Intelligent Data Analysis最新文献_第8页

Editorial 社论

IF 1.7 4区计算机科学

Intelligent Data Analysis Pub Date : 2023-08-10 DOI: 10.3233/ida-239005

引用次数: 0

Mining skyline frequent-utility patterns from big data environment based on MapReduce framework 基于MapReduce框架的大数据环境天际线频繁效用模式挖掘

IF 1.7 4区计算机科学

Intelligent Data Analysis Pub Date : 2023-08-10 DOI: 10.3233/ida-220756

J. Wu, Ranran Li, Mu-En Wu, Jerry Chun‐wei Lin

{"title":"Mining skyline frequent-utility patterns from big data environment based on MapReduce framework","authors":"J. Wu, Ranran Li, Mu-En Wu, Jerry Chun‐wei Lin","doi":"10.3233/ida-220756","DOIUrl":"https://doi.org/10.3233/ida-220756","url":null,"abstract":"When the concentration focuses on data mining, frequent itemset mining (FIM) and high-utility itemset mining (HUIM) are commonly addressed and researched. Many related algorithms are proposed to reveal the general relationship between utility, frequency, and items in transaction databases. Although these algorithms can mine FIMs or HUIMs quickly, these algorithms merely take into account frequency or utility as a unilateral criterion for itemsets but ignore the concurrent itemsets, which are often more valuable for reference. A new skyline framework has been presented to mine frequent high utility patterns (SFUPs) to better support user decision-making. Several new algorithms have been proposed one after another. However, the Internet of Things (IoT), mobile Internet, and traditional Internet are generating massive amounts of data every day, and these cutting-edge standalone algorithms can not satisfy the new challenge of finding interesting patterns from this data. Big Data uses a distributed architecture in the form of cloud computing to filter and process this data to extract useful information. This paper proposes a novel parallel algorithm on Hadoop as a three-stage iterative algorithm based on MapReduce. MapReduce is used to divide the mining tasks of the whole large data set into multiple independent sub-tasks to find frequent and high utility patterns in parallel. Numerous experiments were done in this paper, and from the results, the algorithm can handle large datasets and show good performance on Hadoop clusters.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41984221","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

Temporal attention-aware evidential recurrent network for trustworthy prediction of Alzheimer’s disease progression 时间注意感知证据递归网络对阿尔茨海默病进展的可靠预测

IF 1.7 4区计算机科学

Intelligent Data Analysis Pub Date : 2023-08-10 DOI: 10.3233/ida-230220

Chenran Zhang, Qingsen Bao, Feng-jun Zhang, P. Li, Lei Chen

{"title":"Temporal attention-aware evidential recurrent network for trustworthy prediction of Alzheimer’s disease progression","authors":"Chenran Zhang, Qingsen Bao, Feng-jun Zhang, P. Li, Lei Chen","doi":"10.3233/ida-230220","DOIUrl":"https://doi.org/10.3233/ida-230220","url":null,"abstract":"Accurate and reliable prediction of Alzheimer’s disease (AD) progression is crucial for effective interventions and treatment to delay its onset. Recently, deep learning models for AD progression achieve excellent predictive accuracy. However, their predictions lack reliability due to the non-calibration defects, that affects their recognition and acceptance. To address this issue, this paper proposes a temporal attention-aware evidential recurrent network for trustworthy prediction of AD progression. Specifically, evidential recurrent network explicitly models uncertainty of the output and converts it into a reliability measure for trustworthy AD progression prediction. Furthermore, considering that the actual scenario of AD progression prediction frequently relies on historical longitudinal data, we introduce temporal attention into evidential recurrent network, which improves predictive performance. We demonstrate the proposed model on the TADPOLE dataset. For predictive performance, the proposed model achieves mAUC of 0.943 and BCA of 0.881, which is comparable to the SOTA model MinimalRNN. More importantly, the proposed model provides reliability measures of the predicted results through uncertainty estimation and the ECE of the method on the TADPOLE dataset is 0.101, which is much lower than the SOTA model at 0.147, indicating that the proposed model can provide important decision-making support for risk-sensitive prediction of AD progression.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49395935","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 memetic-based technical indicator portfolio and parameters optimization approach for finding trading signals to construct transaction robot in smart city era 基于模因的技术指标组合和参数优化方法寻找交易信号构建智能城市时代的交易机器人

