Problems ◽  
Recognition Efficiency ◽  
Intelligent Recognition ◽  
Model Support

Fuzzy English recognition is affected by many factors, which leads to certain accuracy problems in intelligent recognition results. In order to improve the automatic recognition efficiency of fuzzy English, based on machine learning technology, this study constructs a neural network model. At the same time, this paper analyzes the research status and existing problems of handwritten character recognition, analyzes the model, and adopts multiple modules for automatic English recognition. In addition, the system is built on the basis of algorithms and model support, which makes fuzzy English recognition intelligent. Finally, in order to study the algorithm and model performance, the fuzzy English recognition is carried out through experiments. The research shows that the model constructed in this paper has certain recognition effect, which can be applied to practice, and can provide theoretical reference for subsequent related research.

scholarly journals A Deep Learning Approach for Retinal Image Feature Extraction

2021 ◽  
Vol 29 (4) ◽  
Author(s):  
Mohammed Enamul Hoque ◽  
Kuryati Kipli ◽  
Tengku Mohd Afendi Zulcaffle ◽  
Abdulrazak Yahya Saleh Al-Hababi ◽  
Dayang Azra Awang Mat ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Retinal Image ◽  
Image Data ◽  
Disease Diagnosis ◽  
Image Features ◽  
Image Feature ◽  
Diagnostic Tools ◽  
Retinal Images ◽  
Retinal Microvasculature

Retinal image analysis is crucially important to detect the different kinds of life-threatening cardiovascular and ophthalmic diseases as human retinal microvasculature exhibits remarkable abnormalities responding to these disorders. The high dimensionality and random accumulation of retinal images enlarge the data size, that creating complexity in managing and understating the retinal image data. Deep Learning (DL) has been introduced to deal with this big data challenge by developing intelligent tools. Convolutional Neural Network (CNN), a DL approach, has been designed to extract hierarchical image features with more abstraction. To assist the ophthalmologist in eye screening and ophthalmic disease diagnosis, CNN is being explored to create automatic systems for microvascular pattern analysis, feature extraction, and quantification of retinal images. Extraction of the true vessel of retinal microvasculature is significant for further analysis, such as vessel diameter and bifurcation angle quantification. This study proposes a retinal image feature, true vessel segments extraction approach exploiting the Faster RCNN. The fundamental Image Processing principles have been employed for pre-processing the retinal image data. A combined database assembling image data from different publicly available databases have been used to train, test, and evaluate this proposed method. This proposed method has obtained 92.81% sensitivity and 63.34 positive predictive value in extracting true vessel segments from the top first tier of colour retinal images. It is expected to integrate this method into ophthalmic diagnostic tools with further evaluation and validation by analysing the performance.

scholarly journals Helmet Detection Based on Deep Learning and Random Forest on UAV for Power Construction Safety

2021 ◽  
Vol 25 (1) ◽  
pp. 40-49
Author(s):  
Guobing Yan ◽  
◽  
Qiang Sun ◽  
Jianying Huang ◽  
Yonghong Chen
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Convolutional Neural Network ◽  
Recognition Algorithm ◽  
Image Features ◽  
Image Feature ◽  
Feature Extraction Method ◽  
Power Construction

Image recognition is one of the key technologies for worker’s helmet detection using an unmanned aerial vehicle (UAV). By analyzing the image feature extraction method for workers’ helmet detection based on convolutional neural network (CNN), a double-channel convolutional neural network (DCNN) model is proposed to improve the traditional image processing methods. On the basis of AlexNet model, the image features of the worker can be extracted using two independent CNNs, and the essential image features can be better reflected considering the abstraction degree of the features. Combining a traditional machine learning method and random forest (RF), an intelligent recognition algorithm based on DCNN and RF is proposed for workers’ helmet detection. The experimental results show that deep learning (DL) is closely related to the traditional machine learning methods. Moreover, adding a DL module to the traditional machine learning framework can improve the recognition accuracy.

A Survey of Access Methods for Image Data

1997 ◽  
Vol 07 (03) ◽  
pp. 305-319 ◽  
Author(s):  
Chee-Yong Chan ◽  
Louis F. Pau
Keyword(s):  
Interface Design ◽  
Image Data ◽  
Difficult Problem ◽  
Image Features ◽  
Image Feature ◽  
Content Based Retrieval ◽  
Access Methods ◽  
Similarity Retrieval ◽  
Efficient Access ◽  
Efficient Retrieval

The feasibility of storing large amount of digital images and the pictorial-oriented nature of many applications have attracted much interest in pictorial information systems. An important issue in such systems is the efficient retrieval of image based on its contents. Supporting content-based retrieval of image data is a difficult problem and embraces different technologies including image processing, user interface design, and database management. To provide efficient content-based retrieval, efficient access methods based on image features are required. This paper presents a survey of indexing techniques that support content-based retrieval of images. In particular, we examine access methods for three types of image features, namely, object shape, semantic objects, and spatial relationships among semantic objects. For each image feature, we look at how various approaches represent and organize the image features and how they support similarity retrieval.

Machine learning improves prediction of delayed cerebral ischemia in patients with subarachnoid hemorrhage

2018 ◽  
Vol 11 (5) ◽  
pp. 497-502 ◽  
Author(s):  
Lucas Alexandre Ramos ◽  
Wessel E van der Steen ◽  
Renan Sales Barros ◽  
Charles B L M Majoie ◽  
Rene van den Berg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Subarachnoid Hemorrhage ◽  
Cerebral Ischemia ◽  
Regression Models ◽  
Aneurysmal Subarachnoid Hemorrhage ◽  
Delayed Cerebral Ischemia ◽  
Image Data ◽  
Image Features ◽  
Logistic Regression Models

Background and purposeDelayed cerebral ischemia (DCI) is a severe complication in patients with aneurysmal subarachnoid hemorrhage. Several associated predictors have been previously identified. However, their predictive value is generally low. We hypothesize that Machine Learning (ML) algorithms for the prediction of DCI using a combination of clinical and image data lead to higher predictive accuracy than previously applied logistic regressions.Materials and methodsClinical and baseline CT image data from 317 patients with aneurysmal subarachnoid hemorrhage were included. Three types of analysis were performed to predict DCI. First, the prognostic value of known predictors was assessed with logistic regression models. Second, ML models were created using all clinical variables. Third, image features were extracted from the CT images using an auto-encoder and combined with clinical data to create ML models. Accuracy was evaluated based on the area under the curve (AUC), sensitivity and specificity with 95% CI.ResultsThe best AUC of the logistic regression models for known predictors was 0.63 (95% CI 0.62 to 0.63). For the ML algorithms with clinical data there was a small but statistically significant improvement in the AUC to 0.68 (95% CI 0.65 to 0.69). Notably, aneurysm width and height were included in many of the ML models. The AUC was highest for ML models that also included image features: 0.74 (95% CI 0.72 to 0.75).ConclusionML algorithms significantly improve the prediction of DCI in patients with aneurysmal subarachnoid hemorrhage, particularly when image features are also included. Our experiments suggest that aneurysm characteristics are also associated with the development of DCI.

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