Yunxiang Liu, Haili Ma, Jianlin Zhu^*, Qiangbo Zhang

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2963-2978, 2024, DOI:10.32604/cmc.2024.053710 - 15 August 2024

Abstract To enhance the efficiency and accuracy of environmental perception for autonomous vehicles, we propose GDMNet, a unified multi-task perception network for autonomous driving, capable of performing drivable area segmentation, lane detection, and traffic object detection. Firstly, in the encoding stage, features are extracted, and Generalized Efficient Layer Aggregation Network (GELAN) is utilized to enhance feature extraction and gradient flow. Secondly, in the decoding stage, specialized detection heads are designed; the drivable area segmentation head employs DySample to expand feature maps, the lane detection head merges early-stage features and processes the output through the Focal Modulation More >

Rana Muhammad Zulqarnain¹, Hafiz Khalil ur Rehman², Imran Siddique³, Hijaz Ahmad^4,5, Sameh Askar⁶, Shahid Hussain Gurmani^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1863-1892, 2024, DOI:10.32604/cmes.2023.031480 - 29 January 2024

Abstract This research is devoted to diagnosing water-borne infectious diseases caused by floods employing a novel diagnosis approach, the Einstein hybrid structure of q-rung orthopair fuzzy soft set. This approach integrates parts of fuzzy logic and soft set theory to develop a robust alternative for disease detection in stressful situations, especially in areas affected by floods. Compared to the traditional intuitionistic fuzzy soft set and Pythagorean fuzzy soft set, the q-rung orthopair fuzzy soft set (q-ROFSS) adequately incorporates unclear and indeterminate facts. The major objective of this investigation is to formulate the q-rung orthopair fuzzy soft… More >

Rana Muhammad Zulqarnain¹, Wen-Xiu Ma^1,2,3,*, Imran Siddique⁴, Hijaz Ahmad^5,6, Sameh Askar⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1829-1862, 2024, DOI:10.32604/cmes.2023.030687 - 29 January 2024

Abstract Green supplier selection is an important debate in green supply chain management (GSCM), attracting global attention from scholars, especially companies and policymakers. Companies frequently search for new ideas and strategies to assist them in realizing sustainable development. Because of the speculative character of human opinions, supplier selection frequently includes unreliable data, and the interval-valued Pythagorean fuzzy soft set (IVPFSS) provides an exceptional capacity to cope with excessive fuzziness, inconsistency, and inexactness through the decision-making procedure. The main goal of this study is to come up with new operational laws for interval-valued Pythagorean fuzzy soft numbers More >

Rana Muhammad Zulqarnain¹, Imran Siddique², Rifaqat Ali³, Fahd Jarad^4,5,6,*, Aiyared Iampan⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 619-651, 2023, DOI:10.32604/cmes.2022.022767 - 29 September 2022

Abstract Experts use Pythagorean fuzzy hypersoft sets (PFHSS) in their investigations to resolve the indeterminate and imprecise information in the decision-making process. Aggregation operators (AOs) perform a leading role in perceptivity among two circulations of prospect and pull out concerns from that perception. In this paper, we extend the concept of PFHSS to interval-valued PFHSS (IVPFHSS), which is the generalized form of interval-valued intuitionistic fuzzy soft set. The IVPFHSS competently deals with uncertain and ambagious information compared to the existing interval-valued Pythagorean fuzzy soft set. It is the most potent method for amplifying fuzzy data in… More >

Xiaobing Mao, Hao Wu, Shuping Wan^*

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.3, pp. 1751-1792, 2022, DOI:10.32604/cmes.2022.019501 - 19 April 2022

Abstract This paper proposes a personalized comprehensive cloud-based method for heterogeneous multi-attribute group decision-making (MAGDM), in which the evaluations of alternatives on attributes are represented by LTs (linguistic terms), PLTSs (probabilistic linguistic term sets) and LHFSs (linguistic hesitant fuzzy sets). As an effective tool to describe LTs, cloud model is used to quantify the qualitative evaluations. Firstly, the regulation parameters of entropy and hyper entropy are defined, and they are further incorporated into the transformation process from LTs to clouds for reflecting the different personalities of decision-makers (DMs). To tackle the evaluation information in the form… More >

Rana Muhammad Zulqarnain¹, Imran Siddique², Aiyared Iampan³, Dumitru Baleanu^4,5,6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.3, pp. 1717-1750, 2022, DOI:10.32604/cmes.2022.019408 - 19 April 2022

Abstract Interval-valued Pythagorean fuzzy so set (IVPFSS) is a generalization of the interval-valued intuitionistic fuzzy so set (IVIFSS) and interval-valued Pythagorean fuzzy set (IVPFS). The IVPFSS handled more uncertainty comparative to IVIFSS; it is the most signicant technique for explaining fuzzy information in the decision-making process. In this work, some novel operational laws for IVPFSS have been proposed. Based on presented operational laws, two innovative aggregation operators (AOs) have been developed such as interval-valued Pythagorean fuzzy so weighted average (IVPFSWA) and interval-valued Pythagorean fuzzy so weighted geometric (IVPFSWG) operators with their fundamental properties. A multi-attribute group More >

A. Sumathi^*, S. Meganathan

Computer Systems Science and Engineering, Vol.40, No.1, pp. 313-325, 2022, DOI:10.32604/csse.2022.017484 - 26 August 2021

Abstract Gestational Diabetes Mellitus (GDM) is an illness that represents a certain degree of glucose intolerance with onset or first recognition during pregnancy. In the past few decades, numerous investigations were conducted upon early identification of GDM. Machine Learning (ML) methods are found to be efficient prediction techniques with significant advantage over statistical models. In this view, the current research paper presents an ensemble of ML-based GDM prediction and classification models. The presented model involves three steps such as preprocessing, classification, and ensemble voting process. At first, the input medical data is preprocessed in four levels… More >

Sifan Long¹, Ming Zhao^2,*, Xinfu He³

CMC-Computers, Materials & Continua, Vol.58, No.3, pp. 727-760, 2019, DOI:10.32604/cmc.2019.04454

Abstract With the development of society and the exhaustion of fossil energy, researcher need to identify new alternative energy sources. Nuclear energy is a very good choice, but the key to the successful application of nuclear technology is determined primarily by the behavior of nuclear materials in reactors. Therefore, we studied the radiation performance of the fusion material reduced activation ferritic/martensitic (RAFM) steel. The main novelty of this paper are the statistical analysis of RAFM steel data sets through related statistical analysis and the formula derivation of the gradient descent method (GDM) which combines the gradient… More >