Intelligent Data Analysis最新文献_第5页

An improved k-NN anomaly detection framework based on locality sensitive hashing for edge computing environment 边缘计算环境下基于局部敏感哈希的改进k-NN异常检测框架

4区计算机科学

Intelligent Data Analysis Pub Date : 2023-10-06 DOI: 10.3233/ida-216461

Cong Gao, Yuzhe Chen, Yanping Chen, Zhongmin Wang, Hong Xia

{"title":"An improved k-NN anomaly detection framework based on locality sensitive hashing for edge computing environment","authors":"Cong Gao, Yuzhe Chen, Yanping Chen, Zhongmin Wang, Hong Xia","doi":"10.3233/ida-216461","DOIUrl":"https://doi.org/10.3233/ida-216461","url":null,"abstract":"Large deployment of wireless sensor networks in various fields bring great benefits. With the increasing volume of sensor data, traditional data collection and processing schemes gradually become unable to meet the requirements in actual scenarios. As data quality is vital to data mining and value extraction, this paper presents a distributed anomaly detection framework which combines cloud computing and edge computing. The framework consists of three major components: k-nearest neighbors, locality sensitive hashing, and cosine similarity. The traditional k-nearest neighbors algorithm is improved by locality sensitive hashing in terms of computation cost and processing time. An initial anomaly detection result is given by the combination of k-nearest neighbors and locality sensitive hashing. To further improve the accuracy of anomaly detection, a second test for anomaly is provided based on cosine similarity. Extensive experiments are conducted to evaluate the performance of our proposal. Six popular methods are used for comparison. Experimental results show that our model has advantages in the aspects of accuracy, delay, and energy consumption.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135302174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Incremental density clustering framework based on dynamic microlocal clusters 基于动态微局部聚类的增量密度聚类框架

4区计算机科学

Intelligent Data Analysis Pub Date : 2023-09-26 DOI: 10.3233/ida-227263

Tao Zhang, Decai Li, Jingya Dong, Yuqing He, Yanchun Chang

{"title":"Incremental density clustering framework based on dynamic microlocal clusters","authors":"Tao Zhang, Decai Li, Jingya Dong, Yuqing He, Yanchun Chang","doi":"10.3233/ida-227263","DOIUrl":"https://doi.org/10.3233/ida-227263","url":null,"abstract":"With the prevailing development of the internet and sensors, various streaming raw data are generated continually. However, traditional clustering algorithms are unfavorable for discovering the underlying patterns of incremental data in time; clustering accuracy cannot be assured if fixed parameters clustering algorithms are used to handle incremental data. In this paper, an Incremental-Density-Micro-Clustering (IDMC) framework is proposed to address this concern. To reduce the succeeding clustering computation, we design the Dynamic-microlocal-clustering method to merge samples from streaming data into dynamic microlocal clusters. Beyond that, the Density-center-based neighborhood search method is proposed for periodically merging microlocal clusters to global clusters automatically; at the same time, these global clusters are updated by the Dynamic-cluster-increasing method with data streaming in each period. In this way, IDMC processes sensor data with less computational time and memory, improves the clustering performance, and simplifies the parameter choosing in conventional and stream data clustering. Finally, experiments are conducted to validate the proposed clustering framework on UCI datasets and streaming data generated by IoT sensors. As a result, this work advances the state-of-the-art of incremental clustering algorithms in the field of sensors’ streaming data analysis.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135721739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploiting scatter matrix on one-class support vector machine based on low variance direction 基于低方差方向的一类支持向量机散射矩阵挖掘

4区计算机科学

Intelligent Data Analysis Pub Date : 2023-09-26 DOI: 10.3233/ida-227036

Soumaya Nheri, Riadh Ksantini, Mohamed Bécha Kaâniche, Adel Bouhoula

引用次数: 0

ACEANet: Ambiguous Context Enhanced Attention Network for skin lesion segmentation 基于模糊上下文增强的关注网络的皮肤病变分割

