Applied Soft Computing最新文献

{"title":"Online ensemble learning-based anomaly detection for IoT systems","authors":"Yafeng Wu,&nbsp;Lan Liu,&nbsp;Yongjie Yu,&nbsp;Guiming Chen,&nbsp;Junhan Hu","doi":"10.1016/j.asoc.2025.112931","DOIUrl":"10.1016/j.asoc.2025.112931","url":null,"abstract":"<div><div>In the modern era of digital transformation, the evolution of fifth-generation (5G) wireless networks has played a pivotal role in revolutionizing communication technology and accelerating the growth of smart technology applications. As an integral element of smart technology, the Internet of Things (IoT) grapples with the problem of limited hardware performance. Cloud and fog computing-based IoT systems offer an effective solution but often encounter concept drift issues in real-time data processing due to the dynamic and imbalanced nature of IoT environments, leading to performance degradation. In this study, we propose a novel framework for drift-adaptive ensemble learning called the Adaptive Exponentially Weighted Average Ensemble (AEWAE), which consists of three stages: IoT data preprocessing, base model learning, and online ensembling. It integrates four advanced online learning methods within an ensemble approach. The crucial parameter of the AEWAE method is fine-tuned using the Particle Swarm Optimization (PSO) technique. Experimental results on four public datasets demonstrate that AEWAE-based anomaly detection effectively detects concept drift and identifies anomalies in imbalanced IoT data streams, outperforming other baseline methods in terms of accuracy, F1 score, false alarm rate (FAR), and latency.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"173 ","pages":"Article 112931"},"PeriodicalIF":7.2,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143550941","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 robust ensemble classifier for imbalanced data via adaptive variety oversampling and embedded sampling rate","authors":"Jun Dou ,&nbsp;Yan Song ,&nbsp;Guoliang Wei ,&nbsp;Xinchen Guo","doi":"10.1016/j.asoc.2025.112922","DOIUrl":"10.1016/j.asoc.2025.112922","url":null,"abstract":"<div><div>This paper introduces a novel hybrid approach for addressing imbalanced data classification. The core concept involves devising a data-based oversampling algorithm to partially re-balance the data and employing an ensemble algorithm to enhance model performance. The merits of the proposed hybrid method can be highlighted as follows: (1) rather than re-balancing the data completely, an incomplete yet rational sampling rate is adopted to synthesize new samples, which can reduce the extreme imbalance ratio as well as avoid the overlap and redundancy by the complete re-balance. After oversampling, an improved adaptive boosting method is used to further contribute to the classification result; (2) with the help of temporarily generating samples in a triangular region of four selected target samples, a new synthesizing method is provided, which contributes greatly to the diversity of the new synthetic samples and the guarantee of the correctness and safety; (3) besides the number of correctly classified minority samples, the imbalance ratio of raw data is considered to make the ensemble classifier serve a further focus on minority samples and proved theoretically effective in mitigating the skew of the classification hyperplane on minority samples.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"174 ","pages":"Article 112922"},"PeriodicalIF":7.2,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570586","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-objective evolutionary algorithm with two balancing mechanisms for heterogeneous UAV swarm path planning","authors":"Xiuju Xu ,&nbsp;Chengyu Xie ,&nbsp;Linru Ma ,&nbsp;Lin Yang ,&nbsp;Tao Zhang","doi":"10.1016/j.asoc.2025.112927","DOIUrl":"10.1016/j.asoc.2025.112927","url":null,"abstract":"<div><div>Unmanned aerial vehicle (UAV) swarm path planning involves creating efficient routes based on task requirements to enable collaborative flight. Compared to homogeneous UAV swarm, the application scenarios of heterogeneous UAV swarm have become increasingly widespread. They can fully leverage the various capabilities of drones and show higher economic benefits. Existing research mainly focuses on homogeneous UAV swarms, and the model for uniformly describing heterogeneous UAV swarm from a functional perspective is insufficient. Differences in dynamic constraints and energy consumption models create challenges for accurately characterizing the path planning problem of heterogeneous UAV swarm. To supplement the above deficiencies, this article designs the scenario and composition structure of heterogeneous UAV swarm. The path-planning problem of heterogeneous UAV swarm is modeled as a multi-objective optimization (MOO) problem, in which a comprehensive energy consumption objective is constructed. To better balance multiple objectives and obtain high-quality solutions, a MOO evolutionary algorithm based on heterogeneous UAV swarm, namely HMOEA, is proposed. Specifically, HMOEA is implemented by combining the proposed two strategies. To verify the model’s feasibility and the algorithm’s effectiveness, numerical simulations and prototype simulations are provided. In numerical simulations, the proposed algorithm was compared with various advanced algorithms, i.e., NSGA-II, CIACO, AP-GWO, CL-DMSPSO, and DSNSGA-III, in two