Applied Intelligence最新文献

{"title":"Adaptive weighted stacking model with optimal weights selection for mortality risk prediction in sepsis patients","authors":"Liang Zhou,&nbsp;Wenjin Li,&nbsp;Tao Wu,&nbsp;Zhiping Fan,&nbsp;Levent Ismaili,&nbsp;Temitope Emmanuel Komolafe,&nbsp;Siwen Zhang","doi":"10.1007/s10489-024-05783-6","DOIUrl":"10.1007/s10489-024-05783-6","url":null,"abstract":"<div><p>Sepsis patients in the ICU face heightened mortality risks. There still exist challenges that hinder the development of mortality risk prediction models for sepsis patients. In the ensemble model, the differences between base classifier performance can affect the model accuracy and efficiency, and overlapping sample training will lead to repetitive learning, which reduces the model generalization. To tackle these challenges, we propose an Adaptive Weighted Stacking based on Optimal Weights Selection (AWS-OWS) model. A random sampling without replacement is employed to prevent repetitive learning in base classifiers. Additionally, a weighted function and the gradient descent algorithm is adopted to select optimal weights for base classifiers, enhancing the performance of stacking model. The MIMIC-IV dataset is used for model training and internal testing, and the independent samples from MIMIC-III are used for external validation. The results show that AWS-OWS achieves the best AUC of 0.88 in the internal test, with a threefold reduction in computation time compared to standard stacking. In external validation, it also demonstrates good model generalization. AWS-OWS significantly improves the prediction performance and model efficiency, facilitates the identification of high-risk patients with sepsis and supports clinicians in determining appropriate management and treatment strategies.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"54 22","pages":"11892 - 11913"},"PeriodicalIF":3.4,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220639","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":"Aerial-view geo-localization based on multi-layer local pattern cross-attention network","authors":"Haoran Li,&nbsp;Tingyu Wang,&nbsp;Quan Chen,&nbsp;Qiang Zhao,&nbsp;Shaowei Jiang,&nbsp;Chenggang Yan,&nbsp;Bolun Zheng","doi":"10.1007/s10489-024-05777-4","DOIUrl":"10.1007/s10489-024-05777-4","url":null,"abstract":"<div><p>Aerial-view geo-localization aims to determine locations of interest to drones by matching drone-view images against a satellite database with geo-tagging. The key underpinning of this task is to mine discriminative features to form a view-invariant representation of the same target location. To achieve this purpose, existing methods usually focus on extracting fine-grained information from the final feature map while neglecting the importance of middle-layer outputs. In this work, we propose a Transformer-based network, named Multi-layer Local Pattern Cross Attention Network (MLPCAN). Particularly, we employ the cross-attention block (CAB) to establish correlations between information of feature maps from different layers when images are fed into the network. Then, we apply the square-ring partition strategy to divide feature maps from different layers and acquire multiple local pattern blocks. For the information misalignment within multi-layer features, we propose the multi-layer aggregation block (MAB) to aggregate the high-association feature blocks obtained by the division. Extensive experiments on two public datasets, i.e., University-1652 and SUES-200, show that the proposed model significantly improves the accuracy of geo-localization and achieves competitive results.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"54 21","pages":"11034 - 11053"},"PeriodicalIF":3.4,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220638","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":"Parallel proportional fusion of a spiking quantum neural network for optimizing image classification","authors":"Zuyu Xu,&nbsp;Kang Shen,&nbsp;Pengnian Cai,&nbsp;Tao Yang,&nbsp;Yuanming Hu,&nbsp;Shixian Chen,&nbsp;Yunlai Zhu,&nbsp;Zuheng Wu,&nbsp;Yuehua Dai,&nbsp;Jun Wang,&nbsp;Fei Yang","doi":"10.1007/s10489-024-05786-3","DOIUrl":"10.1007/s10489-024-05786-3","url":null,"abstract":"<div><p>The recent emergence of the hybrid quantum-classical neural network (HQCNN) architecture has garnered considerable attention because of the potential advantages associated with integrating quantum principles to enhance various facets of machine learning algorithms and computations. However, the current investigated serial structure of HQCNN, wherein information sequentially passes from one network to another, often imposes limitations on the trainability and expressivity of the network. In this study, we introduce a novel architecture termed parallel proportional fusion of spiking and quantum neural networks (PPF-SQNN). The dataset information is simultaneously fed into both the spiking neural network and the variational quantum circuits, with the outputs amalgamated in proportion to their individual contributions. We systematically assess the impact of diverse PPF-SQNN parameters on network performance for image classification, aiming to identify the optimal configuration. On three datasets for image classification tasks, the final classification accuracy reached 98.2%, 99.198%, and 97.921%, respectively, with loss values all below 0.2, outperforming the compared serial networks. In noise testing, it also demonstrates good classification performance even under noise intensities of 0.9 Gaussian and uniform noise. This study introduces a novel and effective amalgamation approach for HQCNN, laying the groundwork for the advancement and application of quantum advantages in artificial intelligence computations.