Applied Intelligence最新文献

{"title":"A novel spatial complex fuzzy inference system for detection of changes in remote sensing images","authors":"Nguyen Truong Thang,&nbsp;Le Truong Giang,&nbsp;Le Hoang Son,&nbsp;Nguyen Long Giang,&nbsp;David Taniar,&nbsp;Nguyen Van Thien,&nbsp;Tran Manh Tuan","doi":"10.1007/s10489-024-06000-0","DOIUrl":"10.1007/s10489-024-06000-0","url":null,"abstract":"<div><p>To enhance the efficacy of change detection in remote sensing images, we propose a novel Spatial Complex Fuzzy Inference System (Spatial CFIS). This system incorporates fuzzy clustering to generate complex fuzzy rules and employs a triangular spatial complex fuzzy rule base to predict changes in subsequent images compared to their original versions. The weight set of the rule base is optimized using the ADAM algorithm to boost the overall performance of Spatial CFIS. Our proposed model is evaluated using datasets from the weather image data warehouse of the USA Navy and the PRISMA mission funded by the Italian Space Agency (ASI). We compare the performance of Spatial CFIS against other relevant algorithms, including PFC-PFR, SeriesNet, and Deep Slow Feature Analysis (DSFA). The evaluation metrics include RMSE (Root Mean Squared Error), R2 (R Squared), and Analysis of Variance (ANOVA). The experimental results demonstrate that Spatial CFIS outperforms other models by up to 40% in terms of accuracy. In summary, this paper presents an innovative approach to handling remote sensing images by applying a spatial-oriented fuzzy inference system, offering improved accuracy in change detection.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142826284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Retraction Note: Location algorithm of transfer stations based on density peak and outlier detection","authors":"Yan Shao-Hong,&nbsp;Niu Jia-Yang,&nbsp;Chen Tai-Long,&nbsp;Liu Qiu-Tong,&nbsp;Yang Cen,&nbsp;Cheng Jia-Qing,&nbsp;Fu Zhi-Zhen,&nbsp;Li Jie","doi":"10.1007/s10489-024-06186-3","DOIUrl":"10.1007/s10489-024-06186-3","url":null,"abstract":"","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142826201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Collision avoidance time-varying group formation tracking control for multi-agent systems","authors":"Weihao Li,&nbsp;Shiyu Zhou,&nbsp;Mengji Shi,&nbsp;Jiangfeng Yue,&nbsp;Boxian Lin,&nbsp;Kaiyu Qin","doi":"10.1007/s10489-024-05959-0","DOIUrl":"10.1007/s10489-024-05959-0","url":null,"abstract":"<div><p>This study considers the time-varying group formation (TVGF) tracking control problem for general linear multi-agent systems (MASs) with collision avoidance, where the MAS is divided into multiple subgroups, enabling followers to form prescribed formations and track trajectories provided by their respective leaders without collisions. Firstly, a distributed TVGF tracking control protocol is introduced using only relative information among neighboring agents. Then, feasibility conditions under which MASs can successfully realize the TVGF tracking without collisions are put forward. Utilizing Lyapunov stability theory, the convergence of the TVGF tracking error systems is confirmed, ensuring the collision-free achievement of the desired formation. Finally, some simulation examples are provided to validate the effectiveness of the theoretical results.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142826286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Unsupervised perturbation based self-supervised federated adversarial training","authors":"Yuyue Zhang,&nbsp;Hanchen Ye,&nbsp;Xiaoli Zhao","doi":"10.1007/s10489-024-05938-5","DOIUrl":"10.1007/s10489-024-05938-5","url":null,"abstract":"<div><p>Similar to traditional machine learning, federated learning is susceptible to adversarial attacks. Existing defense methods against federated attacks often rely on extensive labeling during the local training process to enhance model robustness. However, labeling typically requires significant resources. To address the challenges posed by expensive labeling and the robustness issues in federated learning, we propose the Unsupervised Perturbation based Self-Supervised Federated Adversarial Training (UPFAT) framework. Within local clients, we introduce an innovative unsupervised adversarial sample generation method, which adapts the classical self-supervised framework BYOL (Bootstrap Your Own Latent). This method maximizes the distances between embeddings of various transformations of the same input, generating unsupervised adversarial samples aimed at confusing the model. For model communication, we present the Robustness-Enhanced Moving Average (REMA) module, which adaptively utilizes global model updates based on the local model’s robustness.Extensive experiments demonstrate that UPFAT outperforms existing methods by <span>(varvec{3sim 4%})</span>.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-024-05938-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142826283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MSDformer: an autocorrelation transformer with multiscale decomposition for long-term multivariate time series forecasting","authors":"Guangyao Su,&nbsp;Yepeng Guan","doi":"10.1007/s10489-024-06105-6","DOIUrl":"10.1007/s10489-024-06105-6","url":null,"abstract":"<p>The improvement of performance and efficiency in long-term time series forecasting is significant for practical applications. However, while enhancing overall performance, existing time series forecasting methods often exhibit unsatisfactory capabilities in the restoration of details and prediction efficiency. To address these issues, an autocorrelation Transformer with multiscale decomposition (MSDformer) is proposed for long-term multivariate time series forecasting. Specifically, a multiscale decomposition (MSDecomp) module is designed, which identifies the temporal repeating