با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
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1 |
Risk assessment of the UPIoT construction in China using combined dynamic weighting method under IFGDM environment
ارزیابی ریسک ساخت و ساز UPIoT در چین با استفاده از روش توزین وزن ترکیبی در محیط IFGDM-2020 Large-scale integration of renewable energy systems poses challenges to the ubiquitous power Internet of things (UPIoT) construction in China. This paper aims to study beyond these challenges from a risk assessment per- spective, using the combined dynamic weighting evidence fusion (CDWEF) method under the intuitionistic fuzzy group decision-making (IFGDM) environment. The UPIoT construction risk is identified and characterized by a 17-indicator system which is scored by intuitionistic fuzzy relations (IFRs) from experts. The IFRs are corrected by the dynamic expert weight determined from both the intuitionistic fuzzy entropy and conflicts among IFRs. The IF-AHP-DEMATEL method is adopted to determine the combined indicator weight for correcting the risk mass functions, which are obtained from the IFRs with the evidence fusion theory. The proposed risk assessment method is validated in a case study, indicating that the UPIoT construction risk in China is high in communication networks and business innovation. Keywords: The ubiquitous power Internet of things (UPIoT) | intuitionistic fuzzy group decision-making | combined dynamic weighting | intuitionistic fuzzy AHP-DEMATEL | risk assessment |
مقاله انگلیسی |
2 |
Quantum recurrent encoder–decoder neural network for performance trend prediction of rotating machinery
شبکه عصبی رمزگذار- رمزگذار مکرر کوانتومی برای پیش بینی روند عملکرد ماشین های چرخشی-2020 Traditional neural networks generally neglect the primary and secondary relationships of input information
and process the information indiscriminately, which leads to their bad nonlinear approximation
capacity and low generalization ability. As a result, traditional neural networks always show poor
prediction accuracy in the performance degradation trend prediction of rotating machinery (RM).
In view of this, a novel neural network called quantum recurrent encoder–decoder neural network
(QREDNN) is proposed in this paper. In QREDNN, the attention mechanism is used to simultaneously
reconstruct encoder and decoder of QREDNN, so that QREDNN can fully excavate and pay attention
to important information but suppress the interference of redundant information to obtain better
nonlinear approximation capacity. On the other hand, the quantum neuron is used to construct a
new quantum gated recurrent unit (QGRU) in which activation values and weights are represented
by quantum rotation matrices. The QGRU can traverse the solution space more finely and has a lot
of multiple attractors, so it can replace the traditional recurrent unit of the encoder and decoder
and enhance the generalization ability and response speed of QREDNN. Moreover, the Levenberg–
Marquardt (LM) algorithm is introduced to improve the update speeds of the rotation angles of
quantum rotation matrices and the attention parameters of QREDNN. Based on the superiorities of
QREDNN, a new performance trend prediction method for RM is proposed, in which the denoised
fuzzy entropy (DFE) of vibration acceleration signal of RM is input into QREDNN as the performance
degradation feature for predicting the performance degradation trend of RM. The examples of
predicting the performance trend of rolling bearings demonstrate the effectiveness of our proposed
method. Keywords: Quantum recurrent encoder–decoder | neural network (QREDNN) | Artificial intelligence | Attention mechanism | Quantum neuron | Performance trend prediction | Rotating machinery |
مقاله انگلیسی |
3 |
Pythagorean fuzzy VIKOR approaches based on TODIM for evaluating internet banking website quality of Ghanaian banking industry
رویکردهای VIKOR فازی Pythagorean مبتنی بر TODIM برای ارزیابی کیفیت وب سایت بانکی اینترنتی صنعت بانکی غنا-2019 With the rapid development of Internet banking technology in Ghana, the website quality evaluation
is the essential core of the customer, which is regarded as a multi-criteria decision making (MCDM)
problem. Due to the uncertainty of Internet banking, the evaluation of criteria may be measured
by Pythagorean fuzzy numbers (PFNs). In addition, the customer usually does not exhibit complete
rationality in the decision procedure. Based on the traditional VIKOR (Vise Kriterijumska Optimizacija
I Kompromisno Resenje) method of MCDM, this paper provides a new perspective of a compromised
solution, which can handle the decision maker’s psychological behavior by inducing TODIM (a
Portuguese acronym meaning Interactive Multi-Criteria Decision Making). By defining Pythagorean
fuzzy entropy and cross-entropy measures, we study the determination of the weights of the criteria
in advance. Then, considering the psychological behavior of the customer, we design two types of
strategies for the combination between TODIM and VIKOR. Meanwhile, the corresponding methods
have been developed, i.e., Approaches I and II. After that, a simulated example of ranking Internet
banking websites in the Ghanaian banking industry is given to illustrate the validity and applicability
of our proposed approaches. Finally, we utilize the Wilcoxon signed-rank test and then discuss the
differences among VIKOR, TODIM and our proposed methods. Keywords: Pythagorean fuzzy sets | VIKOR | TODIM | Multi-criteria decision making | Internet banking |
مقاله انگلیسی |
4 |
On Distributed Fuzzy Decision Trees for Big Data
درخت تصمیم گیری فازی توزیع شده برای داده های بزرگ-2018 Fuzzy decision trees (FDTs) have shown to be an effective solution in the framework of fuzzy classification. The approaches proposed so far to FDT learning, however, have generally
neglected time and space requirements. In this paper, we propose a
distributed FDT learning scheme shaped according to the MapReduceprogrammingmodelforgeneratingbothbinaryandmultiway
FDTs from big data. The scheme relies on a novel distributed fuzzy
discretizer that generates a strong fuzzy partition for each continuous attribute based on fuzzy information entropy. The fuzzy
partitions are, therefore, used as an input to the FDT learning algorithm, which employs fuzzy information gain for selecting the
attributes at the decision nodes. We have implemented the FDT
learning scheme on the Apache Spark framework. We have used
ten real-world publicly available big datasets for evaluating the
behavior of the scheme along three dimensions: 1) performance in
terms of classification accuracy, model complexity, and execution
time; 2) scalability varying the number of computing units; and
3) ability to efficiently accommodate an increasing dataset size.
