Mohsen Eid¹, Tamer Medhat^2,*, Manal E. Ali³

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1531-1544, 2023, DOI:10.32604/csse.2023.031745 - 03 November 2022

Abstract An information system is a type of knowledge representation, and attribute reduction is crucial in big data, machine learning, data mining, and intelligent systems. There are several ways for solving attribute reduction problems, but they all require a common categorization. The selection of features in most scientific studies is a challenge for the researcher. When working with huge datasets, selecting all available attributes is not an option because it frequently complicates the study and decreases performance. On the other side, neglecting some attributes might jeopardize data accuracy. In this case, rough set theory provides a… More >

Kanupriya Mittal^*, V. Mary Anita Rajam

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1907-1921, 2023, DOI:10.32604/iasc.2023.029037 - 19 July 2022

Abstract An automated retinal disease detection system has long been in existence and it provides a safe, no-contact and cost-effective solution for detecting this disease. This paper presents a game theory-based dynamic weighted ensemble of a feature extraction-based machine learning model and a deep transfer learning model for automatic retinal disease detection. The feature extraction-based machine learning model uses Gaussian kernel-based fuzzy rough sets for reduction of features, and XGBoost classifier for the classification. The transfer learning model uses VGG16 or ResNet50 or Inception-ResNet-v2. A novel ensemble classifier based on the game theory approach is proposed More >

M. El Sayed^1,*, Wadia Faid Hassan Al-shameri¹, M. A. El Safty²

CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.3, pp. 865-879, 2022, DOI:10.32604/cmes.2022.020066 - 27 June 2022

Abstract A soft, rough set model is a distinctive mathematical model that can be used to relate a variety of real-life data. In the present work, we introduce new concepts of rough set based on soft pre-lower and soft pre-upper approximation space. These concepts are soft pre-rough equality, soft pre-rough inclusion, soft pre-rough belonging, soft predefinability, soft pre-internal lower, and soft pre-external lower. We study the properties of these concepts. Finally, we use the soft pre-rough approximation to illustrate the importance of our method in decision-making for Chikungunya medical illnesses. In reality, the impact factors of More >

Ahmad Taher Azar^1,2,*, Mustafa Samy Elgendy¹, Mustafa Abdul Salam^1,3, Khaled M. Fouad^1,4

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1087-1108, 2022, DOI:10.32604/cmc.2022.028184 - 18 May 2022

Abstract Big data is a vast amount of structured and unstructured data that must be dealt with on a regular basis. Dimensionality reduction is the process of converting a huge set of data into data with tiny dimensions so that equal information may be expressed easily. These tactics are frequently utilized to improve classification or regression challenges while dealing with machine learning issues. To achieve dimensionality reduction for huge data sets, this paper offers a hybrid particle swarm optimization-rough set PSO-RS and Mayfly algorithm-rough set MA-RS. A novel hybrid strategy based on the Mayfly algorithm (MA) More >

Amer Mirkhan¹, Numan Çelebi^2,*

Computer Systems Science and Engineering, Vol.43, No.2, pp. 767-783, 2022, DOI:10.32604/csse.2022.023249 - 20 April 2022

Abstract In most of the scientific research feature selection is a challenge for researcher. Selecting all available features is not an option as it usually complicates the research and leads to performance drop when dealing with large datasets. On the other hand, ignoring some features can compromise the data accuracy. Here the rough set theory presents a good technique to identify the redundant features which can be dismissed without losing any valuable information, however, exploring all possible combinations of features will end with NP-hard problem. In this research we propose adopting a heuristic algorithm to solve More >

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

Energy Engineering, Vol.119, No.3, pp. 929-946, 2022, DOI:10.32604/ee.2022.014816 - 31 March 2022

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… More >

M. A. El Safty^1,*, 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 - 07 September 2021

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 More >

Ahmed B. Khoshaim¹, Saleem Abdullah², Shahzaib Ashraf^3,*, Muhammad Naeem⁴

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4077-4094, 2021, DOI:10.32604/cmc.2021.016973 - 24 August 2021

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… More >

S. A. Al blowi¹, M. El Sayed², M. A. El Safty^3,*

Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 961-972, 2021, DOI:10.32604/iasc.2021.018242 - 20 August 2021

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 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 >