IF 1.7 4区计算机科学

Intelligent Data Analysis Pub Date : 2023-08-10 DOI: 10.3233/ida-220755

C.H. Chen, S. Hung, P.T. Chen, C.S. Wang, R.D. Chiang

{"title":"A memetic-based technical indicator portfolio and parameters optimization approach for finding trading signals to construct transaction robot in smart city era","authors":"C.H. Chen, S. Hung, P.T. Chen, C.S. Wang, R.D. Chiang","doi":"10.3233/ida-220755","DOIUrl":"https://doi.org/10.3233/ida-220755","url":null,"abstract":"With the development of smart cities, the demand for personal financial services is becoming more and more importance, and personal investment suggestion is one of them. A common way to reach the goal is using a technical indicator to form trading strategy to find trading signals as trading suggestion. However, using only a technical indicator has its limitations, a technical indicator portfolio is further utilized to generate trading signals for achieving risk aversion. To provide a more reliable trading signals, in this paper, we propose an optimization algorithm for obtaining a technical indicator portfolio and its parameters for predicting trends of target stock by using the memetic algorithm. In the proposed approach, the genetic algorithm (GA) and simulated annealing (SA) algorithm are utilized for global and local search. In global search, a technical indicator portfolio and its parameters are first encoded into a chromosome using a bit string and real numbers. Then, the initial population is generated based on the encoding scheme. Fitness value of a chromosome is evaluated by the return and risk according to the generated trading signals. In local search, SA is employed to tune parameters of indicators in chromosomes. After that, the genetic operators are continue employed to generate new offspring. Finally, the chromosome with the highest fitness value could be provided to construct transaction robot for making investment plans in smart city environment. Experiments on three real datasets with different trends were made to show the effectiveness of the proposed approach, including uptrend, consolidation, and downtrend. The total returns of them on testing datasets are 26.53% 33.48%, and 9.7% that indicate the proposed approach can not only reach risk aversion in downtrends but also have good returns in others.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45046994","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: 1

A hybrid classification method via keywords screening and attention mechanisms in extreme short text 基于关键词筛选和注意机制的超短文本混合分类方法

IF 1.7 4区计算机科学

Intelligent Data Analysis Pub Date : 2023-08-10 DOI: 10.3233/ida-220417

Xinke Zhou, Yi Zhu, Yun Li, Jipeng Qiang, Yunhao Yuan, Xingdong Wu, Runmei Zhang

{"title":"A hybrid classification method via keywords screening and attention mechanisms in extreme short text","authors":"Xinke Zhou, Yi Zhu, Yun Li, Jipeng Qiang, Yunhao Yuan, Xingdong Wu, Runmei Zhang","doi":"10.3233/ida-220417","DOIUrl":"https://doi.org/10.3233/ida-220417","url":null,"abstract":"Short text classification has provoked a vast amount of attention and research in recent decades. However, most existing methods only focus on the short texts that contain dozens of words like Twitter and Microblog, while pay far less attention to the extreme short texts like news headline and search snippets. Meanwhile, contemporary short text classification methods that extend the features via external knowledge sources always introduce lots of useless concepts, which may be detrimental to classification performance. Moreover, unlike traditional short text classification methods, the classification results of extreme short texts are often determined by a few even one or two keywords. To address these problems, we propose a novel hybrid classification method via Keywords Screening and Attention Mechanisms in extreme short text, called KSAM. More specifically, firstly, the attention-based BiLSTM is introduced in our method to enhance the role of keywords. Secondly, we screen the keywords in the extreme short text for obtaining the true class label, and the concepts concerning the keywords are retrieved from external open knowledge sources like DBpedia. Thirdly, the attention mechanisms are introduced to acquire the weight of these retrieved concepts. Finally, conceptual information is utilized to assist the classification of the extreme short text. Extensive experiments have demonstrated the effectiveness of our method compared to other state-of-the-art methods.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49031201","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