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"54 22","pages":"11876 - 11891"},"PeriodicalIF":3.4,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220643","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":"Fine-grained gaze estimation based on the combination of regression and classification losses","authors":"Ahmed A. Abdelrahman,&nbsp;Thorsten Hempel,&nbsp;Aly Khalifa,&nbsp;Ayoub Al-Hamadi","doi":"10.1007/s10489-024-05778-3","DOIUrl":"10.1007/s10489-024-05778-3","url":null,"abstract":"<div><p>Human gaze is a crucial cue used in various applications such as human-robot interaction, autonomous driving, and virtual reality. Recently, convolution neural network (CNN) approaches have made notable progress in predicting gaze angels. However, estimating accurate gaze direction in-the-wild is still a challenging problem due to the difficulty of obtaining the most crucial gaze information that exists in the eye area which constitutes a small part of the face images. In this paper, we introduce a novel two-branch CNN architecture with a multi-loss approach to estimate gaze angles (pitch and yaw) from face images. Our approach utilizes separate fully connected layers for each gaze angle prediction, allowing explicit learning of discriminative features and emphasizing the distinct information associated with each gaze angle. Moreover, we adopt a multi-loss approach, incorporating both classification and regression losses. This allows for joint optimization of the combined loss for each gaze angle, resulting in improved overall gaze performance. To evaluate our model, we conduct experiments on three popular datasets collected under unconstrained settings: MPIIFaceGaze, Gaze360, and RT-GENE. Our proposed model surpasses current state-of-the-art methods and achieves state-of-the-art performance on all three datasets, showcasing its superior capability in gaze estimation.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"54 21","pages":"10982 - 10994"},"PeriodicalIF":3.4,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-024-05778-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220644","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":"Hierarchical symmetric cross entropy for distant supervised relation extraction","authors":"Yun Liu,&nbsp;Xiaoheng Jiang,&nbsp;Pengshuai Lv,&nbsp;Yang Lu,&nbsp;Shupan Li,&nbsp;Kunli Zhang,&nbsp;Mingliang Xu","doi":"10.1007/s10489-024-05798-z","DOIUrl":"10.1007/s10489-024-05798-z","url":null,"abstract":"<div><p>Distant supervised relation extraction has been increasingly popular in recent years, which generates datasets automatically without human intervention. However, the distant supervised assumption has the limitation that the generated datasets have inevitable labeling errors. This paper proposes the method of Hierarchical Symmetric Cross Entropy for Distant Supervised Relation Extraction (HSCERE) to alleviate the impact of the noisy labels. Specifically, HSCERE simultaneously utilizes two extractors with the same network structure for collaborative learning. This collaborative learning process guides the optimization of the extractor through a joint loss function, namely Hierarchical Symmetric Cross Entropy (HSCE). Within the HSCE loss, the predicted probability distribution of the extractors serves as the supervisory signal, guiding the optimization of the extractors on two levels to reduce the impact of noisy labels. The two levels include the internal optimization within each extractor and the collaborative optimization between extractors. Experiments on generally used datasets show that HSCERE can effectively handle noisy labels and can be incorporated into various methods to enhance their performance.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"54 21","pages":"11020 - 11033"},"PeriodicalIF":3.4,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-024-05798-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220640","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":"Z-number linguistic term set for multi-criteria group decision-making and its application in predicting the acceptance of academic papers","authors":"Yangxue Li,&nbsp;Gang Kou,&nbsp;Yi Peng,&nbsp;Juan Antonio Morente-Molinera","doi":"10.1007/s10489-024-05765-8","DOIUrl":"10.1007/s10489-024-05765-8","url":null,"abstract":"<div><p>Real-world information is often characterized by uncertainty and partial reliability, which led Zadeh to introduce the concept of Z-numbers as a more appropriate formal structure for describing such information. However, the computation of Z-numbers requires solving highly complex optimization problems, limiting their practical