Applied Intelligence最新文献

{"title":"Edge-enhanced dual discriminator generative adversarial network for fast MRI with parallel imaging using multi-view information","authors":"Jiahao Huang,&nbsp;Weiping Ding,&nbsp;Jun Lv,&nbsp;Jingwen Yang,&nbsp;Hao Dong,&nbsp;Javier Del Ser,&nbsp;Jun Xia,&nbsp;Tiaojuan Ren,&nbsp;Stephen T. Wong,&nbsp;Guang Yang","doi":"10.1007/s10489-021-03092-w","DOIUrl":"10.1007/s10489-021-03092-w","url":null,"abstract":"<div><p>In clinical medicine, magnetic resonance imaging (MRI) is one of the most important tools for diagnosis, triage, prognosis, and treatment planning. However, MRI suffers from an inherent slow data acquisition process because data is collected sequentially in <i>k</i>-space. In recent years, most MRI reconstruction methods proposed in the literature focus on holistic image reconstruction rather than enhancing the edge information. This work steps aside this general trend by elaborating on the enhancement of edge information. Specifically, we introduce a novel parallel imaging coupled dual discriminator generative adversarial network (PIDD-GAN) for fast multi-channel MRI reconstruction by incorporating multi-view information. The dual discriminator design aims to improve the edge information in MRI reconstruction. One discriminator is used for holistic image reconstruction, whereas the other one is responsible for enhancing edge information. An improved U-Net with local and global residual learning is proposed for the generator. Frequency channel attention blocks (FCA Blocks) are embedded in the generator for incorporating attention mechanisms. Content loss is introduced to train the generator for better reconstruction quality. We performed comprehensive experiments on Calgary-Campinas public brain MR dataset and compared our method with state-of-the-art MRI reconstruction methods. Ablation studies of residual learning were conducted on the MICCAI13 dataset to validate the proposed modules. Results show that our PIDD-GAN provides high-quality reconstructed MR images, with well-preserved edge information. The time of single-image reconstruction is below 5ms, which meets the demand of faster processing.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"52 13","pages":"14693 - 14710"},"PeriodicalIF":5.3,"publicationDate":"2022-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-021-03092-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9769822","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":"Forecasting patient arrivals at emergency department using calendar and meteorological information","authors":"Yan Zhang,&nbsp;Jie Zhang,&nbsp;Min Tao,&nbsp;Jian Shu,&nbsp;Degang Zhu","doi":"10.1007/s10489-021-03085-9","DOIUrl":"10.1007/s10489-021-03085-9","url":null,"abstract":"<div><p>Overcrowding in emergency departments (EDs) is a serious problem in many countries. Accurate ED patient arrival forecasts can serve as a management baseline to better allocate ED personnel and medical resources. We combined calendar and meteorological information and used ten modern machine learning methods to forecast patient arrivals. For daily patient arrival forecasting, two feature selection methods are proposed. One uses kernel principal component analysis(KPCA) to reduce the dimensionality of all of the features, and the other is to use the maximal information coefficient(MIC) method to select the features related to the daily data first and then perform KPCA dimensionality reduction. The current study focuses on a public hospital ED in Hefei, China. We used the data November 1, 2019 to August 31, 2020 for model training; and patient arrival data September 1, 2020 to November 31, 2020 for model validation. The results show that for hourly patient arrival forecasting, each machine learning model has better forecasting results than the traditional autoRegressive integrated moving average (ARIMA) model, especially long short-term memory (LSTM) model. For daily patient arrival forecasting, the feature selection method based on MIC-KPCA has a better forecasting effect, and the simpler models are better than the ensemble models. The method we proposed could be used for better planning of ED personnel resources.