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检索条件"任意字段=15th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2014"

共 118 条记录，以下是51-60 订阅

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General Structure Preserving Network Embedding 1

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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Zhu, Sinan Jia, Caiyan Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China Beijing Jiaotong Univ Beijing Key Lab Traff Data Anal & Min Beijing 100044 Peoples R China

ISBN: (数字)9783030034931

ISBN: (纸本)9783030034931;9783030034924

Network embedding has attracted increasing attention in recent years since it represents large scale networks in low-dimensional space and provides an easier way to analysis networks. Existing embedding methods either focus on preserving the microscopic topology structure, or incorporate the mesoscopic community structure of a network. However, in the real world, a network may not only contain community structure, but have bipartite-structure, star-structure or other general structures, where nodes in each cluster have similar patterns of connections to other nodes. Empirically, general structure is important for describing the features of networks. In this paper, based on nonnegative matrix factorization framework, we propose GS-NMF which is capable of integrating topology structure and general structure into embedding process. the experimental results show that GS-NMF overcomes the limitation of previous methods and achieves obvious improvement on node clustering, node classification, and visualization.

关键词： Network embedding General structure Nonnegative matrix factorization

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Representation learning of Knowledge Graphs with Multi-scale Capsule Network 20th

Representation Learning of Knowledge Graphs with Multi-scale...

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20th international conference on intelligent data engineering and automated learning (ideal)

作者： Cheng, Jingwei Yang, Zhi Dang, Jinming Pan, Chunguang Zhang, Fu Northeastern Univ Coll Comp Sci & Engn Shenyang Peoples R China

ISBN: (纸本)9783030336073;9783030336066

Representation learning of knowledge graphs has gained wide attention in the field of natural language processing. Most existing knowledge representation models for knowledge graphs embed triples into a continuous low-dimensional vector space through a simple linear transformation. In spite of high computation efficiency, the fitting ability of these models is suboptimal. In this paper, we propose a multi-scale capsule network to model relations between embedding vectors from a deep perspective. We use convolution kernels with different sizes of windows in the convolutional layer inside a Capsule network to extract semantic features of entities and relations in triples. these semantic features are then represented as a continuous vector through a routing process algorithm in the capsule layer. the modulus of this vector is used as the score of confidence of correctness of a triple. Experiments show that the proposed model obtains better performance than state-of-the-art embedding models for the task of knowledge graph completion over two benchmarks, WN18RR and FB15k-237.

关键词： Representation learning Capsule network Multi-scale Dynamic routing Knowledge graph completion

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A Compatible Model of Unstructured data for Cross-Media Retrieval in the Field of Tourism 1

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17th international conference on intelligent data engineering and automated learning (ideal)

作者： Hu, Han Li, Xiaoyu Wu, Wenyi Liu, Zhaoyi Univ Elect Sci & Technol China Chengdu Res Inst Chengdu 610054 Sichuan Peoples R China Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu 610054 Sichuan Peoples R China

ISBN: (数字)9783319462578

ISBN: (纸本)9783319462578;9783319462561

With the development of multimedia, the amount of unstructured data of multimedia is increasing, especially for the tourism. Meanwhile, it is one of the key issue to analysis large-scale unstructured data, which helps us to find the hidden relevance between redundant and different data. How to retrieve efficiently, and recommend accurately for cross-media retrieval is more and more important. this paper proposes a new data mode for cross-media retrieval - unstructured data compatible model, short for UDC model. the UDC model is constructed by its own metadata. All metadata are organized by a certain hierarchical relationship. Every metadata consists of three layers: the feature layer, the semantic layer and the compatibility layer. Furthermore, this paper presents retrieval and recommendation algorithms based on UDC model. the experiment results demonstrate that the retrieval engine based on UDC mode can be more effective for cross-media retrieval and recommendation.

关键词： Tourism Cross-media Retrieval Recommendation Unstructured data model

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Nonconvex functions optimization using an estimation of distribution algorithm based on a multivariate extension of the clayton copula

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15th international conference on intelligent data engineering and automated learning, ideal 2014

作者： De Mello Jr., Harold D. Da Cruz, André V. Abs Vellasco, Marley M. B. R. Department of Electrical Engineering Pontifical Catholic University of Rio de Janeiro Rio de Janeiro RJ Brazil

ISBN: (纸本)9783319108391

this paper presents a copula-based estimation of a distribution algorithm with parameter updating for numeric optimization problems. this model implements an estimation of a distribution algorithm using a multivariate extension of Clayton's bivariate copula (MEC-EDA) to estimate the conditional probability for generating a population of individuals. Moreover, the model uses traditional mutation and elitism operators jointly with a heuristic for a population restarting in the evolutionary process. We show that these approaches improve the overall performance of the optimization compared to other copula-based EDAs. © 2014 Springer international Publishing Switzerland.

