Applied Soft Computing最新文献

{"title":"Evaluation of the anti-disturbance capability of fMRI-based spiking neural network based on speech recognition","authors":"Lei Guo ,&nbsp;Chongming Li ,&nbsp;Youxi Wu ,&nbsp;Menghua Man","doi":"10.1016/j.asoc.2025.113069","DOIUrl":"10.1016/j.asoc.2025.113069","url":null,"abstract":"<div><div>The exterior electromagnetic noise can degrade the performance of neuromorphic hardware based on brain-inspired model. Therefore, enhancing the robustness of a brain-inspired model is a critical issue. However, the topology of a brain-inspired model lacks bio-plausibility. The purpose of this paper is to enhance the anti-disturbance capability of brain-inspired model under exterior electromagnetic noise by improving its bio-plausibility. In this paper, we propose a new spiking neural network (SNN) as a brain-inspired model called fMRI-SNN, in which the topology is constrained by functional magnetic resonance imaging (fMRI) data from the human brain, the nodes are Izhikevich neuron models, and the edges are synaptic plasticity models (SPMs) with time delay co-regulated by excitatory synapses and inhibitory synapses. Then, taken speech recognition (SR) as a case study, the recognition performance of fMRI-SNN is certified. To evaluate its anti-disturbance capability, the SR accuracy of fMRI-SNN under exterior electromagnetic noise is investigated, and is compared with SNNs with alternative topologies. To reveal its anti-disturbance mechanism, the neuroelectric characteristics, adaptive adjustment of synaptic plasticity, and dynamic topological characteristics of fMRI-SNN under exterior electromagnetic noise are discussed. The results indicate that the SR accuracy of fMRI-SNN under exterior electromagnetic noise is higher than that of SNNs with alternative topologies, and our discussion elucidates its anti-damage mechanism. Our results prompt that the brain-inspired model with bio-plausibility can enhance its robustness.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"175 ","pages":"Article 113069"},"PeriodicalIF":7.2,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Designing a cryptocurrency trading system with deep reinforcement learning utilizing LSTM neural networks and XGBoost feature selection","authors":"Hamidreza Ghadiri,&nbsp;Ehsan Hajizadeh","doi":"10.1016/j.asoc.2025.113029","DOIUrl":"10.1016/j.asoc.2025.113029","url":null,"abstract":"<div><div>This paper aims to present a cryptocurrency trading strategy that addresses market volatility and decision-making challenges using advanced machine learning techniques and a wide range of predictor variables. Specifically, the proposed method is designed to enhance trading decisions by improving the accuracy of market trend forecasts. The approach consists of two primary steps. First, the XGBoost algorithm is applied to identify the most relevant features from market variables, technical indicators, macroeconomic factors, and blockchain-specific data for each cryptocurrency. In the second step, these selected features are fed into a Double Deep Q-Network (DDQN) algorithm incorporating LSTM (Long Short-Term Memory), BiLSTM (Bidirectional Long Short-Term Memory), and GRU (Gated recurrent units) layers to generate trading signals (buy, hold, sell). The model’s performance, tested on Bitcoin and Ethereum data from July 2021 to March 2023, demonstrates that blockchain variables provide crucial insights for trading strategies. Furthermore, combining XGBoost for feature selection with the DDQN model improves all key trading performance metrics, highlighting the significance of feature selection in optimizing deep reinforcement learning agents.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"175 ","pages":"Article 113029"},"PeriodicalIF":7.2,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unveiling authenticity with diffusion-based face retouching reversal","authors":"Fengchuang Xing ,&nbsp;Xiaowen Shi ,&nbsp;Yuan-Gen Wang ,&nbsp;Chunsheng Yang","doi":"10.1016/j.asoc.2025.113062","DOIUrl":"10.1016/j.asoc.2025.113062","url":null,"abstract":"<div><div>Unveiling the real appearance of retouched faces to prevent malicious users from deceptive advertising and economic fraud has been an increasing concern in the era of digital economics. This article makes the first attempt to investigate the face retouching reversal (FRR) problem. We first build an FRR dataset, named deepFRR, by collecting 50,000 StyleGAN-generated high-resolution (1024 × 1024) facial images and retouching them via a commercial online API. Then, we present a novel diffusion-based FRR network (FRRffusion) for the FRR task. Our FRRffusion consists of a coarse-to-fine two-stage architecture: A diffusion-based Facial Morpho-Architectonic Restorer (FMAR) is constructed to generate the basic contours of low-resolution faces in the first stage, while a Transformer-based Hyperrealistic Facial Detail Generator (HFDG) is designed to create high-resolution facial details in the second stage. Tested on deepFRR, our FRRffusion surpasses the state-of-the-art image restoration method with 22%, 11%, 20%, and 6% performance improvement in SSIM, PSNR, VGGS, and CLIPS, respectively. Especially, the de-retouched images by our FRRffusion are