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"52 10","pages":"11232 - 11243"},"PeriodicalIF":5.3,"publicationDate":"2022-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-021-03085-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39949190","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":"Cloud vendor selection for the healthcare industry using a big data-driven decision model with probabilistic linguistic information","authors":"R. Krishankumar,&nbsp;R. Sivagami,&nbsp;Abhijit Saha,&nbsp;Pratibha Rani,&nbsp;Karthik Arun,&nbsp;K. S. Ravichandran","doi":"10.1007/s10489-021-02913-2","DOIUrl":"10.1007/s10489-021-02913-2","url":null,"abstract":"<div><p>The role of cloud services in the data-intensive \u0000industry is indispensable. Cision recently reported that the cloud market would grow to 55 billion USD, with an active contribution of the cloud to healthcare around 2025. Inspired by the report, cloud vendors expand their market and the quality of services to seek growth globally. The rapid growth of the cloud sector in the healthcare industry imposes a challenge: making a rational choice of a cloud vendor (CV) out of a diverse set of vendors. Typically, the healthcare industry 4.0 sees the issue as a large-scale group decision-making problem. Previous studies on a CV selection face certain challenges, such as (i) a lack of the ability to handle multiple users’ views, as well as experts’/users’ complex linguistic views; (ii) the confidence level associated with a view is not considered; (iii) the transformation of multiple users’ views into holistic data is lacking; and (iv) the systematic prioritization of CVs with minimum human intervention is a crucial task. Motivated by these challenges and circumventing them, a new big data-driven decision model is put forward in this paper. Initially, the data in the form of complex expressions are collected from multiple cloud users and are further transformed into a holistic decision matrix by adopting probabilistic linguistic information (PLI). PLI represents complex linguistic expressions along with the associated confidence levels. Later, a holistic decision matrix is formed with the missing values imputed by proposing an imputation algorithm. Furthermore, the criteria weights are determined by using a newly proposed mathematical model and partial information. Finally, the evaluation based on the distance from average solution (EDAS) approach is extended to PLI for the rational ranking of CVs. A real-time example of a CV selection for a healthcare center in India is exemplified so as to demonstrate the usefulness of the model, and the comparison reveals the merits and limitations of the model.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"52 12","pages":"13497 - 13519"},"PeriodicalIF":5.3,"publicationDate":"2022-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-021-02913-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39850752","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":"SIRA: Scale illumination rotation affine invariant mask R-CNN for pedestrian detection","authors":"Ujwalla Gawande,&nbsp;Kamal Hajari,&nbsp;Yogesh Golhar","doi":"10.1007/s10489-021-03073-z","DOIUrl":"10.1007/s10489-021-03073-z","url":null,"abstract":"<div><p>In this paper, we resolve the challenging obstacle of detecting pedestrians with the ubiquity of irregularities in scale, rotation, and the illumination of the natural scene images natively. Pedestrian instances with such obstacles exhibit significantly unique characteristics. Thus, it strongly influences the performance of pedestrian detection techniques. We propose the new robust Scale Illumination Rotation and Affine invariant Mask R-CNN (SIRA M-RCNN) framework for overcoming the predecessor’s difficulties. The first phase of the proposed system deals with illumination variation by histogram analysis. Further, we use the contourlet transformation, and the directional filter bank for the generation of the rotational invariant features. Finally, we use Affine Scale Invariant Feature Transform (ASIFT) to find points that are translation and scale-invariant. Extensive evaluation of the benchmark database will prove the effectiveness of SIRA M-RCNN. The experimental results achieve state-of-the-art performance and show a significant performance improvement in pedestrian detection.