关键词： Evolutionary algorithms

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Simultaneously Determining Target Object and Transport Velocity for Manipulator and Moving Vehicle in Piece-Picking Operation 15

Simultaneously Determining Target Object and Transport Veloc...

引用

15th IEEE international conference on Automation Science and engineering (IEEE CASE)

作者： Kimura, Nobutaka Sakai, Ryo Katsumata, Shinichi Chihara, Nobuhiro Hitachi Ltd Intelligent Informat Res Dept Ctr Technol Innovat Digital Technol Res & Dev Grp Kokubunji Tokyo Japan

ISBN: (纸本)9781728103563

We propose a deep learning-based method that simultaneously determines a target object to be picked up by an autonomous manipulator and the velocity of an automated guided vehicle (AGV) that passes in front of the manipulator while the AGV carries a carton case containing the target and other objects. Our method can efficiently perform automated piece-picking operations in warehouses without the AGV needing to pause in front of the manipulator. In our method, for preparing supervised data sets with color images of objects that are randomly piled up in the carton case, a simulator checks whether each object is "pickable" or not by trying to plan the manipulator's motion to have its hand reach the object while avoiding surrounding obstacles by using the depth images in consideration of the carton case's movement and velocity. then, we make each of multiple deep convolutional neural networks (DCNNs) corresponding to multiple levels of velocity learn to detect grasp points for only pickable objects from an RGB image. In our experimental test, using our method, a prototype of the system successfully picked ordered objects up without the AGV pausing while the AGV changed its velocity depending on the layout of the objects in the carton case.

关键词： Task analysis End effectors Cameras Layout Two dimensional displays

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Profiling of high-throughput mass spectrometry data for ovarian cancer detection

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8th international conference on intelligent data engineering and automated learning

作者： He, Shan Li, Xiaoli Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England

ISBN: (纸本)9783540772255

Mass Spectrometry (MS) has been applied to the early detection of ovarian cancer. To date, most of the studies concentrated on the so-called whole-spectrum approach, which treats each point in the spectrum as a separate test, due to its better accuracy than the profiling approach. However, the whole-spectrum approach does not guarantee biologically meaningful results and is difficult for biological interpretation and clinical application. therefore, to develop an accurate profiling technique for early detection of ovarian cancer is required. this paper proposes a novel profiling method for high-resolution ovarian cancer MS data by integrating the Smoothed Nonlinear Energy Operator (SNEO), correlation-based peak selection and Random Forest classifier. In order to evaluate the performance of this novel method without bias, we employed randomization techniques by dividing the data set into testing set and training set to test the whole procedure for many times over. Test results show that the method can find a parsimonious set of biologically meaningful biomarkers with better accuracy than other methods.

关键词： Tumors

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Merging ELMs with Satellite data and Clear-Sky Models for Effective Solar Radiation Estimation 19th

Merging ELMs with Satellite Data and Clear-Sky Models for Ef...

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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Cornejo-Bueno, L. Casanova-Mateo, C. Sanz-Justo, J. Salcedo-Sanz, S. Univ Alcala Dept Signal Proc & Commun Madrid Spain Univ Politecn Madrid Dept Civil Engn Construct Infrastruct & Transport Madrid Spain Univ Valladolid LATUV Lab Teledetecc Valladolid Spain

ISBN: (纸本)9783030034962;9783030034955

this paper proposes a new approach to estimate Global Solar Radiation based on the use of the Extreme learning Machine (ELM) technique combined with satellite data and a clear-sky model. Our study area is the radiometric station of Toledo, Spain. In order to train the Neural Network proposed, one complete year of hourly global solar radiation data (from the 1st of May 2013 to the 30th of April 2014) is used as the target of the experiments, and different input variables are considered: a cloud index, a clear-sky solar radiation model and several reflectivity values from Meteosat visible images. To assess the results obtained by the ELM we have selected as a reference a physical-based method which considers the relation between a clear-sky index and a cloud cover index. then a measure of the Root Mean Square Error (RMSE) and the Pearson's Correlation Coefficient (r(2)) is obtained to evaluate the performance of the suggested methodology against the reference model. We show the improvement of the results obtained by the ELM with respect to those obtained by the physical-based method considered.

