scholarly journals Shoulder Implant Manufacturer Detection by Using Deep Learning: Proposed Channel Selection Layer

Coatings ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 346
Author(s):  
Atınç Yılmaz
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Treatment Process ◽  
Total Shoulder Arthroplasty ◽  
Channel Selection ◽  
Learning Method ◽  
Processing Times ◽  
Accuracy And Precision ◽  
Speed Up ◽  
Feature Filters

Total Shoulder Arthroplasty (TSA) is the process of replacing the damaged ball and socket joint in the shoulder with a prosthesis made with polyethylene and metal components. After this procedure, intervention may be required as a result of damage to the prosthesis, except for the need for an examination regarding the prosthesis at certain periods. If the patient does not have information about the model and manufacturer of the prosthesis, the treatment process is delayed. Artificial intelligence-assisted systems can speed up the treatment process by classifying the manufacturer and model of the prosthesis. In this study, artificial intelligence methods were applied to classify shoulder implants using X-Ray images. The model and manufacturer of the prosthesis is detected by using the proposed deep learning method. Besides, the most commonly used machine learning classifiers were applied for the same problem to compare the results and show the effectiveness of the proposed method. In addition, the accuracy and precision analysis and measurements of the processing times for the applied methods were performed to reveal the performance, accuracy, and efficiency of the study. In order to measure the performance of the proposed method, it was compared with studies on the same problem in the literature. As a result of the comparison, it was found that the proposed method, with an accuracy rate of 97.2%, performed better than the other studies. In the study, the implant manufacturer and model are classified in order to carry out the implant surgery process in the best way with the proposed deep learning model. With the success of the proposed system, the applicability of this model in similar prosthesis classifications has been shown. Differently from the studies in the literature, the channel selection formula is presented in the proposed deep learning method recommended for selecting the most distinctive feature filters.

scholarly journals Risk Assessment for Cardiac Arrest : A Deep Learning Approach With Channel Selection

2020 ◽  
Author(s):  
Atınç Yılmaz
Keyword(s):  
Risk Assessment ◽  
Cardiac Arrest ◽  
Deep Learning ◽  
Heart Attack ◽  
Human Life ◽  
Numerical Data ◽  
Channel Selection ◽  
Heart Attacks ◽  
Precautionary Measures ◽  
Feature Filters

Abstract Background: Risk of developing cardiovascular diseases, in the world, is increasing day by day. Accordingly, the number of deaths due to heart attacks is quite remarkable. Early risk assessment and diagnosis of heart disease are vital to prevent heart attacks by providing effective treatment planning and evaluation of outcomes. When a patient with high risk of heart attack is not treated correctly, chances of survival may reduce dramatically. For this reason, artificial intelligence-assisted systems can support the decision of doctors and it can anticipate risk without fatal consequences.Methods: In this study, individuals who has heart attack risks are predicted by using a proposed CNNs method. A set of medical data from patients with heart attacks and healthy individuals are provided from the UCI database. Reinforced deep learning and ANFIS architectures are also applied to the same problem in order to compare the results and put forth the efficiency of proposed method. In addition, ROC analysis and measurements of processing times for the applied methods were performed to reveal the performance, accuracy and efficiency of the study.Results: The proposed CNNs method and other methods are tested and evaluated. The accuracy performance of the methods were 94.34% for the proposed CNNs method, 91.58% for the ANFIS, and 92.66% for the deep multilayer neural network. Highest accuracy has been obtained by using the proposed CNNs method, which is 94.34%. The reasons why the proposed CNNs method is better than other methods is the use of channel selection layer, the number of convolution and pooling layers, the filter size used in these layers, and the functions used in the loss and activation layers.Conclusions: In the study, the channel selection formula is introduced in the proposed CNNs model to select the most discriminatory feature filters. Besides, the applicability of proposed CNNs method with images obtained from numerical data has been demonstrated. With the early prediction system proposed, it is now possible to take precautionary measures against possible cardiac arrest. In this study; a new method based on CNNs is proposed for early detection of possible heart attack, which is a great risk for human life. Different from studies in the literature, the channel selection formula is presented in the proposed CNNs method to select the most selective feature filters. Besides differently, it was used in the proposed CNNs method by converting all numerical data from dataset into 2D images. Afterwards, to show whether this the proposed method is applicable or not, the dataset which is numerical form was applied to other methods and compared.

scholarly journals An Intelligent Multi-View Active Learning Method Based on a Double-Branch Network

