The Fermatean fuzzy set (FFS) is a momentous generalization of a intuitionistic fuzzy set and a Pythagorean fuzzy set that can more accurately portray the complex vague information of elements and has stronger expert flexibility during decision analysis. The Combined Compromise Solution (CoCoSo) approach is a powerful decision-making technique to choose the ideal objective by fusing three aggregation strategies. In this paper, an integrated, multi-criteria group-decision-making (MCGDM) approach based on CoCoSo and FFS is used to assess green suppliers. To begin, several innovative operations of Fermatean fuzzy numbers based on Schweizer–Sklar norms are presented, and four aggregation operators utilizing the proposed operations are also developed. Several worthwhile properties of the advanced operations and operators are explored in detail. Next, a new Fermatean fuzzy entropy measure is propounded to determine the combined weight of criteria, in which the subjective and objective weights are computed by an improved best-and-worst method (BWM) and entropy weight approach, respectively. Furthermore, MCGDM based on CoCoSo and BWM-Entropy is brought forward and employed to sort diverse green suppliers. Lastly, the usefulness and effectiveness of the presented methodology is validated by comparison, and the stability of the developed MCGDM approach is shown by sensitivity analysis. The results shows that the introduced method is more stable during ranking of green suppliers, and the comparative results expound that the proposed method has higher universality and credibility than prior Fermatean fuzzy approaches.

In order to improve the intelligence of the e‐commerce online intelligent customer service system, this paper proposes a deep rejection recognition algorithm based on the maximum interval squared hinge loss and combines the actual needs of the e‐commerce online customer service system to build an intelligent customer service system with the support of the fuzzy control system. Moreover, this article chooses to build a domain ontology library for structured storage of domain knowledge needed by customer service chatbots. In addition, this article analyzes the dialogue structure based on the speech act model and combines the semantic vector model of the question sentence on the basis of the dialogue structure to understand the question sentence, which helps to improve the accuracy of the answer feedback of the Internet shopping customer service robot. Finally, this article designs experiments to verify the performance of the online customer service system constructed in this article and analyzes the experimental results through statistical methods. The experimental results show that the online intelligent customer service system constructed in this paper has certain practical effects.

Sparse representation based on classification and collaborative representation based classification with regularized least square has been successfully used in face recognition. The over-completed dictionary is crucial for the approaches based on sparse representation or collaborative representation because it directly determines recognition accuracy and recognition time. In this paper, we proposed an algorithm of adaptive dictionary learning according to the inputting testing image. First, nearest neighbors of the testing image are labeled in local configuration pattern (LCP) subspace employing statistical similarity and configuration similarity defined in this paper. Then the face images labeled as nearest neighbors are used as atoms to build the adaptive representation dictionary, which means all atoms of this dictionary are nearest neighbors and they are more similar to the testing image in structure. Finally, the testing image is collaboratively represented and classified class by class with this proposed adaptive over-completed compact dictionary. Nearest neighbors are labeled by local binary pattern and microscopic feature in the very low dimension LCP subspace, so the labeling is very fast. The number of nearest neighbors is changeable for the different testing samples and is much less than that of all training samples generally, which significantly reduces the computational cost. In addition, atoms of this proposed dictionary are these high dimension face image vectors but not lower dimension LCP feature vectors, which ensures not only that the information included in face image is not lost but also that the atoms are more similar to the testing image in structure, which greatly increases the recognition accuracy. We also use the Fisher ratio to assess the robustness of this proposed dictionary. The extensive experiments on representative face databases with variations of lighting, expression, pose, and occlusion demonstrate that the proposed approach is superior both in recognition time and in accuracy.

By using convolutional neural network models, it is possible to quickly and accurately recommend potential tourist attractions and routes of interest to users based on their interests and behaviors, thereby significantly improving the user experience. This article is based on a convolutional neural network model and develops a system that can recommend suitable tourist attractions through users uploading videos and capturing facial images for intelligent sentiment analysis. The experimental results show that the accuracy of the model reaches 83.3%, proving that it can accurately recognize user emotions and provide a more suitable travel recommendation scheme. In the post pandemic era, this system has extremely important practical significance in alleviating social anxiety and relieving people's daily depression.

Ligand-based virtual screening (LBVS) seeks strong early enrichment when searching ultra-large libraries, but practical screening often relies on 1D/2D descriptions while 3D information is expensive and uncertain due to conformer generation and alignment. We propose GNN-MA, a retrieval-style pairwise scoring model for query–candidate molecular pairs that uses molecular graphs as a unified representation. Built on intra-graph message passing, GNN-MA adds cross-graph attention to learn atom-level soft alignment that focuses on key substructures relevant to activity matching, and introduces a bond-to-atom semantic aggregation module to better exploit chemical bond cues for similarity scoring. The framework uses 2D molecular graphs derived from SMILES for retrieval-style matching and does not rely on explicit 3D conformational modeling or alignment. Experiments on DUD-E and LIT-PCBA show that GNN-MA achieves competitive overall discrimination (ROC-AUC) and, relative to its ablated variants, provides consistent gains in early-enrichment metrics (EF@1–5%) on DUD-E, while on LIT-PCBA the improvements are more target-dependent. The learned atom-level soft alignment also provides a qualitative interpretability cue in case studies. Throughput benchmarks suggest that GNN-MA is most suitable as a re-ranking/refinement model after a fast prefiltering stage.