Applied Intelligence最新文献

{"title":"Facial StO2-based personal identification: dataset construction, feasibility study, and recognition framework","authors":"Zheyuan Zhang,&nbsp;Xinyu Liu,&nbsp;Yingjuan Jia,&nbsp;Ju Zhou,&nbsp;Hanpu Wang,&nbsp;Jiaxiu Wang,&nbsp;Tong Chen","doi":"10.1007/s10489-025-06267-x","DOIUrl":"10.1007/s10489-025-06267-x","url":null,"abstract":"<div><p>Biometrics have been extensively utilized in the realm of identity recognition. However, each biometric method has its inherent limitations in specific scenarios. For example, identity recognition based on facial images is contactless but can be forged; finger vein recognition is very secure but generally requires contact collection to ensure accurate identification. In some scenarios with high security requirements, there is often a need for contactless acquisition of biometric features that cannot be forged to recognize identity. Therefore, a novel biometric, facial tissue oxygen saturation (StO2) with the advantages of robust anti-spoofing capabilities and non-contact measurement, is proposed for identity recognition. To more comprehensively verify the feasibility of facial StO2 for identity recognition, a Facial StO2 Identity Dataset (FSID148) containing 148 identities is collected and the feasibility of facial StO2 identity recognition is validated by performing verification, close-set identification, and open-set identification tasks. In order to enhance the performance of facial StO2 identity recognition, an attention-guided contrastive learning framework that enables backbones to derive discriminative identity representations from both local and global facial StO2 regions is proposed. The method proposed has achieved accuracies of 96.11%, 94.60%, and 88.51% in the aforementioned tasks, positioning facial StO2 as a promising biometric for a wide array of application scenarios.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143109457","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":"Control of traffic network signals based on deep deterministic policy gradients","authors":"Huifeng Hu,&nbsp;Shu Lin,&nbsp;Ping Wang,&nbsp;Jungang Xu","doi":"10.1007/s10489-024-06208-0","DOIUrl":"10.1007/s10489-024-06208-0","url":null,"abstract":"<div><p>The centralized control of traffic signals is a challenging problem due to the high randomness and complexity of traffic flow on urban road networks and the interaction between intersections. Centralized control leads to high spatial dimensionality of joint actions for traffic road network signal control. However, the decisive action output can solve the problem of “dimensional explosion” caused by joint actions. In this paper, we propose a deep deterministic policy gradient-based algorithm for centralized control of urban traffic road network signals. We simplify the traffic signal control to a four-phase green signal ratio, and the deep deterministic policy gradient-based algorithm deterministically outputs the control signal for each intersection based on the information of the whole traffic network, thus avoiding the problem of “dimensional explosion”. In particular, a new normalization function is proposed to generate the green rate of traffic signals and constrain it to a range of maximum and minimum sustained green time by linear transformation, which makes the generated traffic signals more realistic. Our proposed algorithm is shown to be optimal and robust compared to Deep Q-Network(DQN) based and fixed time control for 7-hour SUMO simulation of a single-peak traffic network with three intersections.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-024-06208-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143109458","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":"The utility of hyperplane angle metric in detecting financial concept drift","authors":"ZhiPeng Jiang,&nbsp;Dengyi Zhang,&nbsp;Xiaolei Luo,&nbsp;Fazhi He","doi":"10.1007/s10489-025-06292-w","DOIUrl":"10.1007/s10489-025-06292-w","url":null,"abstract":"<div><p>In financial time series analysis, introducing a new metric for concept drift is essential to address the limitations of existing evaluation methods, particularly in terms of speed, interpretability, and stability. Performance-based metrics and model-based metrics are the most commonly used to detect concept drift. For example, the error rate, which belongs to performance-based metric, is a frequently used metric that directly reflects the difference between the model’s output and the actual results, making it suitable for quick decision-making. Mahalanobis Distance, being a model-based metric, detects concept drift by evaluating deviations in the sample distribution, offering deeper interpretability and stability. Generally speaking, performance-based metrics excel in speed but lack deeper interpretability and stability, while model-based metrics are opposite. To achieve speed, deeper interpretability, and stability, we propose a novel metric termed the Angle Between Hyperplanes (ABH), which calculates the angle between earlier and later hyperplanes at two distinct time points through an arc-cosine function. This metric quantifies the similarity between two decision boundaries, with the angle reflecting the degree of concept drift detection. In other words, a larger angle indicates a higher probability of detecting concept drift. ABH offers good interpretability, as its angle has a geometric presentation, and it is time-efficient, requiring only the calculation of an arc-cosine function. To validate the effectiveness of the ABH, we integrate it into the Drift Detection Model (DDM) framework, replacing error rate-based metrics to monitor data distribution over time. Empirical studies on synthetic datasets show that ABH achieves approximately a 50% reduction in the Coefficient of Variation (Cv) compared to error rate-based approaches, demonstrating the stability of ABH. On the Shanghai and Shenzhen Stock Exchanges, our model outperforms the recent models. For instance, our model outperforms the Weighted Increment-Decrement Support Vector Machine (WIDSVM), reducing the error rate by 4% and 1%, respectively.