Fangfang Shan^1,2,*, Mengyao Liu^1,2, Menghan Zhang^1,2, Zhenyu Wang^1,2

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1521-1542, 2024, DOI:10.32604/cmc.2024.053937

Abstract Social media has become increasingly significant in modern society, but it has also turned into a breeding ground for the propagation of misleading information, potentially causing a detrimental impact on public opinion and daily life. Compared to pure text content, multmodal content significantly increases the visibility and share ability of posts. This has made the search for efficient modality representations and cross-modal information interaction methods a key focus in the field of multimodal fake news detection. To effectively address the critical challenge of accurately detecting fake news on social media, this paper proposes a fake… More >

Zhan Shi^*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 973-994, 2024, DOI:10.32604/cmc.2024.051530

Abstract The convergence of Internet of Things (IoT), 5G, and cloud collaboration offers tailored solutions to the rigorous demands of multi-flow integrated energy aggregation dispatch data processing. While generative adversarial networks (GANs) are instrumental in resource scheduling, their application in this domain is impeded by challenges such as convergence speed, inferior optimality searching capability, and the inability to learn from failed decision making feedbacks. Therefore, a cloud-edge collaborative federated GAN-based communication and computing resource scheduling algorithm with long-term constraint violation sensitiveness is proposed to address these challenges. The proposed algorithm facilitates real-time, energy-efficient data processing by More >

Zhijun Guo¹, Yun Sun^2,*, Ying Wang¹, Chaoqi Fu³, Jilong Zhong^4,*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2375-2398, 2024, DOI:10.32604/cmc.2024.048112

Abstract Due to the time-varying topology and possible disturbances in a conflict environment, it is still challenging to maintain the mission performance of flying Ad hoc networks (FANET), which limits the application of Unmanned Aerial Vehicle (UAV) swarms in harsh environments. This paper proposes an intelligent framework to quickly recover the cooperative coverage mission by aggregating the historical spatio-temporal network with the attention mechanism. The mission resilience metric is introduced in conjunction with connectivity and coverage status information to simplify the optimization model. A spatio-temporal node pooling method is proposed to ensure all node location features… More >

Teng Wang, Yao Zhang, Jiangguo Liang, Shuai Wang, Shuanggen Liu^*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3891-3907, 2024, DOI:10.32604/cmc.2024.048115

Abstract With the widespread data collection and processing, privacy-preserving machine learning has become increasingly important in addressing privacy risks related to individuals. Support vector machine (SVM) is one of the most elementary learning models of machine learning. Privacy issues surrounding SVM classifier training have attracted increasing attention. In this paper, we investigate Differential Privacy-compliant Federated Machine Learning with Dimensionality Reduction, called Fed_DPDR-DPML, which greatly improves data utility while providing strong privacy guarantees. Considering in distributed learning scenarios, multiple participants usually hold unbalanced or small amounts of data. Therefore, Fed_DPDR-DPML enables multiple participants to collaboratively learn a global… More >

Xingli Wu^{1,{{sup}}#{{/sup}}}, Huchang Liao^{1,{{sup}}#{{/sup}}}, Shuxian Sun¹, Zhengjun Wan^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3125-3146, 2024, DOI:10.32604/cmes.2023.047031

Abstract Agricultural investment project selection is a complex multi-criteria decision-making problem, as agricultural projects are easily influenced by various risk factors, and the evaluation information provided by decision-makers usually involves uncertainty and inconsistency. Existing literature primarily employed direct preference elicitation methods to address such issues, necessitating a great cognitive effort on the part of decision-makers during evaluation, specifically, determining the weights of criteria. In this study, we propose an indirect preference elicitation method, known as a preference disaggregation method, to learn decision-maker preference models from decision examples. To enhance evaluation ease, decision-makers merely need to compare… 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

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 >

Tariq Qayyum¹, Zouheir Trabelsi^1,*, Asadullah Tariq¹, Muhammad Ali², Kadhim Hayawi³, Irfan Ud Din⁴

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1739-1757, 2023, DOI:10.32604/cmc.2023.043684

Abstract Federated Learning (FL) enables collaborative and privacy-preserving training of machine learning models within the Internet of Vehicles (IoV) realm. While FL effectively tackles privacy concerns, it also imposes significant resource requirements. In traditional FL, trained models are transmitted to a central server for global aggregation, typically in the cloud. This approach often leads to network congestion and bandwidth limitations when numerous devices communicate with the same server. The need for Flexible Global Aggregation and Dynamic Client Selection in FL for the IoV arises from the inherent characteristics of IoV environments. These include diverse and distributed… More >

Salma Khan¹, Muhammad Gulistan¹, Nasreen Kausar², Seifedine Kadry^3,4,5, Jungeun Kim^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1951-1979, 2024, DOI:10.32604/cmes.2023.030896

Abstract Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons, including leisure, pleasure, or business. A recent study has proposed a unique mathematical concept called a q−Rung orthopair fuzzy hypersoft set (ROFHS) to enhance the formal representation of human thought processes and evaluate tourism carrying capacity. This approach can capture the imprecision and ambiguity often present in human perception. With the advanced mathematical tools in this field, the study has also incorporated the Einstein aggregation operator and score function into the ROFHS values to support multi-attribute More >

Yixia Chen^1,2, Mingwei Lin^1,2,*

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 845-866, 2023, DOI:10.32604/cmc.2023.040970

Abstract The consensus scheme is an essential component in the real blockchain environment. The Delegated Proof of Stake (DPoS) is a competitive consensus scheme that can decrease energy costs, promote decentralization, and increase efficiency, respectively. However, how to study the knowledge representation of the collective voting information and then select delegates is a new open problem. To ensure the fairness and effectiveness of transactions in the blockchain, in this paper, we propose a novel fine-grained knowledge representation method, which improves the DPoS scheme based on the linguistic term set (LTS) and proportional hesitant fuzzy linguistic term… More >

Zhihong Ouyang^*, Lei Xue, Feng Ding, Yongsheng Duan

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 983-1008, 2023, DOI:10.32604/cmc.2023.042222

Abstract Affinity propagation (AP) is a widely used exemplar-based clustering approach with superior efficiency and clustering quality. Nevertheless, a common issue with AP clustering is the presence of excessive exemplars, which limits its ability to perform effective aggregation. This research aims to enable AP to automatically aggregate to produce fewer and more compact clusters, without changing the similarity matrix or customizing preference parameters, as done in existing enhanced approaches. An automatic aggregation enhanced affinity propagation (AAEAP) clustering algorithm is proposed, which combines a dependable partitioning clustering approach with AP to achieve this purpose. The partitioning clustering… More >