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Artificial Intelligence and Computational Intelligence 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Jingsheng Lei Fu Lee Wang Hepu Deng Duoqian Miao

ISBN: (数字)9783642334788

ISBN: (纸本)9783642334771

this volume proceedings contains revised selected papers from the 4th international conference on Artificial Intelligence and Computational Intelligence, AICI 2012, held in Chengdu, China, in October 2012. the total of 163 high-quality papers presented were carefully reviewed and selected from 724 submissions. the papers are organized into topical sections on applications of artificial intelligence, applications of computational intelligence, data mining and knowledge discovery, evolution strategy, expert and decision support systems, fuzzy computation, information security, intelligent control, intelligent image processing, intelligent information fusion, intelligent signal processing, machine learning, neural computation, neural networks, particle swarm optimization, and pattern recognition.

关键词： Artificial Intelligence pattern recognition Image Processing and Computer Vision data mining and Knowledge Discovery Computation by Abstract Devices Algorithm Analysis and Problem Complexity

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Frontiers in Algorithmics and Algorithmic Aspects in Information and Management 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Jack Snoeyink Pinyan Lu Kaile Su Lusheng Wang

ISBN: (数字)9783642297007

ISBN: (纸本)9783642296994

this book constitutes the refereed proceedings of the 6th international Frontiers of Algorithmics Workshop, FAW 2012, and the 8th international conference on Algorithmic Aspects in Information and Management, AAIM 2012, jointly held in Beijing, China, in May 2012. the 33 revised full papers presented together with 4 invited talks were carefully reviewed and selected from 81 submissions. the papers are organized in topical sections on algorithms and data structures, algorithmic game theory and incentive analysis, biomedical imaging algorithms, communication networks and optimization, computational learning theory, knowledge discovery, and data mining, experimental algorithmic methodologies, optimization algorithms in economic and operations research, pattern recognition algorithms and trustworthy algorithms and trustworthy software.

关键词： Algorithm Analysis and Problem Complexity Discrete Mathematics in Computer Science Mathematics of Computing theory of Computation Management of Computing and Information Systems data Structures

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Tutorial 2: Visual Knowledge Discovery and machine learning

Tutorial 2: Visual Knowledge Discovery and Machine Learning

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international conference on Informatics, Electronics & Vision (ICIEV)

作者： Boris Kovalerchuk Dept. of Computer Science Central Washington University USA

the goal of this tutorial is presenting new visual knowledge discovery and machine learning methods that allow making knowledge discovery and predictive models more effective and rigorous. Specifically, we focus on learning tasks of classification and clustering of n-D data using lossless visual representation of n-D data as graphs.

关键词： Visualization Knowledge discovery machine learning Tutorials data visualization Principal component analysis data mining

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Facial expression recognition from video using geometric features

Facial expression recognition from video using geometric fea...

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8th international conference of pattern recognition Systems (ICPRS 2017)

作者： Yacine Yaddaden Mehdi Adda Abdenour Bouzouane Sebastien Gaboury Bruno Bouchard Universite du Quebec a Chicoutimi Chicoutimi QC CA Universite du Quebec a Rimouski Rimouski QC CA ACM Professional Specialist in Artificial Intelligence

ISBN: (纸本)9781785616525

Human Computer Interaction has a significant impact in different fields of Information and Communication Technology. It is mainly due to the importance of interaction between human beings and the technologies they are using. therefore, facial expression recognition has been widely used to enhance this interaction and make it more natural. Most of the proposed methods are based on images and even if they showed good performances, they do not match the real interaction model of people. Video is a rich source of information when considering the presence of temporal aspect. In this paper, we propose an approach that uses videos to recognize facial expressions and based on geometric features. We have tested it on a popular dataset and the carried experimentations showed promising results.