designed terrain problems. The results demonstrate that HMOEA not only outperforms the compared algorithms on convergence and diversity indicators increased over 4% and 2% respectively. Normal flight results were achieved in the two scenarios served by the prototype simulation, namely, urban buildings and forest scenes. Specific implementation and application can be achieved in military or civilian scenarios like reconnaissance and strike missions, search and rescue missions. The proposed model can adapt to more task scenarios, and the proposed method can provide faster and higher quality results for heterogeneous UAV swarm routes. In actual deployment, adjusting model parameters and optimizing the computing environment according to application requirements are worth further investigation to achieve optimal effect.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"173 ","pages":"Article 112927"},"PeriodicalIF":7.2,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143550943","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":"Dual residual learning of frequency fingerprints in detecting synthesized biomedical imagery","authors":"Misaj Sharafudeen,&nbsp;Vinod Chandra S.S.","doi":"10.1016/j.asoc.2025.112930","DOIUrl":"10.1016/j.asoc.2025.112930","url":null,"abstract":"<div><div>Artificial synthesis of biomedical imagery is an evolving threat yet under-addressed. The integrity of medical imaging is important for accurate diagnosis and treatment. This study addresses the potential threat of fabricated biomedical imagery, focusing on synthetic dermatological lesions and CT nodules. The Representation Similarity Matrix measured the quantitative authenticity to account for similarities of synthesized data with authentic data. The study explores traces of manipulation from frequency signatures of synthesized imagery. We propose a novel combinatorial architecture, the Dual Residual Network (DRN), capturing hidden residual traces from low-frequency fingerprints of synthetic data and exposing hidden forgeries. DRN achieves near-perfect detection rates with an accuracy of 98.80% for CT nodules and 98.97% for lesions. Equal Error Rates of the model on the two datasets exhibited a marginal improvement of 57.87% in the CT nodules compared to the skin lesions. Sensitivity and specificity play a significant role in medical diagnostics. The model achieved sensitivities of 99.31% and 98.45% and specificity of 98.80% and 99.60% for each dataset, respectively. Further verification of the frequency traces was performed by analyzing gradients in the target concepts that led to decision-making. This study equips the medical field with a powerful tool to combat the evolving threat of synthetic fraud, safeguarding patient and client safety. The potential of the technique extends beyond healthcare, offering a blueprint for tackling synthetic data across diverse domains.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"173 ","pages":"Article 112930"},"PeriodicalIF":7.2,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143551048","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":"Automatic optimization for generating adversarial malware based on prioritized evolutionary computing","authors":"Yaochang Xu,&nbsp;Yong Fang,&nbsp;Yijia Xu,&nbsp;Zhan Wang","doi":"10.1016/j.asoc.2025.112933","DOIUrl":"10.1016/j.asoc.2025.112933","url":null,"abstract":"<div><div>Machine learning has been widely applied to malware detection tasks; but unfortunately, they exhibit significant vulnerability to adversarial attacks and can be easily circumvented using perturbation carefully crafted. Concurrently, we are witnessing a corresponding increase in the attention dedicated to adversarial attacks against malware detection models. Nevertheless, current research on adversarial examples still faces obstacles such as poor escape effectiveness and difficulty in preserving functionality. Particularly, greedily recruiting the best manipulations from a vast search space often leads to poor diversity of adversarial perturbation sequence. To rectify these shortcomings, this paper proposes an automated, continuously optimized approach for generating malware adversarial examples based on evolutionary computing. Our method filters effective action sequences from a large pool of random manipulations, assigning different priorities to different actions. The generation and optimization of adversarial examples are formalized as a sparse minimization optimization problem based on a fixed-length action vector. We introduce AOP-Mal, a novel genetic framework to automatically generate and optimize adversarial examples. The initialization and evolution of the population depend on the priority of actions, as well as the proposed novel evolutionary operator. The experimental results demonstrate that our attack strategy effectively bypasses the detection mechanisms and outperforms most state-of-the-art malware adversarial frameworks. Our hope is to help researchers understand the intentions of attackers and explore more powerful defense mechanisms.