scholarly journals Prototyping neural networks to evaluate the risk of adverse cardiovascular outcomes in the population

2021 ◽  
Vol 6 (4) ◽  
pp. 67-81
Author(s):  
L. A. Bogdanov ◽  
E. A. Komossky ◽  
V. V. Voronkova ◽  
D. E. Tolstosheev ◽  
G. V. Martsenyuk ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Risk Factors ◽  
Neural Networks ◽  
User Interface ◽  
Arterial Hypertension ◽  
Graphical User Interface ◽  
Cross Validation ◽  
Cardiovascular Outcomes ◽  
Artery Disease

Aim. To develop a neural network basis for the design of artificial intelligence software to predict adverse cardiovascular outcomes in the population.Materials and Methods. Neural networks were designed using the database of 1,525 participants of PURE (Prospective Urban Rural Epidemiology Study), an international, multi-center, prospective study investigating disease risk factors in the urban and rural areas. As this study is still ongoing, we analysed only baseline data, therefore switching prognosis and diagnosis task. Because of its leading prevalence among other cardiovascular diseases, arterial hypertension was selected as an adverse outcome. Neural networks were designed employing STATISTICA Automated Neural Networks (SANN) software, manually selected, cross-validated, and transferred to the original graphical user interface software.Results. Input risk factors were gender, age, place of residence, concomitant diseases (i.e., coronary artery disease, chronic heart failure, diabetes mellitus, chronic obstructive pulmonary disease, and asthma), active or passive smoking, regular use of medications, family history of arterial hypertension, coronary artery disease or stroke, heart rate, body mass index, fasting blood glucose and cholesterol, high- and low-density lipoprotein cholesterol, and serum creatinine levels. Our neural networks showed a moderate efficacy in the virtual diagnostics of arterial hypertension (84.5%, or 1,289 successfully predicted outcomes out of 1,525, area under the ROC curve = 0.88), with almost equal sensitivity (83.6%) and specificity (85.3%), and were successfully integrated into graphical user interface that is necessary for the development of the commercial prognostication software. Cross-validation of this neural network on bootstrapped samples of virtual patients demonstrated sensitivity of 82.7 – 84.7%, specificity of 84.5 – 87.3%, and area under the ROC curve of 0.88 – 0.89.Conclusion. The artificial intelligence prognostication software to predict adverse cardiovascular outcomes in the population can be developed by a combination of automated neural network generation and analysis followed by manual selection, cross-validation, and integration into graphical user interface.

scholarly journals Artificial Intelligence Techniques for Phishing Detection

2019 ◽  
Vol 8 (11) ◽  
pp. 2330-2335
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Neural Systems ◽  
Element Determination ◽  
Artificial Intelligence Techniques ◽  
The Neural Network ◽  
Proper Number ◽  
Phishing Detection

The objective of this undertaking is to apply neural systems to phishing email recognition and assess the adequacy of this methodology. We structure the list of capabilities, process the phishing dataset, and execute the Neural Network frameworks. we analyze its exhibition against that of other real Artificial Intelligence Techniques – DT , K-nearest , NB and SVM machine.. The equivalent dataset and list of capabilities are utilized in the correlation. From the factual examination, we infer that Neural Networks with a proper number of concealed units can accomplish acceptable precision notwithstanding when the preparation models are rare. Additionally, our element determination is compelling in catching the qualities of phishing messages, as most AI calculations can yield sensible outcomes with it.

scholarly journals Prediksi Pemakaian Listrik Kelompok Tarif Menggunakan Jaringan Syaraf Tiruan dan ARIMA

2013 ◽  
Vol 7 (2) ◽  
pp. 189
Author(s):  
Silviani E Rumagit ◽  
Azhari SN
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Statistical Method ◽  
Moving Average ◽  
Electric Energy ◽  
Backpropagation Neural Network ◽  
The Difference ◽  
The Government ◽  
Increasing Demand

