Soft Computing最新文献

It is known that the so-called Beck’s conjecture, i.e. the equality of the finite clique and chromatic numbers of a zero-divisor graph, holds for partially ordered sets Halaš and Jukl (Discrete Math 309(13):4584–4589, 2009). In this paper we present a simple direct proof of this fact. Also, the case when the finiteness assumption of the clique number is omitted is investigated. We have shown that the conjecture fails in general and a bunch of counterexamples is presented.

Our study presents a novel method for analyzing biological sequences, utilizing Pairwise Amino Acid Composition and Amino Acid physicochemical properties to construct a feature vector. This step is pivotal, as by utilizing pairwise analysis, we consider the order of amino acids, thereby capturing subtle nuances in sequence structure. Simultaneously, by incorporating physicochemical properties, we ensure that the hidden information encoded within amino acids is not overlooked. Furthermore, by considering both the frequency and order of amino acid pairs, our method mitigates the risk of erroneously clustering different sequences as similar, a common pitfall in older methods. Our approach generates a concise 48-member vector, accommodating sequences of arbitrary lengths efficiently. This compact representation retains essential amino acid-specific features, enhancing the accuracy of sequence analysis. Unlike traditional approaches, our algorithm avoids the introduction of sparse vectors, ensuring the retention of important information. Additionally, we introduce fuzzy equivalence relationships to address uncertainty in the clustering process, enabling a more nuanced and flexible clustering approach that captures the inherent ambiguity in biological data. Despite these advancements, our algorithm is presented in a straightforward manner, ensuring accessibility to researchers with varying levels of computational expertise. This enhancement improves the robustness and interpretability of our method, providing researchers with a comprehensive and user-friendly tool for biological sequence analysis.

With the increasing popularity of social media, Online Social Networks (OSNs) are being used for promoting or discrediting various products or persons. As such, rumors are spread in the networks to increase or decrease the popularity of the target. Limiting the spread of rumors is an important research problem. In a promotion or smear campaign, we see multiple rumors about the target. Many existing works have explored rumor propagation and mitigation in social networks for a single rumor. However, users become biased towards the topic due to multiple rumors about it. A user influenced by the previous rumors about a topic is more likely to believe a rumor with similar content. Therefore, in this work, we analyze the spread of multiple rumors about a topic and formulate an optimization problem to identify the top k rumor spreaders. A Bayesian Inference has been applied to model the user bias caused by multiple rumors based on rumor content and user opinion about the topic. An Adaptive Ant Colony Optimization algorithm has been proposed to determine the top k rumor spreaders, who may be isolated from the network to reduce the impact of the rumors in the OSN. The efficacy of the proposed approaches is shown through experimentation on two datasets by considering the budget k.

This article highlights the expanding issue of e-waste caused by the accessibility and widespread utilisation of electronics. Because precious metals in e-waste have high value, it is vital to recycle them while minimising their loss. Batteries in e-waste are identified and located using image processing techniques, such as semantic segmentation, which categorizes each pixel in an image. The article describes a modified U-Net Convolutional Neural Network approach with pre-processing procedures to assure clean raw photos for image segmentation of the battery component. Key matrices were used to analyse the output of three distinct CNN models with loss functions. The study comes to the conclusion that the improved model for battery segmentation of X-ray images is the modified U-Net with dice coefficient. The development of more efficient e-waste recycling methods with the help of this research could lead to a more sustainable future.

This work addresses the Knapsack Problem with Forfeit Sets, a recently introduced variant of the 0/1 Knapsack Problem considering subsets of items associated with contrasting choices. Some penalty costs need to be paid whenever the number of items in the solution belonging to a forfeit set exceeds a predefined allowance threshold. We propose an effective metaheuristic to solve the problem, based on the Biased Random-Key Genetic Algorithm paradigm. An appropriately designed decoder function assigns a feasible solution to each chromosome, and improves it using some additional heuristic procedures. We show experimentally that the algorithm outperforms significantly a previously introduced metaheuristic for the problem.

We consider a set (N = { 1,ldots ,n }), (nge 2), of interacting agents whose individual opinions ( x_{i}), with (i in N ), take values in some domain (mathbb {D}subseteq mathbb {R}). The interaction among the agents represents the degree of reciprocal influence which the agents exert upon each other and it is expressed by a general asymmetric interaction matrix with null diagonal and off-diagonal coefficients in the open unit interval. The present paper examines the asymmetric generalization of the linear consensus dynamics model discussed in previous publications by the same authors, in which symmetric interaction was assumed. We are mainly interested in determining the form of the asymptotic convergence towards the consensual opinion. In this respect, we present some general results plus the study of three particular versions of the linear consensus dynamics, depending on the relation between the interaction structure and the degrees of proneness to evaluation review of the various individual opinions. In the general asymmetric case, the analytic form of the asymptotic consensual solution (tilde{x}) is highly more complex than that under symmetric interaction, and we have obtained it only in two low-dimensional cases. Nonetheless, we are able to write those complex analytic forms arranging the numerous terms in an intelligible way which might provide useful clues to the open quest for the analytic form of the asymptotic consensual solution in higher-dimensional cases.