关键词： Solar radiation estimation Extreme learning machines Meteosat data

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Scalable Bootstrap Clustering for Massive data 15

Scalable Bootstrap Clustering for Massive Data

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15th IEEE/ACIS international conference on Software engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)

作者： Wang, Haocheng Zhuang, Fuzhen Ao, Xiang He, Qing Shi, Zhongzhi Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9781479956043

the bootstrap provides a simple and powerful means of improving the accuracy of clustering. However, for today's increasingly large datasets, the computation of bootstrap-based quantities can be prohibitively demanding. In this paper we introduce the Bag of Little Bootstraps Clustering (BLBC), a new procedure which utilizes the Bag of Little Bootstraps technique to obtain a robust, computationally efficient means of clustering for massive data. Moreover, BLBC is suited to implementation on modern parallel and distributed computing architectures which are often used to process large datasets. We investigate empirically the performance characteristics of BLBC and compare to the performances of existing methods via experiments on simulated data and real data. the results show that BLBC has a significantly more favorable computational profile than the bootstrap based clustering while maintaining good statistical correctness.

关键词： data mining machine learning bag of little bootstraps clustering parallel and distributed computing

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Deep-learning-Based Classification of Rat OCT Images After Intravitreal Injection of ET-1 for Glaucoma Understanding 1

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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Fuentes-Hurtado, Felix Morales, Sandra Mossi, Jose M. Naranjo, Valery Fedulov, Vadim Woldbye, David Klemp, Kristian Torm, Marie Larsen, Michael Univ Politecn Valencia Inst Invest & Innovac Bioingn Camino Vera S-N Valencia 46022 Spain Univ Copenhagen Dept Neurosci & Pharmacol Copenhagen Denmark Univ Copenhagen Lab Neural Plast Dept Neurosci Copenhagen Denmark Rigshosp Glostrup Dept Ophthalmol Copenhagen Denmark

ISBN: (数字)9783030034931

ISBN: (纸本)9783030034931;9783030034924

Optical coherence tomography (OCT) is a useful technique to monitor retinal damage. We present an automatic method to accurately classify rodent OCT images in healthy and pathological (before and after 14 days of intravitreal injection of Endothelin-1, respectively) making use of the DenseNet-201 architecture fine-tuned and a customized top-model. We validated the performance of the method on 1912 OCT images yielding promising results (AUC = 0.99 +/- 0.01 in a P = 15 leave-P-out cross-validation). Besides, we also compared the results of the fine-tuned network with those achieved training the network from scratch, obtaining some interesting insights. the presented method poses a step forward in understanding pathological rodent OCT retinal images, as at the moment there is no known discriminating characteristic which allows classifying this type of images accurately. the result of this work is a very accurate and robust automatic method to distinguish between healthy and a rodent model of glaucoma, which is the backbone of future works dealing with human OCT images.

关键词： Optical coherence tomography Deep-learning Glaucoma

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Smart Recruitment System 15

Smart Recruitment System

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15th international conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Dugyala, Rakshitha Gaddam, Vinay Kumar Eroju, Harika Dantuluri, Mohana Varma Madhubabu, C.H. Bv Raju Institute Of Technology Dept. of Computer Science Engineering Medak Telangana Narsapur India

ISBN: (纸本)9798350370249

this project proposes the development of an advanced job applicants assessment and recruitment system that harnesses the power of artificial intelligence (AI) to streamline and enhance the hiring process. the system integrates cuttingedge AI algorithms, including natural language processing (NLP) and machine learning, to automate and optimize key stages of recruitment. the system begins with an AI-driven resume screening process that analyzes candidate resumes, extracting relevant information and evaluating qualifications against predefined criteria. Additionally, NLP algorithms are employed to comprehend the context and nuances of the resumes, enabling a more accurate assessment of candidates' skills and experiences. Furthermore, the system incorporates machine learning models to dynamically adapt and learn from past recruitment data, continually improving its ability to identify suitable candidates. this iterative learning process ensures a more effective and efficient selection of candidates over time. the intelligent-based system also facilitates automated interview scheduling, taking into account both candidate availability and interviewer preferences. By automating this process, the system optimizes the coordination of interviews, reducing the time and effort required from human resources personnel. Collaboration with HR professionals and data scientists is crucial for refining the system's algorithms and ensuring alignment with industry standards and ethical considerations. the proposed solution aims to revolutionize the traditional recruitment process by providing a robust, intelligent, and data-hy7driven approach to talent acquisition. © 2024 IEEE.

关键词： Artificial intelligence(AI) data-driven approach Ethical considerations HR collaboration intelligent recruitment Iterative learning Job applicants assessment Machine learning Natural language processing (NLP) Resume screening Talent acquisition

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