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Hybrid subjective evaluation method using weighted subsethood - based (WSBA) rule generation algorithm
Published 2013“…The use of fuzzy rules, which were extracted directly from input data through Weighted Subsethood-based (WSBA) Rule Generation Algorithm.WSBA rule generation use the subsethood values to generate the weights which finally produced the fuzzy general rules.The rules generated through the data provided knowledge in developed fuzzy rule The fuzzy rules embedded in the framework of subjective evaluation method showed advantages in generalizing the evaluation of the performance achievement, where the evaluation process can be conducted consistently in producing good evaluation results with the use of the membership set score.The results from the numerical examples are comparable to other fuzzy evaluation methods, even with the use of small rule size.…”
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Fuzzy Type-1 Triangular Membership Function Approximation Using Fuzzy C-Means
Published 2020“…This research focuses on generating the parametric values of the triangular membership function using a novel method. …”
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3
Hybrid subjective evaluation of rule Exraction Algorithm using Weighted Subsethood-Based (WSBA)
Published 2013“…Fuzzy rules are important elements that being highlighted in any fuzzy expert system.This research proposes the framework of subjective performance evaluation using fuzzy technique for ranking the performance of the financial performance of a company under a multi criteria environment.There are a lot of techniques used such as fuzzy similarity function, fuzzy synthetic decision and satisfaction function have been adopted.The framework is based on fuzzy multi-criteria decision-making that consists of fuzzy rules.The use of fuzzy rules, which were extracted directly from input data through Weighted Subsethood-based (WSBA) Rule Generation Algorithm.WSBA rule generation use the subsethood values to generate the weights which finally produced the fuzzy general rules.The rules generated through the data provided knowledge in developed fuzzy rule The fuzzy rules embedded in the framework of subjective evaluation method showed advantages in generalizing the evaluation of the performance achievement, where the evaluation process can be conducted consistently in producing good evaluation results with the use of the membership set score.The results from the numerical examples are comparable to other fuzzy evaluation methods, even with the use of small rule size.…”
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4
New Learning Models for Generating Classification Rules Based on Rough Set Approach
Published 2000“…Recently, different models were used to generate knowledge from vague and uncertain data sets such as induction decision tree, neural network, fuzzy logic, genetic algorithm, rough set theory, and others. …”
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An Innovative Signal Detection Algorithm in Facilitating the Cognitive Radio Functionality for Wireless Regional Area Network Using Singular Value Decomposition
Published 2011“…In order to use the algorithm effectively, users need to balance between detection accuracy and execution time. …”
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6
Efficient genetic partitioning-around-medoid algorithm for clustering
Published 2019“…Accordingly, the corresponding genetic operators are adapted to suite the medoid and to incorporate much clustering-specific domain knowledge. The algorithm is also preceded with careful seeding using mathematically proved to converge k-means++ algorithm. …”
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7
Discovering decision algorithm from a distance relay event report
Published 2009“…In helping the protection engineers deal with this overwhelming data, this study relied merely on digital protective relay’s recorded event report because, among other intelligent electronic devices, digital protective relay sufficiently provided virtually most attributes needed for data mining process in knowledge discovery in database. The method of discovering the distance relay decision algorithm essentially involved formulating rough set discernibility matrix and function from relay event report, finding reducts of pertinent attributes using genetic algorithm and finally generating relay prediction rules. …”
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Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining.
Published 2005“…Total rules number, rules length and rules accuracy for the generation rules are recorded. The accuracy for rules and classification resulted from the TIP method are compared with other methods such as Standard Integer Programming (SIP) and Decision Related Integer Programming (DRIP) from Rough Set, Genetic Algorithm (GA), Johnson reducer, HoltelR method, Multiple Regression (MR), Neural Network (NN), Induction of Decision Tree Algorithm (ID3) and Base Learning Algorithm (C4.5); all other classifiers that are mostly used in the classification tasks. …”
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Effective query structuring with ranking using named entity categories for XML retrieval
Published 2016“…The method employs Semantic Tags Extraction (STSE) algorithm to extract semantic tags of an element and Element Enrichment (EERM) algorithm to enrich the elements. …”
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A Model Development And Comparison Study On The Microwave Remote Sensing Of Snow Medium Using A Coupled Finite Element Method And Method Of Moment, And The Relaxed Hierarchical Equ...
Published 2022“…The investigatio n is done by integrating two computational techniques, which are the coupled Finite Element Method (FEM) and Method of Moment (MoM) and the Relaxed Hierarchical Equivalent Source Algorithm (RHESA). …”
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Combining Recursive Least Square and Principal Component Analysis for Assisted History Matching
Published 2014“…Forward model was also involved in the process of defining the objective function. Next, using simulated data together with historical data, objective function will be computed. …”
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Bayesian logistic regression model on risk factors of type 2 diabetes mellitus
Published 2016“…The significant variables determined by maximum likelihood method were then estimated using the BLR method. The BLR approach via Gibbs sampler and the random walk metropolis algorithm suggests that family history of diabetes, waist circumference and the body mass index are the significant risk factors associated with the type 2 diabetes mellitus. …”
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What, how and when to use knowledge in neural network application
Published 2004“…These weights are assigned randomly or generated using other procedures such as Nguyen-Widrow initialization algorithm. …”
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Electricity load profile determination by using fuzzy C-means and probability neural network / Norhasnelly Anuar
Published 2015“…The objectives of this project are to use FCM as the clustering algorithm to establish TLPs. …”
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Blind motion image deblurring using canny edge detector with generative adversarial networks / Idriss Moussa Idriss
Published 2021“…Experiment s are conducted using the GoPro dataset. The proposed combined method has achieved good deblurring with edge-preserving results based on the evaluation metrics used. …”
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17
Extended Haar Wavelet Quasilinearization method for solving boundary value problems / Nor Artisham Che Ghani
Published 2018“…This method can therefore serve as very useful tool in many physical applications.…”
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A framework of modified adaptive neuro-fuzzy inference engine
Published 2012“…The Takagi-Sugeno-Kang (TSK) type fuzzy inference system was chosen and constructed by an automatic generation of clusters as well as membership functions and minimal rules through the use of hybrid fuzzy clustering and the modified apriori algorithms respectively. …”
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Fuzzy-Genetic based approach in decision making for repair of turbochargers using additive manufacturing
Published 2023“…Genetic algorithm optimization method was used to optimize the cost of the repairing process once the decision on whether the turbocharger was repairable was determined by the Fuzzy system. …”
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Effective keyword query structuring using NER for XML retrieval
Published 2015“…It also include a ranking function computes a score for each generated query by using both semantic information and data statistic as opposed to data statistic only approach used by the existing approaches.…”
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