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检索条件"任意字段=4th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2005"

共 135 条记录，以下是11-20 订阅

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Efficient Binary Static Code data Flow Analysis Using Unsupervised learning 4

Efficient Binary Static Code Data Flow Analysis Using Unsupe...

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4th international conference on Artificial Intelligence for Industries, AI4I 2021

作者： Obert, James Loffredo, Tim Sandia National Labs AlbuquerqueNM United States

ISBN: (纸本)9781665434102

the ever increasing need to ensure that code is reliably, efficiently and safely constructed has fueled the evolution of popular static binary code analysis tools. In identifying potential coding flaws in binaries, tools such as IDA Pro are used to disassemble the binaries into an opcode/assembly language format in support of manual static code analysis. Because of the highly manual and resource intensive nature involved with analyzing large binaries, the probability of overlooking potential coding irregularities and inefficiencies is quite high. In this paper, a light-weight, unsupervised data flow methodology is described which uses highly-correlated data flow graph (CDFGs) to identify coding irregularities such that analysis time and required computing resources are minimized. Such analysis accuracy and efficiency gains are achieved by using a combination of graph analysis and unsupervised machine learning techniques which allows an analyst to focus on the most statistically significant flow patterns while performing binary static code analysis. © 2021 IEEE

关键词： data flow analysis

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SCHEMATA: 3D Classification and Categorization of Ancient Terracotta Figurines 12

SCHEMATA: 3D Classification and Categorization of Ancient Te...

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12th international conference on Agents and Artificial Intelligence (ICAART)

作者： Zeckey, Alexander Langner, Martin Georg August Univ Gottingen Inst Digital Humanities Heinrich Duker Weg 12 Gottingen Germany

ISBN: (纸本)9789897583957

the goal of the starting case-study is not only to develop procedures for automatically generating corpora using 3D pattern recognition, but also to reflect on the associated schematizations and how they can be applied in computer science and visual sciences. For this purpose, methods of object mining in 3D data are to be developed. We chose an object group which is defined by its complexity in shape and the similarity between the objects: In 4th and 3rd century BC ancient Greece small terracotta figurines used to be an art form that was quite common. Based on 200 of those terracottas, a classification system will be elaborated with digital methods, which is able to meet the complexity of the artefacts. In close cooperation between computer science and archaeology, this experimental process leads to a fundamental examination of the concept of pattern recognition as a humanities category. the discussion of the various concepts and methods will be carried out in two complementary dissertations.

关键词： Object mining Image Exploration Greek Terracotta Figurines machine learning Typology

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A Transformer District Line Loss Anomaly Discrimination Model Incorporating Cross-attention and Deep learning Algorithm 4

A Transformer District Line Loss Anomaly Discrimination Mode...

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4th international conference on Artificial Intelligence and pattern recognition, AIPR 2021

作者： Zhang, Songhui Xu, Xinguang Xing, Yu Liu, Tao Chen, Zijian Yang, Jian Wang, Yuqi Dong, Xianguang China

ISBN: (纸本)9781450384087

Transformer district line loss anomaly discrimination has an important influence on the stable operation of the power system. How to improve the accuracy of anomaly discrimination is a research hotspot. At present, most researches use data-driven methods (such as decision trees, support vector machine, neural networks, etc.) to implement line loss rate calculation. However, these methods ignore the dependence between electricity consumption data and the influence of weather on transformer district line loss anomaly discrimination. therefore, a line loss anomaly discrimination model based on cross-attention and deep learning algorithms is proposed. First, an embedding vector is used to map the electricity consumption data and weather data into user information sequences (including user basic information and electricity consumption) and weather sequences (including temperature, wind speed, etc.), and a cross-attention mechanism is introduced to capture the dependency information between the dual sequences, while a Gated Recurrent Unit (GRU) is used to learn the user s electricity consumption behavior representation vector. then, we use Convolutional Neural Networks (CNN) to extract the context features of electricity consumption data based on the trend of user electricity consumption, and obtain the trend feature vector of electricity consumption to enhance the representation ability of the feature vector, thereby improving the performance of line loss anomaly discrimination. Finally, a verification analysis is carried out based on the electricity data of a certain region of China. the experimental results show that the proposed method can effectively improve the accuracy of line loss anomaly discrimination. © 2021 Association for Computing machinery. All rights reserved.

关键词： Vectors

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eSNN for Spatio-Temporal fMRI Brain pattern recognition with a Graphical Object recognition Case Study 4th

eSNN for Spatio-Temporal fMRI Brain Pattern Recognition with...