patterns in time series with different scales to retain more historical details while extracting trend components. An Encoder layer is proposed based on the MSDecomp module and Auto-Correlation mechanism, which discovers the similarity of subsequences in a periodic manner and effectively captures the seasonal components to improve the degree of restoration of prediction details while extracting the residual trend components. Finally, unlike the traditional Transformer structure, the decoder structure is replaced by the proposed Autoregressive module to simplify the output mode of the decoder and enhance linear information. Compared to other advanced and representative models on six real-world datasets, the experimental results demonstrate that the MSDformer has a relative performance improvement of an average of 8.1%. MSDformer also has lower memory usage and temporal consumption, making it more advantageous for long-term time series forecasting.</p>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142826285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Comparative performance of machine learning-selected portfolios from dynamic CSI300 constituents: forward vs. backward adjusted stock prices","authors":"Ligang Zhou,&nbsp;Xiaoguo Chen,&nbsp;Xiaolei Tang","doi":"10.1007/s10489-024-06107-4","DOIUrl":"10.1007/s10489-024-06107-4","url":null,"abstract":"<p>Most existing studies utilize backward-adjusted stock prices from data platforms to develop and backtest investment strategies using machine learning models. However, these prices are not point-in-time data and may introduce look-ahead bias, raising concerns about the reliability of model performance. To examine the impact of different price adjustment methods, we compare the predictive performance of various machine learning models and the backtesting results of portfolios constructed using these models with both forward-adjusted and backward-adjusted stock prices. Our study, conducted from 2012 to 2022, evaluates the real-world viability of investment strategies on the dynamic constituents of the CSI300 index. The empirical results reveal that while certain measures of machine learning models’ predictive performance may not be significantly affected by the stock price adjustment method, the backtesting performance under backward-adjusted stock prices is overestimated compared to that under forward-adjusted stock prices. This research provides evidence for the impact of historical stock price adjustments in developing machine learning models and presents a comprehensive framework for applying these techniques to the management of index constituent portfolios, thereby bridging the gap between predictive modeling and practical investment strategies.</p>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142826184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Spatio-temporal attention-based hybrid deep network for time series prediction of industrial process","authors":"Dong Lu,&nbsp;Xiaofeng Zhou,&nbsp;Shuai Li","doi":"10.1007/s10489-024-06033-5","DOIUrl":"10.1007/s10489-024-06033-5","url":null,"abstract":"<div><p>Industrial time series involves a large amount of production process information, which effectively reflects the production status of the industrial process. To better understand characteristics and patterns of changes in production conditions, it is crucial to analyze and predict industrial time series data. Given the involvement of numerous parameters and complex physical-chemical reactions in industrial processes, attaining precise predictive performance utilizing a single model remains a formidable challenge. In this paper, we propose a novel hybrid deep learning prediction method based on spatio-temporal attention and temporal convolution network. The proposed method aims to handle the multivariate coupling characteristics and dynamic nonlinear features in industrial time series through different model structures for accurate prediction. In this method, historical data are first segmented into multiple consecutive inputs along the temporal dimension, which are then used as inputs to the subsequent attention mechanism module. To realize the mapping from points to series in the temporal dimension, the segmented input is processed using both the adaptive attention mechanism and one-dimensional convolution. Then the spatio-temporal coupling features are further explored through the spatio-temporal attention model. In addition, to extract dynamic nonlinear features from historical data, a parallel temporal convolutional network with temporal pattern attention is utilized. In order to evaluate the prediction performance of the proposed model, we use two different real-world industrial time series datasets for comprehensive evaluation. The experimental results demonstrate the effectiveness and accuracy of the proposed method. Code is available at https://github.com/TensorPulse/MACnet.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142826405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"UEFN: Efficient uncertainty estimation fusion network for reliable multimodal sentiment analysis","authors":"Shuai Wang,&nbsp;K. Ratnavelu,&nbsp;Abdul Samad Bin Shibghatullah","doi":"10.1007/s10489-024-06113-6","DOIUrl":"10.1007/s10489-024-06113-6","url":null,"abstract":"<div><p>The rapid evolution of the digital era has greatly transformed social media, resulting in more diverse emotional expressions and increasingly complex public discourse. Consequently, identifying relationships within multimodal data has become increasingly challenging. Most current multimodal sentiment analysis (MSA) methods concentrate on merging data from diverse modalities into an integrated feature representation to enhance recognition performance by leveraging the complementary nature of multimodal data. However, these approaches often overlook prediction