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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

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data Mining for Early Gastric Cancer Etiological Factors from Diet-Lifestyle Characteristics

Data Mining for Early Gastric Cancer Etiological Factors fro...

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international conference on ent Computing and Control Systems (ICICCS)

作者： Senthil Kumar Brindha Nachimuthu Senthil Kumar Saia Chenkual S. T. Lalruatfela thomas Zomuana Zothansanga Ralte Arindam Maitra Analabha Basu Prem Nath Department of Computer Science Mizoram University Aizawl Mizoram India Department of Biotechnology Mizoram Univeristy Aizawl Mizoram India Department of Surgery Civil Hospital Aizawl Mizoram India National Institute of Biomedical Genomics Kalyani West Bengal India Department of Computer Engineering Mizoram University Aizawl Mizoram India

ISBN: (数字)9781728148762

ISBN: (纸本)9781728148779

Gastric cancer is predominantly caused by demographic-diet factors as compared to other cancer types. the aim of the study is to predict Early Gastric Cancer (EGC) factors from diet and lifestyle characteristics of Mizo-ethnicity using supervised machine learning algorithms. For this study, 80 cases and 160 controls are selected and a dataset containing 11 features that are core risk factors for the gastric cancer have been chosen for data mining. the learning curves show Naive Bayes, Logistic Regression and Multilayer perceptron are the best fit classification algorithms for our dataset. data models are constructed and evaluated using: brier score, accuracy, precision_recall curves for cases (patients) and controls (healthy individuals), and false positives. the data interpretation shows Naive Bayes has the highest classification results having an accuracy of 90%, with the lowest Brier score of 0.1, and a false positive rate of 3% as compared to other models. Logistic regression classifier shows equally good performances with setback in brier_score and false positives. this study found that extra salt, tuibur, smoking and alcohol are the non_invasive etiological factors for gastric cancer in Mizoram population as predicted by the Naive Bayes algorithm. this knowledge will be helpful for initiating early screening and to educate the public about the risk of dietary and lifestyle factors in high risk population with unique habits.

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Research on Classification of Power Load data Based on LIBSVM

Research on Classification of Power Load Data Based on LIBSV...

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international conference on intelligent Human-Machine Systems and Cybernetics, IHMSC

作者： Xiao Qi Xiao Wu Yu Ji Xu Wang Hui Li School of Electrical Engineering Nantong University Nantong Jiangsu China State Grid Nantong Power Supply Company Nantong Jiangsu China

In order to effectively classify the increasingly massive load data, the method of applying support vector machine to load classification is studied. By comparing and selecting the C-SVC classification and the RBF kernel function, the optimization method of the grid search is used to find the optimal parameter combination of the LIBSVM classifier and establish the model. the visualization accuracy and index function are used to observe the classification accuracy of each class. Operations such as weighting the unbalanced samples are conducted to adjust the accuracy gap between large and small samples. Experimental results show that the method is feasible in load classification.

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Enhancing ENIGMA Given Clause Guidance 1

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11th international conference on intelligent Computer Mathematics (CICM)

作者： Jakubuv, Jan Urban, Josef Czech Tech Univ Prague Czech Republic

ISBN: (数字)9783319968124

ISBN: (纸本)9783319968124;9783319968117

ENIGMA is an efficient implementation of learning-based guidance for given clause selection in saturation-based automated theorem provers. In this work, we describe several additions to this method. this includes better clause features, adding conjecture features as the proof state characterization, better data pre-processing, and repeated model learning. the enhanced ENIGMA is evaluated on the MPTP2078 dataset, showing significant improvements.

关键词： data handling

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Study of Machine learning Based Rice Breeding Decision Support Methods and Technologies 1

引用

11th IFIP WG 5.14 international conference on Computer and Computing Technologies in Agriculture (CCTA)

作者： Cui, Yun-peng Wang, Jian Liu, Shi-hong Liu, En-ping Liu, Hai-qing Chinese Acad Agr Sci Agr Informat Inst Key Lab Agriinformat Serv Technol Minist Agr Beijing Peoples R China Inst Sci & Tech Informat CATS Key Lab Trop Crops Informat Technol Applicat Danzhou Peoples R China

ISBN: (数字)9783030061371

ISBN: (纸本)9783030061371;9783030061364

the Objective of the study is to Analyze and mining rice breeding data with data explore and machine learning algorithms to discover how rice biological characters influence the economic characters, explore effective methods and technologies for breeders and help them find appropriate breeding parents, and provide tools for parental selection in rice breeding. the author developed a B/S application with Python and Django, which implement real-time data mining of rice breeding data. data analysis and processing result generated from decision tree algorithm can find effective breeding knowledge and patterns, and spectral biclustering algorithm can find required varieties with their local features follow certain patterns. the system can help breeders find useful knowledge and patterns more quickly, and improves the accuracy and efficiency of crop breeding.