AbstrakLatar Belakang penelitian ini dibuat dimana semakin meningkatnya kebutuhan listrik di setiap kelompok tarif. Yang dimaksud dengan kelompok tarif dalam penelitian ini adalah kelompok tarif sosial, kelompok tarif rumah tangga, kelompok tarif bisnis, kelompok tarif industri dan kelompok tarif pemerintah. Prediksi merupakan kebutuhan penting bagi penyedia tenaga listrik dalam mengambil keputusan berkaitan dengan ketersediaan energi listik. Dalam melakukan prediksi dapat dilakukan dengan metode statistik maupun kecerdasan buatan.            ARIMA merupakan salah satu metode statistik yang banyak digunakan untuk prediksi dimana ARIMA mengikuti model autoregressive (AR) moving average (MA). Syarat dari ARIMA adalah data harus stasioner, data yang tidak stasioner harus distasionerkan dengan differencing. Selain metode statistik, prediksi juga dapat dilakukan dengan teknik kecerdasan buatan, dimana dalam penelitian ini jaringan syaraf tiruan backpropagation dipilih untuk melakukan prediksi. Dari hasil pengujian yang dilakukan selisih MSE ARIMA, JST dan penggabungan ARIMA, jaringan syaraf tiruan tidak berbeda secara signifikan. Kata Kunci— ARIMA, jaringan syaraf tiruan, kelompok tarif.  AbstractBackground this research was made where the increasing demand for electricity in each group. The meaning this group is social, the household, business, industry groups and the government fare. Prediction is an important requirement for electricity providers in making decisions related to the availability of electric energy. In doing predictions can be made by statistical methods and artificial intelligence.            ARIMA is a statistical method that is widely used to predict where the ARIMA modeled autoregressive (AR) moving average (MA). Terms of ARIMA is the data must be stationary, the data is not stationary should be stationary  use differencing. In addition to the statistical method, predictions can also be done by artificial intelligence techniques, which in this study selected Backpropagation neural network to predict. From the results of tests made the difference in MSE ARIMA, ANN and merging ARIMA, artificial neural networks are not significantly different. Keyword—ARIMA, neural network, tarif groups

scholarly journals Demand Modelling in Telecommunications

10.14311/1121 ◽  
2009 ◽  
Vol 49 (2) ◽  
Author(s):  
M. Chvalina
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Network Models ◽  
Short Term ◽  
Neural Network Models ◽  
Term Forecast ◽  
Artificial Intelligence Methods ◽  
Demand Modelling

This article analyses the existing possibilities for using Standard Statistical Methods and Artificial Intelligence Methods for a short-term forecast and simulation of demand in the field of telecommunications. The most widespread methods are based on Time Series Analysis. Nowadays, approaches based on Artificial Intelligence Methods, including Neural Networks, are booming. Separate approaches will be used in the study of Demand Modelling in Telecommunications, and the results of these models will be compared with actual guaranteed values. Then we will examine the quality of Neural Network models. 

Introduction

2013 ◽  
pp. 1-5
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Computational Intelligence ◽  
Evolutionary Computing ◽  
Historical Background ◽  
Intelligent Computing ◽  
Biometric Security ◽  
Introductory Overview ◽  
Over Time

This chapter presents an introductory overview of the application of computational intelligence in biometrics. Starting with the historical background on artificial intelligence, the chapter proceeds to the evolutionary computing and neural networks. Evolutionary computing is an ability of a computer system to learn and evolve over time in a manner similar to humans. The chapter discusses swarm intelligence, which is an example of evolutionary computing, as well as chaotic neural network, which is another aspect of intelligent computing. At the end, special concentration is given to a particular application of computational intelligence—biometric security.