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4th international conference on Soft Computing and data mining (SCDM)

作者： Murli, Norhanifah Kasabov, Nikola Paham, Nurul Amirah Univ Tun Hussein Onn Parit Raja Malaysia Auckland Univ Technol Auckland New Zealand

ISBN: (纸本)9783030360566;9783030360559

this paper describes an experiment involving visual object fMRI brain data and the NeuCube [1] architecture. fMRI spatio- and spectro- temporal data (SSTD), apart from EEG, audio and video data, comprises both space and time information, that requires a specific and specialized architecture to process, interpret and visualize the data for better understanding and interpretation of the information it may carries. At the same time, any patterns can be better recognized and thus new knowledge that may be embedded within the pattern can be extracted. From the experiment with the case study of Haxby fMRI data, NeuCubeB has accomplished better accuracy in recognizing the brain patterns compared with the standard machine learning techniques (i.e. SVM and MLP). In addition, the NeuCube method assists deep learning of the SSTD and deeper analysis of the spatio-temporal characteristics and patterns in the fMRI SSTD.

关键词： NeuCube Spatio- spectro- temporal pattern Functional magnetic resonance imaging (fMRI) Evolving spiking neural network Deep learning

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Research and Application of data mining Technology in Library Office Information Construction 4

Research and Application of Data Mining Technology in Librar...

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2020 4th international Workshop on Advanced Algorithms and Control Engineering, IWAACE 2020

作者： Wang, Weifeng Meng, Luyang Wu, Lei Zhang, Jing Library University of Jinan Jinan Shandong Province250022 China

With the rapid development of the Internet, the amount of data related to users has shown a rapid increase. At the same time, in the library office system, the problem of how to effectively use and process massive data needs to be solved urgently. data mining technology, as a technology combining machine learning, pattern recognition, and data visualization, can effectively solve many problems in the process of library office information construction. the data mining technology mainly includes many analysis methods such as association analysis, data segmentation, and deviation analysis. the mining results of these algorithm models are presented to users in a visual way, and the results of the rules, trends and concepts discovered can be applied to During the application of the system. © Published under licence by IOP Publishing Ltd.

关键词： data visualization

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4th international conference on Artificial Intelligence and Evolutionary Computations in Engineering Systems, ICAIECES 2019

4th International Conference on Artificial Intelligence and ...

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4th international conference on Artificial Intelligence and Evolutionary Computations in Engineering Systems, ICAIECES 2019

ISBN: (纸本)9789811501982

the proceedings contain 67 papers. the special focus in this conference is on Artificial Intelligence and Evolutionary Computations in Engineering Systems. the topics include: Development of a Novel Embedded Board: CANduino;Sales Demand Forecasting Using LSTM Network;privacy-Enhanced Emotion recognition Approach for Remote Health Advisory System;an Effective Approach for Plant Monitoring, Classification and Prediction Using IoT and machine learning;encrypted Transfer of Confidential Information Using Steganography and Identity Verification Using Face data;intelligent Wireless Sensor Networks for Precision Agriculture;mining of Removable Closed patterns in Goods dataset;Comparative Analysis and Performance of DSTATCOM Device Using PI and Second-Order Sliding Mode Control;stock Market Prediction Using Long Short-Term Memory;Dragonfly Algorithm for Optimal Allocation of D-STATCOM in Distribution Systems;plant Disease Identification and Detection Using Support Vector machines and Artificial Neural Networks;SRF Control Algorithm for Five-Level Cascaded H-Bridge D-STATCOM in Single-Phase Distribution System;a Robust Multi-unit Feature-Level Fusion Framework for Iris Biometrics;providing data Security in Deep learning by Using Genomic Procedure;fault Identification in Deadline Sensitive Applications in Cloud Computing;web Service Selection Mechanism in Service-Oriented Architecture Based on Publish–Subscribe pattern in Fog Environment;heterogeneous Defect Prediction Using Ensemble learning Technique;fuzzy Lattice-Based Orthogonal Image Transformation Technique for Natural Image Analysis;DC Smart Grid System for EV Charging Station;PVW: An Efficient Dynamic Symmetric Cipher to Enable End-to-End Secure Communication;smart Monitoring System for Football Players;efficiency Comparison and Analysis of Pseudo-random Generators in Network Security;artificial Intelligence and Cybersecurity: Past, Presence, and Future.

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4th international conference on data mining and Big data, DMBD 2019

4th International Conference on Data Mining and Big Data, DM...

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4th international conference on data mining and Big data, DMBD 2019

ISBN: (纸本)9789813295629

the proceedings contain 34 papers. the special focus in this conference is on data mining and Big data. the topics include: Using K-means algorithm for description analysis of text in RSS news format;differential evolution clustering and data mining for determining learning routes in moodle;student performance assessment using clustering techniques;a visual analytics on mortality of malignant neoplasm and organic food;classification of radio galaxy images with semi-supervised learning;neuronal environmental pattern recognizer: Optical-by-distance lstm model for recognition of navigation patterns in unknown environments;discovering strategy in navigation problem;Topic mining of Chinese scientific literature research about "the Belt and Road Initiative"¥ based on LDA model from the sub disciplinary perspective;early warning system based on data mining to identify crime patterns;fuzzy C-means in lower dimensional space for topics detection on indonesian online news;a novel big data platform for city power grid status estimation;what are you reading: A big data analysis of online literary content;taxi service pricing based on online machine learning;IBA-buffer: Interactive buffer analysis method for big geospatial data;semi-automated augmentation of pandas dataframes;PRESENT cipher implemented on an ARM-based system on chip;enhancing quality of movies recommendation through contextual ontological user profiling;Determinants of researchgate (RG) score for the top100 of Latin American Universities at Webometrics;BIOG: An Effective and efficient algorithm for influence blocking maximization in social networks;Factors affecting the big data adoption as a marketing tool in SMEs;data mining to identify risk factors associated with university students dropout.

