scholarly journals Artificial neural networks in cardiology: analysis of graphic data

2022 ◽  
Vol 20 (4) ◽  
pp. 193-204
Author(s):  
P. S. Onishchenko ◽  
K. Yu. Klyshnikov ◽  
E. A. Ovcharenko
Keyword(s):  
Neural Networks ◽  
Cardiac Surgery ◽  
Artificial Neural Networks ◽  
Convolutional Neural Networks ◽  
Limiting Factors ◽  
Scientific Databases ◽  
Existing Problems ◽  
Graphic Data ◽  
Area Of Interest ◽  
Artificial Neural

Aim. To consider application of convolutional neural networks for processing medical images in various fields of cardiology and cardiac surgery using publications from 2016 to 2019 as an example.Materials and methods. In the study, we used the following scientific databases: PubMed Central, ArXiv, ResearchGate. The cited publications were grouped by the area of interest (heart, aorta, carotid arteries).Results. The general principle of work of the technology under consideration was described, the results were shown, and the main areas of application of this technology in the studies under consideration were described. For most of the studies, sample sizes were given. The author’s view on the development of convolutional neural networks in medicine was presented and some limiting factors for their distribution were listed.Conclusion. A brief overview shows possible areas of application of convolutional neural networks in the fields of cardiology and cardiac surgery. Without denying the existing problems, this type of artificial neural networks may help many doctors and researchers in the future. 

scholarly journals KLASIFIKASI BATIK RIAU DENGAN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORKS (CNN)

2020 ◽  
Vol 9 (1) ◽  
pp. 7-10
Author(s):  
Hendry Fonda
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
18Th Century ◽  
The Public ◽  
Artificial Neural ◽  
The Difference ◽  
Fully Connected

ABSTRACT Riau batik is known since the 18th century and is used by royal kings. Riau Batik is made by using a stamp that is mixed with coloring and then printed on fabric. The fabric used is usually silk. As its development, comparing Javanese  batik with riau batik Riau is very slowly accepted by the public. Convolutional Neural Networks (CNN) is a combination of artificial neural networks and deeplearning methods. CNN consists of one or more convolutional layers, often with a subsampling layer followed by one or more fully connected layers as a standard neural network. In the process, CNN will conduct training and testing of Riau batik so that a collection of batik models that have been classified based on the characteristics that exist in Riau batik can be determined so that images are Riau batik and non-Riau batik. Classification using CNN produces Riau batik and not Riau batik with an accuracy of 65%. Accuracy of 65% is due to basically many of the same motifs between batik and other batik with the difference lies in the color of the absorption in the batik riau. Kata kunci: Batik; Batik Riau; CNN; Image; Deep Learning   ABSTRAK   Batik Riau dikenal sejak abad ke 18 dan digunakan oleh bangsawan raja. Batik Riau dibuat dengan menggunakan cap yang dicampur dengan pewarna kemudian dicetak di kain. Kain yang digunakan biasanya sutra. Seiring perkembangannya, dibandingkan batik Jawa maka batik Riau sangat lambat diterima oleh masyarakat. Convolutional Neural Networks (CNN) merupakan kombinasi dari jaringan syaraf tiruan dan metode deeplearning. CNN terdiri dari satu atau lebih lapisan konvolutional, seringnya dengan suatu lapisan subsampling yang diikuti oleh satu atau lebih lapisan yang terhubung penuh sebagai standar jaringan syaraf. Dalam prosesnya CNN akan melakukan training dan testing terhadap batik Riau sehingga didapat kumpulan model batik yang telah terklasi    fikasi berdasarkan ciri khas yang ada pada batik Riau sehingga dapat ditentukan gambar (image) yang merupakan batik Riau dan yang bukan merupakan batik Riau. Klasifikasi menggunakan CNN menghasilkan batik riau dan bukan batik riau dengan akurasi 65%. Akurasi 65% disebabkan pada dasarnya banyak motif yang sama antara batik riau dengan batik lainnya dengan perbedaan terletak pada warna cerap pada batik riau. Kata kunci: Batik; Batik Riau; CNN; Image; Deep Learning

scholarly journals Artificial Neural Networks as Decision Support Tools in Cytopathology: Past, Present, and Future

2016 ◽  
Vol 7 ◽  
pp. BECB.S31601 ◽  
Author(s):  
Abraham Pouliakis ◽  
Efrossyni Karakitsou ◽  
Niki Margari ◽  
Panagiotis Bountris ◽  
Maria Haritou ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Diagnostic System ◽  
Test Environment ◽  
Scientific Databases ◽  
Support Tools ◽  
Artificial Neural ◽  
Hybrid Screening ◽  
System Organ

Objective This study aims to analyze the role of artificial neural networks (ANNs) in cytopathology. More specifically, it aims to highlight the importance of employing ANNs in existing and future applications and in identifying unexplored or poorly explored research topics. Study Design A systematic search was conducted in scientific databases for articles related to cytopathology and ANNs with respect to anatomical places of the human body where cytopathology is performed. For each anatomic system/organ, the major outcomes described in the scientific literature are presented and the most important aspects are highlighted. Results The vast majority of ANN applications are related to cervical cytopathology, specifically for the ANN-based, semiautomated commercial diagnostic system PAPNET. For cervical cytopathology, there is a plethora of studies relevant to the diagnostic accuracy; in addition, there are also efforts evaluating cost-effectiveness and applications on primary, secondary, or hybrid screening. For the rest of the anatomical sites, such as the gastrointestinal system, thyroid gland, urinary tract, and breast, there are significantly less efforts relevant to the application of ANNs. Additionally, there are still anatomical systems for which ANNs have never been applied on their cytological material. Conclusions Cytopathology is an ideal discipline to apply ANNs. In general, diagnosis is performed by experts via the light microscope. However, this approach introduces subjectivity, because this is not a universal and objective measurement process. This has resulted in the existence of a gray zone between normal and pathological cases. From the analysis of related articles, it is obvious that there is a need to perform more thorough analyses, using extensive number of cases and particularly for the nonexplored organs. Efforts to apply such systems within the laboratory test environment are required for their future uptake.

