scholarly journals Adaptive Neuro-Fuzzy Inference System for Measuring software quality Product

2021 ◽  
Author(s):  
Atrin Barzegar ◽  
Yas Barzegar
Keyword(s):  
Neural Network ◽  
Software Quality ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Quality Model ◽  
Inference System ◽  
Software Products ◽  
Software Product ◽  
Neural Fuzzy

Computer systems are involved in many critical human applications today, so that a small error can lead to serious and dangerous problems. These errors can be from an error in the incorrect design of the user interface to an error in the program code. The success of a software product depends on several factors. Given that different organizations and institutions use software products, the need to have a quality and desirable Software according to the goals and needs of the organization makes measuring the quality of software products. an important issue for most organizations and institutions, To be sure of having the right software. It is necessary to use a standard quality model to examine the features and sub-features for a detailed and principled study in the quality discussion. In this study, the quality of Word software was measured by Adaptive Neural Fuzzy Inference System. In recent years, powerful systems called fuzzy inference systems on The basis of adaptive neural network (ANFIS) has been used in various sciences. Using the power of neural network training and the linguistic advantage of fuzzy systems, these types of systems have been able to realize the advantages of the two in terms of analyzing very powerful complex processes. Considering the importance of software quality and to have a good and usable software in terms of quality and measuring the quality of software during the study. It was applied at different levels to make the result of measuring the quality of Word software more accurate and closer to reality. In this research, the quality of the software product is measured based on the adaptive neural-fuzzy inference system in ISO standard. According to the results obtained in this study, it is understood that quality is a continuous and hierarchical concept and the quality of each part of the software at any stage of production can lead to high quality products.

scholarly journals Measuring Software Quality Product Based on Fuzzy Inference System Techniques in ISO Standard

2021 ◽  
Author(s):  
Atrin Barzegar
Keyword(s):  
Software Quality ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Quality Model ◽  
Inference System ◽  
Iso Standard ◽  
Software Products ◽  
Software Product ◽  
The Impact

The success of a software product depends on several factors. Given that different organizations and institutions use software products, the need to have a quality and desirable software according to the goals and needs of the organization makes measuring the quality of software products an important issue for most organizations and institutions. To be sure of having the right software. It is necessary to use a standard quality model to examine the features and sub-features for a detailed and principled study in the quality discussion. In this study, the quality of Word software was measured. Considering the importance of software quality and to have a good and usable software in terms of quality and measuring the quality of software during the study, experts and skilled in this field were used and the impact of each factor and quality characteristics. It was applied at different levels according to their opinion to make the result of measuring the quality of Word software more accurate and closer to reality. In this research, the quality of the software product is measured based on the fuzzy inference system in ISO standard. According to the results obtained in this study, it is understood that quality is a continuous and hierarchical concept and the quality of each part of the software at any stage of production can lead to high quality products.

scholarly journals Genetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Quality

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2822
Author(s):  
Tamas Galli ◽  
Francisco Chiclana ◽  
Francois Siewe
Keyword(s):  
Product Quality ◽  
Fuzzy Inference System ◽  
Software Maintenance ◽  
Fuzzy Inference ◽  
Quality Model ◽  
Inference System ◽  
Linguistic Rules ◽  
Software Product ◽  
Execution Tracing

Execution tracing is a tool used in the course of software development and software maintenance to identify the internal routes of execution and state changes while the software operates. Its quality has a high influence on the duration of the analysis required to locate software faults. Nevertheless, execution tracing quality has not been described by a quality model, which is an impediment while measuring software product quality. In addition, such a model needs to consider uncertainty, as the underlying factors involve human analysis and assessment. The goal of this study is to address both issues and to fill the gap by defining a quality model for execution tracing. The data collection was conducted on a defined study population with the inclusion of software professionals to consider their accumulated experiences; moreover, the data were processed by genetic algorithms to identify the linguistic rules of a fuzzy inference system. The linguistic rules constitute a human-interpretable rule set that offers further insights into the problem domain. The study found that the quality properties accuracy, design and implementation have the strongest impact on the quality of execution tracing, while the property legibility is necessary but not completely inevitable. Furthermore, the quality property security shows adverse effects on the quality of execution tracing, but its presence is required to some extent to avoid leaking information and to satisfy legal expectations. The created model is able to describe execution tracing quality appropriately. In future work, the researchers plan to link the constructed quality model to overall software product quality frameworks to consider execution tracing quality with regard to software product quality as a whole. In addition, the simplification of the mathematically complex model is also planned to ensure an easy-to-tailor approach to specific application domains.

scholarly journals Inverse Kinematic Solution of 5R Manipulator using ANN and ANFIS

2015 ◽  
Vol 4 (2) ◽  
pp. 109
Author(s):  
Panchand Jha
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Inference System ◽  
Inverse Kinematics ◽  
Fuzzy Inference ◽  
Robot Manipulator ◽  
Ann Model ◽  
Inference System ◽  
Artificial Neural ◽  
Neural Fuzzy

<span>Inverse kinematics of manipulator comprises the computation required to find the joint angles for a given Cartesian position and orientation of the end effector. There is no unique solution for the inverse kinematics thus necessitating application of appropriate predictive models from the soft computing domain. Artificial neural network and adaptive neural fuzzy inference system techniques can be gainfully used to yield the desired results. This paper proposes structured artificial neural network (ANN) model and adaptive neural fuzzy inference system (ANFIS) to find the inverse kinematics solution of robot manipulator. The ANN model used is a multi-layered perceptron Neural Network (MLPNN). Wherein, gradient descent type of learning rules is applied. An attempt has been made to find the best ANN configuration for the problem. It is found that ANFIS gives better result and minimum error as compared to ANN.</span>

