scholarly journals Evaluation of the efficacy of neural network technology in the analysis of the condition of the optic nerve disc and peripapillary retina in healthy individuals examined for glaucoma

2020 ◽  
pp. 43-47
Author(s):  
A. B. Movsisyan ◽  
A. V. Kuroyedov ◽  
V. V. Gorodnichy ◽  
G. A. Ostapenko ◽  
S. V. Podvigin ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Optic Nerve ◽  
High Efficiency ◽  
Study Groups ◽  
Automated Perimetry ◽  
Healthy Individuals ◽  
Study Results ◽  
Artificial Intelligence Technology ◽  
Peripapillary Retina

Objective: Evaluation of efficacy of the application of artificial intelligence technology and neural networks in the analysis of the condition of the optic disc and the peripapillary retina in healthy individuals.Methods: Prospective analysis of the condition of visual organs in 54 patients aged from 49 to 71 years (100 eyes) was conducted. The examination included autorefractometry, visometry, tonometry, automated perimetry, spectral domain optical coherence tomography, Heidelberg retina tomography. A pre-trained neural network evaluated only a photograph of the optic nerve disc and the peripapillary retina.Results: Neural network identified twelve images with suspected glaucoma, five of which were selected by medical experts. The comparison of all study groups has demonstrated the presence of statistically significant differences between them according to a range of visiometric indicators.Conclusions: The study results showed high efficiency of artificial intelligence and the prospects of its use for the diagnosis of glaucoma. 

scholarly journals Artificial intelligence in artistic practices

2021 ◽  
pp. 41-50
Author(s):  
Asmati Chibalashvili
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Visual Arts ◽  
Human Condition ◽  
Human Intervention ◽  
Basic Principles ◽  
Generative Art ◽  
Works Of Art ◽  
Artificial Intelligence Technology ◽  
Emotionally Intelligent

The article considers methods of involving artificial intelligence in artistic practices. Based on the analysis of ways to use this technology in visual arts and music, the basic principles of working with artificial intelligence technology are identified, including: imitation of historical art, implemented in projects The Next Rembrandt and Choral; generative art, which is found in the works “Hyperbolic Composition І” and “Hyperbolic Composition ІІ” of S. Eaton and also in the AIVA program (Artificial Intelligence Virtual Artist). The importance of the mechanisms of neurobiology in the process of working with artificial intelligence on the example of the project “Neural Zoo” of S. Crespo, Iamus program, in which the development of musical material is based on the principle of evolution, is stated. In the application Endel and in the opera “Emotionally intelligent” Artificially Intelligent Brainwave Opera» of E. Perlman, a neural network is used to read information about the human condition and its further processing for modification into a sound landscape or image. The development of artificial intelligence and its use in artistic practices opens up new opportunities, expanding both the field of authors of artistic content and attracting new audience. This phenomenon provokes many issues, including: the ability to think artificially of artificial intelligence, the ability to create works of art without human intervention, as well as issues related to copyright.

Recent Advances in Medical Image Processing

2020 ◽  
pp. 1-14
Author(s):  
Zhen Huang ◽  
Qiang Li ◽  
Ju Lu ◽  
Junlin Feng ◽  
Jiajia Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Processing ◽  
Medical Imaging ◽  
Medical Image ◽  
Medical Image Processing ◽  
Convolution Neural Network ◽  
Artificial Intelligence Technology ◽  
Deep Convolution Neural Network

<b><i>Background:</i></b> Application and development of the artificial intelligence technology have generated a profound impact in the field of medical imaging. It helps medical personnel to make an early and more accurate diagnosis. Recently, the deep convolution neural network is emerging as a principal machine learning method in computer vision and has received significant attention in medical imaging. <b><i>Key Message:</i></b> In this paper, we will review recent advances in artificial intelligence, machine learning, and deep convolution neural network, focusing on their applications in medical image processing. To illustrate with a concrete example, we discuss in detail the architecture of a convolution neural network through visualization to help understand its internal working mechanism. <b><i>Summary:</i></b> This review discusses several open questions, current trends, and critical challenges faced by medical image processing and artificial intelligence technology.

scholarly journals Application of Artificial Intelligence Technology Optimized by Deep Learning to Rural Financial Development and Rural Governance