关键词： Facial Expressions Emotion recognition Human Computer Interaction machine learning data mining computer human interaction Facial Expression emotion recognition Videotapes data mining Information source expression recognition Artificial Intelligence machine learning Aspects(Temporal) Facial recognition Information and communication technology

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Crime prediction using data mining and machine learning 8th

Crime prediction using data mining and machine learning

引用

8th international conference on Computer Engineering and Networks, CENet 2018

作者： Wu, Shaobing Wang, Changmei Cao, Haoshun Jia, Xueming Institute of Information Security Yunnan Police College Kunming650223 China Solar Energy Institute Yunnan Normal University Kunming650092 China

ISBN: (纸本)9783030146795

In order to predict the crime in YD county, data mining and machine learning are used in this paper. the aim of the study is to show the pattern and rate of crime in YD county based on the data collected and to show the relationships that exist among the various crime types and crime Variable. Analyzing this data set can provide insight on crime activities within YD county. By introducing formula and methods of Bayesian network, random tree and neural network in machine learning and big data, to analyze the crime rules from the collected data. According to the statistics released by the YD county From 2012-09-01 to 2015-07-21, the crime of smuggling, selling, transporting and manufacturing drugs, theft, Intentional injury, Illegal business crime, Illegal possession of drugs, Rape, Crime of fraud, Gang fighting, manslaughter, Robbery made the top ten list of crime types with high number of crimes. the crime rate of drugs was the highest, reaching 46.86%, farmers are the majority, accounting for 97.07%, people under the age of 35 are the subject of crime. Males accounted for 90.17% of crimes committed, while females accounted for 9.83%. For ethnic groups, the top five were han, yi, wa, dai and lang, accounting for 68.43%, 23.43%, 1.88%, 1.67% and 1.25% respectively. By adopting random forest, Bayesian networks, and neural network methods, we obtained the decision rules for criminal variables. By comparison, the classification effect of Random Trees is better than that of Neural Networks and Bayesian Networks. through the data collection of the three algorithms, the validity and accuracy of the random tree algorithm in predicting crime data are observed. the performance of the Bayesian network algorithm is relatively poor, probably due to the existence of certain random factors in various crimes and related features (the correlation between the three algorithms is low). © Springer Nature Switzerland AG 2020.

关键词： Forecasting

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BIOADI: A machine learning approach to identifying abbreviations and definitions in biological literature

BIOADI: A machine learning approach to identifying abbreviat...

引用

8th international conference on Bioinformatics: Bioinformatics, InCoB 2009

作者： Kuo, Cheng-Ju Ling, Maurice H.T. Lin, Kuan-Ting Hsu, Chun-Nan Institute of Information Science Academia Sinica Taipei 115 Taiwan Singapore Polytechnic School of Chemical and Life Sciences Singapore Singapore Institute of Biomedical Informatics National Yang-Ming University Taipei 112 Taiwan Department of Zoology University of Melbourne Parkville VIC Australia

Background: To automatically process large quantities of biological literature for knowledge discovery and information curation, text mining tools are becoming essential. Abbreviation recognition is related to NER and can be considered as a pair recognition task of a terminology and its corresponding abbreviation from free text. the successful identification of abbreviation and its corresponding definition is not only a prerequisite to index terms of text databases to produce articles of related interests, but also a building block to improve existing gene mention tagging and gene normalization tools. Results: Our approach to abbreviation recognition (AR) is based on machine-learning, which exploits a novel set of rich features to learn rules from training data. Tested on the AB3P corpus, our system demonstrated a F-score of 89.90% with 95.86% precision at 84.64% recall, higher than the result achieved by the existing best AR performance system. We also annotated a new corpus of 1200 PubMed abstracts which was derived from BioCreative II gene normalization corpus. On our annotated corpus, our system achieved a F-score of 86.20% with 93.52% precision at 79.95% recall, which also outperforms all tested systems. Conclusion: By applying our system to extract all short form-long form pairs from all available PubMed abstracts, we have constructed BIOADI. mining BIOADI reveals many interesting trends of bio-medical research. Besides, we also provide an off-line AR software in the download section on http://***. ***/BIOADI/. © 2012 BioMed Central Ltd .

关键词： Genes

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An attribute reduction method based on SVM

An attribute reduction method based on SVM

引用

international conference on Natural Computation (ICNC)

作者： Xiao Tang Zhiwen Mo School of Mathematical Sciences University of Electronic Science and Technology Chengdu China College of Mathematics and Software Science Sichuan Normal University Chengdu China

Recently, support vector machine (SVM), which is based on statistical learning theory emerges as a hot spot in artificial intelligence. It has been widely applied in pattern recognition and many fields. A new attribute reduction method based on SVM is proposed and realized. this paper provides a new way of thinking for attribute reduction.