Effectiveness prediction of abrasive jetting stream of accelerator tank using normalized sparse autoencoder-adaptive neural fuzzy inference system

2020 ◽  
Vol 234 (13) ◽  
pp. 1615-1639 ◽  
Author(s):  
Zhongwei Liang ◽  
Xiaochu Liu ◽  
Guilin Wen ◽  
Jinrui Xiao
Keyword(s):  
Neural Network ◽  
Simulated Annealing ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Impact Stress ◽  
Jet Impact ◽  
Effectiveness Prediction ◽  
Sparse Autoencoder ◽  
Neural Fuzzy

Abrasive jetting stream generated from accelerator tank is crucial to the precision machining of industrial products during the process of strengthen jet grinding. In this article, its effectiveness prediction using normalized sparse autoencoder-adaptive neural fuzzy inference system is carried out to provide an optimal result of jetting stream. A normalized sparse autoencoder-adaptive neural fuzzy inference system capable of calculating the concentration density of abrasive impact stress by normalized sparse autoencoder and identifying the effectiveness indexes of abrasive jetting by adaptive neural fuzzy inference system is proposed to predict the stream effectiveness index in grinding practices, indicating that when turbulence root-mean-square velocity ( VRMS) is 420 m/s, turbulence intensity ( Ti) is 570, turbulence kinetic energy ( Tc) is 540 kJ, turbulence entropy ( Te) is 620 J/K, and Reynolds shear stress ( Rs) is 430 kPa (Error tolerance = ± 5%, the same as follows), the optimized effectiveness quality of abrasive jetting stream could be ensured. The effectiveness prediction involve the following steps: measuring the jet impact data on the interior boundary surface of accelerator tank, calculating the concentration density of abrasive impact stress, establishing the descriptive analytical frame work of normalized sparse autoencoder-adaptive neural fuzzy inference system, adaptive prediction of abrasive jetting stream effectiveness through normalized sparse autoencoder-adaptive neural fuzzy inference system computation, and performance verification of actual effectiveness prediction in the efficiency quantification and quality assessment when it compared to that of alternative approaches, such as genetic, simulated annealing–genetic algorithm, Taguchi, artificial neural network–simulated annealing, and genetically optimized neural network system methods. Objective of this research is to adaptive predict the abrasive jetting stream effectiveness using a new-proposed prediction system, a stable and reliable abrasive jetting stream therefore can be achieved using jetting pressure ( Pw) at 320 MPa, mass of cast steel grits ( Mc) at 270 g, mass of bearing steel grits ( Mb) at 310 g, mass of brown-fused alumina grits ( Ma) at 360 g, and mass rate of abrasives ( Fa) at 0.46 kg/min. It is concluded that normalized sparse autoencoder-adaptive neural fuzzy inference system owns an outstanding predictive capability and possesses a much better working advancement in typical calibration indexes of accuracy and efficiency, meanwhile a high agreement between the fuzzy predicted and actual measured values of effectiveness indexes is ensured. This novel method could be promoted constructively to improve the quality uniformity for abrasive jetting stream and to facilitate the productive managements of abrasive jet machining consequently.

scholarly journals Quality of Experience Measurement for Video Streaming Based On Adaptive Neural Fuzzy Inference System

2019 ◽  
pp. 1609-1617
Author(s):  
Rana Fareed Ghani ◽  
Amal Sufiuh Ajrash
Keyword(s):  
Video Streaming ◽  
Fuzzy Inference System ◽  
Quality Of Experience ◽  
Fuzzy Inference ◽  
Technological Development ◽  
Video Quality ◽  
Quality Measurement ◽  
Inference System ◽  
Neural Fuzzy

Technological development in recent years leads to increase the access speed in the networks that allow a huge number of users watching videos online. Video streaming is one of the most popular applications in networking systems. Quality of Experience (QoE) measurement for transmitted video streaming may deal with data transmission problems such as packet loss and delay. This may affect video quality and leads to time consuming. We have developed an objective video quality measurement algorithm that uses different features, which affect video quality. The proposed algorithm has been estimated the subjective video quality with suitable accuracy. In this work, a video QoE estimation metric for video streaming services is presented where the proposed metric does not require information on the original video. This work predicts QoE of videos by extracting features. Two types of features have been used, pixel-based features and network-based features. These features have been used to train an Adaptive Neural Fuzzy Inference System (ANFIS) to estimate the video QoE. 

scholarly journals HANDWRITING IDENTIFICATION USING ANFIS

2014 ◽  
Vol 3 (2) ◽  
pp. 464-471
Author(s):  
T. Devi
Keyword(s):  
Neural Network ◽  
Membership Function ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Column Vector ◽  
Great Efficiency ◽  
Inference System ◽  
Handwritten Text ◽  
Triangular Membership Function ◽  
Neural Fuzzy

A new method for handwriting identification was presented.Individual characters was separated from a word choosed from a paragraph of handwritten text image which is given as input to the system. Then each of the separated characters are converted into column vectors of 625 values that are later fed into the adaptive neural fuzzy inference system(ANFIS), which was calculate membership function(MF) and normalized firing strength.In our paper we were used triangular membership function and compare with others MF.The networks has been designed with single layered neural network corresponding to a character from a-z, the outputs of all the column vector is fed into network the which has been developed using the concepts of correlation, with the help of this the overall network is optimized with the help of column vector thus providing us with recognized outputs with great efficiency.

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