关键词： Machine learning Rice Breeding Decision support

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16th international conference on intelligent data engineering and automated learning, IDEAL 2015

16th International Conference on Intelligent Data Engineerin...

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16th international conference on intelligent data engineering and automated learning, IDEAL 2015

the proceedings contain 64 papers. the special focus in this conference is on intelligent data engineering and automated learning. the topics include: data streams fusion by frequent correlations mining;web genre classification via hierarchical multi-label classification;multi-agent reinforcement learning for control systems: challenges and proposals;optimal filtering for time series classification;managing monotonicity in classification by a pruned random forest;ensemble selection based on discriminant functions in binary classification task;an extension of multi-label binary relevance models based on randomized reference classifier and local fuzzy confusion matrix;fusion of self-organizing maps with different sizes;a particle swarm clustering algorithm with fuzzy weighted step sizes;a bacterial colony algorithm for association rule mining;information retrieval and data forecasting via probabilistic nodes combination;knowledge discovery in enterprise databases for forecasting new product success;deterministic extraction of compact sets of rules for subgroup discovery;variable transformation for granularity change in hierarchical databases in actual data mining solutions;an empirical evaluation of robust Gaussian process models for system identification;throughput analysis of automatic production lines based on simulation methods;study of collective robotic tasks based on the behavioral model of the agent;minimalist artificial eye for autonomous robots and path planning;intelligent automated design of machine components using antipatterns;application of fuzzy logic controller for machine load balancing in discrete manufacturing system;local search based on a local utopia point for the multiobjective travelling salesman problem;a hybrid programming framework for resource-constrained scheduling problems and building an efficient evolutionary algorithm for forex market predictions.

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Research on Fault Diagnosis Algorithm Based on Convolutional Neural Network

Research on Fault Diagnosis Algorithm Based on Convolutional...

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international conference on intelligent Human-Machine Systems and Cybernetics, IHMSC

作者： Xiaolong Li Sen Wang Wei Zhou Qi Huang Bowen Feng Lilan Liu Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University Shanghai China Research Institute of Informatization Business Division Shanghai Baosight Software Corporation Shanghai China

Most of the traditional fault diagnosis methods rely on the expert knowledge of artificial extraction features and related fields, and these algorithms are not accurate, and the robustness and generalization ability are poor. Convolutional neural network is one of the most widely used deep learning models. Based on its unique convolution-pooling network structure, convolutional neural network has powerful feature extraction and expression capabilities. In this paper, based on the characteristics of one-dimensional vibration signals, a fault diagnosis algorithm model based on one-dimensional convolutional neural network is proposed. through the experiment of the bearing fault public data set, the proposed algorithm has more than 99% fault recognition rate.

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An Agent-Based Support System for Suburban Railway Traffic Control

An Agent-Based Support System for Suburban Railway Traffic C...

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international conference on intelligent Human-Machine Systems and Cybernetics, IHMSC

作者： Taomei Zhu Jose Manuel Mera Xiuqing Guo Yidan Xing Wei Nai Electronics and Information Engineering Tongji Zhejiang College Jiaxing Zhejiang China Railway Technology Research Center (CITEF) ETSII of Technical University of Madrid (UPM) Madrid Spain Tongji Zhejiang College Jiaxing China

Suburban railway lines are emerging in China in the last few years. Not as definite as in high-speed railway or urban rail transit, the applications of signaling and traffic control system in suburban railway have not been strictly defined. Based on the investigation of the traffic scale and its characteristics in suburban railway, and traffic control systems implemented respectively in national railway and urban rail transit, an agent-based system, named D-Agent, is presented to support suburban railway traffic control and management. It consists of six basic modules: local data, knowledge base, skills, reasoning mechanism, data processing, and communication. Infrastructure characteristics, train performances, safety and operational restrictions are stored in local data and knowledge base. Functions of traffic control and adaptive support become the skills of D-Agent, and all possible interfaces of traffic control system are learned as the languages of D-Agent for communication with other systems. through continuous learning, D-Agent can be trained to support the traffic control system or dispatcher in handling traffic problems, as well in adapting to different signaling systems that may be applied in suburban railway.