4区计算机科学

Intelligent Data Analysis Pub Date : 2023-09-21 DOI: 10.3233/ida-230298

Yun Jiang, Hao Qiao

{"title":"ACEANet: Ambiguous Context Enhanced Attention Network for skin lesion segmentation","authors":"Yun Jiang, Hao Qiao","doi":"10.3233/ida-230298","DOIUrl":"https://doi.org/10.3233/ida-230298","url":null,"abstract":"Skin lesion segmentation from dermatoscopic images is essential for the diagnosis of skin cancer. However, it is still a challenging task due to the ambiguity of the skin lesions, the irregular shape of the lesions and the presence of various interfering factors. In this paper, we propose a novel Ambiguous Context Enhanced Attention Network (ACEANet) based on the classical encoder-decoder architecture, which is able to accurately and reliably segment a variety of lesions with efficiency. Specifically, a novel Ambiguous Context Enhanced Attention module is embedded in the skip connection to augment the ambiguous boundary information. A Dilated Gated Fusion block is employed in the end of the encoding phase, which effectively reduces the loss of spatial location information due to continuous downsampling. In addition, we propose a novel Cascading Global Context Attention to fuse feature information generated by the encoder with features generated by the decoder of the corresponding layer. In order to verify the effectiveness and advantages of the proposed network, we have performed comparative experiments on ISIC2018 dataset and PH2 dataset. Experiments results demonstrate that the proposed model has superior segmentation performance for skin lesions.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136237978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GT-CHES: Graph transformation for classification in human evolutionary systems GT-CHES:人类进化系统分类的图变换

4区计算机科学

Intelligent Data Analysis Pub Date : 2023-09-21 DOI: 10.3233/ida-230194

J. Johnson, C. Giraud-Carrier

引用次数: 0

Pure large kernel convolutional neural network transformer for medical image registration 用于医学图像配准的纯大核卷积神经网络变压器

4区计算机科学

Intelligent Data Analysis Pub Date : 2023-09-14 DOI: 10.3233/ida-230197

Zhao Fang, Wenming Cao

{"title":"Pure large kernel convolutional neural network transformer for medical image registration","authors":"Zhao Fang, Wenming Cao","doi":"10.3233/ida-230197","DOIUrl":"https://doi.org/10.3233/ida-230197","url":null,"abstract":"Deformable medical image registration is a fundamental and critical task in medical image analysis. Recently, deep learning-based methods have rapidly developed and have shown impressive results in deformable image registration. However, existing approaches still suffer from limitations in registration accuracy or generalization performance. To address these challenges, in this paper, we propose a pure convolutional neural network module (CVTF) to implement hierarchical transformers and enhance the registration performance of medical images. CVTF has a larger convolutional kernel, providing a larger global effective receptive field, which can improve the network’s ability to capture long-range dependencies. In addition, we introduce the spatial interaction attention (SIA) module to compute the interrelationship between the target feature pixel points and all other points in the feature map. This helps to improve the semantic understanding of the model by emphasizing important features and suppressing irrelevant ones. Based on the proposed CVTF and SIA, we construct a novel registration framework named PCTNet. We applied PCTNet to generate displacement fields and register medical images, and we conducted extensive experiments and validation on two public datasets, OASIS and LPBA40. The experimental results demonstrate the effectiveness and generality of our method, showing significant improvements in registration accuracy and generalization performance compared to existing methods. Our code has been available at https://github.com/fz852/PCTNet.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134913998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A feature-aware long-short interest evolution network for sequential recommendation 时序推荐的特征感知多空兴趣演化网络

4区计算机科学

Intelligent Data Analysis Pub Date : 2023-09-14 DOI: 10.3233/ida-230288

Jing Tang, Yongquan Fan, Yajun Du, Xianyong Li, Xiaoliang Chen

{"title":"A feature-aware long-short interest evolution network for sequential recommendation","authors":"Jing Tang, Yongquan Fan, Yajun Du, Xianyong Li, Xiaoliang Chen","doi":"10.3233/ida-230288","DOIUrl":"https://doi.org/10.3233/ida-230288","url":null,"abstract":"Recommendation systems are an effective solution to deal with information overload, particularly in the e-commerce sector, in which sequential recommendation is extensively utilized. Sequential recommendations aim to acquire users’ interests and provide accurate recommendations by analyzing users’ historical interaction sequences. To improve recommendation performance, it is vital to take into account the long- and short-term interests of users. Despite significant advancements in this domain, some issues need to be addressed. Conventional sequential recommendation models typically express each item with a uniform embedding, ignoring evolutionary patterns among item attributes, such as category, brand, and price. Moreover, these models often model users’ long- and short-term interests independently, failing to adequately address the issues of interest drift and short-term interest evolution. This study proposes a new model, the Feature-aware Long-Short Interest Evolution Network (FLSIE), to address the above-mentioned issues. Specifically, the model uses explicit feature embedding to represent item attribute information and employs a two-dimensional (2D) attention mechanism to distinguish the significance of individual features in a specific item and the relevance of each item in the interaction sequence. Furthermore, to avoid the issue of interest drift, the model employs a long-term interest guidance mechanism to enhance the representation of short-term interest and adopts a gated recurrent unit with attentional update gate to model the dynamic evolution of users’ short-term interest. Experimental results indicate that our presented model outperforms existing methods on three real-world datasets.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134914000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning hierarchical embedding space for image-text matching 学习图像-文本匹配的分层嵌入空间