reliability. To address this, we propose the uncertainty estimation fusion network (UEFN), a reliable MSA method based on uncertainty estimation. UEFN combines the Dirichlet distribution and Dempster-Shafer evidence theory (DSET) to predict the probability distribution and uncertainty of text, speech, and image modalities, fusing the predictions at the decision level. Specifically, the method first represents the contextual features of text, speech, and image modalities separately. It then employs a fully connected neural network to transform features from different modalities into evidence forms. Subsequently, it parameterizes the evidence of different modalities via the Dirichlet distribution and estimates the probability distribution and uncertainty for each modality. Finally, we use DSET to fuse the predictions, obtaining the sentiment analysis results and uncertainty estimation, referred to as the multimodal decision fusion layer (MDFL). Additionally, on the basis of the modality uncertainty generated by subjective logic theory, we calculate feature weights, apply them to the corresponding features, concatenate the weighted features, and feed them into a feedforward neural network for sentiment classification, forming the adaptive weight fusion layer (AWFL). Both MDFL and AWFL are then used for multitask training. Experimental comparisons demonstrate that the UEFN not only achieves excellent performance but also provides uncertainty estimation along with the predictions, enhancing the reliability and interpretability of the results.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142826381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"HFL-GAN: scalable hierarchical federated learning GAN for high quantity heterogeneous clients","authors":"Lewis Petch,&nbsp;Ahmed Moustafa,&nbsp;Xinhui Ma,&nbsp;Mohammad Yasser","doi":"10.1007/s10489-024-05924-x","DOIUrl":"10.1007/s10489-024-05924-x","url":null,"abstract":"<div><p>This paper introduces a novel approach for training generative adversarial networks using federated machine learning. Generative adversarial networks have gained plenty of attention in the research community especially with their abilities to produce high quality synthetic data for a variety of use-cases. Yet, when combined with federated learning, those models suffer from degradation in both training time and quality of results. To address this challenge, this paper introduces a novel approach that uses hierarchical learning techniques to enable the efficient training of federated GAN models. The proposed approach introduces an innovative mechanism that dynamically clusters participant clients to edge servers as well as a novel multi-generator GAN architecture that utilizes non-identical model aggregation stages. The proposed approach has been evaluated on a number of benchmark datasets to measure its performance on higher numbers of participating clients. The results show that HFL-GAN outperforms other comparative state-of-the-art approaches in the training of GAN models in complex non-IID federated learning settings.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-024-05924-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142826382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving multi-class classification: scaled extensions of harmonic mean-based adaptive k-nearest neighbors","authors":"Mustafa Açıkkar,&nbsp;Selçuk Tokgöz","doi":"10.1007/s10489-024-06109-2","DOIUrl":"10.1007/s10489-024-06109-2","url":null,"abstract":"<div><p>This paper proposes a novel extension of the harmonic mean-based adaptive <i>k</i>-nearest neighbors (<i>HMAKNN</i>) algorithm, called scaled <i>HMAKNN</i> (<i>SHMAKNN</i>), which builds on <i>HMAKNN</i>’s strengths to achieve improved multi-class classification accuracy. <i>HMAKNN</i> uses a modified voting mechanism based on the harmonic mean and adaptive <i>k</i>-value selection to address issues like the sensitivity to <i>k</i>-value selection and the limitations of majority voting. <i>SHMAKNN</i> further improves the decision process by adjusting the components of the harmonic mean, focusing on voting values and the average distances of each class label. Additionally, <i>SHMAKNN</i> applies a re-scaling process to adjust the distances of the nearest neighbors within a specific range, enhancing the consistency of distances at different scales. These improvements help align the elements of the harmonic mean more effectively, leading to a balanced and less biased classification process. The study utilized 26 benchmark datasets, carefully curated to ensure accuracy and consistency, selected from diverse domains to evaluate the proposed method on real-world problems. These datasets were chosen to represent challenges like noise, imbalance, and sparsity, ensuring robustness in handling common data complexities. Additionally, small to medium-sized datasets were used to reduce computational burden and allow for efficient evaluation. The evaluation results show that the proposed <i>SHMAKNN</i> models outperform existing methods in both <i>accuracy</i> and <i>F1-score</i> for datasets with four or more classes. Specifically, <i>SHMAKNN</i> achieved the highest average <i>accuracy</i> and <i>F1-score</i> (86.36% and 86.16%) compared to <i>HMAKNN</i> (86.10% and 85.74%) and traditional <i>k</i>-nearest neighbors (84.87% and 84.69%). The performance improvements were validated using Friedman’s test at a significance level of 0.05, confirming their statistical significance of the results. Consequently, the findings indicate that the proposed algorithm exhibits remarkable performance, thereby confirming its reliability and validity in the context of real-world applications, particularly those involving multiple classes.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142826380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}