visually much closer to the raw face images than both the retouched face images and those restored by the state-of-the-art, like GP-UNIT and Stable Diffusion, in terms of qualitative evaluation with 85 subjects. These results sufficiently validate the efficacy of our FRRffusion, bridging the gap between the FRR and generic image restoration tasks. The code is available at <span><span>https://github.com/GZHU-DVL/FRRffusion</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"174 ","pages":"Article 113062"},"PeriodicalIF":7.2,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-objective cuckoo search algorithm using generalized Lèvy flight and dissimilar egg identification for multispectral image thresholding","authors":"Ramen Pal ,&nbsp;Pritam Roy ,&nbsp;Srijon Mallick ,&nbsp;Somnath Mukhopadhyay ,&nbsp;Sunita Sarkar ,&nbsp;Mike Hinchey","doi":"10.1016/j.asoc.2025.113054","DOIUrl":"10.1016/j.asoc.2025.113054","url":null,"abstract":"<div><div>Cuckoo Search (CS) stands as a highly efficient meta-heuristic optimization algorithm. Existing literature showcases the ability of CS in multi-objective scenarios, delineated by the three fundamental rules. However, the first rule of the algorithm incurs the cost of a generation to update a single nest, while the third rule necessitates hit-and-trial methods for parameter adjustment. To address these concerns, a multi-objective cuckoo search algorithm is proposed in this paper. The algorithm builds upon a generalized concept of Lèvy Flight for generating new solutions. Problem-specific, constraint-based strategies for identifying the best nest and dissimilar eggs are also introduced. The algorithm is further applied to solve multispectral remote sensing image thresholding problem. Prior studies have underscored the efficiency of entropy and clustering-based thresholding methods over other techniques. Nevertheless, most entropy-based approaches entail converting color images to grayscale before segmentation, potentially sacrificing crucial spectral information and consequently degrading segmentation algorithm’s performance. To avoid these limitations, this research introduces an entropy-based thresholding method to segment a color image without converting it to grayscale. The experiments are carried out using very high resolution (VHR) and coarse resolution (CR) multispectral (MS) images from the satellite sensors Pl’eidas-1B and Sentinel-2b, respectively. The proposed methods undergo validation against four state-of-the-art techniques on benchmark functions and six clustering indexes, respectively.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"175 ","pages":"Article 113054"},"PeriodicalIF":7.2,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-expression multi-label feature selection based on fuzzy decision","authors":"Shibing Pei ,&nbsp;Minghao Chen ,&nbsp;Changzhong Wang","doi":"10.1016/j.asoc.2025.113046","DOIUrl":"10.1016/j.asoc.2025.113046","url":null,"abstract":"<div><div>The large amount of high-dimensional data poses a great challenge to multi-label learning. Feature selection is an effective method to alleviate this problem. However, many existing multi-label feature selection models either ignore the intrinsic spatial structure of samples or have no restrictions on the predicted label values. To solve the above problems, a sample self-representation multi-label feature selection method based on fuzzy decision is proposed in this paper. Firstly, a self-representation coefficient matrix of samples is proposed, which not only retains the original data structure information, but also reflects the distribution structure of data. Then, a fuzzy decision function is introduced to fuzzy prediction labels which well represents the membership of a sample to a class and is more consistent with the real label distribution. The <span><math><msub><mrow><mi>L</mi></mrow><mrow><mn>2</mn><mo>,</mo><mn>1</mn></mrow></msub></math></span>-norm is imposed on the feature weight matrix to ensure sparsity and the <span><math><mi>F</mi></math></span>-norm is introduced into the self-expression matrix to weaken the effects of redundancy and anomalous samples. Finally, the gradient descent method is used to optimize the objective function. Experimental results on 12 multi-label datasets show that the proposed method performs better than other state-of-the-art multi-label feature selection methods, and obtain a significant increase in classification accuracy of about 2%–3% over all the compared approaches.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"175 ","pages":"Article 113046"},"PeriodicalIF":7.2,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-view self-supervised learning on heterogeneous graphs for recommendation","authors":"Yunjia Zhang ,&nbsp;Yihao Zhang ,&nbsp;Weiwen Liao ,&nbsp;Xiaokang Li ,&nbsp;Xibin Wang","doi":"10.1016/j.asoc.2025.113056","DOIUrl":"10.1016/j.asoc.2025.113056","url":null,"abstract":"<div><div>Graph neural networks (GNNs) have significantly contributed to data mining but face challenges due to sparse graph data and lack of labels. Typically, GNNs rely on simple feature aggregation to leverage unlabeled information, neglecting the richness inherent in unlabeled data within graphs. Graph self-supervised learning methods effectively capitalize on unlabeled information. Nevertheless, most existing graph self-supervised learning methods focus on homogeneous graphs, ignoring the heterogeneity of graphs and mainly considering the graph structure from a single perspective. These methods cannot fully capture the complex semantics and correlations in heterogeneous graphs. It is challenging to design self-supervised learning tasks that can fully capture and represent complex relationships in heterogeneous graphs.