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3rd international conference on Soft Computing in Data Science, SCDS 2017

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3rd international conference on Soft Computing in Data Science, SCDS 2017

ISBN: (纸本)9789811072413

The proceedings contain 26 papers. The special focus in this conference is on Soft Computing in Data Science. The topics include: Time series machine learning: Implementing ARIMA and hybrid ARIMA-ANN for electricity forecasting modeling;a design of a reminiscence system for tacit knowledge recall and transfer;pattern search based on particle swarm optimization technique for block matching motion estimation algorithm;enhancing parallel self-organizing map on heterogeneous system architecture;geovisualization using hexagonal tessellation for spatiotemporal earthquake data analysis in Indonesia;Incremental filtering visualization of jobstreet Malaysia ICT jobs;software application for analyzing photovoltaic module panel temperature in relation to climate factors;budget visual: Malaysia budget visualization;fuzzy arithmetical modeling of pressurizer in a nuclear power plant;road lane segmentation using deconvolutional neural network;nitrogen fertilizer recommender for paddy fields;Ranking performance of modified fuzzy TOPSIS variants based on different similarity measures;prediction of new bioactive molecules of chemical compound using boosting ensemble methods;Pragmatic miner to risk analysis for intrusion detection (PMRA-ID);intelligent system E-learning modeling according to learning styles and level of ability of students;mining textual terms for stock market prediction analysis using financial news;content quality of latent dirichlet allocation summaries constituted using unique significant words;rare event prediction using similarity majority under-sampling technique;pattern recognition of balinese carving motif using learning vector quantization (LVQ);Feature extraction for image content retrieval in thai traditional painting with SIFT algorithms;modeling of the gaussian-based component analysis on the kernel space to extract face image.

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pattern recognition of balinese carving motif using learning vector quantization (LVQ) 3rd

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3rd international conference on Soft Computing in Data Science, SCDS 2017

作者： Mahawan, I. Made Avendias Harjoko, Agus Department of Computer Science and Electronics University of Gadjah Mada Yogyakarta Indonesia

ISBN: (纸本)9789811072413

Bali is a world tourism destination with its cultural uniqueness, one of the Balinese cultural products that need to be maintained is the art of Balinese carvings in traditional buildings and sacred buildings, to inherit the culture it needs a management, documentation and dissemination of information by utilizing technology. Digital image processing and pattern recognition can be utilized to preserve arts and culture, the technology can be utilized to classify images into specific classes. Balinese carving is one of the carvings that have many variations, if these carvings are analyzed then required an appropriate method for feature extraction process to produce special features in the image. So they can be recognized and classified well and provide information that helps preserve Bali. The aim of this research is to get the right feature extraction method to recognize and classify Bali carving pattern image based on the accuracy of HOG feature extraction method with PCA trained using LVQ. The results of the test data obtained the best accuracy of HOG is 90% with cell size 32 × 32 and block size 2 × 2, PCA obtained 23.67% with threshold 0.01 and 0.001, from training input with learning rate = 0.001 and epoch = 1000. © Springer Nature Singapore Pte Ltd. 2017.

关键词： Principal component analysis

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Linear Dynamical Systems Modeling for EEG-Based Motor Imagery Brain-Computer Interface 1

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3rd international conference on Cognitive Systems and Information Processing (ICCSIP)

作者： Zhang, Wenchang Sun, Fuchun Tan, Chuanqi Liu, Shaobo Tsinghua Univ Comp Sci & Technol Sch State Key Lab Intelligent Technol & Syst FIT Bldg Beijing 100084 Peoples R China Inst Med Equipment Wandong Rd Tianjin Peoples R China