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"52 9","pages":"10398 - 10416"},"PeriodicalIF":5.3,"publicationDate":"2022-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-021-03073-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39829776","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":"Semantic consistency generative adversarial network for cross-modality domain adaptation in ultrasound thyroid nodule classification","authors":"Jun Zhao,&nbsp;Xiaosong Zhou,&nbsp;Guohua Shi,&nbsp;Ning Xiao,&nbsp;Kai Song,&nbsp;Juanjuan Zhao,&nbsp;Rui Hao,&nbsp;Keqin Li","doi":"10.1007/s10489-021-03025-7","DOIUrl":"10.1007/s10489-021-03025-7","url":null,"abstract":"<div><p>Deep convolutional networks have been widely used for various medical image processing tasks. However, the performance of existing learning-based networks is still limited due to the lack of large training datasets. When a general deep model is directly deployed to a new dataset with heterogeneous features, the effect of domain shifts is usually ignored, and performance degradation problems occur. In this work, by designing the semantic consistency generative adversarial network (SCGAN), we propose a new multimodal domain adaptation method for medical image diagnosis. SCGAN performs cross-domain collaborative alignment of ultrasound images and domain knowledge. Specifically, we utilize a self-attention mechanism for adversarial learning between dual domains to overcome visual differences across modal data and preserve the domain invariance of the extracted semantic features. In particular, we embed nested metric learning in the semantic information space, thus enhancing the semantic consistency of cross-modal features. Furthermore, the adversarial learning of our network is guided by a discrepancy loss for encouraging the learning of semantic-level content and a regularization term for enhancing network generalization. We evaluate our method on a thyroid ultrasound image dataset for benign and malignant diagnosis of nodules. The experimental results of a comprehensive study show that the accuracy of the SCGAN method for the classification of thyroid nodules reaches 94.30%, and the AUC reaches 97.02%. These results are significantly better than the state-of-the-art methods.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"52 9","pages":"10369 - 10383"},"PeriodicalIF":5.3,"publicationDate":"2022-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-021-03025-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39829775","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":"NetNMSP: Nonoverlapping maximal sequential pattern mining","authors":"Yan Li,&nbsp;Shuai Zhang,&nbsp;Lei Guo,&nbsp;Jing Liu,&nbsp;Youxi Wu,&nbsp;Xindong Wu","doi":"10.1007/s10489-021-02912-3","DOIUrl":"10.1007/s10489-021-02912-3","url":null,"abstract":"<div><p>Nonoverlapping sequential pattern mining, as a kind of repetitive sequential pattern mining with gap constraints, can find more valuable patterns. Traditional algorithms focused on finding all frequent patterns and found lots of redundant short patterns. However, it not only reduces the mining efficiency, but also increases the difficulty in obtaining the demand information. To reduce the frequent patterns and retain its expression ability, this paper focuses on the Nonoverlapping Maximal Sequential Pattern (NMSP) mining which refers to finding frequent patterns whose super-patterns are infrequent. In this paper, we propose an effective mining algorithm, Nettree for NMSP mining (NetNMSP), which has three key steps: calculating the support, generating the candidate patterns, and determining NMSPs. To efficiently calculate the support, NetNMSP employs the backtracking strategy to obtain a nonoverlapping occurrence from the leftmost leaf to its root with the leftmost parent node method in a Nettree. To reduce the candidate patterns, NetNMSP generates candidate patterns by the pattern join strategy. Furthermore, to determine NMSPs, NetNMSP adopts the screening method. Experiments on biological sequence datasets verify that not only does NetNMSP outperform the state-of-the-arts algorithms, but also NMSP mining has better compression performance than closed pattern mining. On sales datasets, we validate that our algorithm guarantees the best scalability on large scale datasets. Moreover, we mine NMSPs and frequent patterns in SARS-CoV-1, SARS-CoV-2 and MERS-CoV. The results show that the three viruses are similar in the short patterns but different in the long patterns. More importantly, NMSP mining is easier to find the differences between the virus sequences.