</div><div>In order to address the above problems, we investigate the problem of self-supervised HGNN and propose a new self-supervised learning mechanism for HGNN called Multi-view Self-supervised Learning on Heterogeneous Graphs for Recommendation (MSRec). We introduce a maximum entropy path sampler to help sample meta-paths containing structural context. Encoding information from diverse views defined by various meta-paths, decoding it into a semantic space different from own and optimizing tasks in both local-view and global-view contrastive learning, which facilitates collaborative and mutually supervisory interactions between the two views, leveraging unlabeled information for node embedding learning effectively. According to experimental results, our method demonstrates an optimal performance improvement of approximately 7% in NDCG@10 and about 8% in Prec@10 compared to state-of-the-art models. The experimental results on three real-world datasets demonstrate the superior performance of MSRec compared to state-of-the-art recommendation methods.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"174 ","pages":"Article 113056"},"PeriodicalIF":7.2,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intra and inter-series pattern representations fusion network for multiple time series forecasting","authors":"Canghong Jin ,&nbsp;Tianyi Chen ,&nbsp;Hao Ni ,&nbsp;Qihao Shi","doi":"10.1016/j.asoc.2025.113024","DOIUrl":"10.1016/j.asoc.2025.113024","url":null,"abstract":"<div><div>Multiple time series (MTS) can comprise data collected from various wireless sensor networks in the actual application, and each source provides a distinctive pattern. Most existing neural network methods attempt to model the patterns of individual time series by training a global model using the entire dataset, suffering from insufficient ability to consider the differences among source patterns and lowering the predictability. To address this limitation, we propose the Multiple Time Series Pattern Representation Network(MTS-PRNet), a unified framework consisting of two modules to forecast multiple time series from diverse sources. The first is the intra-series correlation learning module, which explicitly learns the temporal dependencies of time series. The second is the inter-series discriminative representation learning module that learns shapelets as discriminative representations to capture shared features among series. By integrating the covariates map generated by the second module, both intra and inter-series characteristics are captured to provide transferable guidance for increasing predictability. Experiments conducted on 9 datasets verify that our model achieves state-of-the-art performance. In particular, we carry out an ablation study to validate the effectiveness of discriminative representations.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"175 ","pages":"Article 113024"},"PeriodicalIF":7.2,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A dynamic parameters genetic algorithm for collaborative strike task allocation of unmanned aerial vehicle clusters towards heterogeneous targets","authors":"Chao Zhang ,&nbsp;Jianlu Guo ,&nbsp;Fei Wang ,&nbsp;Boyuan Chen ,&nbsp;Chunshi Fan ,&nbsp;Linghui Yu ,&nbsp;Zhiwen Wang","doi":"10.1016/j.asoc.2025.113075","DOIUrl":"10.1016/j.asoc.2025.113075","url":null,"abstract":"<div><div>Collaborative strikes by unmanned aerial vehicle clusters (UAVCs) is becoming a key focus in the future air warfare, which can significantly enhance warfare effectiveness and reduce costs. To exactly describe the real battlefield scenarios, various heterogeneous strike-targets should be embedded. However, it will significantly increase the complexity of multi-constraint combinatorial optimization problem, thus the traditional genetic algorithm (GA) is difficult to solve efficiently due to its unchanged gene operator. In this paper, a dynamic parameters genetic algorithm has been proposed for UAVCs collaborative task allocation towards heterogeneous targets. Firstly, according to the differences of type, value, combat and defense, the heterogeneous strike-targets have been abstracted into strike target points and the UAVCs have been formulated into a set. Secondly, an innovative multiple unmanned aerial vehicles duplicate tasks orienteering problem (MUDTOP) model has been built to achieve multiple strikes on certain targets. Finally, the new triple-chromosome encoding and duplicate gene segments have been designed, and a novel genetic algorithm called DPGA-TEDG has been presented through dynamic gene operator. Experimental comparison results across various battlefield scales demonstrate that the outcomes of the proposed DPGA-TEDG algorithm not only meet practical requirements, but also outperform that of the other three algorithms in both optimality and robustness. Especially, in the battlefield scale environment of 180 km* 180 km, the average objective value of DPGA-TEDG is better than that of traditional genetic algorithm (GA-TEDG), simulated annealing algorithm (SA) and particle swarm optimization algorithm (PSO) about 2.71 %, 6.58 % and 20.49 %, respectively.