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143109786","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":"Advances in deep neural network-based hyperspectral image classification and feature learning with limited samples: a survey","authors":"Farhan Ullah,&nbsp;Irfan Ullah,&nbsp;Khalil Khan,&nbsp;Salabat Khan,&nbsp;Farhan Amin","doi":"10.1007/s10489-024-06139-w","DOIUrl":"10.1007/s10489-024-06139-w","url":null,"abstract":"<div><p>Advancements in sensor technologies have brought about significant improvements in the resolution and quality of imagery by enhancing spatial, temporal, spectral, and radiometric aspects. These remarkable progressions have sparked enhancements in hyperspectral image classification (HSIC) applications, including land cover mapping, vegetation classification, urban monitoring, and resource understanding, which are crucial for optimal earth resource management. Effective HSIC demands advanced algorithms that exhibit high accuracy, low computational complexity, and robustness in extracting intricate spectral-spatial features. The advent of deep convolutional neural networks (DCNNs) has revolutionized image classification, introducing robust architectures that continue to evolve. However, a notable challenge remains in supervised HSIC due to the scarcity of training samples, a bottleneck that has yet to be comprehensively addressed in the literature. To catalyze further exploration, this study reviews existing methods designed to mitigate the limitations posed by limited labeled data. It also examines current techniques for feature learning in HSIC using DCNNs. Additionally, the study presents results obtained from various methods applied to the most widely recognized public HSIC datasets, accompanied by insightful observations that lay the groundwork for future research endeavors within the hyperspectral community.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143109049","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":"Room-level localization method in industrial workshops using LiDAR-based point cloud registration and object recognition","authors":"Yunzhi Li,&nbsp;Libin Tan,&nbsp;Xiangrong Xu,&nbsp;Zequn Zhang","doi":"10.1007/s10489-025-06244-4","DOIUrl":"10.1007/s10489-025-06244-4","url":null,"abstract":"<div><p>In this work, we aim to achieve room-level localization for mobile robots in industrial workshops. It is difficult to obtain precise localization information via common methods because of the complexity of the industrial environment. Our findings show that precise room-level localization can be achieved via LiDAR-based point cloud registration and object recognition. For this purpose, we formulate room-level localization as a classification problem. Registration and object recognition are used to extract features from point clouds. After the data enhancement algorithm, called Stacked Auto Encoder is employed to overcome the issue of limited feature data, the neural network algorithm is leveraged to address the classification problem. To this end, we collected point cloud data from industrial workshops and performed experimental validation. We evaluated the recognition performance of the algorithm in a metallurgical workshop and achieved good accuracy.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143109173","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":"Multimodal information fusion detection of fall-related disability based on video images and sensing signals","authors":"Yuke Qiu,&nbsp;Yuchen He,&nbsp;Yangwei Ying,&nbsp;Xiaoyu Ma,&nbsp;Hong Zhou,&nbsp;Kewei Liang","doi":"10.1007/s10489-024-06193-4","DOIUrl":"10.1007/s10489-024-06193-4","url":null,"abstract":"<div><p>Fall-related disability is prevalent among older adults. This paper introduces a novel multimodal data fusion detection approach aimed at the early identification of such conditions in everyday settings, thereby enabling prompt intervention. The methodology utilizes both video cameras and waist sensors to gather visual and sensory data during human motion. The video-based analysis investigates the spatial-temporal characteristics and the interrelations of human joint points. These features are extracted by the ST-GCN network and effectively distinguished through classification, achieving an accuracy rate of 73.85<span>(%)</span>. The sensor-based analysis focuses on the examination of the amplitude and frequency variations in 3D acceleration and declination data. By integrating the Mann-Whitney U test and DTW analysis for refined data differentiation, this method achieves an accuracy rate of 80.77<span>(%)</span>. The paper finally presents a fusion analysis technique that gives precedence to samples yielding consistent results from both methods. When encountering inconsistent results, a multi-layer neural network is developed to determine the fusion weights for the two data types. These weights are used to generate the final assessment outcomes. The fusion method demonstrates a marked increase in accuracy, reaching 91.54<span>(%)</span>, which significantly surpasses the performance of the individual methods.