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 901
Author(s):  
Fucong Liu ◽  
Tongzhou Zhang ◽  
Caixia Zheng ◽  
Yuanyuan Cheng ◽  
Xiaoli Liu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Active Learning ◽  
Sample Selection ◽  
Training Dataset ◽  
Learning Method ◽  
Active Learning Method ◽  
Level Information ◽  
The Stability ◽  
Different Characteristics

Artificial intelligence is one of the most popular topics in computer science. Convolutional neural network (CNN), which is an important artificial intelligence deep learning model, has been widely used in many fields. However, training a CNN requires a large amount of labeled data to achieve a good performance but labeling data is a time-consuming and laborious work. Since active learning can effectively reduce the labeling effort, we propose a new intelligent active learning method for deep learning, which is called multi-view active learning based on double-branch network (MALDB). Different from most existing active learning methods, our proposed MALDB first integrates two Bayesian convolutional neural networks (BCNNs) with different structures as two branches of a classifier to learn the effective features for each sample. Then, MALDB performs data analysis on unlabeled dataset and queries the useful unlabeled samples based on different characteristics of two branches to iteratively expand the training dataset and improve the performance of classifier. Finally, MALDB combines multiple level information from multiple hidden layers of BCNNs to further improve the stability of sample selection. The experiments are conducted on five extensively used datasets, Fashion-MNIST, Cifar-10, SVHN, Scene-15 and UIUC-Sports, the experimental results demonstrate the validity of our proposed MALDB.

Image intelligent automatic processing analysis based on artificial intelligence vision generator

2020 ◽  
pp. 1-10
Author(s):  
Ruijuan Wang ◽  
Wei Zhuo
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Deep Learning ◽  
Human Error ◽  
Processing System ◽  
Image Data ◽  
Visual Image ◽  
Automated Analysis ◽  
Learning Method ◽  
Deep Learning Model

The image intelligent processing analysis technology uses a computer to imitate and execute some intellectual functions of the human brain, and realizes an image processing system with artificial intelligence, that is, an image processing analysis technology is an understanding of an image. The degree of intelligent automated analysis and processing is low, many operations need to be done manually, causing human error, inaccurate detection, and time-consuming and laborious. Deep learning method can extract features step by step in the original image from the bottom to the top. Therefore, based on feature analysis technology, this paper uses the deep learning method to intelligently and automatically analyse the visual image. This method only needs to send the image into the system, and then the manual analysis is not needed, and the analysis result of the final image can be obtained. The process is completely intelligent and automatically processed. First, improve the deep learning model and use massive image data to choose and optimize parameters. Results indicate that our method not only automatically derives the semantic information of the image, but also accurately understands the image accurately and improve the work efficiency.

scholarly journals Application of artificial intelligence principles in mechanical engineering

2018 ◽  
Vol 244 ◽  
pp. 01027 ◽  
Author(s):  
Ivan Zajačko ◽  
Tomáš Gál ◽  
Zuzana Ságová ◽  
Vasyl Mateichyk ◽  
Dariusz Wiecek
Keyword(s):  
Artificial Intelligence ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Defect Detection ◽  
Mechanical Engineering ◽  
Technical Diagnostics ◽  
Learning Method ◽  
Special Meaning

The article deals with methods of Artificial Intelligence and their utilisation in technical diagnostics. Special meaning will be given on methods such as Deep learning. The deep learning method seems to be a very good candidate for defect detection and pattern recognition. The method was applied for technical diagnostic in automotive factory and the problem will be described in the paper.

scholarly journals Earth Observation and Artificial Intelligence for Improving Safety to Navigation in Canada Low-Impact Shipping Corridors

2020 ◽  
Vol 9 (6) ◽  
pp. 383
Author(s):  
René Chénier ◽  
Mesha Sagram ◽  
Khalid Omari ◽  
Adam Jirovec
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Satellite Imagery ◽  
New Technologies ◽  
Earth Observation ◽  
Machine Learning Techniques ◽  
The Arctic ◽  
Marine Transportation ◽  
Processing Times ◽  
The Government