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"52 9","pages":"9861 - 9884"},"PeriodicalIF":5.3,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-021-02912-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39825115","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":"COVID-CT-Mask-Net: prediction of COVID-19 from CT scans using regional features","authors":"Aram Ter-Sarkisov","doi":"10.1007/s10489-021-02731-6","DOIUrl":"10.1007/s10489-021-02731-6","url":null,"abstract":"<div><p>We present COVID-CT-Mask-Net model that predicts COVID-19 in chest CT scans. The model works in two stages: in the first stage, Mask R-CNN is trained to localize and detect two types of lesions in images. In the second stage, these detections are fused to classify the whole input image. To develop the solution for the three-class problem (COVID-19, Common Pneumonia and Control), we used the COVIDx-CT data split derived from the dataset of chest CT scans collected by China National Center for Bioinformation. We use 3000 images (about 5<i>%</i> of the train split of COVIDx-CT) to train the model. Without any complicated data normalization, balancing and regularization, and training only a small fraction of the model’s parameters, we achieve a <b>9</b><b>0</b><b>.</b><b>8</b><b>0</b><b>%</b> COVID-19 sensitivity, <b>9</b><b>1</b><b>.</b><b>6</b><b>2</b><b>%</b> Common Pneumonia sensitivity and <b>9</b><b>2</b><b>.</b><b>1</b><b>0</b><b>%</b> true negative rate (Control sensitivity), an overall accuracy of <b>9</b><b>1</b><b>.</b><b>6</b><b>6</b><b>%</b> and F1-score of <b>9</b><b>1</b><b>.</b><b>5</b><b>0</b><b>%</b> on the test data split with 21192 images, bringing the ratio of test to train data to <b>7.06</b>. We also establish an important result that regional predictions (bounding boxes with confidence scores) detected by Mask R-CNN can be used to classify whole images. The full source code, models and pretrained weights are available on https://github.com/AlexTS1980/COVID-CT-Mask-Net.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"52 9","pages":"9664 - 9675"},"PeriodicalIF":5.3,"publicationDate":"2022-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-021-02731-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39824695","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":"Rescuing emergency cases of COVID-19 patients: An intelligent real-time MSC transfusion framework based on multicriteria decision-making methods","authors":"M. A. Alsalem,&nbsp;O. S. Albahri,&nbsp;A. A. Zaidan,&nbsp;Jameel R. Al-Obaidi,&nbsp;Alhamzah Alnoor,&nbsp;A. H. Alamoodi,&nbsp;A. S. Albahri,&nbsp;B. B. Zaidan,&nbsp;F. M. Jumaah","doi":"10.1007/s10489-021-02813-5","DOIUrl":"10.1007/s10489-021-02813-5","url":null,"abstract":"<div><p>Mesenchymal stem cells (MSCs) have shown promising ability to treat critical cases of coronavirus disease 2019 (COVID-19) by regenerating lung cells and reducing immune system overreaction. However, two main challenges need to be addressed first before MSCs can be efficiently transfused to the most critical cases of COVID-19. First is the selection of suitable MSC sources that can meet the standards of stem cell criteria. Second is differentiating COVID-19 patients into different emergency levels automatically and prioritising them in each emergency level. This study presents an efficient real-time MSC transfusion framework based on multicriteria decision-making(MCDM) methods. In the methodology, the testing phase represents the ability to adhere to plastic surfaces, the upregulation and downregulation of specific surface protein markers and finally the ability to differentiate into different kinds of cells. In the development phase, firstly, two scenarios of an augmented dataset based on the medical perspective are generated to produce 80 patients with different emergency levels. Secondly, an automated triage algorithm based on a formal medical guideline is proposed for real-time monitoring of COVID-19 patients with different emergency levels (i.e. mild, moderate, severe and critical) considering the improvement and deterioration procedures from one level to another. Thirdly, a unique decision matrix for each triage level (except mild) is constructed on the basis of the intersection between the evaluation criteria of each emergency level and list of COVID-19 patients. Thereafter, MCDM methods (i.e. analytic hierarchy process [AHP] and <i>vlsekriterijumska optimizcija i kaompromisno resenje</i> [VIKOR]) are integrated to assign subjective weights for the evaluation criteria within each triage level and then prioritise the COVID-19 patients on the basis of individual and group decision-making(GDM) contexts. Results show that: (1) in both scenarios, the proposed algorithm effectively classified the patients into four emergency levels, including mild, moderate, severe and critical, taking into consideration the improvement and deterioration cases. (2) On the basis of experts’ perspectives, clear differences in most individual prioritisations for patients with different emergency levels in both scenarios were found. (3) In both scenarios, COVID-19 patients were prioritised identically between the internal and external group VIKOR. During the evaluation, the statistical objective method indicated that the patient prioritisations underwent systematic ranking. Moreover, comparison analysis with previous work proved the efficiency of the proposed framework. Thus, the real-time MSC transfusion for COVID-19 patients can follow the order achieved in the group VIKOR results.