REVISITING SYSTEMS AND APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS IN CONSTRUCTION ENGINEERING AND MANAGEMENT

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Alireza Shojaei ◽  
Amirsaman Mahdavian
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Construction Industry ◽  
Construction Engineering ◽  
Existing Problems ◽  
Modeling Methods ◽  
Recent Developments ◽  
Clustering And Classification ◽  
Artificial Neural ◽  
Complex Relationships

Artificial neural networks have been widely used for modeling and simulation of different problems in the construction industry, including, but not limited to, regression, clustering, and classification. They provide solutions for complex problems where other modeling methods often fail. For instance, they can capture nonlinear and complex relationships between the variables while many traditional modeling methods fail. However, they have their own limitations. They often can only be trained for a specific problem with a predetermined number of inputs and outputs. As a result, any change that requires an update in the architecture of the network cannot be automatically done and require human intervention. The recent developments in the field of artificial neural networks resulted in new concepts such as neural architecture search, reinforcement learning, and neuroevolution. These new areas can provide new methods for solving past and existing problems facing the construction industry in a more efficient, elegant, and versatile manner. One of the main contributions of the recent developments is networks that can optimize their own architecture and networks that are able to evolve and change their architecture. This paper aims to briefly review the application areas of the artificial neural networks in construction engineering and management and discuss how the recent developments in this field can be applied and provide better solutions.

scholarly journals Risk factor identification and mortality prediction in cardiac surgery using artificial neural networks

2006 ◽  
Vol 132 (1) ◽  
pp. 12-19.e1 ◽  
Author(s):  
Johan Nilsson ◽  
Mattias Ohlsson ◽  
Lars Thulin ◽  
Peter Höglund ◽  
Samer A.M. Nashef ◽  
...  
Keyword(s):  
Neural Networks ◽  
Cardiac Surgery ◽  
Risk Factor ◽  
Artificial Neural Networks ◽  
Mortality Prediction ◽  
Artificial Neural

scholarly journals Building Recognition in Air and Satellite Photos

2019 ◽  
Author(s):  
Дмитрий Булатицкий ◽  
Dmitriy Bulatitskiy ◽  
Александр Буйвал ◽  
Aleksandr Buyval ◽  
Михаил Гавриленков ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Artificial Neural Networks ◽  
Convolutional Neural Networks ◽  
Information Platform ◽  
Software Complex ◽  
Building Recognition ◽  
Artificial Neural

The paper deals with the algorithms of building recognition in air and satellite photos. The use of convolutional artificial neural networks to solve the problem of image segmentation is substantiated. The choice between two architectures of artificial neural networks is considered. The development of software implementing building recognition based on convolutional neural networks is described. The architecture of the software complex, some features of its construction and interaction with the cloud geo-information platform in which it functions are described. The application of the developed software for the recognition of buildings in images is described. The results of experiments on building recognition in pictures of various resolutions and types of buildings using the developed software are analysed.

scholarly journals PERSIANN-CNN: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Convolutional Neural Networks

2019 ◽  
Vol 20 (12) ◽  
pp. 2273-2289 ◽  
Author(s):  
Mojtaba Sadeghi ◽  
Ata Akbari Asanjan ◽  
Mohammad Faridzad ◽  
Phu Nguyen ◽  
Kuolin Hsu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
High Resolution ◽  
Convolutional Neural Networks ◽  
Spatial Scales ◽  
Remotely Sensed ◽  
Proposed Model ◽  
Precipitation Estimation ◽  
Artificial Neural ◽  
Rainfall Estimates

Abstract Accurate and timely precipitation estimates are critical for monitoring and forecasting natural disasters such as floods. Despite having high-resolution satellite information, precipitation estimation from remotely sensed data still suffers from methodological limitations. State-of-the-art deep learning algorithms, renowned for their skill in learning accurate patterns within large and complex datasets, appear well suited to the task of precipitation estimation, given the ample amount of high-resolution satellite data. In this study, the effectiveness of applying convolutional neural networks (CNNs) together with the infrared (IR) and water vapor (WV) channels from geostationary satellites for estimating precipitation rate is explored. The proposed model performances are evaluated during summer 2012 and 2013 over central CONUS at the spatial resolution of 0.08° and at an hourly time scale. Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)–Cloud Classification System (CCS), which is an operational satellite-based product, and PERSIANN–Stacked Denoising Autoencoder (PERSIANN-SDAE) are employed as baseline models. Results demonstrate that the proposed model (PERSIANN-CNN) provides more accurate rainfall estimates compared to the baseline models at various temporal and spatial scales. Specifically, PERSIANN-CNN outperforms PERSIANN-CCS (and PERSIANN-SDAE) by 54% (and 23%) in the critical success index (CSI), demonstrating the detection skills of the model. Furthermore, the root-mean-square error (RMSE) of the rainfall estimates with respect to the National Centers for Environmental Prediction (NCEP) Stage IV gauge–radar data, for PERSIANN-CNN was lower than that of PERSIANN-CCS (PERSIANN-SDAE) by 37% (14%), showing the estimation accuracy of the proposed model.

Sign in / Sign up

Export Citation Format

Share Document