Applied Soft Computing最新文献

{"title":"Sentiment analysis and emotion recognition in social media: A comprehensive survey","authors":"Mrunmayee Bachate,&nbsp;Suchitra S","doi":"10.1016/j.asoc.2025.112958","DOIUrl":"10.1016/j.asoc.2025.112958","url":null,"abstract":"<div><div>Sentiment Analysis (SA) and emotion recognition is the fundamental dialogue system that recently gained more attention. It is applied in many scenarios like mining the opinions of the speaker’s conversation and enhancing the feedback of the robot agent. Furthermore, the live conversation is used to generate the talks through certain sentiments to enhance the human-machine interaction. This survey focuses the researchers on handling the SA and classification of various sentences in social media by reviewing various approaches. This analysis explains the 50 research articles from different methods used for SA and sentiment classification in social media. Finally, the evaluation of this survey is performed based on the publication year, various approaches, evaluation metrics, and tools. Moreover, the collected 50 research papers are categorized into different techniques, such as deep learning (DL) based methods, machine learning (ML) based methods, lexicon-based methods, hybrid-based methods, and dependency-based methods. Therefore, from this survey, it is clearly shown that the DL-based method is the most utilized approach in many research papers. Similarly, python is the most used tool for SA and classification, and real-time dataset is a commonly used dataset for SA and classification. Likewise, accuracy is repeatedly employed in metrics with the highest value.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"174 ","pages":"Article 112958"},"PeriodicalIF":7.2,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629922","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":"Short-term forecasting for port throughput time series based on multi-modal fuzzy information granule","authors":"Fang Li ,&nbsp;Wen Tong ,&nbsp;Xiyang Yang","doi":"10.1016/j.asoc.2025.112957","DOIUrl":"10.1016/j.asoc.2025.112957","url":null,"abstract":"<div><div>Port throughput forecasting is a crucial task that enables port managers to efficiently plan operations, optimize resource utilization, and manage risks. Simultaneously, accurate throughput predictions can prevent port congestion, reduce logistics delays, and enhance cargo handling efficiency, thereby improving port operational efficiency and customer satisfaction. While the existing models often show poor prediction accuracy, because they fail to capture data information comprehensive and produce the iterative errors in short-term forecasting. To address these challenges, a novel short-term time series prediction model is designed, fuzzy information granule (FIG) based model. Different from the existing models, our model incorporates an algorithm based on <span><math><msub><mrow><mi>l</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span>-trend filtering to dissect port throughput data into linear trend series and random series, effectively revealing the multi-modal information within data--linear modality and non-linear modality. These multiple modalities allow for a better understanding of throughput changes. Following such multi-modal characteristics, the multi-modal FIG, comprising Gaussian polynomial FIG and Gaussian FIG, is constructed, where the former reflects data linear modality and the latter reflects non-linear modality. Through meticulous data information mining and description, the innovative model achieves short-term forecasting at the granular level, reducing the cumulative errors in iteration. The novel designed FIG based model demonstrates superior accuracy and reliability compared to other 10 models across four metrics, mean absolute error, root mean squared error, mean absolute percentage error, and Wilcoxon signed rank test, which are tested on the data from ports including Ningbo, New York, Shanghai, Singapore, Qingdao, and Malaysia. The application of our model in short-term port throughput forecasting holds significant potential impact in both port operations management and computer science domains.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"174 ","pages":"Article 112957"},"PeriodicalIF":7.2,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601205","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":"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":"A particle swarm optimization and constraint programming-based approach for integrated process planning and scheduling with lot streaming problem","authors":"Mengya Zhang,&nbsp;Xinyu Li,&nbsp;Liang Gao,&nbsp;Qihao Liu","doi":"10.1016/j.asoc.2025.112938","DOIUrl":"10.1016/j.asoc.2025.112938","url":null,"abstract":"<div><div>This paper studies the integrated process planning and scheduling with lot streaming (IPPS-LS) problem, which consists of lot splitting, process planning, and shop scheduling. Although the IPPS-LS problem is common in the manufacturing of flexible process products, it has not been extensively studied due to its high complexity. Hence, this study develops an enhanced particle swarm optimization algorithm based on constraint programming (CP) to minimize makespan. The proposed algorithm employs finite condition and relaxation models for particle reconfiguration and re-optimization. To achieve it, two types of relaxation models are constructed by decomposing the multiple constraints of the CP model. The algorithm dynamically updates particle encoding sequences based on model accuracy, effectively reducing invalid searches and accelerating the search process. The proposed algorithm is compared with models and other metaheuristic algorithms on 120 test instances. The impact of the relaxed CP strategy and particle swarm optimization algorithm on the proposed algorithm performance is also analyzed. Finally, a significance of difference validation is performed. Computational experiments demonstrate the efficiency of the proposed algorithm in solving the IPPS-LS problem of varying scales. In addition, the relaxed CP strategy exhibits a more significant improvement effect for medium-scale problems compared to small and large-scale problems.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"174 ","pages":"Article 112938"},"PeriodicalIF":7.2,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592747","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":"Software and hardware synergy for accelerated plant disease identification","authors":"Hongxing Wen,&nbsp;Chuandong Li,&nbsp;Xinpei Wang,&nbsp;Ling Chen","doi":"10.1016/j.asoc.2025.112926","DOIUrl":"10.1016/j.asoc.2025.112926","url":null,"abstract":"<div><div>Plant diseases are one of the main causes of reduced crop yields. Therefore, it is necessary to adopt timely and effective identification methods and take corresponding measures. Some physical and biological detection methods have been proposed by researchers, but these methods require specialized techniques and expensive detection costs. Furthermore, the limited number of skilled technicians means that disease identification is not always timely or effective. To achieve real-time identification of plant diseases in remote areas where network and circuit are not well connected, We adopted a hardware and software co-acceleration approach to implement the design. First, we designed a lightweight convolutional neural network (CNN) and trained this network using a knowledge distillation approach. Then, we quantified the model parameters into int8 type using a method of model quantization to further compress the model size. After compression, the model size of the network is 0.035 MB and the recognition accuracy is 94.06% on the test set of the experiment. In order to deploy the proposed network on resource constrained Field-Programmable Gate Array (FPGA) devices, we used time-division multiplexing and feature map segmentation to deploy the network. Finally, our design is implemented on ZYNQ7020 with an inference speed of 35.73ms/frame and a power consumption of 1.97 W. The experimental results show that our design has the advantages of consuming less FPGA resources, low power consumption, high speed and portability. It can be used for disease recognition in multiple plant classes.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"174 ","pages":"Article 112926"},"PeriodicalIF":7.2,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592940","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}