PREDICTING SOFTWARE QUALITY, DURING TESTING, USING NEURAL NETWORK MODELS: A COMPARATIVE STUDY

1994 ◽  
Vol 01 (03) ◽  
pp. 303-319 ◽  
Author(s):  
TAGHI M. KHOSHGOFTAAR ◽  
ROBERT M. SZABO
Keyword(s):  
Neural Network ◽  
Software Quality ◽  
Principal Components ◽  
Network Modeling ◽  
Network Models ◽  
Neural Nets ◽  
Neural Network Modeling ◽  
Neural Network Models ◽  
Predictive Quality

The application of statistical modeling techniques has been an intensely pursued area of research in the field of software engineering. The goal has been to model software quality and use that information to better understand the software development process. Neural network modeling methods have recently been applied to this field. The results reported indicate that neural network models have better predictive quality than some statistical models when predicting reliability and the number of faults. In this paper, we will investigate the application of principal components analysis to neural network modeling as a way of improving the predictive quality of neural network quality models. Using data we collected from a large commercial software system, we developed a multiple regression model using the principal components. Then, we trained two neural nets, one with raw data, and one with principal components. Then, we compare the predictive quality of the three competing models for a variety of quality measures.

scholarly journals EXPERIENCE OF NEURAL NETWORK MODELING IN MANAGING THE ACHIEVEMENT OF CRITERIA FOR A NEW MODEL OF A MEDICAL ORGANIZATION THAT USES LEAN TECHNOLOGIES

2020 ◽  
Vol 30 (5) ◽  
pp. 673-678
Author(s):  
S.B. Petrov ◽  
S.D. Mazunina
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Models ◽  
Performance Criteria ◽  
Neural Network Modeling ◽  
Neural Network Models ◽  
Medical Organization ◽  
New Model ◽  
Generalized Regression

Nowadays the scientific developments connected with increase of readiness of the medical institutions, rendering primary medical and sanitary aid, to work with application of methods and tools of lean technologies for increase of level of availability and quality of medical aid to the population of Russia acquire urgency. The aim of the study is to assess the prognostic importance of common neural network models to analyze the value components of the reception of a local therapist, affecting the level of satisfaction with the quality of medical care, from the position of management to achieve the criteria of a new model of a medical organization using lean technologies. The following types of neural network models were studied: based on a multilayer perceptron, a radial basis function, and a generalized regression neural network. Models based on multiple linear regression equations were used as a control group of networks. In total, 50 artificial neural networks were obtained and analyzed. The effectiveness of neural network models was evaluated based on the following parameters: the ratio of standard deviations of the forecast error and the source data, as well as the Pearson correlation between the observed and predicted indicators of the model. Among the studied neural network models, models based on a multi-layer perceptron and generalized regression neural networks have the highest quality of prediction, which makes them promising for use in systems that monitor and predict the structure of the value component of the main processes in medical organizations for patients. The proposed neural network models can become the basis for creating information management systems that monitor the achievement of performance criteria for a new model of a medical organization that uses lean technologies.

scholarly journals Discourseology of Linguistic Consciousness: Neural Network Modeling of some Structural and Semantic Relationships

2021 ◽  
Vol 29 (1) ◽  
pp. 193-207
Author(s):  
Vitalii Shymko
Keyword(s):  
Neural Network ◽  
Structural Parameters ◽  
Network Modeling ◽  
Network Models ◽  
Well Being ◽  
Neural Network Modeling ◽  
Validity And Reliability ◽  
Neural Network Models ◽  
Linear Sequence ◽  
Artificial Neural

Objective. Study of the validity and reliability of the discourse approach for the psycholinguistic understanding of the nature, structure, and features of the linguistic consciousness functioning. Materials & Methods. This paper analyzes artificial neural network models built on the corpus of texts, which were obtained in the process of experimental research of the coronavirus quarantine concept as a new category of linguistic consciousness. The methodology of feedforward artificial neural networks (multilayer perceptron) was used in order to assess the possibility of predicting the leading texts semantics based on the discourses ranks and their place in the respective linear sequence. Same baseline parameters were used to predict respondents' self-assessments of changes in their psychological well-being and in daily life routine during the quarantine, as well as to predict their preferences of the quarantine strategies. The study relied on basic ideas about discourse as a meaning constituted by the dispersion of other meanings (Foucault). The same dispersion mechanism realizes itself in interdiscourse interaction, forming a discursive formation at a higher level. The method of T-units (Hunt) was used to identify and count discourses in the texts. The ranking of discourses was provided based on the criterion of their semantic-syntactic autonomy. Results. The conducted neural network modeling revealed a high accuracy in predicting the work of the linguistic consciousness functions associated with retrospective self-assessment and anticipatory imagination of the respondents. Another result of this modeling is a partial confirmation of the assumption concerning existence a relationship between the structural parameters of the discursive field (the rank of the discourses and their place in the respective linear sequence) and the leading semantics of the text. Conclusions. A discourse approach to the study of linguistic consciousness, understanding of its structure and functioning features seems to be reasonably appropriate. The implementation of the approach presupposes the need to form a base of linguistic corpora with the inclusion in each text markup of such parameters as: the presence of specific discourses, their ranks, positions in the linear sequence of discourses.