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"175 ","pages":"Article 113075"},"PeriodicalIF":7.2,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Railway prioritized food logistics in developing countries using fuzzy decision making under interval-valued pythagorean fuzzy environment","authors":"Ali Atilla Arisoy ,&nbsp;S. Jeevaraj ,&nbsp;Ilgin Gokasar ,&nbsp;Muhammet Deveci ,&nbsp;Seifedine Kadry ,&nbsp;Zhe Liu","doi":"10.1016/j.asoc.2025.113066","DOIUrl":"10.1016/j.asoc.2025.113066","url":null,"abstract":"<div><div>The current state of agricultural logistics is vulnerable to global crises and oil price fluctuations, especially in developing countries that depend heavily on highway transportation. Experts are seeking efficient and eco-friendly solutions, exploring options such as railroad transport and innovative concepts such as synchromodality for improvement. In this study, a decision-making approach for policymakers and logistics experts to improve the efficiency and resilience of agricultural logistics by using more sustainable transport modes and synchromodality is proposed. The approach is based on a new total ordering principle on the class of Interval-Valued Pythagorean Fuzzy Numbers (IVPFNs), which is compared with existing ranking methods. In this paper, we have used IVPFNs for modelling our problem. The idea of IVPFNs (generalising interval-valued intuitionistic fuzzy numbers) introduced by Yager in 2013. However, the total ordering of the class of IVPFNs has not been studied so far. The main Mathematical contribution of this work lies in defining the total order relation on the set of IVPFNs for the first time in the literature. To do this, firstly, the Four new score functions on the set of IVPFNs are introduced and various mathematical properties of them are studied. Secondly, a new total ordering principle is introduced by combining all these score functions, and their mathematical proofs are given. Thirdly, a new group decision-making algorithm based on interval-valued Pythagorean fuzzy extent analysis (IVPFEA) is proposed and applied to a real-life case study problem. Finally, the sensitivity analysis has been done properly to show the robustness of the proposed algorithm and the results. The case study involves seven experts role-playing as advisors for the Republic of Türkiye, which is a developing country, on choosing the best agricultural logistics system alternative among four alternatives. Twelve criteria, under four aspects, are presented for participants to consider. Based on the responses of the experts, the railway-prioritized food logistics system is the primary alternative. Overall, the results of this study provide a mathematical and data-driven approach to deciding on a new logistics system that policymakers and sector experts can utilize.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"175 ","pages":"Article 113066"},"PeriodicalIF":7.2,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FT-GPNN: A finite-time convergence solution for multi-set constrained optimization","authors":"Huiting He ,&nbsp;Chengze Jiang ,&nbsp;Zhiyuan Song ,&nbsp;Xiuchun Xiao ,&nbsp;Neal Xiong","doi":"10.1016/j.asoc.2025.113030","DOIUrl":"10.1016/j.asoc.2025.113030","url":null,"abstract":"<div><div>Gradient Neural Networks (GNNs) have demonstrated remarkable progress in handling optimization problems. However, applying GNNs to multi-constrained optimization problems, particularly those with those involving multi-set constraints, poses several challenges. These challenges arise from the complexity of the derivations and the increasing number of constraints. As the number of constraints increases, the optimization problem becomes more complex, making it more challenging for GNN-based methods to effectively identify the optimal solution. Motivated by these challenges, the Finite-Time Gradient Projection Neural Network (FT-GPNN) is introduced for tackling Multi-set Constrained Optimization (MCO). This innovative solution incorporates an Enhanced Sign-Bi-Power (ESBP) activation function and simplifies the design tailored explicitly for MCO. Furthermore, within the Lyapunov stability framework, the theoretical foundation of this model is strengthened by rigorous proof of local convergence. Building upon this foundation, we further establish that our model can achieve convergence within a finite time. To validate the effectiveness of our approach, we present empirical results from numerical experiments conducted under consistent conditions. Notably, our experiments demonstrate that the model using the ESBP activation function outperforms others in terms of finite-time convergence.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"175 ","pages":"Article 113030"},"PeriodicalIF":7.2,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}