NonPC: Non-parametric clustering algorithm with adaptive noise detecting NonPC:具有自适应噪声检测的非参数聚类算法

IF 1.7 4区计算机科学

Intelligent Data Analysis Pub Date : 2023-08-10 DOI: 10.3233/ida-220427

Lin Li, Xiang Chen, Chengyun Song

{"title":"NonPC: Non-parametric clustering algorithm with adaptive noise detecting","authors":"Lin Li, Xiang Chen, Chengyun Song","doi":"10.3233/ida-220427","DOIUrl":"https://doi.org/10.3233/ida-220427","url":null,"abstract":"Graph-based clustering performs efficiently for identifying clusters in local and nonlinear data Patterns. The existing methods face the problem of parameter selection, such as the setting of k of the k-nearest neighbor graph and the threshold in noise detection. In this paper, a non-parametric clustering algorithm (NonPC) is proposed to tackle those inherent limitations and improve clustering performance. The weighted natural neighbor graph (wNaNG) is developed to represent the given data without any prior knowledge. What is more, the proposed NonPC method adaptively detects noise data in an unsupervised way based on some attributes extracted from wNaNG. The algorithm works without preliminary parameter settings while automatically identifying clusters with unbalanced densities, arbitrary shapes, and noises. To assess the advantages of the NonPC algorithm, extensive experiments have been conducted compared with some classic and recent clustering methods. The results demonstrate that the proposed NonPC algorithm significantly outperforms the state-of-the-art and well-known algorithms in Adjusted Rand index, Normalized Mutual Information, and Fowlkes-Mallows index aspects.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41379047","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 novel deep learning framework for the identification of tortuous vessels in plus diseased infant retinal images 一种新的深度学习框架，用于识别患病婴儿视网膜图像中的弯曲血管

IF 1.7 4区计算机科学

Intelligent Data Analysis Pub Date : 2023-08-07 DOI: 10.3233/ida-220451

Sivakumar Ramachandran

{"title":"A novel deep learning framework for the identification of tortuous vessels in plus diseased infant retinal images","authors":"Sivakumar Ramachandran","doi":"10.3233/ida-220451","DOIUrl":"https://doi.org/10.3233/ida-220451","url":null,"abstract":"Retinopathy of prematurity ROP, sometimes known as Terry syndrome, is an ophthalmic condition that affects premature babies. It is the main cause of childhood blindness and morbidity of vision throughout life. ROP frequently coexists with a disease stage known as Plus disease, which is marked by severe tortuosity and dilated retinal blood vessels. The goal of this research is to create a diagnostic technique that can discriminate between infants with Plus disease from healthy subjects. Blood vascular tortuosity is used as a prognostic indicator for the diagnosis. We examine the quantification of retinal blood vessel tortuosity and propose a computer-aided diagnosis system that can be used as a tool for ROP identification. Deep neural networks are used in the proposed approach to segment retinal blood vessels, which is followed by the prediction of tortuous vessel pixels in the segmented vessel map. Digital fundus images obtained from Retcam3TM is used for screening. We use a proprietary data set of 289 infant retinal images (89 with Plus disease and 200 healthy) from Narayana Nethralaya in Bangalore, India, to illustrate the efficacy of our methodology. The findings of this study demonstrate the reliability of the suggested method as a computer-aided diagnostic tool that can help medical professionals make an early diagnosis of ROP.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45520391","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

Detection of multi-size peach in orchard using RGB-D camera combined with an improved DEtection Transformer model RGB-D相机结合改进的检测变压器模型对果园中多粒桃进行检测