application. Although linguistic Z-numbers have been explored for their computational straightforwardness, they lack theoretical support from Z-number theory and exhibit certain limitations. To address these issues and provide theoretical support from Z-numbers, we propose a Z-number linguistic term set to facilitate more efficient processing of Z-number-based information. Specifically, we redefine linguistic Z-numbers as Z-number linguistic terms. By analyzing the hidden probability density functions of these terms, we identify patterns for ranking them. These patterns are used to define the Z-number linguistic term set, which includes all Z-number linguistic terms sorted in order. We also discuss the basic operators between these terms. Furthermore, we develop a multi-criteria group decision-making (MCGDM) model based on the Z-number linguistic term set. Applying our method to predict the acceptance of academic papers, we demonstrate its effectiveness and superiority. We compare the performance of our MCGDM method with five existing Z-number-based MCGDM methods and eight traditional machine learning clustering algorithms. Our results show that the proposed method outperforms others in terms of accuracy and time consumption, highlighting the potential of Z-number linguistic terms for enhancing Z-number computation and extending the application of Z-number-based information to real-world problems.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"54 21","pages":"10962 - 10981"},"PeriodicalIF":3.4,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220603","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":"Task-based dialogue policy learning based on diffusion models","authors":"Zhibin Liu,&nbsp;Rucai Pang,&nbsp;Zhaoan Dong","doi":"10.1007/s10489-024-05810-6","DOIUrl":"10.1007/s10489-024-05810-6","url":null,"abstract":"<div><p>The purpose of task-based dialogue systems is to help users achieve their dialogue needs using as few dialogue rounds as possible. As the demand increases, the dialogue tasks gradually involve multiple domains and develop in the direction of complexity and diversity. Achieving high performance with low computational effort has become an essential metric for multi-domain task-based dialogue systems. This paper proposes a new approach to guided dialogue policy. The method introduces a conditional diffusion model in the reinforcement learning Q-learning algorithm to regularise the policy in a diffusion Q-learning manner. The conditional diffusion model is used to learn the action value function, regulate the actions using regularisation, sample the actions, use the sampled actions in the policy update process, and additionally add a loss term that maximizes the value of the actions in the policy update process to improve the learning efficiency. Our proposed method is based on a conditional diffusion model, combined with the reinforcement learning TD3 algorithm as a dialogue policy and an inverse reinforcement learning approach to construct a reward estimator to provide rewards for policy updates as a way of completing a multi-domain dialogue task.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"54 22","pages":"11752 - 11764"},"PeriodicalIF":3.4,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220601","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":"Research on the prediction algorithm of aero engine lubricating oil consumption based on multi-feature information fusion","authors":"Qifan Zhou,&nbsp;Yingqing Guo,&nbsp;Kejie Xu,&nbsp;Bosong Chai,&nbsp;Guicai Li,&nbsp;Kun Wang,&nbsp;Yunhui Dong","doi":"10.1007/s10489-024-05759-6","DOIUrl":"10.1007/s10489-024-05759-6","url":null,"abstract":"<div><p>The lubrication system supplies lubrication and cleans the rotating parts and contacting machinery during the operation of an aero-engine. It is crucial to maintain an adequate amount of lubricant by predicting and analyzing the consumption rate to ensure endurance and maintenance programs are effective. This paper examines the combination of temporal and non-temporal data that impact the characteristic parameters of lubricant consumption rate in aero-engines. Our study focuses on the merging of LSTM (Long Short-Term Memory) + LightGBM (Light Gradient Boosting Machine) + CatBoost, and uses KPCA dimensionality reduction optimization, along with Stacking for the fusion of a multi-feature regression prediction algorithm. On the one hand, this study utilizes integrated learning to fuse feature extractions from LSTM for temporal information and non-temporal information by GDBT (Gradient Boosting Decision Tree). This approach considers the trend and distribution of feature samples to develop a more robust feature extraction method. On the other hand, the integrated learning framework incorporates multi-decision making and feature importance extraction to strengthen the mapping relationship with the predicted output of lubrication oil consumption rate, enabling regression prediction. The algorithm for regression prediction has been executed and the results indicate a final regression prediction MAPE (Mean Absolute Percentage Error) of less than 3%. MSE and RMSE reached 1.28% and 1.33%, the results are in an ideal state. The algorithms used in this paper will be applied in the future to aero-engine lubricant systems and eventually to engines in general.