ISBN: (数字)9789811052309

ISBN: (纸本)9789811052309;9789811052293

Motor imagery-based Brain Computer Interfaces (MI-BCI) has attracted more and more attention due to its effectivity for stroke and spinal cord injury patients' rehabilitation. Common Spatial pattern (CSP) and other spatio-spectral feature extraction methods become the most effective and principle successful solutions for MI-BCI pattern recognition in the recent few years. This paper applies Linear dynamical systems (LDS) referring to control field for EEG signals feature extraction and classification. Compared to other state-of-the-art methods, this model has lots of obvious advantages, such as simultaneous generation spatial and temporal feature matrix, without complex preprocessing or post-processing, ease of use, and low cost. A study is shown to program by computer and assess the performance of feature selection and classification algorithms for use with the LDS. Extensive experimental results are presented on public dataset from 'BCI Competition III Data Sets IVa'. The results show that LDS, using Martin Distance and k-Nearest Neighbors classification algorithm, yields higher accuracies compared to prevailing approaches.

关键词： BCI MI EEG LDS Feature extraction

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3rd international conference of Pioneer Computer Scientists, Engineers, and Educators, ICPCSEE 2017

3rd International Conference of Pioneer Computer Scientists,...

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3rd international conference of Pioneer Computer Scientists, Engineers, and Educators, ICPCSEE 2017

ISBN: (纸本)9789811063848

The proceedings contain 112 papers. The special focus in this conference is on Pioneer Computer Scientists, Engineers, and Educators. The topics include: A fine-grained emotion analysis method for chinese microblog;research of detection algorithm for time series abnormal subsequence;an improved SVM based wind turbine multi-fault detection method;GPU based hash segmentation index for fast t-overlap query;research on pattern matching method of multivariate hydrological time series;desktop data driven approach to personalize query recommendation;disease prediction based on transfer learning in individual healthcare;research on fuzzy matching query algorithm based on spatial multi-keyword;a new approach to dense spectrum analysis of infrasonic signals;research on XDR bill compression under big data technology;the scalability of volunteer computing for mapreduce big data applications;an improved FP-growth algorithm based on SOM partition;a novel recommendation service method based on cloud model and user personality;a cooperative abnormal behavior detection framework based on big data analytics;composite graph publication considering important data;hierarchical access control scheme of private data based on attribute encryption;template protection based on chaotic map for face recognition;a fast and secure transmission method based on optocoupler for mobile storage;elderly health care - security and privacy issue;secure multi-party comparison protocol and application;security analysis of secret image sharing;a real-time visualization defense framework for DDoS attack and an improved data packet capture method based on multicore platform.

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Query From Sketch: A Common Subgraph Correspondence Mining Framework 3

Query From Sketch: A Common Subgraph Correspondence Mining F...

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IEEE 3rd international conference on Multimedia Big Data (BigMM)

作者： Zhu, Lingheng Liu, Wu Chu, Lingyang Liu, Peiye Gu, Xiaoyan Beijing Univ Posts & Telecommun Lab Intelligent Telecommun Software & Multimedia Beijing 100876 Peoples R China Simon Fraser Univ Sch Comp Sci Burnaby BC V5A 1S6 Canada Chinese Acad Sci Informat Informat Engn Beijing 100093 Peoples R China

ISBN: (纸本)9781509065493

We investigate a subgraph mining framework, that can connect similar entities according to their structure and attribute similarities. We take one mapping between two related points chosen from the query and target graph as one vertex in the correspondence graph and decide the weight of the edge based on the similarity score. In this way, we transform the problem to a dense subgraph discovery problem. To adapt this method to large scale, we choose the candidate group by some effective pruning methods. We also add some techniques to make our method more flexible to fit uncertain user sketched input. We investigate how changes to certain parameters in the algorithm can influence the results. By integrating all these adjustments into the framework, we can provide a method that exhibits both accuracy and flexibility in many situations with a degree of generality. Experiments on both certain and uncertain query graphs can give satisfactory and informative results.

