Applied Soft Computing最新文献

In this study, a novel soft sensor modeling approach using Takagi–Sugeno (TS) fuzzy models and Prediction Intervals (PIs) is presented to quantify uncertainties in Electric Arc Furnace (EAF) steel production processes, namely to estimate the dissolved oxygen content in the steel bath. In real EAF operation, dissolved oxygen content is measured only a few times in the refining stage; therefore, the approach addresses the challenge of predicting unobserved output under conditions of irregular and scarce output measurements, using two distinct methods: Instant TS (I-TS) and Input Integration TS (II-TS). In the I-TS method, the model is computed for each individual indirect measurement, while the II-TS approach integrates these indirect measurements. The inclusion of PIs in TS models allows the derivation of the narrowest band containing a prescribed percentage of data, despite the presence of heteroscedastic noise. These PIs provide valuable insight into potential variability and allow decision-makers to evaluate worst-case scenarios. When evaluated against real EAF data, these methods were shown to effectively overcome the obstacles posed by scarce output measurements. Despite its simplicity, the I-TS model performed better in terms of interpretability and robustness to the operational reality of the EAF process. The II-TS model, on the other hand, showed excellent performance on all metrics but exhibited theoretical inconsistencies when deviating from typical operations. In addition, the proposed method successfully estimates carbon content in the steel bath using the established dissolved oxygen/carbon equilibrium, eliminating the need for direct carbon measurements. This shows the potential of the proposed methods to increase productivity and efficiency in the EAF steel industry.

Appropriate studies on drug–drug interactions (DDIs) can evade possible adverse side effects due to the ingestion of multiple drugs. This paper proposes a novel framework called Similarity Network Fusion and Hybrid Convolutional Neural Network (SNF–HCNN) to predict the DDIs better. The proposed framework leverages data from DrugBank, PubChem, and SIDER. Seven critical drug features are extracted: Target, Transporter, Enzymes, Chemical substructure, Carrier, Offside, and Side effects. The Jaccard Similarity measure evaluates the similarity of drug features to construct a comprehensive similarity matrix that effectively captures potential drug relationships and patterns. The similarity selection process identifies the most relevant features, reduces redundancy, and enhances identifying potential drug interactions. Integrating Similarity Network Fusion (SNF) with the selected similarity matrix ensures a comprehensive representation of drug features and leads to superior accuracy compared to conventional methods. Our experimental results demonstrate the effectiveness of the proposed hybrid convolutional neural network (HCNN) architectures, such as CNN+LR (CNN+Logistic Regression), CNN+RF (CNN+Random Forest), and CNN+SVM (CNN+Support Vector Machine), showing impressive accuracies of 95.19%, 94.45%, and 93.65%, respectively. Moreover, CNN+LR outperforms other approaches regarding precision, sensitivity, F1-score, and AUC score, which implicate better outcomes for ensuring medication safety aspects in clinical settings in the future.

A smart contract is a software program executed on a blockchain, designed to facilitate functionalities such as contract execution, asset administration, and identity validation within a secure and decentralized ecosystem. Summarizing the code of Solidity smart contracts aids developers in promptly grasping essential functionalities, thereby enhancing the security posture of Ethereum-based projects. Existing smart contract code summarization works mainly use traditional information retrieval and single code features, resulting in suboptimal performance. In this study, we propose a fusing multiple code features (FMCF) approach based on Transformer for Solidity summarization. First, FMCF created contract integrity modeling and state immutability modeling in the data preprocessing stage to process and filter data that meets security conditions. At the same time, FMCF retains the self-attention mechanism to construct the Graph Attention Network (GAT) encoder and CodeBERT encoder, which respectively extract multiple feature vectors of the code to ensure the integrity of the source code information. Furthermore, the FMCF uses a weighted summation method to input these two types of feature vectors into the feature fusion module for fusion and inputs the fused feature vectors into the Transformer decoder to obtain the final smart contract code summarization. The experimental results show that FMCF outperforms the standard baseline methods by 12.45% in the BLEU score and maximally preserves the semantic information and syntax structures of the source code. The results demonstrate that the FMCF can provide a good direction for future research on smart contract code summarization, thereby helping developers enhance the security of development projects.