Comprehensive Overview of Neural Networks and Its Applications in Autonomous Vehicles

2019 ◽  
pp. 159-173
Author(s):  
Jay Rodge ◽  
Swati Jaiswal
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Learning Algorithms ◽  
Activation Functions ◽  
Comprehensive Overview ◽  
Unit Component

Deep learning and Artificial intelligence (AI) have been trending these days due to the capability and state-of-the-art results that they provide. They have replaced some highly skilled professionals with neural network-powered AI, also known as deep learning algorithms. Deep learning majorly works on neural networks. This chapter discusses about the working of a neuron, which is a unit component of neural network. There are numerous techniques that can be incorporated while designing a neural network, such as activation functions, training, etc. to improve its features, which will be explained in detail. It has some challenges such as overfitting, which are difficult to neglect but can be overcome using proper techniques and steps that have been discussed. The chapter will help the academician, researchers, and practitioners to further investigate the associated area of deep learning and its applications in the autonomous vehicle industry.

scholarly journals Optimization of control program for CNC machines with regarding dynamic stability of cutting process and optimum modes of treatment in conditions of digital production

2018 ◽  
Vol 226 ◽  
pp. 04016
Author(s):  
Yuri G. Kabaldin ◽  
Dmitrii A. Shatagin ◽  
Pavel V. Kolchin
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Dynamic Stability ◽  
Control Program ◽  
Cutting Process ◽  
Cnc Machines ◽  
Control Programs ◽  
Digital Production ◽  
Optimizing Control

The method for optimizing control programs for CNC machines based on artificial intelligence approaches, in particular, the apparatus of artificial neural networks, is outlined. A neural network model of the dynamic stability of the cutting process is proposed, which makes it possible to simulate the dynamics of the cutting process using the CAM system.

An artificial intelligence algorithm that differentiates anterior ethmoidal artery location on sinus computed tomography scans

2019 ◽  
Vol 134 (1) ◽  
pp. 52-55 ◽  
Author(s):  
J Huang ◽  
A-R Habib ◽  
D Mendis ◽  
J Chong ◽  
M Smith ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Computed Tomography ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Sinus Surgery ◽  
Computed Tomography Scans ◽  
Ethmoidal Artery ◽  
Anterior Ethmoidal Artery

AbstractObjectiveDeep learning using convolutional neural networks represents a form of artificial intelligence where computers recognise patterns and make predictions based upon provided datasets. This study aimed to determine if a convolutional neural network could be trained to differentiate the location of the anterior ethmoidal artery as either adhered to the skull base or within a bone ‘mesentery’ on sinus computed tomography scans.MethodsCoronal sinus computed tomography scans were reviewed by two otolaryngology residents for anterior ethmoidal artery location and used as data for the Google Inception-V3 convolutional neural network base. The classification layer of Inception-V3 was retrained in Python (programming language software) using a transfer learning method to interpret the computed tomography images.ResultsA total of 675 images from 388 patients were used to train the convolutional neural network. A further 197 unique images were used to test the algorithm; this yielded a total accuracy of 82.7 per cent (95 per cent confidence interval = 77.7–87.8), kappa statistic of 0.62 and area under the curve of 0.86.ConclusionConvolutional neural networks demonstrate promise in identifying clinically important structures in functional endoscopic sinus surgery, such as anterior ethmoidal artery location on pre-operative sinus computed tomography.

scholarly journals Diuretics in treatment of patients with arterial hypertension

2009 ◽  
Vol 6 (4) ◽  
pp. 9-12
Author(s):  
Irina Evgen'evna Chazova ◽  
Lyudmila Gennadievna Ratova
Keyword(s):  
Risk Factors ◽  
Blood Pressure ◽  
Coronary Artery Disease ◽  
Cardiovascular Risk ◽  
Arterial Hypertension ◽  
Disease Process ◽  
Low Doses ◽  
Treatment Of Hypertension ◽  
Artery Disease ◽  
High Doses

Since all cardiovascular risk factors contribute to coronary artery disease in patients with arterial hypertension, they should all be considered in the management of this disease process. Thiazides diuretics when used at high doses, negatively impact lipid and glucose metabolism. These findings have resulted in decreased use of diuretics in favor of newer agents such as ACE inhibitors and calcium antagonists. However, recent data have demonstrated that when used at low doses (6.25 or 25 mg of hydrochlorothiazide), diuretics lack significant metabolic side effects while bringing about significant reductions in blood pressure. Thus, at these doses, hydrochlorothiazide is a useful drug in the treatment of hypertension, both as monotherapy and in combination therapy.

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