关键词： data mining graph search pattern recognition similarity search

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36th international conference on Conceptual Modeling, ER 2017 held in Conjuction with the 3rd international Workshop on Modeling for Ambient Assistance and Healthy Ageing, AHA 2017, 6th international Workshop on Modeling and Management of Big Data, MoBiD 2017, 4th international Workshop on Conceptual Modeling in Requirements and Business Analysis, MREBA 2017, 5th international Workshop on Ontologies and Conceptual Modeling, OntoCom 2017 and 4th Workshop on Quality of Models and Models of Quality, QMMQ 2017

36th International Conference on Conceptual Modeling, ER 201...

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36th international conference on Conceptual Modeling, ER 2017 held in Conjuction with the 3rd international Workshop on Modeling for Ambient Assistance and Healthy Ageing, AHA 2017, 6th international Workshop on Modeling and Management of Big Data, MoBiD 2017, 4th international Workshop on Conceptual Modeling in Requirements and Business Analysis, MREBA 2017, 5th international Workshop on Ontologies and Conceptual Modeling, OntoCom 2017 and 4th Workshop on Quality of Models and Models of Quality, QMMQ 2017

ISBN: (纸本)9783319706245

The proceedings contain 22 papers. The special focus in this conference is on Conceptual Modeling. The topics include: Towards an ontology for strategic decision making: The Case of quality in rapid software development projects;detecting bad smells of refinement in goal-oriented requirements analysis;Requirements engineering for data warehouses (RE4DW): From strategic goals to multidimensional model;towards formal strategy analysis with goal models and semantic web technologies;assisting process modeling by identifying business process elements in natural language texts;Using multidimensional concepts for detecting problematic Sub-KPIs in analysis systems;automatically annotating business process models with ontology concepts at design-time;OPL-ML: A modeling language for representing ontology pattern languages;Evaluating quality issues in BPMN models by extending a technical debt software platform;data modelling for dynamic monitoring of vital signs: Challenges and perspectives;utility-driven data management for data-intensive applications in fog environments;Assessing the positional planimetric accuracy of DBpedia georeferenced resources;Assessing the completeness evolution of DBpedia: A case study;towards care systems using model-driven adaptation and monitoring of autonomous multi-clouds;clustering event traces by behavioral similarity;goal-based selection of visual representations for big data analytics;A four V’s design approach of NoSQL graph databases;towards efficient and informative omni-channel customer relationship management;stream clustering of chat messages with applications to twitch streams;towards consistent demarcation of enterprise design domains.

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A New Method of Shredded Paper Image Stitching and Restoration 3

A New Method of Shredded Paper Image Stitching and Restorati...

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3rd international conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration, ICIICII 2017

作者： Pan, Zixiao Wang, Mei School of Automation Wuhan University of Technology Wuhan China Laboratory of Image Processing and Pattern Recognition Yantai Vocational College Yan China

ISBN: (纸本)9781538624340

For the image restoration problem of shredded paper broken by shredder with the same marginal feature, a new method based on spatial feature classification is proposed in this paper. Because no obvious marginal feature for rule shredder paper can be used to split, the feature similarity classification processing can greatly reduce the solution scale and improve the algorithm efficiency. The horizontal projection position of shredded paper image can be used to distinguish the same line for the shredded paper with words. Then the marginal moment invariants distance are defined by the left and right part spatial correlation information. The original order of adjacent shredded paper in the same line can be ensured by the minimum marginal distance. Similarly ordering up and down can be completed by the minimum marginal distance among up-down part stitching images. For the image stitching and restoration of large-scale rule shredded paper, the actual experimental result shows the good stability, efficiency and strong robustness. © 2017 IEEE.

关键词： Restoration

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Survey on fault diagnosis of diesel engine based on vibration signal

Survey on fault diagnosis of diesel engine based on vibratio...