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"52 9","pages":"9676 - 9700"},"PeriodicalIF":5.3,"publicationDate":"2022-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-021-02813-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39824694","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":"A survey of group decision making methods in Healthcare Industry 4.0: bibliometrics, applications, and directions","authors":"Keyu Lu,&nbsp;Huchang Liao","doi":"10.1007/s10489-021-02909-y","DOIUrl":"10.1007/s10489-021-02909-y","url":null,"abstract":"<div><p>Healthcare Industry 4.0 refers to intelligent operation processes in the medical industry. With the development of information technology, large-scale group decision making (GDM), which allows a larger number of decision makers (DMs) from different places or sectors to participate in decision making, has been rapidly developed and applied in Healthcare Industry 4.0 to help to make decisions efficiently and smartly. To make full use of GDM methods to promote the developments of the medical industry, it is necessary to review the existing relevant achievements. Therefore, this paper conducts an overview to generate a comprehensive understanding of GDM in Healthcare Industry 4.0 and to identify future development directions. Bibliometric analyses are conducted in order to learn the development trends from published papers. The implementations of GDM methods in Healthcare Industry 4.0 are reviewed in accordance with the paradigm of the general GDM process, which includes information representation, dimension reduction, consensus reaching, and result elicitation. We also provide current research challenges and future directions regarding medical GDM. It is hoped that our study will be helpful for researchers in the field of GDM in Healthcare Industry 4.0.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"52 12","pages":"13689 - 13713"},"PeriodicalIF":5.3,"publicationDate":"2022-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-021-02909-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39676478","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":"Two-dimensional Bhattacharyya bound linear discriminant analysis with its applications","authors":"Yan-Ru Guo,&nbsp;Yan-Qin Bai,&nbsp;Chun-Na Li,&nbsp;Lan Bai,&nbsp;Yuan-Hai Shao","doi":"10.1007/s10489-021-02843-z","DOIUrl":"10.1007/s10489-021-02843-z","url":null,"abstract":"<div><p>The recently proposed L2-norm linear discriminant analysis criterion based on Bhattacharyya error bound estimation (L2BLDA) was an effective improvement over linear discriminant analysis (LDA) and was used to handle vector input samples. When faced with two-dimensional (2D) inputs, such as images, converting two-dimensional data to vectors, regardless of the inherent structure of the image, may result in some loss of useful information. In this paper, we propose a novel two-dimensional Bhattacharyya bound linear discriminant analysis (2DBLDA). 2DBLDA maximizes the matrix-based between-class distance, which is measured by the weighted pairwise distances of class means and minimizes the matrix-based within-class distance. The criterion of 2DBLDA is equivalent to optimizing the upper bound of the Bhattacharyya error. The weighting constant between the between-class and within-class terms is determined by the involved data that make the proposed 2DBLDA adaptive. The construction of 2DBLDA avoids the small sample size (SSS) problem, is robust, and can be solved through a simple standard eigenvalue decomposition problem. The experimental results on image recognition and face image reconstruction demonstrate the effectiveness of 2DBLDA.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"52 8","pages":"8793 - 8809"},"PeriodicalIF":5.3,"publicationDate":"2021-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-021-02843-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39612345","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}