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"173 ","pages":"Article 112933"},"PeriodicalIF":7.2,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143550940","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":"Enhanced function approximation and applications to image scaling: A new family of exponential sampling neural network Kantorovich operators","authors":"P.N. Agrawal ,&nbsp;Behar Baxhaku ,&nbsp;Artan Berisha","doi":"10.1016/j.asoc.2025.112923","DOIUrl":"10.1016/j.asoc.2025.112923","url":null,"abstract":"<div><div>This paper introduces a novel family of exponential sampling type neural network Kantorovich operators, extending the work of Bajpeyi and Kumar (2021) and Bajpeyi (2023). Unlike previous research focused on approximating continuous functions, our operators are designed to handle Lebesgue integrable functions, offering enhanced versatility. We establish convergence theorems, analyze asymptotic behavior, and demonstrate the effectiveness of linear combinations for improving convergence rates. Our analysis extends to the multivariate setting, highlighting the operators’ capability in approximating a wide range of functions. To evaluate the practical performance of our proposed operators, we conducted numerical experiments with different sigmoidal functions and parameter values. Our findings reveal that operators activated by the Parametric sigmoid function consistently outperform those activated by other sigmoidal functions, achieving up to 20.70% reduction in maximum absolute error and 10.03% reduction in root mean squared errors. When applied to image scaling, our operators demonstrated superior performance compared to state-of-the-art methods like nearest neighbor, bilinear, and bicubic interpolation. For the ’Baboon’ image, we observed up to 5.62% increase in Peak Signal-to-Noise Ratio (PSNR) and 5.25% increase in Structural Similarity Index Measure (SSIM). Similar enhancements were observed for the ’Flowers’ and ’Retina’ images. The paper includes a detailed description of the image processing algorithm, along with a flowchart illustrating the implementation. These results underscore the operators’ potential in various machine learning tasks, motivating further research into their applications and optimization.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"174 ","pages":"Article 112923"},"PeriodicalIF":7.2,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570583","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 random searching algorithm for efficiently solving the connectivity-oriented robust optimization problem on large-scale networked systems","authors":"Wei Wei,&nbsp;Guobin Sun,&nbsp;Qinghui Zhang","doi":"10.1016/j.asoc.2025.112924","DOIUrl":"10.1016/j.asoc.2025.112924","url":null,"abstract":"<div><div>By consolidating part of the links to be invulnerable, there will be no connectivity degradation in a network under expected network failure intensity. Although existing link consolidation methods can handle large-scale networks, their solutions are far from optimal. Redundancy in existing solutions can be quantified by the connectivity of the pure graph consisting of the necessary subset of links, and existing methods improve pure graph connectivity to far above the expected value. Fortunately, we have found a special superset of link cuts, and proved that it can reduce consolidation links by removing the right set of links from existing solutions while maintaining the desired connectivity. In response to the high complexity of searching for the optimal superset, we found one kind of superset that is close to the optimal solution and easy to locate, significantly reducing the number of links that need to be consolidated with a slight increase in preprocessing overhead. Experiments have shown that in large networks, the algorithm can provide a protection effect of over 99.9%, and can lead to 60% overhead savings compared to existing high-speed algorithms under the same computing time. On small-scale networks where the optimal algorithm is feasible, the average additional cost compared to the optimal result can be controlled within 1%. Thus, while ensuring accuracy, it can further approach the optimal solution compared to existing algorithms, significantly reducing the overhead of infrastructure consolidation.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"173 ","pages":"Article 112924"},"PeriodicalIF":7.2,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143550939","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":"Forecasting time series using convolutional neural network with multiplicative neuron","authors":"Shobhit Nigam","doi":"10.1016/j.asoc.2025.112921","DOIUrl":"10.1016/j.asoc.2025.112921","url":null,"abstract":"<div><div>Convolutional neural networks (CNNs) are proven to be efficient in time series forecasting, however architectural selection remains a challenging task. This work aims to propose CNN, utilizing single multiplicative neuron model in forecasting time series, intended to eliminate architectural complexities of classical CNN ensuring its computational efficiency. Applicability of proposed approach is employed on financial time series datasets such as Index, Stocks, Cryptocurrencies and a commodity in evaluating the model’s performance on the basis of RMSE, MAE and <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> values. Further, time-delay effects were also observed in datasets which has been analyzed to improve the accuracy of the proposed model. Based on the lowest RMSE and MAE values, and higher <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> values, the optimal delay value has been analyzed which has been used for forecasting. The result demonstrates that in data sets like NIFTY50, SBI, Bitcoin, and Natural Gas, the forecasting efficiency is improved when compared to classical CNN. The results obtained can be used to draw valuable insights for decision making, which will enable future studies and facilitate easy adaptation in analyzing time series.