4区计算机科学

Intelligent Data Analysis Pub Date : 2023-09-14 DOI: 10.3233/ida-230214

Sun Hao, Xiaolin Qin, Xiaojing Liu

{"title":"Learning hierarchical embedding space for image-text matching","authors":"Sun Hao, Xiaolin Qin, Xiaojing Liu","doi":"10.3233/ida-230214","DOIUrl":"https://doi.org/10.3233/ida-230214","url":null,"abstract":"There are two mainstream strategies for image-text matching at present. The one, termed as joint embedding learning, aims to model the semantic information of both image and sentence in a shared feature subspace, which facilitates the measurement of semantic similarity but only focuses on global alignment relationship. To explore the local semantic relationship more fully, the other one, termed as metric learning, aims to learn a complex similarity function to directly output score of each image-text pair. However, it significantly suffers from more computation burden at retrieval stage. In this paper, we propose a hierarchically joint embedding model to incorporate the local semantic relationship into a joint embedding learning framework. The proposed method learns the shared local and global embedding spaces simultaneously, and models the joint local embedding space with respect to specific local similarity labels which are easy to access from the lexical information of corpus. Unlike the methods based on metric learning, we can prepare the fixed representations of both images and sentences by concatenating the normalized local and global representations, which makes it feasible to perform the efficient retrieval. And experiments show that the proposed model can achieve competitive performance when compared to the existing joint embedding learning models on two publicly available datasets Flickr30k and MS-COCO.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134972788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cross-modality semantic guidance for multi-label image classification 多标签图像分类的跨模态语义引导

4区计算机科学

Intelligent Data Analysis Pub Date : 2023-09-14 DOI: 10.3233/ida-230239

Jun Huang, Dian Wang, Xudong Hong, Xiwen Qu, Wei Xue

{"title":"Cross-modality semantic guidance for multi-label image classification","authors":"Jun Huang, Dian Wang, Xudong Hong, Xiwen Qu, Wei Xue","doi":"10.3233/ida-230239","DOIUrl":"https://doi.org/10.3233/ida-230239","url":null,"abstract":"Multi-label image classification aims to predict a set of labels that are present in an image. The key challenge of multi-label image classification lies in two aspects: modeling label correlations and utilizing spatial information. However, the existing approaches mainly calculate the correlation between labels according to co-occurrence among them. While the result is easily affected by the label noise and occasional co-occurrences. In addition, some works try to model the correlation between labels and spatial features, but the correlation among labels is not fully considered to model the spatial relationships among features. To address the above issues, we propose a novel cross-modality semantic guidance-based framework for multi-label image classification, namely CMSG. First, we design a semantic-guided attention (SGA) module, which applies the label correlation matrix to guide the learning of class-specific features, which implicitly models semantic correlations among labels. Second, we design a spatial-aware attention (SAA) module to extract high-level semantic-aware spatial features based on class-specific features obtained from the SGA module. The experiments carried out on three benchmark datasets demonstrate that our proposed method outperforms existing state-of-the-art algorithms on multi-label image classification.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134913990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Boosting active domain adaptation with exploration of samples 通过对样本的探索增强主动域适应能力

4区计算机科学

Intelligent Data Analysis Pub Date : 2023-09-14 DOI: 10.3233/ida-230150

Qing Tian, Heng Zhang

{"title":"Boosting active domain adaptation with exploration of samples","authors":"Qing Tian, Heng Zhang","doi":"10.3233/ida-230150","DOIUrl":"https://doi.org/10.3233/ida-230150","url":null,"abstract":"Nowadays, the idea of active learning is gradually adopted to assist domain adaptation. However, due to the existence of domain shift, the traditional active learning methods originating from semi-supervised scenarios can not be directly applied to domain adaptation. To solve the problem, active domain adaptation is proposed as a new domain adaptation paradigm, which aims to improve the performance of the model by annotating a small amount of target domain samples. In this regard, we propose an active domain adaptation method named Boosting Active Domain Adaptation with Exploration of Samples (BADA), dividing Active DA into two related issues: sample selection and sample utilization. We design the instability selection criterion based on predictive consistency and the diversity selection criterion. For the remaining unlabeled samples, we design a self-training framework, which screens out reliable samples and unreliable samples through the sample screening mechanism similar to selection criteria. And we adopt respective loss functions for reliable samples and unreliable samples. Experiments show that BADA remarkably outperforms previous active learning methods and Active DA methods on several domain adaptation datasets.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134913992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0