Xiaoqin Ma^1,2, Jun Wang¹, Wenchang Yu¹, Qinli Zhang^1,2,*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2063-2083, 2024, DOI:10.32604/cmc.2024.049147 - 15 May 2024

Abstract The presence of numerous uncertainties in hybrid decision information systems (HDISs) renders attribute reduction a formidable task. Currently available attribute reduction algorithms, including those based on Pawlak attribute importance, Skowron discernibility matrix, and information entropy, struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values, and attributes with fuzzy boundaries and abnormal values. In order to address the aforementioned issues, this paper delves into the study of attribute reduction within HDISs. First of all, a novel metric based on the decision attribute is introduced to solve… More >

Rodyna A. Hosny¹, Radwan Abu-Gdairi², Mostafa K. El-Bably^3,*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3073-3085, 2023, DOI:10.32604/iasc.2023.034234 - 15 March 2023

Abstract The theory of rough set represents a non-statistical methodology for analyzing ambiguity and imprecise information. It can be characterized by two crisp sets, named the upper and lower approximations that are used to determine the boundary region and accurate measure of any subset. This article endeavors to achieve the best approximation and the highest accuracy degree by using the minimal structure approximation space via ideal . The novel approach (indicated by ) modifies the approximation space to diminish the boundary region and enhance the measure of accuracy. The suggested method is more accurate than Pawlak’s More >

Ibrahim Noaman¹, Abd El Fattah El Atik², Tamer Medhat^3,*, Manal E. Ali⁴

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6893-6908, 2023, DOI:10.32604/cmc.2023.033539 - 28 December 2022

Abstract This article focuses on the relationship between mathematical morphology operations and rough sets, mainly based on the context of image retrieval and the basic image correspondence problem. Mathematical morphological procedures and set approximations in rough set theory have some clear parallels. Numerous initiatives have been made to connect rough sets with mathematical morphology. Numerous significant publications have been written in this field. Others attempt to show a direct connection between mathematical morphology and rough sets through relations, a pair of dual operations, and neighborhood systems. Rough sets are used to suggest a strategy to approximate… More >

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 - 21 July 2021

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… More >

Mohamed Abdel-Basset^1,*, Rehab Mohamed¹, Florentin Smarandache², Mohamed Elhoseny³

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2751-2769, 2021, DOI:10.32604/cmc.2021.013092 - 28 December 2020

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… More >

L. Ezhilarasi^1,*, A.P. Shanthi², V. Uma Maheswari¹

Intelligent Automation & Soft Computing, Vol.26, No.3, pp. 465-478, 2020, DOI:10.32604/iasc.2020.013923

Abstract Development of new Artificial Intelligence related data analy sis methodologies w ith rev olutionary information technology has made a radical change in prediction, forecasting, and decision making for real-w orld data. The challenge arises w hen the real w orld dataset consisting of v oluminous data is uncertain. The rough set is a mathematical formalism that has emerged significantly for uncertain datasets. It represents the know ledge of the datasets as decision rules. It does not need any metadata. The rules are used to predict or classify unseen ex amples. The objectiv e of this… More >

Haresh Kumar Sharma, Kriti Kumari, Samarjit Kar

Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 1-14, 2019, DOI:10.31209/2018.100000036

Abstract This article focuses on the use of the rough set theory in modeling of time series forecasting. In this paper, we have used the double exponential smoothing (DES) model for forecasting. The classical DES model has been improved by using the rough set technique. The improved double exponential smoothing (IDES) method can be used for the time series data without any statistical assumptions. The proposed method is applied on tourism demand of the air transportation passenger data set in Australia and the results are compared with the classical DES model. It has been observed that More >