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"174 ","pages":"Article 112921"},"PeriodicalIF":7.2,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570585","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 unified transductive and inductive learning framework for Few-Shot Learning using Graph Neural Networks","authors":"Jie Chang ,&nbsp;Haodong Ren ,&nbsp;Zuoyong Li ,&nbsp;Yinlong Xu ,&nbsp;Taotao Lai","doi":"10.1016/j.asoc.2025.112928","DOIUrl":"10.1016/j.asoc.2025.112928","url":null,"abstract":"<div><div>Graph Neural Networks (GNNs) have shown their effectiveness in integrating feature embeddings for image and video processing tasks. While initially developed for inductive learning, GNNs have been extended to support transductive learning, enabling them to learn from partially labeled graphs. However, the combination of transductive and inductive learning in existing GNN-based models lacks proper theoretical justification, and GNNs with information propagation mechanisms often encounter the over-smoothing problem, especially in Few-Shot Learning (FSL) tasks. In this paper, we propose a unified transductive and inductive learning GNN model named FGCN for FSL tasks. The proposed FGCN differentiates between the roles of inductive and transductive learning, while quantifying the contributions of intra-properties within entities and inner-relationships between neighboring entities. By addressing the over-smoothing problem comprehensively, the FGCN offers a promising approach for FSL tasks. Our findings demonstrate that the proposed FGCN model achieves a significant improvement in accuracy over state-of-the-art methods, as evidenced by experiments on four standard Few-Shot Learning benchmarks. For example, in the 5-Way 5-Shot scenario, the proposed FGCN achieved an accuracy increase of 7.70% on the Mini-ImageNet, compared to the state-of-the-art result obtained by the MCGN model.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"173 ","pages":"Article 112928"},"PeriodicalIF":7.2,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143551047","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":"An Advanced Ensemble Framework for defending against obfuscated Windows, Android, and IoT malware","authors":"Danish Vasan ,&nbsp;Junaid Akram ,&nbsp;Mohammad Hammoudeh ,&nbsp;Adel F. Ahmed","doi":"10.1016/j.asoc.2025.112908","DOIUrl":"10.1016/j.asoc.2025.112908","url":null,"abstract":"<div><div>The detection and analysis of malware binaries pose significant challenges due to their obfuscated and packed nature, rendering traditional static analysis techniques ineffective. Extracting static features in a dynamic environment where malware exhibits its actual behavior becomes crucial to detecting malware accurately. This article addresses this challenge by analyzing static features extracted from real-time Windows, Android, and IoT applications within a dynamic environment. To tackle this problem, we propose an Advanced Ensemble Framework (AEF) that combines embedded feature selection and an advanced stacking ensemble technique. The embedded feature selection approach effectively reduces the number of highly correlated features by over 70%, employing a combination of filter and wrapper methods. Furthermore, the advanced stacking ensemble approach combines two-level learners: a base learner with state-of-the-art classifiers adept at handling raw features and meta-learner trains using transfer features and probabilities obtained from the previous base classifiers. A 5-fold cross-training scheme based on cross-validation is introduced to prevent overfitting during the training. It also helps to reduce overfitting by training the model on multiple subsets of the data. The model learns patterns from different parts of the dataset, which can lead to a more generalized model. Pre-processed datasets from the Canadian Institute of Cybersecurity comprising obfuscated Windows malware, Android malware, and IoT malicious attacks are used to evaluate AEF. Additionally, to further assess the efficiency, compatibility, and robustness of AEF, we utilized an additional dataset of obfuscated Windows malware that includes memory dump images. Extensive experiments are conducted to evaluate the proposed defender using publicly available real-time datasets. The results show that AEF effectively counters obfuscation techniques, offering a flexible, practical, and efficient solution for malware detection across various datasets. Furthermore, the prediction time of the proposed approach is <span><math><mrow><mn>0</mn><mo>.</mo><mn>042</mn><mspace></mspace><mi>ms</mi></mrow></math></span> for CICMalDroid-2020, <span><math><mrow><mn>0</mn><mo>.</mo><mn>16</mn><mspace></mspace><mi>ms</mi></mrow></math></span> for IoMT-2024, <span><math><mrow><mn>0</mn><mo>.</mo><mn>055</mn><mspace></mspace><mi>ms</mi></mrow></math></span> for CIC-MalMemory-2022, and <span><math><mrow><mn>0</mn><mo>.</mo><mn>15</mn><mspace></mspace><mi>ms</mi></mrow></math></span> for Dumpaware10 malware datasets.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"173 ","pages":"Article 112908"},"PeriodicalIF":7.2,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562806","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}