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  • Open Access


    Power Quality Assessment Based on Rough AHP and Extension Analysis

    Guofeng Liu*, Can Zhang, Zhengyi Zhu, Xuyan Wang

    Energy Engineering, Vol.119, No.3, pp. 929-946, 2022, DOI:10.32604/ee.2022.014816

    Abstract Due to the increasing power consumption of whole society and widely using of new non-linear and asymmetric electrical equipment, power quality assessment problem in the new period has attracted more and more attention. The mathematical essence of comprehensive assessment of power quality is a multi-attribute optimal decision-making problem. In order to solve the key problem of determining the indicator weight in the process of power quality assessment, a rough analytic hierarchy process (AHP) is proposed to aggregate the judgment opinions of multiple experts and eliminate the subjective effects of single expert judgment. Based on the advantage of extension analysis for… More >

  • Open Access


    Soft -Rough Set and Its Applications in Decision Making of Coronavirus

    M. A. El Safty1,*, Samirah Al Zahrani1, M. K. El-Bably2, M. El Sayed3

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 267-285, 2022, DOI:10.32604/cmc.2022.019345

    Abstract In this paper, we present a proposed method for generating a soft rough approximation as a modification and generalization of Zhaowen et al. approach. Comparisons were obtained between our approach and the previous study and also. Eventually, an application on Coronavirus (COVID-19) has been presented, illustrated using our proposed concept, and some influencing results for symptoms of Coronavirus patients have been deduced. Moreover, following these concepts, we construct an algorithm and apply it to a decision-making problem to demonstrate the applicability of our proposed approach. Finally, a proposed approach that competes with others has been obtained, as well as realistic… More >

  • Open Access


    Emergency Decision-Making Based on q-Rung Orthopair Fuzzy Rough Aggregation Information

    Ahmed B. Khoshaim1, Saleem Abdullah2, Shahzaib Ashraf3,*, Muhammad Naeem4

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4077-4094, 2021, DOI:10.32604/cmc.2021.016973

    Abstract With the frequent occurrences of emergency events, emergency decision making (EDM) plays an increasingly significant role in coping with such situations and has become an important and challenging research area in recent times. It is essential for decision makers to make reliable and reasonable emergency decisions within a short span of time, since inappropriate decisions may result in enormous economic losses and social disorder. To handle emergency effectively and quickly, this paper proposes a new EDM method based on the novel concept of q-rung orthopair fuzzy rough (q-ROPR) set. A novel list of q-ROFR aggregation information, detailed description of the… More >

  • Open Access


    Decision Making Based on Fuzzy Soft Sets and Its Application in COVID-19

    S. A. Al blowi1, M. El Sayed2, M. A. El Safty3,*

    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 961-972, 2021, DOI:10.32604/iasc.2021.018242

    Abstract Real-world applications are now dealing with a huge amount of data, especially in the area of high-dimensional features. Trait reduction is one of the major steps in decision making problems. It refers to the determination of a minimum subset of attributes which preserves the final decision based on the entire set of attributes. Unfortunately, most of the current features are irrelevant or redundant, which makes these systems unreliable and imprecise. This paper proposes a new paradigm based on fuzzy soft relationship and level fuzzy soft relationship, called Union - Intersection decision making method. Using these new principles, the decision-making strategy… More >

  • Open Access


    Optimization of Sentiment Analysis Using Teaching-Learning Based Algorithm

    Abdullah Muhammad, Salwani Abdullah, Nor Samsiah Sani*

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1783-1799, 2021, DOI:10.32604/cmc.2021.018593

    Abstract Feature selection and sentiment analysis are two common studies that are currently being conducted; consistent with the advancements in computing and growing the use of social media. High dimensional or large feature sets is a key issue in sentiment analysis as it can decrease the accuracy of sentiment classification and make it difficult to obtain the optimal subset of the features. Furthermore, most reviews from social media carry a lot of noise and irrelevant information. Therefore, this study proposes a new text-feature selection method that uses a combination of rough set theory (RST) and teaching-learning based optimization (TLBO), which is… More >

  • Open Access


    A Multi-Agent Stacking Ensemble Hybridized with Vaguely Quantified Rough Set for Medical Diagnosis