Power load forecasting is of great significance to the electricity management. However, extant research is insufficient in comprehensively combining data processing and further optimization of existing prediction models. Therefore, this paper propose an improved power load prediction methods from two aspects: data processing and optimization of Sequence to Sequence (Seq2Seq) model. Firstly, in the data processing, Extreme Gradient Boosting (XGBoost) is adopted to eliminate the redundant features for feature extraction. Meanwhile, Successive Variational Mode Decomposition (SVMD) is employed to solve the unsteadiness and nonlinearities present in electricity data during the decomposition process. Secondly, the Seq2Seq model is selected and improved with a variety of machine learning methods. Specifically, input data features are extracted using Convolutional Neural Networks (CNN), enhancing the decoder's focus on vital information with the Attention mechanism (AM). Temporal Convolutional Network (TCN) serves as both the encoder and decoder of Seq2Seq, with further optimization of the model parameters through the Bayesian Optimization (BO) algorithm. Finally, the cases of two real power market datasets in Switzerland and Singapore illustrate the efficiency and superiority of proposed hybrid forecasting method. Through a comprehensive comparison and analysis with the other six models and four commonly used evaluation metrics, it is evident that the proposed method excels in performance, attaining the highest level of prediction accuracy, with the highest accuracy rate of 95.83 %. Consequently, it exhibits significant practical utility in the realm of power load forecasting.

Given the ubiquity of non-separable optimization problems in real worlds, in this paper we analyze and extend the large-scale version of the well-known cooperative coevolution (CC), a divide-and-conquer black-box optimization framework, on non-separable functions. First, we reveal empirical reasons of when decomposition-based methods are preferred or not in practice on some non-separable large-scale problems, which have not been clearly pointed out in many previous CC papers. Then, we formalize CC to a continuous-game model via simplification, but without losing its essential property. Different from previous evolutionary game theory for CC, our new model provides a much simpler but useful viewpoint to analyze its convergence, since only the pure Nash equilibrium concept is needed and more general fitness landscapes can be explicitly considered. Based on convergence analyses, we propose a hierarchical decomposition strategy for better generalization, as for any decomposition, there is a risk of getting trapped into a suboptimal Nash equilibrium. Finally, we use powerful distributed computing to accelerate it under the recent multi-level learning framework, which combines the fine-tuning ability from decomposition with the invariance property of CMA-ES. Experiments on a set of high-dimensional test functions validate both its search performance and scalability (w.r.t. CPU cores) on a clustering computing platform with 400 CPU cores.

This study introduced a novel ant colony optimization algorithm that implements the population selection strategy of the Estimation of Distribution Algorithm and a new pheromone updating formula. It aimed to optimize the performance of G-type random high-order satisfiability logic structures embedded in Discrete Hopfield Neural Networks, thereby enhancing the efficiency of the Hopfield Neural Network learning algorithm. Through comparative analysis with other metaheuristic algorithms, our model demonstrated superior performance in terms of global convergence, time complexity, and algorithm complexity. Additionally, we evaluated the learning phase, retrieval phase, and similarity analysis using various ratios of literals and clauses. It was shown that our proposed model exhibits stronger search ability compared to other metaheuristic algorithms and Exhaustive Search. Our model enhanced the efficiency of the learning phase, resulting in the number of global solutions accounting for 100 %, and significantly improved the global solution diversity. These advancements contributed to the efficiency of the model in convergence, rendering it applicable to a wide range of nonlinear classification and prediction problems.