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143109048","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 sub-model selection algorithm considering model interactions in combination forecasting for carbon price forecasting","authors":"Jingling Yang,&nbsp;Liren Chen,&nbsp;Huayou Chen","doi":"10.1007/s10489-025-06252-4","DOIUrl":"10.1007/s10489-025-06252-4","url":null,"abstract":"<div><p>In model selection for combination forecasting, it is essential not only to consider the relevance and redundancy of models but also to emphasize their interactions. However, current methods often overlook this aspect, resulting in the selection of model subsets that fail to fully account for the synergistic effects between models, thereby limiting improvements in predictive accuracy. To address this issue, this paper proposes a novel model selection method based on mutual information theory, which defines relevance, redundancy, and interaction within combination forecasting. The method first selects the model outputs most correlated with the actual values through mutual information maximization and then assesses the potential interactions between the selected outputs and other candidate models to form an interaction set. From this set, the superior subset is selected according to the criteria of maximizing mutual information and minimizing error. This approach efficiently selects superior subsets without using an exhaustive search over all combinations. Empirical analysis using carbon price datasets confirms that the selected subsets outperform their derived subsets, the best individual predictions, and those chosen by the benchmark selection algorithms. The results further demonstrate that the incorporation of model interactions in the selection process effectively enhances forecasting performance.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143109172","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":"Multi-instance embedding space set-kernel fusion with discriminability metric","authors":"Mei Yang,&nbsp;Jing-Yu Zhang,&nbsp;Zhen Pan,&nbsp;Fan Min","doi":"10.1007/s10489-024-06160-z","DOIUrl":"10.1007/s10489-024-06160-z","url":null,"abstract":"<div><p>Multi-instance learning (MIL) is a weakly supervised approach where labeled bags contain multiple unlabeled instances. Some popular methods demonstrate the efficacy of the set-kernel in capturing bag-level information. However, they face challenges in simultaneously utilizing diverse perspective information extracted from different kernels. In this paper, we propose a multi-instance embedding space set-kernel fusion with discriminability metric (MIKF) algorithm with three techniques. First, the embedding space set-kernel (ESK) construction technique obtains perspective-specific information efficiently. A flexible strategy is in charge of generating various ESKs based on different embedding spaces. Second, the embedding space construction technique creates three types of concrete spaces. It selects three types of instances containing different perspective information according to instance correlation. Third, the kernel fusion technique employs bag labels to construct a discriminability metric for obtaining adaptive weights for base kernels. These weights facilitate the effective integration of diverse perspective information. Experimental results on 29 datasets show that MIKF outperforms rival set-kernels and state-of-the-art MIL algorithms in terms of average classification performance. <i>Source codes are available at</i> https://github.com/whale2024/MIKF.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143109050","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":"Fake advertisements detection using automated multimodal learning: a case study for Vietnamese real estate data","authors":"Duy Nguyen,&nbsp;Trung T. Nguyen,&nbsp;Cuong V. Nguyen","doi":"10.1007/s10489-025-06238-2","DOIUrl":"10.1007/s10489-025-06238-2","url":null,"abstract":"<div><p>The popularity of e-commerce has given rise to fake advertisements that can expose users to financial and data risks while damaging the reputation of these e-commerce platforms. For these reasons, detecting and removing such fake advertisements are important for the success of e-commerce websites. In this paper, we propose FADAML, a novel end-to-end machine learning system to detect and filter out fake online advertisements. Our system combines techniques in multimodal machine learning and automated machine learning to achieve a high detection rate. As a case study, we apply FADAML to detect fake advertisements on popular Vietnamese real estate websites. Our experiments show that we can achieve 91.5% detection accuracy, which significantly outperforms three different state-of-the-art fake news detection systems.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-025-06238-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143108890","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":"RWGCN: Random walk graph convolutional network for group activity recognition","authors":"Junpeng Kang,&nbsp;Jing Zhang,&nbsp;Lin Chen,&nbsp;Hui Zhang,&nbsp;Li Zhuo","doi":"10.1007/s10489-024-06017-5","DOIUrl":"10.1007/s10489-024-06017-5","url":null,"abstract":"<div><p>Group activity recognition can remarkably improve the understanding of video content by analyzing human behaviors and activities in videos. We propose a random walk graph convolutional network (RWGCN) for group activity recognition. (1) Considering the limitation of the convolutional structure to the visual information of group activities, the position feature extraction module is used to compensate for the loss of visual information. (2) A graph convolutional network (GCN) with distance-adaptive edge relations is constructed using individuals as graph nodes to identify the intrinsic relationships among the individuals in the group activities. (3) A Levy flight random walk mechanism is introduced into the GCN to obtain information from different nodes and integrate the previous position information to recognize group activity. Extensive experiments on the publicly available CAD, CAE datasets, and self-built BJUT-GAD dataset show that our RWGCN achieves MPCA of 95.49%, 94.82%, and 96.02%, respectively, which provides a better competitiveness in group activity recognition compared to other methods.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143108891","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}