scholarly journals Skill Oriented Online Master’s Course “Neural Network Modeling of Complex Technical Systems”

2020 ◽  
Vol 35 ◽  
pp. 01011
Author(s):  
Ekaterina V. Panfilova
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Modeling ◽  
Research Work ◽  
Network Models ◽  
Neural Network Modeling ◽  
Neural Network Models ◽  
Study Results ◽  
Complex Technical Systems ◽  
Technical Systems

In this paper we consider the application of online course “Neural Network modeling of Complex Technical Systems” in the Master’s degree programs in the field of nanotechnology and nanoengineering in Bauman Moscow State Technical University. The course has rather practical than theoretical nature. The aim of this course is skill oriented learning. Nowadays neural network models have become a powerful tool of scientific research for engineers and students. The methods studied during the study of the discipline can be applied to estimation, modeling, classification, clustering, forecasting and more. The neural networks modeling plays a significant role in Master’s education and student’s research work. Neural Networks models are successfully presented in graduation theses. Thanks to online educations students can practice at their own pace and study modern neural networks software products, methods of data preparing, designing and training neural network and then apply these algorithms in practice. According to the steps of neural network modeling algorithm the course consists of three main parts and conclusive one. In this paper course structure and study results are presented.

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. 

Evaluating the quality of argon‐arc welded joints using neural network models with regression input

2000 ◽  
Vol 14 (7) ◽  
pp. 559-564
Author(s):  
E A Gladkov ◽  
A V Maloletkov ◽  
R A Perkovskii ◽  
A I Gavrilov
Keyword(s):  
Neural Network ◽  
Welded Joints ◽  
Network Models ◽  
Neural Network Models

Predicting the quality of TIG butt welded joints using neural network models

1998 ◽  
Vol 12 (3) ◽  
pp. 215-219 ◽  
Author(s):  
E A Gladkov ◽  
A V Maloletkov ◽  
R A Perkovskii
Keyword(s):  
Neural Network ◽  
Welded Joints ◽  
Network Models ◽  
Neural Network Models

scholarly journals Artificial neural network modeling of jatropha oil fueled diesel engine for emission predictions

2009 ◽  
Vol 13 (3) ◽  
pp. 91-102 ◽  
Author(s):  
Thirunavukkarasu Ganapathy ◽  
Parkash Gakkhar ◽  
Krishnan Murugesan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Models ◽  
Neural Network Modeling ◽  
Data Sets ◽  
Nox Emissions ◽  
Jatropha Oil ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Artificial Neural Network Models

This paper deals with artificial neural network modeling of diesel engine fueled with jatropha oil to predict the unburned hydrocarbons, smoke, and NOx emissions. The experimental data from the literature have been used as the data base for the proposed neural network model development. For training the networks, the injection timing, injector opening pressure, plunger diameter, and engine load are used as the input layer. The outputs are hydrocarbons, smoke, and NOx emissions. The feed forward back propagation learning algorithms with two hidden layers are used in the networks. For each output a different network is developed with required topology. The artificial neural network models for hydrocarbons, smoke, and NOx emissions gave R2 values of 0.9976, 0.9976, and 0.9984 and mean percent errors of smaller than 2.7603, 4.9524, and 3.1136, respectively, for training data sets, while the R2 values of 0.9904, 0.9904, and 0.9942, and mean percent errors of smaller than 6.5557, 6.1072, and 4.4682, respectively, for testing data sets. The best linear fit of regression to the artificial neural network models of hydrocarbons, smoke, and NOx emissions gave the correlation coefficient values of 0.98, 0.995, and 0.997, respectively.

scholarly journals System of providing sustainability of tower cranes from overturn in extreme wind loads

2018 ◽  
Vol 224 ◽  
pp. 02086
Author(s):  
Pavel Sorokin ◽  
Alexey Mishin ◽  
Vitaliy Antsev ◽  
Alexey Red’kin
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Network Models ◽  
Wind Loads ◽  
Neural Network Models ◽  
Quality Of Work ◽  
Development Stages ◽  
The Neural Network ◽  
Extreme Wind

The article is devoted to the issues of ensuring stability of tower cranes from overturn. The development stages of devices for ensuring tower cranes safety are examined and their shortcomings are revealed. The system consisting of subsystems and drives is proposed and their interaction is presented. The article deals with a subsystem based on artificial intelligence methods. The neural network models of forecasting wind parameters are developed. The quality of work of neural network models is estimated. The ways of further topic development are suggested.