In 2014, through the World-Class Tanker Safety System (WCTSS) initiative, the Government of Canada launched the Northern Marine Transportation Corridors (NMTC) concept. The corridors were created as a strategic framework to guide Federal investments in marine transportation in the Arctic. With new government investment, under the Oceans Protection Plan (OPP), the corridors initiative, known as the Northern Low-Impact Shipping Corridors, will continue to be developed. Since 2016, the Canadian Hydrographic Service (CHS) has been using the corridors as a key layer in a geographic information system (GIS) model known as the CHS Priority Planning Tool (CPPT). The CPPT helps CHS prioritize its survey and charting efforts in Canada’s key traffic areas. Even with these latest efforts, important gaps in the surveys still need to be filled in order to cover the Canadian waterways. To help further develop the safety to navigation and improve survey mission planning, CHS has also been exploring new technologies within remote sensing. Under the Government Related Initiatives Program (GRIP) of the Canadian Space Agency (CSA), CHS has been investigating the potential use of Earth observation (EO) data to identify potential hazards to navigation that are not currently charted on CHS products. Through visual interpretation of satellite imagery, and automatic detection using artificial intelligence (AI), CHS identified several potential hazards to navigation that had previously gone uncharted. As a result, five notices to mariners (NTMs) were issued and the corresponding updates were applied to the charts. In this study, two AI approaches are explored using deep learning and machine learning techniques: the convolution neural network (CNN) and random forest (RF) classification. The study investigates the effectiveness of the two models in identifying shoals in Sentinel-2 and WorldView-2 satellite imagery. The results show that both CNN and RF models can detect shoals with accuracies ranging between 79 and 94% over two study sites; however, WorldView-2 images deliver results with higher accuracy and lower omission errors. The high processing times of using high-resolution imagery and training a deep learning model may not be necessary in order to quickly scan images for shoals; but training a CNN model with a large training set may lead to faster processing times without the need to train individual images.

scholarly journals Research on Deep Learning-Based Financial Risk Prediction

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Boning Huang ◽  
Junkang Wei
Keyword(s):  
Deep Learning ◽  
Social Network ◽  
Risk Prediction ◽  
Classification Accuracy ◽  
Financial Risk ◽  
Financial Analysis ◽  
Extraction Process ◽  
Sentiment Classification ◽  
Learning Method ◽  
Speed Up

Financial text-based risk prediction is an important subset for financial analysis. Through automatic analysis of public financial comments, fundamentals on current financial expectations can be evaluated. A deep learning method for financial risk prediction based on sentiment classification is proposed in this paper. The proposed method consists of two steps. Firstly, the abstract of the financial message is extracted according to the seq2seq model. During the extraction process, the seq2seq model can cope with the situation of different input message lengths. After the abstraction, invalid information in the financial messages can be effectively filtered, thus accelerating the subsequent sentiment classification step. The sentiment classification step is performed through the GRU model according to the abstracted texts. The proposed method has the following advantages: (1) it can handle financial messages of different lengths; (2) it can filter out the invalid information of financial messages; (3) because the extracted abstract is more refined, it can speed up the subsequent sentiment classification step; and (4) it has better sentiment classification accuracy. The proposed method in this paper is then verified through financial message dataset from the financial social network StockTwits. By comparing the classification performances, it can be seen that compared with the classical SVM and LSTM methods, the proposed method in this paper can improve the accuracy of sentiment classification by 5.57% and 2.58%, respectively.

scholarly journals Research on the Application of Deep Learning in Artificial Intelligence Courses

2021 ◽  
Vol 5 (6) ◽  
pp. 14-18
Author(s):  
Ruijue Wang
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Basic Knowledge ◽  
Learning Method ◽  
Practical Skills ◽  
Classroom Learning ◽  
Important Goal ◽  
Teaching Reform ◽  
Key Points ◽  
New Learning

With the in-depth reform of education, taking students as the center, letting students master the basic knowledge of the theory, but also training students’ practical skills, is an important goal of the current artificial intelligence curriculum teaching reform. As a new learning method, deep learning is applied to the teaching of artificial intelligence courses, which can not only give play to students’ subjective initiative, but also improve the efficiency of students’ classroom learning. In order to explore the specific application of deep learning in the teaching of artificial intelligence courses, this article analyzes the key points of the application of deep learning in artificial intelligence courses. In addition, further explores the application strategies of deep learning in artificial intelligence courses. As it aims to provide some useful references to improve the actual efficiency of artificial intelligence course teaching.

scholarly journals 297 Deep Ackerman a novel deep learning method to develop dermatopathology diagnosis by artificial intelligence

2018 ◽  
Vol 138 (5) ◽  
pp. S51
Author(s):  
Y. Ota ◽  
K. Shido ◽  
K. Kojimako ◽  
K. Yamasaki ◽  
M. Nagasaki ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Learning Method
Sign in / Sign up

Export Citation Format

Share Document