Automatic detection of defects on painted wall surfaces (DPWSs) based on machine vision is meaningful for reducing manpower consumption and shorting lead time, which is one of the critical components of intelligent construction. Conventional detection methods suffer from some challenges due to the multi-scale defects and unstructured detection environment. In this study, a detection network for DPWSs is developed based on the enhanced You Only Look Once version 5 (YOLOv5). First, the convolutional block attention module (CBAM) is inserted into the backbone of YOLOv5 to boost the feature extraction and suppress noise, which can sufficiently extract the features of the defects with blurry edges. Then, to improve the adaptability for multi-scale defects and reduce the model size, the Bi-directional Feature Pyramid Network (BiFPN) is employed in the neck of YOLOv5 to enhance the feature fusion, where the multi-scale objects can be fully captured. Finally, the decoupled head is proposed to replace the original convolution layer in the You Only Look Once (YOLO) head, which separates the classification and localization tasks to improve detection speed and robustness. Since there is no publicly available data set, a data set of DPWSs is constructed, and a series of comparative experiments are conducted. The results show that the detection accuracy is improved by 15.6% and the model size is reduced by 30.8% compared with YOLOv5. Meanwhile, the proposed network has better adaptability to DPWSs with higher detection accuracy and smaller model sizes compared with other advanced methods. As to the general applicability aspect of the model, the proposed model holds significant academic and practical implications in the realms of intelligent construction. Besides the model’s primary application domain of construction quality control, it can also be applied to defect detection in other scenarios that have multi-scale defects and unstructured environments. This versatility benefits a wide spectrum of construction projects.

Abstract

This paper aims to solve a solid transportation problem, wherein the uncertain parameters related to the problem are represented using triangular Fermatean fuzzy numbers. Fermatean fuzzy sets offer a relatively novel and wider alternative by providing the decision-makers with more versatile means of managing the uncertain information throughout the decision-making process. As per our literature survey, no algorithm exists in the literature for fuzzy solid transportation problems with parameters as triangular Fermatean fuzzy numbers. Therefore, in this study, the existing Vogel’s approximation method for the initial basic feasible solution (IBFS) of the traditional transportation problems is extended for the Fermatean fuzzy solid transportation problems. Further, a new method for getting the optimal solution from the obtained IBFS is proposed. The computational complexity and operational efficacy of the proposed algorithm are also discussed. Two application examples have been solved to illustrate the practicality of the proposed algorithm. For the comparative study, the problems are also solved with LINGO 20.0 software. The obtained results of IBFS are found to be significantly lower than those obtained by certain existing methods and the optimal values are comparable with those generated by the LINGO 20.0 optimization solver. Through a comparative analysis, it has been found that the proposed algorithm consistently produces optimal transportation costs for both application examples, adding to the novelty of the proposed work. Finally, the conclusion and future scope of this study are described.

Graphical abstract

Graphical depiction of abstract

This article explores pure-endowment contracts with investments that are simultaneously allocated in risk-free and risky financial markets. Employing the optimal stochastic control method and assuming that the jumps of the risky financial market follow either a finite or infinite activity Lévy process, and that the policyholder’s utility function is a Constant Relative Risk Aversion (CRRA) utility function, the article derives an optimal investment strategy and optimal policyholder consumption, with dependency on the mortality rate. Various mortality models and jump parameters are utilized to investigate the sensitivity of our findings. Finally, the article establishes the fair price of such contracts under different circumstances, showcasing practical applications through several examples.

The diffusion of AI and big data is reshaping decision-making processes by increasing the amount of information that supports decisions, while reducing direct interaction with data and empirical evidence. This paradigm shift introduces new sources of uncertainty, as limited data observability results in ambiguity and a lack of interpretability. The need for the proper analysis of data-driven strategies motivates the search for new models that can describe this type of bounded access to knowledge.This contribution presents a novel theoretical model for uncertainty in knowledge representation and its transfer mediated by agents. We provide a dynamical description of knowledge states by endowing our model with a structure to compare and combine them. Specifically, an update is represented through combinations, and its explainability is based on its consistency in different dimensional representations. We look at inequivalent knowledge representations in terms of multiplicity of inferences, preference relations, and information measures. Furthermore, we define a formal analogy with two scenarios that illustrate non-classical uncertainty in terms of ambiguity (Ellsberg’s model) and reasoning about knowledge mediated by other agents observing data (Wigner’s Friend). Finally, we discuss some implications of the proposed model for data-driven strategies, with special attention to reasoning under uncertainty about business value dimensions and the design of measurement tools for their assessment.