    Ali M. Aseere1,*, Ayodele Lasisi2

    Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 683-699, 2021, DOI:10.32604/iasc.2021.014811

    Abstract In the absence of fast and adequate measures to combat them, life-threatening diseases are catastrophic to human health. Computational intelligent algorithms characterized by their adaptability, robustness, diversity, and recognition abilities allow for the diagnosis of medical diseases. This enhances the decision-making process of physicians. The objective is to predict and classify diseases accurately. In this paper, we proposed a multi-agent stacked ensemble classifier based on a vaguely quantified rough set, simple logistic algorithm, sequential minimal optimization (SMO), and JRip. The vaguely quantified rough set (VQRS) is used for feature selection and eradicating noise in the data. There are two classifier… More >

  • Open Access


    Multi-Span and Multiple Relevant Time Series Prediction Based on Neighborhood Rough Set

    Xiaoli Li1, Shuailing Zhou1, Zixu An2,*, Zhenlong Du1

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3765-3780, 2021, DOI:10.32604/cmc.2021.012422

    Abstract Rough set theory has been widely researched for time series prediction problems such as rainfall runoff. Accurate forecasting of rainfall runoff is a long standing but still mostly significant problem for water resource planning and management, reservoir and river regulation. Most research is focused on constructing the better model for improving prediction accuracy. In this paper, a rainfall runoff forecast model based on the variable-precision fuzzy neighborhood rough set (VPFNRS) is constructed to predict Watershed runoff value. Fuzzy neighborhood rough set define the fuzzy decision of a sample by using the concept of fuzzy neighborhood. The fuzzy neighborhood rough set… More >

  • Open Access


    A New Decision-Making Model Based on Plithogenic Set for Supplier Selection

    Mohamed Abdel-Basset1,*, Rehab Mohamed1, Florentin Smarandache2, Mohamed Elhoseny3

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2751-2769, 2021, DOI:10.32604/cmc.2021.013092

    Abstract Supplier selection is a common and relevant phase to initialize the supply chain processes and ensure its sustainability. The choice of supplier is a multi-criteria decision making (MCDM) to obtain the optimal decision based on a group of criteria. The health care sector faces several types of problems, and one of the most important is selecting an appropriate supplier that fits the desired performance level. The development of service/product quality in health care facilities in a country will improve the quality of the life of its population. This paper proposes an integrated multi-attribute border approximation area comparison (MABAC) based on… More >

  • Open Access


    Non-Deterministic Outlier Detection Method Based on the Variable Precision Rough Set Model

    Alberto Fernández Oliva1, Francisco Maciá Pérez2, José Vicente Berná-Martinez2,*, Miguel Abreu Ortega3

    Computer Systems Science and Engineering, Vol.34, No.3, pp. 131-144, 2019, DOI:10.32604/csse.2019.34.131

    Abstract This study presents a method for the detection of outliers based on the Variable Precision Rough Set Model (VPRSM). The basis of this model is the generalisation of the standard concept of a set inclusion relation on which the Rough Set Basic Model (RSBM) is based. The primary contribution of this study is the improvement in detection quality, which is achieved due to the generalisation allowed by the classification system that allows a certain degree of uncertainty. From this method, a computationally efficient algorithm is proposed. The experiments performed with a real scenario and a comparison of the results with… More >

  • Open Access


    A Novel Fuzzy Rough Sets Theory Based CF Recommendation System

    C. Raja Kumar1, VE. Jayanthi2

    Computer Systems Science and Engineering, Vol.34, No.3, pp. 123-129, 2019, DOI:10.32604/csse.2019.34.123

    Abstract Collaborative Filtering (CF) is one of the popular methodology in recommender systems. It suffers from the data sparsity problem, recommendation inaccuracy and big-error in predictions. In this paper, the efficient advisory tool is implemented for the younger generation to choose their right career based on their knowledge. It acquires the notions of indiscernible relation from Fuzzy Rough Sets Theory (FRST) and propose a novel algorithm named as Fuzzy Rough Set Theory Based Collaborative Filtering Algorithm (FRSTBCF). To evaluate the model, data is prepared using the cross validation method. Based on that, ratings are evaluated by calculating the MAE (mean average… More >

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