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Back-propagation neural network versus logistic regression in heart disease classification 11th

Back-propagation neural network versus logistic regression i...

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11th international conference on Advanced Computing and Communication Technologies, ICACCT 2018

作者： Desai, Shrinivas D. Giraddi, Shantala Narayankar, Prashant Pudakalakatti, Neha R. Sulegaon, Shreya School of Computer Science and Engineering KLE Technological University Hubballi India

ISBN: (纸本)9789811306792

Globally, cardiovascular (heart) diseases are the major cause of death. About 80% of deaths are reported in developing countries. Looking at the trend and lifestyle, one can predict that by 2030 around 23.6 million people may die due to heart disease (mainly from heart attacks and strokes). Each and every healthcare unit generates enormous heart disease data which unfortunately are not "mined" to discover pattern and knowledge for effective decision making. Practical knowledge by domain experts plays vital role. However, there is a need for effective analysis tools to discover unknown relationships and trends in data. Objective of this paper is to assess the accuracy of classification model for the prediction of heart disease for Cleveland dataset. A comparative study of parametric and nonparametric approach in classifying heart disease is presented. Two classification models, back-propagation neural network (BPNN) and logistic regression (LR), are used for the study. the developed classification model will assist domain experts to take effective diagnostic decision. 10-fold cross validation method is used to measure the unbiased estimate of these classification models. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Population health assessment based on entropy modeling of multidimensional stochastic systems 11th

Population health assessment based on entropy modeling of mu...

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11th international conference on intelligent data Processing, IDP 2016

作者： Tyrsin, Alexander N. Gevorgyan, Garnik G. Ural Federal University Yekaterinburg Russia Scientific and Engineering Center Reliability and Resource of Large Systems and Machine of UB RAS Yekaterinburg Russia

ISBN: (纸本)9783030353995

the article attempts to demonstrate the possible application opportunities of multidimensional stochastic systems entropy modeling in medicine on a specific example. An assessment of rural male population health state has been done by the criteria of "healthy", "practically healthy", and "diseased". the entropy levels are determined for the parameters, characterizing the main risk factors for chronic non-communicable diseases, depending on the health group. For each group a single parameter with the largest contribution to the entropy of the population is highlighted, and the dependence degree of this parameter from the others is determined. It was concluded that with the deterioration of health state the population entropy increases. this growth is caused by the increase of randomness entropy. It is shown that with the help of the entropy model it was possible to quite confidently study population health simultaneously by several risk factors. this allows us to count on the successful application of the proposed approach in medicine, as well as in other spheres. © Springer Nature Switzerland AG 2019.

关键词： Entropy

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the influence of sampling on imbalanced data classification 8

The influence of sampling on imbalanced data classification

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8th Brazilian conference on intelligent Systems, BRACIS 2019

作者： Barella, Victor H. Garcia, Luís P.F. De Carvalho, André C.P.L.F. ICMC/University of São Paulo São Carlos Brazil CIC/University of Brasilia Brasília Brazil

ISBN: (纸本)9781728142531

Classification tasks using imbalanced data are not challenging on their own. When the classes are linearly separable, a regular classification algorithm usually induces predictive models able to distinguish the classes properly. Imbalanced data poses difficulty for the minority class when the training sets have classes overlapping or a complex border decision. Assessing these characteristics is fundamental to understand the classification task difficulty and to choose adequate pre-processing techniques for imbalanced data. Measures able to identify the complexity of a classification task for a given dataset have been proposed. these measures use different criteria to identify how difficult it is to induce a classifier from a dataset. In this paper, we investigate the use of data complexity measures to estimate the best sample size for data imbalance pre-processing techniques. For such, this paper assesses the predictive performance and the data complexity of real datasets after applying pre-processing techniques using different sample sizes. According to experiments, the data complexity measures are a tool to help in choosing a proper sample size to improve the predictive performance of the classifiers. We also observe that only the difficulty of predicting the minority class is not enough when dealing with sampling. As an alternative to deal with this deficiency, we suggest a combination of the data complexity of both classes. © 2019 IEEE.

关键词： Sampling

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