Technique of scientific and technical forecasting using neural networks

2018 ◽  
pp. 103-110
Author(s):  
V. V. Nefedev
Keyword(s):  
Mathematical Modeling ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Original Method ◽  
Forecasting Methods ◽  
Expert Assessments ◽  
Artificial Neural

For the definition and implementation of breakthrough technologies the most important is the role of scientific and technical forecasting. Well-known forecasting methods based on extrapolation, expert assessments and mathematical modeling are not universal and have a number of significant disadvantages. The article proposes an original method of scientific and technical forecasting based on the use of the methodology of artificial neural networks. 

scholarly journals Cardiovascular events in patients with mild autonomous cortisol secretion: analysis with artificial neural networks

2017 ◽  
Vol 177 (1) ◽  
pp. 73-83 ◽  
Author(s):  
Valentina Morelli ◽  
Serena Palmieri ◽  
Andrea Lania ◽  
Alberto Tresoldi ◽  
Sabrina Corbetta ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Characteristic Curve ◽  
Smoking Habit ◽  
Adrenal Incidentaloma ◽  
Cortisol Secretion ◽  
Autonomous Cortisol Secretion ◽  
Artificial Neural

Background The independent role of mild autonomous cortisol secretion (ACS) in influencing the cardiovascular event (CVE) occurrence is a topic of interest. We investigated the role of mild ACS in the CVE occurrence in patients with adrenal incidentaloma (AI) by standard statistics and artificial neural networks (ANNs). Methods We analyzed a retrospective record of 518 AI patients. Data regarding cortisol levels after 1 mg dexamethasone suppression (1 mg DST) and the presence of obesity (OB), hypertension (AH), type-2 diabetes (T2DM), dyslipidemia (DL), familial CVE history, smoking habit and CVE were collected. Results The receiver-operating characteristic curve analysis suggested that 1 mg DST, at a cut-off of 1.8 µg/dL, had the best accuracy for detecting patients with increased CVE risk. In patients with 1 mg-DST ≥1.8 µg/dL (DST+, n = 223), age and prevalence of AH, T2DM, DL and CVE (66 years, 74.5, 25.9, 41.4 and 26.8% respectively) were higher than that of patients with 1 mg-DST ≤1.8 µg/dL (61.9 years, 60.7, 18.5, 32.9 and 10%, respectively, P < 0.05 for all). The CVE were associated with DST+ (OR: 2.46, 95% CI: 1.5–4.1, P = 0.01), regardless of T2DM, AH, DL, smoking habit, gender, observation period and age. The presence of at least two among AH, T2DM, DL and OB plus DST+ had 61.1% sensitivity in detecting patients with CVE. By using the variables selected by ANNs (familial CVE history, age, T2DM, AH, DL and DST+) 78.7% sensitivity was reached. Conclusions Cortisol after 1 mg-DST is independently associated with the CVE occurrence. The ANNs might help for assessing the CVE risk in AI patients.

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.

scholarly journals Practical Aspects of the Design and Use of the Artificial Neural Networks in Materials Engineering

Metals ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1832
Author(s):  
Wojciech Sitek ◽  
Jacek Trzaska
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Vital Role ◽  
Materials Engineering ◽  
The Neural Network ◽  
Critical Issues ◽  
Artificial Neural ◽  
The Individual

Artificial neural networks are an effective and frequently used modelling method in regression and classification tasks in the area of steels and metal alloys. New publications show examples of the use of artificial neural networks in this area, which appear regularly. The paper presents an overview of these publications. Attention was paid to critical issues related to the design of artificial neural networks. There have been presented our suggestions regarding the individual stages of creating and evaluating neural models. Among other things, attention was paid to the vital role of the dataset, which is used to train and test the neural network and its relationship to the artificial neural network topology. Examples of approaches to designing neural networks by other researchers in this area are presented.

Applying Artificial Neural Networks in the Modelling of Copper Recovery Process by Ionic Exchange in Aqueous Solutions

2008 ◽  
Vol 59 (7) ◽  
Author(s):  
Leonard Mihaly Cozmuta ◽  
Camelia Varga ◽  
Monica Marian ◽  
Anca Mihaly Cozmuta ◽  
Tutu Hlanganani
Keyword(s):  
Mathematical Modeling ◽  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Aqueous Solutions ◽  
Exchange Process ◽  
Theoretical Data ◽  
Copper Recovery ◽  
Ionic Exchange ◽  
Artificial Neural

Artificial neuronal networks are widely used in different fields such as finance, medicine, engineering, geology, chemistry, physics in order to predict, classify and control the development of different processes. The paper presents experimental data on the adsorption process of copper from aqueous solutions and compares it with theoretical data obtained by mathematical modeling using artificial neural networks (ANN). The aim of the paper is to demonstrate that ANN ensure a high accuracy in the mathematical modeling of the process. We have collected experimental data by using synthetic solutions with different pH and cooper ions concentrations, which were retained on a PUROLITE S930 cationic resin. Both experimental and theoretical data obtained using ANN show the correlation between factors which influence the ionic exchange process (pH, temperature, initial copper concentration, activation energy).

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