2022 ◽  
Vol 30 (7) ◽  
pp. 1-23
Author(s):  
Hongwei Hou ◽  
Kunzhi Tang ◽  
Xiaoqian Liu ◽  
Yue Zhou
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Research Result ◽  
Customer Information ◽  
Rural Governance ◽  
Artificial Intelligence Technology ◽  
Reasonable Prediction

The aim of this article is to promote the development of rural finance and the further informatization of rural banks. Based on DL (deep learning) and artificial intelligence technology, data pre-processing and feature selection are conducted on the customer information of rural banks in a certain region, including the historical deposit and loan, transaction record, and credit information. Besides, four DL models are proposed with a precision of more than 87% by test to improve the simulation effect and explore the application of DL. The BLSTM-CNN (Bi-directional Long Short-Term Memory-Convolutional Neural Network) model with a precision of 95.8%, which integrates RNN (Recurrent Neural Network) and CNN (Convolutional Neural Network) in parallel, solves the shortcomings of RNN and CNN separately. The research result can provide a more reasonable prediction model for rural banks, and ideas for the development of rural informatization and promoting rural governance.

scholarly journals An Automatic Logistics System using Artificial Intelligence

2019 ◽  
Vol 8 (2S4) ◽  
pp. 924-926
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Optimal Route ◽  
Logistics System ◽  
The Neural Network ◽  
Artificial Intelligence Technology ◽  
Artificial Neural ◽  
Set Up

In this paper, we propose a method to utilize machine learning to automate the system of classifying and transporting large quantities of logistics. First, establish an environment similar to the task of transferring logistics to the desired destination, and set up basic rules for classification and transfer. Next, each of the logistics that need sorting and transportation is defined as one entity, and artificial intelligence is introduced so that each individual can go to an optimal route without collision between the objects to the destination. Artificial intelligence technology uses artificial neural networks and uses genetic algorithms to learn neural networks. The artificial neural network is generated by each chromosome, and it is evolved based on the most suitable artificial neural network, and a score is given to each operation to evaluate the fitness of the neural network. In conclusion, the validity of this algorithm is evaluated through the simulation of the implemented system.

The Application of Artificial Intelligence Technology in the Selective Laster Sintering

2014 ◽  
Vol 1037 ◽  
pp. 236-239
Author(s):  
Li Yuan Cai ◽  
Qing Shun Wang ◽  
Wei Sun
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Bp Neural Network ◽  
Research Method ◽  
Control Method ◽  
Laser Sintering ◽  
Sintering Process ◽  
Optimal Parameters ◽  
Artificial Intelligence Technology ◽  
Neural Network Algorithm

Based on laser sintering constituency as the research object, this paper aimed at the perspective of artificial intelligence technology. It uses the new control theory and research method of BP neural network algorithm and tries to provide reference for optimizing the sintering process of laser district. This paper argues that the application of artificial intelligence technology to laser sintering constituency. Through the simulation, it can make up for the inadequacy of the traditional control method. Under certain conditions, the goal of process optimization will be achieved by finding the optimal parameters.

The Application of Wavelet Neural Network in Prediction of the Fish Price

2014 ◽  
Vol 687-691 ◽  
pp. 1945-1949
Author(s):  
Hong Wei Li ◽  
Xiao Xiang Gao ◽  
Ke Jun Cheng
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Success Rate ◽  
Bp Algorithm ◽  
Price Forecasting ◽  
Bp Network ◽  
Market Prices ◽  
Short Period ◽  
Artificial Intelligence Technology

The market fish price is an important factor that affects the income of fishermen, so how to accurately analyze and predict the fish pricet o obtain huge profits has caught people's attention. As science advances, various price forecasting and analysis methods have come into being. How to build a prediction theories and models with relatively high success rate has been the study of many scholars over the years. With the development of artificial intelligence, neural networks have become an important tool of predicting and analyzing changes in market prices. Neural networks are important artificial intelligence technology, which have simple structures, but are able to solve complicated problems. They have strong applicability in predicting the mature index fluctuations in a short period. This paper considers some shortcomings and deficiencies the BP network prototype, which tries to use the wavelet Functions to replace the excitation function in the traditional BP algorithm on the basis of a network of neurons and then forms into WNN. We can verify the feasibility of WNN by perch price forecasts, and then this method is used in price forecasts of the three main fish of the Ulungur Lake Aquatic, to provide the basis for the aquatic base decision

scholarly journals Robust model reconstruction for intelligent health monitoring of tunnel structures