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Discriminative Graph learning via Low-Rank Constraint for Semi-Supervised learning 13

Discriminative Graph Learning via Low-Rank Constraint for Se...

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13th EAI international conference on Mobile Multimedia Communications, Mobimedia 2020

作者： Wang, Ying Li, Ao Sun, Gunaglu Liu, Xin School of Computer Science and Technology Harbin University of Science and Technology Harbin China

ISBN: (纸本)9781631902710

Graph learning is more and more widely used in discovering associations and mining relationships between data and data. Our paper proposed a discriminative graph learning for semi-supervised learning method discriminative graph learning with low rank constraints. Traditional semi- supervised learning methods exist the problem of similarity between similar species is not high. How to improve the similarity between similar species has become a problem we need to solve in this field. In order to solve this problem, this paper proposed a new semi-supervised learning means, which mainly includes two parts: 1) Representing the local structure of the research sample by low-rank representation, compressing similar content, and easily finding the focus of image classification;2) In order to improve the similarity between classes, a model that uses similarity constraints between the data to be classified. the proposed means is used for comparative experiments of classification on multiple data sets. through experiments show that the means proposed in this paper performs better than traditional semi-supervised classification methods in terms of accuracy of classification. Copyright © 2020 EAI.

关键词： Image classification

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Rule induction partitioning estimator: A new deterministic algorithm for data dependent partitioning estimate 14th

Rule induction partitioning estimator: A new deterministic a...

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14th international conference on machine learning and data mining in pattern recognition, mldm 2018

作者： Margot, Vincent Baudry, Jean-Patrick Guilloux, Frederic Wintenberger, Olivier Laboratoire de Probabilités Statistique et Modélisation Sorbonne Universités Campus Pierre et Marie Curie Tour 16-26 1er étage 4 Place Jussieu Paris75005 France

ISBN: (纸本)9783319961323

RIPE is a novel deterministic and easily understandable prediction algorithm developed for continuous and discrete ordered data. It infers a model, from a sample, to predict and to explain a real variable Y given an input variable X∈ X (features). the algorithm extracts a sparse set of hyperrectangles r⊂ X, which can be thought of as rules of the form If-then. this set is then turned into a partition of the features space X of which each cell is explained as a list of rules with satisfied their If conditions. the process of RIPE is illustrated on simulated datasets and its efficiency compared with that of other usual algorithms. © Springer international Publishing AG, part of Springer Nature 2018.

关键词： data mining

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mining Student Feedback to Improve the Quality of Higher Education through Multi Label Classification, Sentiment Analysis, and Trend Topic

Mining Student Feedback to Improve the Quality of Higher Edu...

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international conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

作者： Calandra Alencia Hariyani Achmad Nizar Hidayanto Nur Fitriah Zaenal Abidin theresia Wati Fakultas Ilmu Komputer Universitas Indonesia Fakultas Ilmu Komputer UPN “Veteran” Jakarta Jakarta Indonesia

ISBN: (数字)9781728151182

ISBN: (纸本)9781728151199

this research carried out the label aspect classification, sentiment analysis, and topic trends on the Open-Ended Question (OEQ) section for Student Feedback Questionnaire (SFQ). Multi-Class aspect label classification for SFQ will choose the best classification model by comparing the results of the evaluation of accuracy, precision, recall, and Flscore for each feature combination and comparison of four classification algorithms namely Decision Tree (DT), Naive Bayes (NB), K-Nearest Neighbor (KNN), and Support Vector machine (SVM). the results of this research are Classification Techniques using a combination of features of TFIDF, Unigranb and Bigram with the SVM algorithm which is the best Multi-Class classification model for labeling SFQ aspects. In addition, the SentiStrenghtID algorithm used to get sentiments and also the LDA (Latent Dirichlet Allocation) used to get annual topic trends on each survey aspect label. the findings can help Higher Education to support decision making in taking proactive actions towards improvement for self-evaluation and quality.

关键词： Bayes methods computer aided instruction data mining decision making decision trees further education learning (artificial intelligence) nearest neighbour methods pattern classification support vector machines Web sites Continuing Education Support Vector Network computer-assisted instruction Decision Trees Bayes methods pattern recognition higher education websites data mining nearest neighbor algorithm RSFQ Taxonomy decision making

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