scholarly journals A Hybrid Approach for Modular Neural Network Design Using Intercriteria Analysis and Intuitionistic Fuzzy Logic

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Sotir Sotirov ◽  
Evdokia Sotirova ◽  
Vassia Atanassova ◽  
Krassimir Atanassov ◽  
Oscar Castillo ◽  
...  
Keyword(s):  
Neural Networks ◽  
Research Work ◽  
Hybrid Approach ◽  
Complex Problem ◽  
Neural Models ◽  
Modular Neural Networks ◽  
Intuitionistic Fuzzy ◽  
Real Value ◽  
Intuitionistic Fuzzy Logic ◽  
Goal Value

Intercriteria analysis (ICA) is a new method, which is based on the concepts of index matrices and intuitionistic fuzzy sets, aiming at detection of possible correlations between pairs of criteria, expressed as coefficients of the positive and negative consonance between each pair of criteria. Here, the proposed method is applied to study the behavior of one type of neural networks, the modular neural networks (MNN), that combine several simple neural models for simplifying a solution to a complex problem. They are a tool that can be used for object recognition and identification. Usually the inputs of the MNN can be fed with independent data. However, there are certain limits when we may use MNN, and the number of the neurons is one of the major parameters during the implementation of the MNN. On the other hand, a high number of neurons can slow down the learning process, which is not desired. In this paper, we propose a method for removing part of the inputs and, hence, the neurons, which in addition leads to a decrease of the error between the desired goal value and the real value obtained on the output of the MNN. In the research work reported here the authors have applied the ICA method to the data from real datasets with measurements of crude oil probes, glass, and iris plant. The method can also be used to assess the independence of data with good results.

scholarly journals Recommendation Systems for Education: Systematic Review

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1611
Author(s):  
María Cora Urdaneta-Ponte ◽  
Amaia Mendez-Zorrilla ◽  
Ibon Oleagordia-Ruiz
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Formal Education ◽  
Research Work ◽  
Hybrid Approach ◽  
Recommendation Systems ◽  
Educational Resources ◽  
Future Research ◽  
Collaborative Approach ◽  
Developmental Approach

Recommendation systems have emerged as a response to overload in terms of increased amounts of information online, which has become a problem for users regarding the time spent on their search and the amount of information retrieved by it. In the field of recommendation systems in education, the relevance of recommended educational resources will improve the student’s learning process, and hence the importance of being able to suitably and reliably ensure relevant, useful information. The purpose of this systematic review is to analyze the work undertaken on recommendation systems that support educational practices with a view to acquiring information related to the type of education and areas dealt with, the developmental approach used, and the elements recommended, as well as being able to detect any gaps in this area for future research work. A systematic review was carried out that included 98 articles from a total of 2937 found in main databases (IEEE, ACM, Scopus and WoS), about which it was able to be established that most are geared towards recommending educational resources for users of formal education, in which the main approaches used in recommendation systems are the collaborative approach, the content-based approach, and the hybrid approach, with a tendency to use machine learning in the last two years. Finally, possible future areas of research and development in this field are presented.

Hybrid Approach and Architecture to Detect Fake News on Twitter in Real-Time using Neural Networks

2020 ◽  
Author(s):  
Madusha Prasanjith Thilakarathna ◽  
Vihanga Ashinsana Wijayasekara ◽  
Yasiru Gamage ◽  
Kavindi Hanshani Peiris ◽  
Chanuka Abeysinghe ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Hybrid Approach ◽  
Fake News

scholarly journals Fast Motion Model of Road Vehicles with Artificial Neural Networks

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 928
Author(s):  
Ferenc Hegedüs ◽  
Péter Gáspár ◽  
Tamás Bécsi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Dynamic Model ◽  
Piecewise Linear ◽  
Complex Problem ◽  
Computational Effort ◽  
Road Vehicles ◽  
Vehicle Motion ◽  
Artificial Neural ◽  
Original Dynamic

Nonlinear optimization-based motion planning algorithms have been successfully used for dynamically feasible trajectory planning of road vehicles. However, the main drawback of these methods is their significant computational effort and thus high runtime, which makes real-time application a complex problem. Addressing this field, this paper proposes an algorithm for fast simulation of road vehicle motion based on artificial neural networks that can be used in optimization-based trajectory planners. The neural networks are trained with supervised learning techniques to predict the future state of the vehicle based on its current state and driving inputs. Learning data is provided for a wide variety of randomly generated driving scenarios by simulation of a dynamic vehicle model. The realistic random driving maneuvers are created on the basis of piecewise linear travel velocity and road curvature profiles that are used for the planning of public roads. The trained neural networks are then used in a feedback loop with several variables being calculated by additional numerical integration to provide all the outputs of the original dynamic model. The presented model can be capable of short-term vehicle motion simulation with sufficient precision while having a considerably faster runtime than the original dynamic model.

scholarly journals Prediction of Blended Yarn Evenness and Tensile Properties by Using Artificial Neural Network and Multiple Linear Regression

2016 ◽  
Vol 16 (2) ◽  
pp. 43-50 ◽  
Author(s):  
Samander Ali Malik ◽  
Assad Farooq ◽  
Thomas Gereke ◽  
Chokri Cherif
Keyword(s):  
Neural Networks ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Tensile Properties ◽  
Regression Models ◽  
Prediction Models ◽  
Research Work ◽  
Absolute Error ◽  
Knowledge Domain ◽  
Artificial Neural

Abstract The present research work was carried out to develop the prediction models for blended ring spun yarn evenness and tensile parameters using artificial neural networks (ANNs) and multiple linear regression (MLR). Polyester/cotton blend ratio, twist multiplier, back roller hardness and break draft ratio were used as input parameters to predict yarn evenness in terms of CVm% and yarn tensile properties in terms of tenacity and elongation. Feed forward neural networks with Bayesian regularisation support were successfully trained and tested using the available experimental data. The coefficients of determination of ANN and regression models indicate that there is a strong correlation between the measured and predicted yarn characteristics with an acceptable mean absolute error values. The comparative analysis of two modelling techniques shows that the ANNs perform better than the MLR models. The relative importance of input variables was determined using rank analysis through input saliency test on optimised ANN models and standardised coefficients of regression models. These models are suitable for yarn manufacturers and can be used within the investigated knowledge domain.

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