We have demonstrated that the proposed scheme turns out to be
suitable for managing big datasets even with a modest commodity
hardware support. Finally, we have used the distributed decision
tree learning algorithm implemented in the MLLib library and the
Chi-FRBCS-BigData algorithm, a MapReduce distributed fuzzy
rule-based classification system, for comparative analysis
Index Terms: Apache spark, big data, fuzzy decision trees (FDTs), fuzzy discretizer, fuzzy entropy, fuzzy partitioning,MapReduce |
مقاله انگلیسی |
5 |
A Distributed Fuzzy Associative Classifier for Big Data
طبقه بندی کننده انجمنی توزیع شده فازی برای داده های بزرگ-2018 Fuzzy associative classification has not been widely
analyzed in the literature, although associative classifiers (ACs)
have proved to be very effective in different real domain applications. The main reason is that learning fuzzy ACs is a very heavy
task, especially when dealing with large datasets. To overcome
this drawback, in this paper, we propose an efficient distributed
fuzzy associative classification approach based on the MapReduce
paradigm. The approach exploits a novel distributed discretizer
based on fuzzy entropy for efficiently generating fuzzy partitions
of the attributes. Then, a set of candidate fuzzy association rules
is generated by employing a distributed fuzzy extension of the
well-known FP-Growth algorithm. Finally, this set is pruned by
using three purposely adapted types of pruning. We implemented
our approach on the popular Hadoop framework. Hadoop allows
distributing storage and processing of very large data sets on
computer clusters built from commodity hardware. We have performed an extensive experimentation and a detailed analysis of
the results using six very large datasets with up to 11 000 000
instances. We have also experimented different types of reasoning
methods. Focusing on accuracy, model complexity, computation
time, and scalability, we compare the results achieved by our
approach with those obtained by two distributed nonfuzzy ACs
recently proposed in the literature. We highlight that, although
the accuracies result to be comparable, the complexity, evaluated
in terms of number of rules, of the classifiers generated by the
fuzzy distributed approach is lower than the one of the nonfuzzy
classifiers.
Index Terms: Associative classifier (AC), big data, fuzzy AC (FAC), fuzzy FP-Growth, Hadoop, MapReduce |
مقاله انگلیسی |
6 |
On Distributed Fuzzy Decision Trees for Big Data
درخت تصمیم گیری فازی توزیع شده برای داده های بزرگ-2017 Fuzzy decision trees (FDTs) have shown to be an
effective solution in the framework of fuzzy classification. The
approaches proposed so far to FDT learning, however, have
generally neglected time and space requirements. In this paper,
we propose a distributed FDT learning scheme shaped according
to the MapReduce programming model for generating both
binary and multi-way FDTs from big data. The scheme relies
on a novel distributed fuzzy discretizer that generates a strong
fuzzy partition for each continuous attribute based on fuzzy
information entropy. The fuzzy partitions are therefore used
as input to the FDT learning algorithm, which employs fuzzy
information gain for selecting the attributes at the decision
nodes. We have implemented the FDT learning scheme on
the Apache Spark framework. We have used ten real-world
publicly available big datasets for evaluating the behavior of
the scheme along three dimensions: i) performance in terms of
classification accuracy, model complexity and execution time,
ii) scalability varying the number of computing units and iii)
ability to efficiently accommodate an increasing dataset size. We
have demonstrated that the proposed scheme turns out to be
suitable for managing big datasets even with modest commodity
hardware support. Finally, we have used the distributed decision
tree learning algorithm implemented in the MLLib library and
the Chi-FRBCS-BigData algorithm, a MapReduce distributed
fuzzy rule-based classification system, for comparative analysis.
Keywords: Fuzzy Decision Trees | Big Data | Fuzzy Entropy | Fuzzy Discretizer | Apache Spark | MapReduce | Fuzzy Partitioning |
مقاله انگلیسی |