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142091083
Author(s):  
Xiangyang Xu ◽  
Hao Yang
Keyword(s):  
Artificial Intelligence ◽  
Health Monitoring ◽  
High Efficiency ◽  
Spline Approximation ◽  
Robotic Systems ◽  
Large Area ◽  
Cloud Data ◽  
Robust Model ◽  
Robot Systems ◽  
Artificial Intelligence Technology

Advanced robotic systems will encounter a rapid breakthrough opportunity and become increasingly important, especially with the aid of the accelerated development of artificial intelligence technology. Nowadays, advanced robotic systems are widely used in various fields. However, the development of artificial intelligence-based robot systems for structural health monitoring of tunnels needs to be further investigated, especially for data modeling and intelligent processing for noises. This research focuses on integrated B-spline approximation with a nonparametric rank method and reveals its advantages of high efficiency and noise resistance for the automatic health monitoring of tunnel structures. Furthermore, the root-mean-square error and time consumption of the rank-based and Huber’s M-estimator methods are compared based on various profiles. The results imply that the rank-based method to model point cloud data has a comparative advantage in the monitoring of tunnel, as well as the large-area structures, which requires high degrees of efficiency and robustness.

scholarly journals THE USE OF AI (ARTIFICIAL INTELLIGENCE) IN ENGLISH LEARNING AMONG UNIVERSITY STUDENT: CASE STUDY IN ENGLISH DEPARTMENT, UNIVERSITAS AIRLANGGA

2021 ◽  
Author(s):  
Hemas Kumala Dewi ◽  
Raselly Elfa Putri ◽  
Nur Annisa Rahim ◽  
Tia Ivanka Wardani ◽  
Moses Glorino Rumambo Pandin
Keyword(s):  
Artificial Intelligence ◽  
Student Performance ◽  
English Learning ◽  
Literature Study ◽  
Learning And Teaching ◽  
Study Results ◽  
Artificial Intelligence Technology ◽  
Depth Interviews

Aim: This research aim is to analyze an artificial intelligence platform that can be used in imparting education as well as evaluating student performance. Method: This research was conducted with a qualitative method by conducting in-depth interviews and a literature study. Results: The findings of this study shows that Artificial Intelligence technology can be used as a means of developing English learning for students. Discussion: There have been several studies that support research results, that AI can be used to improve students' English skills through applications, websites, Virtual Reality technology, and other AI-based learning and teaching systems. Limitation: The limitation of this research is that it does not examine how far the role of AI in students' English learning is. Suggestion: For further research, it is expected to test how far the role of AI is to improve students' English skills, especially Universitas Airlangga students.

scholarly journals Development of a new empirical correlation for predicting formation volume factor of reservoir oil using artificial intelligence

2021 ◽  
Vol 44 (4) ◽  
pp. 408-416
Author(s):  
E. V. Shakirova ◽  
A. A. Aleksandrov ◽  
M. V. Semykin
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Fuzzy Inference ◽  
Empirical Correlation ◽  
Support Vector ◽  
Volume Factor ◽  
Inference System ◽  
Study Results ◽  
Artificial Neural

It is known that oil in reservoir conditions is characterized by the content of a certain amount of dissolved gas. As reservoir pressure decreases this gas is released from oil significantly changing its physical properties, primarily its density and viscosity. In addition, the oil volume also reduces, sometimes by 50–60 %. In this regard, when calculating reserves, it is necessary to justify the reduction amount of the reservoir oil volume when oil is extracted to the surface. For this purpose, the concept of formation volume factor of reservoir oil has been introduced. The formation volume factor of oil is considered one of the main characterizing parameters of crude oil. It is also required for modeling and predicting the characteristics of an oil reservoir. The purpose of the present work is to develop a new empirical correlation for predicting the formation volume factor of reservoir oil using artificial intelligence methods based on MATLAB software, such as: an artificial neural network, an adaptive neuro-fuzzy inference system, and a support vector machine. The article presents a new empirical correlation extracted from the artificial neural network based on 503 experimental data points for oils from the Eastern Siberia field, which was able to predict the formation volume factor of oil with the correlation coefficient of 0.969 and average absolute error of less than 1 %. The conducted study shows that the prediction accuracy of the desired parameter in the developed artificial intelligence model exceeds the accuracy of study results obtained by conventional statistical methods. Moreover, the model can be useful in the prospect of process optimization in field planning and development.

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