Neural network environment to evaluate the learning of Software Engineering in university students

2016 ◽  
pp. 153-176
Author(s):  
Álvaro Enrique Salcedo-Olivero
Keyword(s):  
Neural Network ◽  
Software Engineering ◽  
University Students ◽  
Network Environment

scholarly journals Red neuronal artificial de predicción de situación de dominio del estudiante ante el examen TOEFL

2019 ◽  
Vol 7 (1) ◽  
pp. 19-23
Author(s):  
Silvia Soledad Moreno Gutiérrez ◽  
Jesús Francisco Álvarez Cornejo ◽  
Mónica García Munguía
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Software Engineering ◽  
University Students ◽  
Language Proficiency ◽  
English Language ◽  
English Language Proficiency ◽  
Educational Institutions ◽  
Engineering Degree ◽  
Estudiantes Universitarios

El examen TOEFL es una prueba orientada a identificar el nivel de dominio del idioma inglés en estudiantes universitarios, por ello, diferentes instituciones educativas del país solicitan este resultado incluso como requisito de titulación, la Universidad Autónoma del Estado de Hidalgo es una de ellas, el 100% de sus alumnos de la licenciatura en ingeniería de software presentan el examen, asumiendo que están preparados, no obstante, en más del 50% de los casos el puntaje obtenido es no satisfactorio. Por ello, se propone una red neuronal artificial de tipo Backpropagation basada en la trayectoria escolar del alumno, que predice con un 95% de precisión la situación de dominio del estudiante, y sugiere presentar el examen o participar en el curso de preparación respectivo, de esta forma, la propuesta contribuye a incrementar el porcentaje de alumnos con resultados satisfactorios desde la primera aplicación y reducir el número de intentos. The TOEFL test is a test aimed at identifying the level of English language proficiency in university students, therefore, different educational institutions in the country request this result even as a qualification requirement, the Autonomous University of the State of Hidalgo is one of them, the 100% of its students in the software engineering degree present the exam, however, in more than 50% of the cases the score obtained is unsatisfactory, so the student must present it again. For this reason, an artificial neural network of Backpropagation type is proposed that predicts with 95% accuracy the situation of the student's domain and suggests presenting the exam or participating in the respective preparation course, in this way, the proposal contributes to reduce the number of attempts and increase the percentage of students with satisfactory results from the first application.

Neural Network Model to Calculate the Creep Data Using Size

2017 ◽  
Vol 7 (8) ◽  
pp. 257
Author(s):  
Seetharam .K ◽  
Sharana Basava Gowda ◽  
. Varadaraj
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Software Engineering ◽  
Network Model ◽  
Neural Network Model ◽  
Software Metrics ◽  
Creep Data ◽  
Engineering Software ◽  
Function Points ◽  
Different Levels

In Software engineering software metrics play wide and deeper scope. Many projects fail because of risks in software engineering development[1]t. Among various risk factors creeping is also one factor. The paper discusses approximate volume of creeping requirements that occur after the completion of the nominal requirements phase. This is using software size measured in function points at four different levels. The major risk factors are depending both directly and indirectly associated with software size of development. Hence It is possible to predict risk due to creeping cause using size.

An artificial intelligence data mining technology based evaluation model of education on political and ideological strategy of students

2020 ◽  
pp. 1-12
Author(s):  
Zheng Rong ◽  
Zheng Gang
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
College Students ◽  
Data Mining ◽  
University Students ◽  
Evaluation System ◽  
Evaluation Model ◽  
Ideological And Political Education ◽  
Political Education ◽  
Ideological Education

The student’s political and ideological practices is a vital portion of education, and it is related to optimization of task based on fundamental scenario in establishing morality. In order to establish a scientific, reasonable and operable evaluation model for students’ ideological education, and evaluate the status of college students’ ideological education. In this paper, firstly, in view of the shortcomings of evaluation objectives, single evaluation methods, lack of pertinence of evaluation indicators and subjectivity of evaluation standards in the current evaluation system of university students’ ideological and political education, the basic principles for constructing evaluation models of university students’ ideological and political education are put forward. Secondly, in case to meet changing needs of the times, an artificial neural network algorithm based on artificial intelligence data mining and a traditional multi-layer fuzzy evaluation model are designed to evaluate the ideological and political education of college students. This newly proposed model integrates learning, association, recognition, self-adaptive and fuzzy information processing, and at the same time, it overcomes their respective shortcomings. Finally, an example analysis is carried out with a nearby university as an example. The evaluation results display that the evaluation model of students’ ideological education established in this paper is in good agreement with the previous evaluation results. It fully shows that the comprehensive evaluation model of fuzzy neural network for college students’ ideological and political education established in this paper is scientific and effective.

Bitcoin Prediction Using Multi-Layer Perceptron Regressor, PCA, and Support Vector Regression (SVR)

2022 ◽  
pp. 225-236
Author(s):  
Aatif Jamshed ◽  
Asmita Dixit
Keyword(s):  
Neural Network ◽  
Software Engineering ◽  
Support Vector Regression ◽  
Evaluation Process ◽  
Support Vector ◽  
Actual Rate ◽  
Multi Layer Perceptron ◽  
Bayesian Neural Network ◽  
Price Schedule ◽  
Tremendous Amount

Bitcoin has gained a tremendous amount of attention lately because of the innate nature of entering cryptographic technologies and money-related units in the fields of banking, cybersecurity, and software engineering. This chapter investigates the effect of Bayesian neural structures or networks (BNNs) with the aid of manipulating the Bitcoin process's timetable. The authors also choose the maximum extensive highlights from Blockchain records that are carefully applied to Bitcoin's marketplace hobby and use it to create templates to enhance the influential display of the new Bitcoin evaluation process. They endorse actual inspection to check and expect the Bitcoin technique, which compares the Bayesian neural network and other clean and non-direct comparison models. The exact tests show that BNN works well for undertaking the Bitcoin price schedule and explain the intense unpredictability of Bitcoin's actual rate.

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