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international conference on Information Management (ICIM)

作者： Husheng Wu Qian Yan Xiaodong Ling Materiel Engineering College Armed Police Force Engineering University Xi'an China Joint Laboratory for Marine Measurement and control Satellite Maritime Tracking and control center Jiangying China

ISBN: (纸本)9781509063079

The diesel engine, an efficient power machinery, is widely applied in many fields, such as vehicle, watercraft and petroleum machinery. The significance and difficulties of fault diagnosis on diesel engine is stated. Some domestic and foreign conventional methods of diesel state monitoring and fault diagnosis and their principles is discussed. Then, the applications of vibration signal time frequency analysis method and pattern recognition method in fault diagnosis for diesel engine are analyzed. What's more, the established fault diagnosis model which combined signal time frequency analysis method with pattern recognition method is discussed in detail. Finally, the development trend of marine diesel fault diagnosis technology is forecasted.

关键词： Fault diagnosis Diesel engines Vibrations Time-frequency analysis pattern recognition Monitoring Signal analysis

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Android Malware Detection Using Local Binary pattern and Principal Component Analysis 3rd

Android Malware Detection Using Local Binary Pattern and Pri...

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3rd international conference of Pioneering Computer Scientists, Engineers and Educators (ICPCSEE)

作者： Wu, Qixin Qin, Zheng Zhang, Jinxin Yin, Hui Yang, Guangyi Hu, Kuangsheng Hunan Univ Coll Comp Sci & Elect Engn Changsha Hunan Peoples R China Hunan Key Lab Big Data Res & Applicat Changsha Hunan Peoples R China Hunan Inst Metrol & Test Changsha Hunan Peoples R China

ISBN: (纸本)9789811063855;9789811063848

Nowadays, analysis methods based on big data have been widely used in malicious software detection. Since Android has become the dominator of smartphone operating system market, the number of Android malicious applications are increasing rapidly as well, which attracts attention of malware attackers and researchers alike. Due to the endless evolution of the malware, it is critical to apply the analysis methods based on machine learning to detect malwares and stop them from leakaging our privacy information. In this paper, we propose a novel Android malware detection method based on binary texture feature recognition by Local Binary pattern and Principal Component Analysis, which can visualize malware and detect malware accurately. Also, our method analyzes malware binary directly without any decompiler, sandbox or virtual machines, which avoid time and resource consumption caused by decompiler or monitor in this process. Experimentation on 5127 benigns and 5560 malwares shows that we obtain a detection accuracy of 90%.

关键词： Android malware detection Binary texture feature Local binary pattern Principal component analysis

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Recent Research and Prospect on Feature recognition of Three-dimensional Model

Recent Research and Prospect on Feature Recognition of Three...

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2018 3rd international conference on Control, Automation and Artificial Intelligence (CAAI 2018)

作者： Yangdong Shi Zhaohui Deng Junwu Zhong Hunan Provincial Key Laboratory of High Efficiency and Precision Machining of Difficult-to-Cut Material Hunan University of Science and Technology Intelligent manufacturing institute of HNUST

A feature recognition technique is the intelligent interface of CAD/CAPP. Through extracting and recognizing the tolerance and surface roughness, the design features such asholes, steps and other design features in a 3D model of parts, the CAD/CAPP can be combined efficiently, and then the CAD/CAPP/CAM system can be integrated. This technique also helps to shorten the cycle of product development, especially process preparation, and reduce the design cost. In this paper, the 3D model features recognition approaches about boundary pattern matching, volume composition, manufacturing resources, and hybridization method are reviewed, and compared in terms of identifying efficiency, identifying accuracy, identifying flexibility, machinability of features, and utilization of manufacturing resource information in a process environment is conducted on the 3D model feature recognition approaches. The main issues of machining feature recognition of the part model are also discussed, and the direction of future researches in this field is presented. It is pointed out that the development of an efficient, accurate, flexible, automatic and intelligent machining feature recognition system will become an important research topic in the field of computer integrated manufacturing in the future.

关键词： Three-dimensional model Feature recognition CAD/CAPP/CAM Machining feature recognition based on boundary pattern matching Machining feature recognition based on volume decomposition Machining feature recognition based on manufacturing resources Hybrid recognition of Machining Feature

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