Neural network modeling of carcass measurements to predict beef tenderness

2000 ◽  
Vol 80 (2) ◽  
pp. 311-318 ◽  
Author(s):  
B. D. Hill ◽  
S. D. M. Jones ◽  
W. M. Robertson ◽  
I. T. Major
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Large Data ◽  
Research Centre ◽  
Neural Network Modeling ◽  
Marbling Score ◽  
Neural Network Models ◽  
Cooking Method ◽  
Beef Tenderness ◽  
Plant Weight

Neural network (NN) models were developed for predicting and classifying an objective measurement of tenderness using carcass data such as pre-slaughter information (sex, age, kill order), weights, pH, temperatures, lean color readings, lab-determined measurements, grade measurements and organ weights. Tenderness was expressed objectively as Warner-Bratzler shear (WBS) force measured on steaks, aged 6 d, from the longissimus thoracis et lumborum (LTL) muscle. Carcass data from experiments conducted between 1985 and 1995 at the Lacombe Research Centre were combined to form large data sets (n = 775–1177) for modeling. Neural network models to predict actual shear values showed limited potential (R2 = 0.37–0.45) and were only marginally better than a multiple linear regression (MLR) model (R2 = 0.34). Neural network models that classified carcasses into tenderness categories showed better potential (mean accuracy 51–53%). The best four-category (tender, probably tender, probably tough, tough) model classified tender and tough steaks with accuracies of 0.64 and 0.79, respectively. This model reduced tough and probably tough carcasses by 55% in our population. The model required the following 11 inputs, which, except for cooking method, are available by 24 h postmortem: sex, live plant weight, hot carcass weight, 24-h cooler shrink, 24-h pH, 24-h CIE color b*, 24-h CIE lightness L* × hue angle, rib eye area, grader's marbling score (AMSA%), grade, and cooking method. By implementing techniques outlined in this study in a plant situation, the current 23% unacceptable consumer rating for Canadian beef could be reduced to 10–12%. Key words: Neural networks, beef, tenderness, carcass measurements, longissimus muscle

DEVELOPMENT OF AN EXPERT SYSTEM FOR DIAGNOSTICS OF LUNG DISEASES BASED ON NEURAL NETWORK MODELING

2021 ◽  
pp. 155-159
Author(s):  
Екатерина Ивановна Новикова ◽  
Екатерина Александровна Андрианова ◽  
Елена Евгеньевна Удодова ◽  
Анастасия Юрьевна Корниенко ◽  
Александр Станиславович Панов
Keyword(s):  
Neural Network ◽  
Lung Diseases ◽  
Network Models ◽  
The Body ◽  
Neural Network Modeling ◽  
Diagnostic Study ◽  
Topological Groups ◽  
Social Significance ◽  
Neural Network Models ◽  
Information Process

В статье рассматриваются вопросы диагностики заболеваний легких, таких как кавернозный, инфильтративный, очаговой, диссеминированный туберкулез, онкология и пневмония. Медико-социальное значение болезней органов дыхания в современных условиях велико и определяется, прежде всего, их крайне высокой частотой среди различных контингентов населения. Учитывая значимость дыхания для организма, необходимо вовремя выявлять различные патологии и применять незамедлительные меры лечения. Одним из средств повышения эффективности диагностики данных патологий является автоматизация обработки диагностических данных с использованием современных технологий, а также создание компьютерной системы поддержки принятия решений, которая принимала бы во внимание большой объем диагностической информации и исключала ошибки субъективного характера. Выделение топологических групп по легочным заболеваниям проводилось с использованием самоорганизующихся карт Кохонена. По результатам классификации было проведено обучения нейронных сетей, используя алгоритм «многослойного персептрона» методом «обратного распространения», и получены математические модели. В медицинской практике постоянно следует учитывать то обстоятельство, что достоверные и адекватные медицинские данные, например, лабораторные анализы, результаты инструментального диагностического исследования, данные опроса больного или физикального исследования, потеряют свою актуальность, если информационный процесс длительно растянут по времени. Разработанные нейросетевые модели, были реализованы в информационно-программном обеспечении, которое позволит повысить эффективность процесса диагностики заболеваний легких The article deals with the diagnosis of lung diseases such as cavernous, infiltrative, focal, disseminated tuberculosis, oncology and pneumonia. The medical and social significance of respiratory diseases in modern conditions is great and is determined, first of all, by their extremely high frequency among various contingents of the population. Given the importance of breathing for the body, it is necessary to timely identify various pathologies and apply immediate treatment measures. One of the means of increasing the efficiency of diagnosing these pathologies is the automation of the processing of diagnostic data using modern technologies, as well as the creation of a computer decision support system that would take into account a large amount of diagnostic information and exclude subjective errors. The selection of topological groups for pulmonary diseases was carried out using self-organizing Kohonen maps. Based on the classification results, neural networks were trained using the "multilayer perceptron" algorithm by the "backpropagation" method and mathematical models were obtained. In medical practice, one should constantly take into account the fact that reliable and adequate medical data, for example, laboratory tests, the results of an instrumental diagnostic study, data from a patient survey or physical examination, will lose their relevance if the information process is prolonged for a long time. The developed neural network models were implemented in information and software that will improve the efficiency of the process of diagnosing lung diseases

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