关键词： Support vector machines Rough sets Approximation methods Educational institutions data mining Classification algorithms

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Variable Width Elliptic Gaussian Kernels for Probability Density Estimation

Variable Width Elliptic Gaussian Kernels for Probability Den...

引用

international conference on Telecommunications in Modern Satellite, Cable and Broadcasting Service (TELSIKS)

作者： Dragoljub Pokrajac Longin Jan Latecki Aleksandar Lazarevic Jelena Nikolic Computer and Information Science Department Applied Mathematics Research Center and Center for Research and Education in Optical Sciences and Applications (CREOSA) Delaware State University Dover DE USA CIS Department Temple University Philadelphia PA USA CREOSA and United Technologies Research Center Hartford CT USA Faculty of Electronic Engineering University of Nis Nis Serbia

Estimation of probability density functions based on available data is important problem arising in various fields, such as telecommunications, machine learning, data mining, pattern recognition and computer vision. In this paper, we consider Kernel-based non-parametric density estimation methods and derive formulae for variable kernel density estimation using generalized, elliptic Gaussian kernels. the proposed technique is verified on simulated data.

关键词： Kernel Probability density function machine learning data mining pattern recognition Computer vision Eigenvalues and eigenfunctions Random variables State estimation Optical computing

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Analysis of Images, Social Networks and Texts 1

引用

丛书名： Communications in Computer and Information Science

1000年

作者： Wil M. P. van der Aalst Vladimir Batagelj Dmitry I. Ignatov Valentina Kuskova Sergei O. Kuznetsov Irina A. Lomazova Michael Khachay Andrey Kutuzov Natalia Loukachevitch Amedeo Napoli Panos M. Pardalos Marcello Pelillo Andrey V. Savchenko Elena Tutubalina

this book constitutes the proceedings of the 8th international conference on Analysis of Images, Social Networks and Texts, AIST 2019, held in Kazan, Russia, in July 2019.

ISBN: (数字)9783030395759

ISBN: (纸本)9783030395742

this book constitutes the proceedings of the 8th international conference on Analysis of Images, Social Networks and Texts, AIST 2019, held in Kazan, Russia, in July 2019.

关键词： data mining and Knowledge Discovery machine learning Information Systems Applications (incl. Internet) Computer Appl. in Social and Behavioral Sciences Computing Milieux Computer Imaging, Vision, pattern recognition and Graphics

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Parametric Study of Student learning in IT Using data mining to Improve Academic Performance

Parametric Study of Student Learning in IT Using Data Mining...

引用

international conference on Informatics, Electronics & Vision (ICIEV)

作者： Rubyeat Islam Muhammad Tawsif Sazid Sharifa Rania Mahmud Chowdhury Nawrin Ferdous Reshad Reza Syed Akhter Hossain Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh

data mining in education is a developing interdisciplinary research field also known as educational data mining (EDM). the goal is to understand students' learning process and identify the way by which they can learn to improve educational outcomes. learning using IT is one of the most widely used methods for education in modern days. Digital learning gives students an experience of individual learning at any time as well as anywhere, so students get more interest, flexibility at learning. Knowing the preferences of students learning will help the instructors to design better learning materials and teaching styles. We have surveyed on the students of undergraduate level and evaluated the students in three categories: good, average and excellent. We have used four classification models: Support Vector machine (SVM), Logistic Regression (LR), Decision Tree and Random Forest (RF) model to predict the performance of students on basis of the impact of IT and other study mediums based on their results. In this article, we have identified different parameters or features from five different learning sectors or fields which can give an impact on the student's learning process. So, we have processed in a way that will find out the data mining model which can give better accuracy of student's performance and also can find out which parameters or features among the five fields are playing a great role in their academic results. Moreover, we can apply these features by inspiring good or average students to improve their learning process.

关键词： data mining Decision trees Predictive models Support vector machines data models Logistics

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