IF 1.7 4区计算机科学

Intelligent Data Analysis Pub Date : 2023-08-07 DOI: 10.3233/ida-220449

Yu Yang, Xin Wang, Zhenfang Liu, Min Huang, Shangpeng Sun, Qibing Zhu

引用次数: 0

A hybrid quantum-behaved particle swarm optimization solution to non-convex economic load dispatch with multiple fuel types and valve-point effects 多燃料类型和阀点效应非凸经济负荷调度的混合量子粒子群优化解

IF 1.7 4区计算机科学

Intelligent Data Analysis Pub Date : 2023-08-04 DOI: 10.3233/ida-220415

Qidong Chen, Sun Jun, V. Palade

{"title":"A hybrid quantum-behaved particle swarm optimization solution to non-convex economic load dispatch with multiple fuel types and valve-point effects","authors":"Qidong Chen, Sun Jun, V. Palade","doi":"10.3233/ida-220415","DOIUrl":"https://doi.org/10.3233/ida-220415","url":null,"abstract":"Economic dispatch problems (EDPs) can be reduced to non-convex constrained optimization problems, and most of the population-based algorithms are prone to have problems of premature and falling into local optimum when solving EDPs. Therefore, this paper proposes a hybrid quantum-behaved particle swarm optimization (HQPSO) algorithm to alleviate the above problems. In the HQPSO, the Solis and Wets local search method is used to enhance the local search ability of the QPSO so that the algorithm can find solutions that is close to optimal when the constraints are met, and two evolution operators are proposed and incorporated for the purpose of making a better balance between local search and global search abilities at the later search stage. The performance comparison is made among the HQPSO and the other ten population-based random search methods under two different experimental configurations and four different power systems in terms of solution quality, robustness, and convergence property. The experimental results show that the HQPSO improves the convergence properties of the QPSO and finally obtains the best total generation cost without violating any constraints. In addition, the HQPSO outperforms all the other algorithms on 7 cases of all 8 experimental cases in terms of global best position and mean position, which verifies the effectiveness of the algorithm.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44719390","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

Adversarial unsupervised domain adaptation based on generative adversarial network for stock trend forecasting 基于生成对抗网络的无监督域自适应股票趋势预测

IF 1.7 4区计算机科学

Intelligent Data Analysis Pub Date : 2023-08-02 DOI: 10.3233/ida-220414

Qiheng Wei, Qun Dai

{"title":"Adversarial unsupervised domain adaptation based on generative adversarial network for stock trend forecasting","authors":"Qiheng Wei, Qun Dai","doi":"10.3233/ida-220414","DOIUrl":"https://doi.org/10.3233/ida-220414","url":null,"abstract":"Stock trend forecasting, which refers to the prediction of the rise and fall of the next day’s stock price, is a promising research field in financial time series forecasting, with a large quantity of well-performing algorithms and models being proposed. However, most of the studies focus on trend prediction for stocks with a large number of samples, while the trend prediction problem of newly listed stocks with only a small number of samples is neglected. In this work, we innovatively design a solution to the Small Sample Size (SSS) trend prediction problem of newly listed stocks. Traditional Machine Learning (ML) and Deep Learning (DL) techniques are based on the assumption that the available labeled samples are substantial, which is invalid for SSS trend prediction of newly listed stocks. In order to break out of this dilemma, we propose a novel Adversarial Unsupervised Domain Adaptation Network (AUDA-Net), based on Generative Adversarial Network (GAN), ad hoc for SSS stock trend forecasting. Different from the traditional domain adaptation algorithms, we employ a GAN model, which is trained on basis of the target stock dataset, to effectively solve the absence problem of available samples. Notably, AUDA-Net can reasonably and successfully transfer the knowledge learned from the source stock dataset to the newly listed stocks with only a few samples. The stock trend forecasting performance of our proposed AUDA-Net model has been verified through extensive experiments conducted on several real stock datasets of the U.S. stock market. Using stock trend forecasting as a case study, we show that the SSS forecasting results produced by AUDA-Net are favorably comparable to the state-of-the-art.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43390592","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