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"54 22","pages":"11845 - 11875"},"PeriodicalIF":3.4,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220637","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":"A novel differential evolution algorithm based on periodic intervention and systematic regulation mechanisms","authors":"Guanyu Yuan,&nbsp;Gaoji Sun,&nbsp;Libao Deng,&nbsp;Chunlei Li,&nbsp;Guoqing Yang","doi":"10.1007/s10489-024-05781-8","DOIUrl":"10.1007/s10489-024-05781-8","url":null,"abstract":"<p>Differential evolution (DE) has attracted widespread attention due to its outstanding optimization performance and ease of operation, but it cannot avoid the dilemmas of premature convergence or stagnation when faced with complex optimization problems. To reduce the probability of such difficulties for DE, we sort out the factors that influence the balance between global exploration and local exploitation in the DE algorithm, and we design a novel DE variant (abbreviated as PISRDE) by integrating the corresponding influence factors through a periodic intervention mechanism and a systematic regulation mechanism. The periodic intervention mechanism divides the optimization operations of PISRDE into routine operation and intervention operation, and it balances global exploration and local exploitation at the macro level by executing the two operations alternately. The systematic regulation mechanism treats the involved optimization strategies and parameter settings as an organic system for targeted design, to balance global exploration and local exploitation at the meso or micro level. To evaluate and verify the optimization performance of PISRDE, we employ seven DE variants with excellent optimization performance to conduct comparison experiments on the IEEE CEC 2014 and IEEE CEC 2017 benchmarks. The comparison results indicate that PISRDE outperforms all competitors overall, and its relative advantage is even more significant when dealing with high-dimensional and complex optimization problems.</p><p>Schematic design philosophy of PISRDE</p>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"54 22","pages":"11779 - 11803"},"PeriodicalIF":3.4,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220642","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":"A comprehensive review of model compression techniques in machine learning","authors":"Pierre Vilar Dantas,&nbsp;Waldir Sabino da Silva Jr,&nbsp;Lucas Carvalho Cordeiro,&nbsp;Celso Barbosa Carvalho","doi":"10.1007/s10489-024-05747-w","DOIUrl":"10.1007/s10489-024-05747-w","url":null,"abstract":"<p>This paper critically examines model compression techniques within the machine learning (ML) domain, emphasizing their role in enhancing model efficiency for deployment in resource-constrained environments, such as mobile devices, edge computing, and Internet of Things (IoT) systems. By systematically exploring compression techniques and lightweight design architectures, it is provided a comprehensive understanding of their operational contexts and effectiveness. The synthesis of these strategies reveals a dynamic interplay between model performance and computational demand, highlighting the balance required for optimal application. As machine learning (ML) models grow increasingly complex and data-intensive, the demand for computational resources and memory has surged accordingly. This escalation presents significant challenges for the deployment of artificial intelligence (AI) systems in real-world applications, particularly where hardware capabilities are limited. Therefore, model compression techniques are not merely advantageous but essential for ensuring that these models can be utilized across various domains, maintaining high performance without prohibitive resource requirements. Furthermore, this review underscores the importance of model compression in sustainable artificial intelligence (AI) development. The introduction of hybrid methods, which combine multiple compression techniques, promises to deliver superior performance and efficiency. Additionally, the development of intelligent frameworks capable of selecting the most appropriate compression strategy based on specific application needs is crucial for advancing the field. The practical examples and engineering applications discussed demonstrate the real-world impact of these techniques. By optimizing the balance between model complexity and computational efficiency, model compression ensures that the advancements in AI technology remain sustainable and widely applicable. This comprehensive review thus contributes to the academic discourse and guides innovative solutions for efficient and responsible machine learning practices, paving the way for future advancements in the field.</p>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"54 22","pages":"11804 - 11844"},"PeriodicalIF":3.4,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-024-05747-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220600","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}