To enhance the two-sided matching efficiency in a multi-source heterogeneous environment, this paper takes the randomness and unstructured features of the online comments into consideration, and proposes a new matching mechanism by introducing the complex information representation tool. Firstly, the concept of the probabilistic linguistic normal cloud (PLNC) model is introduced to preserve information cohesion and characteristic distribution. Next, the basic operation laws and corresponding operators are given. Then, an innovative maximum boundary concept skipping the indirect approach is presented to update the similarity degree and distance measures, also the correlation coefficient. Furthermore, for the multi-indicator systems with interactions, the peer experts are invited to evaluate the relationship between indicators, a modified algorithm based on the Decision-Making and Trial Evaluation Laboratory (DEMATEL) method is applied to obtain the subjective weights of indicators. After that, a whole matching process and a correlation coefficient cluster method-based recommendation algorithm are presented. A case study is provided to illustrate the method, wherein a new indicator system is constructed by analyzing the correlation of multiple indicators based on online linguistic evaluations. The random forest model is combined to obtain the objective weights and balance its reliability. Finally, sensitivity analysis and comparative analysis are employed to validate the effectiveness and applicability.

Malignant tumors still have a high incidence and mortality rate worldwide. Pathological examination remains the clinical gold standard for tumor diagnosis. However, some patients cannot undergo pathological examination due to advanced age and special lesion location. Therefore, making full use of PET/CT to assist doctors in tumor classification has important clinical significance. Since category labels are calibrated according to pathological images, it is difficult to obtain effective pathological category features directly using PET-CT image modeling. In response to this problem, this paper proposes a novel tumor classification algorithm. This method fully utilizes multi-gray-level 3D gray-level co-occurrence matrix and the proposed rough and fine constraint network under the constraint loss of rough and fine labels. Based on single- and multi-objective consistency, a parallel collaborative optimization method is proposed, including category consistency loss and feature specificity loss. To reduce the interference of redundant features, an improved Boruta feature selection method using multiple classifiers and multiple steps is proposed. The final result is obtained through a conditional weighted voting function. The proposed method shows excellent performance in both the submodels and the fusion model. We validated the proposed tumor classification method on three datasets and achieved good performance with the accuracy of 0.80–0.85 and F1-score of 0.78–0.88. The results indicate that the proposed method has good performance and generalization ability.

This study addresses energy-efficient multi-objective distributed assembly permutation flowshop scheduling problems with minimisation of maximum completion time, mean of earliness and tardiness, and total carbon emission simultaneously. A mathematical model is introduced to describe the concerned problems. Five meta-heuristics are employed and improved, including the artificial bee colony, genetic algorithms, particle swarm optimization, iterated greedy algorithms, and Jaya algorithms. To improve the quality of solutions, five critical path-based neighborhood structures are designed. Q-learning, a value-based reinforcement learning algorithm that learns an optimal strategy by repeatedly interacting with the environment, is embedded into meta-heuristics. The Q-learning guides algorithms intelligently select appropriate neighborhood structures in the iterative process. Then, two machine speed adjustment strategies are developed to further optimize the obtained solutions. Finally, extensive experimental results show that the Jaya algorithm with Q-learning has the best performance for solving the considered problems.

The idea of the metaverse has been rather popular in many different sectors since it provides immersive virtual reality where users may connect and participate. Leveraging metaverse technologies—such as virtual reality and augmented reality—for project visualization, collaborative design, and training simulations—in the building industry is attracting increasing attention. Notwithstanding its possible advantages, there is a clear knowledge vacuum on the obstacles preventing the general acceptance of metaverse technology in building. The study intends to close this gap by means of a literature review and analysis of the identified barriers using the fuzzy-DEMATEL technique, therefore separating the causal links among them. There were 26 obstacles found in the literature review and expert comments, arranged technically, organizationally, environmentally, socially, and economically. The most important obstacles shown by results are Security Concerns, Resistance to Change, Lack of Expertise, Siloed Departments, and Training and Education Needs. The results of this research provide building companies and legislators with important new perspectives and direction in developing plans to remove obstacles and encourage the use of metaverse technology. Moreover, the findings of the study provide a road map for industry players in tackling the important issue of restricted data capacities, thereby enabling a better and more successful integration of metaverse technology into building methods.