Recently, machinelearning models have been applied to neuroimaging data, allowing to make predictions about a variable of interest based on the pattern of activation or anatomy over a set of voxels. These pattern rec...
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ISBN:
(纸本)9780769550619
Recently, machinelearning models have been applied to neuroimaging data, allowing to make predictions about a variable of interest based on the pattern of activation or anatomy over a set of voxels. These patternrecognition based methods present undeniable assets over classical (univariate) techniques, by providing predictions for unseen data, as well as the weights of each voxel in the model. However, the obtained weight map cannot be thresholded to perform regionally specific inference, leading to a difficult localization of the variable of interest. In this work, we provide local averages of the weights according to regions defined by anatomical or functional atlases (e. g. Brodmann atlas). These averages can then be ranked, thereby providing a sorted list of regions that can be (to a certain extent) compared with univariate results. Furthermore, we defined a "ranking distance", allowing for the quantitative comparison between localized patterns. These concepts are illustrated with two datasets.
作者：
He, JingWuhan Univ
Econ & Management Sch Wuhan 430072 Peoples R China
The datamining field proposes the development of methods and techniques for assigning useful meanings for data stored in databases. It gathers researches from many study fields like machinelearning, pattern recognit...
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ISBN:
(纸本)9780769538594
The datamining field proposes the development of methods and techniques for assigning useful meanings for data stored in databases. It gathers researches from many study fields like machinelearning, patternrecognition, databases, statistics, artificial intelligence, knowledge acquisition for expert systems, data visualization and grids. datamining represents a set of specific algorithms of finding useful meanings in stored data. This paper aims to point the most important steps that were made in the datamining field of study in recent years and to show how the overall process of discovering can be improved in the future.
Deep learning architectures based on convolutional neural networks (CNN) are very successful in image recognition tasks. These architectures use a cascade of convolution layers and activation functions. The setup of t...
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ISBN:
(纸本)9781467391870
Deep learning architectures based on convolutional neural networks (CNN) are very successful in image recognition tasks. These architectures use a cascade of convolution layers and activation functions. The setup of the number of layers and the number of neurons in each layer, the choice of activation functions and training optimization algorithm are very important. I present GPU implementation of CNN with feature extractors designed for building recognition, learned in a supervised way and achieve very good results.
The outbreak of COVID-19 has dramatically changed peoples' lives over the past two years. The goal of this project is to analyze COVID-19 data from John Hopkins University for the USA, Brazil, India, and Iran betw...
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In order to reduce the time and memory consumption of frequent itemsets mining in stream data, and weaken the impact of historical transactions on datapatterns, this paper proposes a frequent itemsets mining algorith...
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ISBN:
(纸本)9781450375511
In order to reduce the time and memory consumption of frequent itemsets mining in stream data, and weaken the impact of historical transactions on datapatterns, this paper proposes a frequent itemsets mining algorithm SWFIUT-stream based on sliding decay time window. In this algorithm, the time attenuation factor is introduced to assign different weights to each window unit to weaken their influence on data mode. In order to realize the fast stream datamining processing, when mining the frequent itemsets, the two-dimensional table is used to scan and decompose the itemsets synchronously to mine all the frequent itemsets in the window, and the distributed parallel computing processing is carried out based on storm framework. Experimental data show that the algorithm consumes less time and consumes less memory space than conventional algorithms when mining frequent itemsets in stream data.
Fish identification is a challenge to recreational anglers, but critically important to the management of fisheries. The state of Louisiana currently provides printed illustrations of species as the sole aid to angler...
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ISBN:
(纸本)9781450366359
Fish identification is a challenge to recreational anglers, but critically important to the management of fisheries. The state of Louisiana currently provides printed illustrations of species as the sole aid to anglers for the process of fish identification. This work describes the application of Google's TensorFlow machinelearning library to the task of fish identification as a case study on the application of the image classification capabilities. We describe the implementation and results of the project.
datamining is a technique of using available information of an entity to extract useful patterns from the large database which can be implemented in various sectors. It is an essential process where intelligent metho...
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ISBN:
(纸本)9781538647653
datamining is a technique of using available information of an entity to extract useful patterns from the large database which can be implemented in various sectors. It is an essential process where intelligent methods are applied to extract relationship within data. datamining holds great potential to improve health systems like mental issues, finance, bioinformatics, agriculture, healthcare and business. In this survey, we aim to compare efficiency of various datamining techniques and examine the role played by machinelearning and fuzzy logic in the field of healthcare. The paper mainly focuses on determining the pregnancy related issues and health status of a newborn by analysing the demographic information, medical history, lifestyle information including smoking and drug use and many other useful attributes of a pregnant lady during gestation period, a stage wherein every women undergoes many physiological changes, sometimes inducing severe health problems leading to death of both mother and foetus.
Face clustering is a pre-processing stage for face detection and face recognition tasks, alleviating the heavy manual labeling work. The goodness of clustering depends on the data features and clustering algorithm. Th...
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ISBN:
(纸本)9781665417907
Face clustering is a pre-processing stage for face detection and face recognition tasks, alleviating the heavy manual labeling work. The goodness of clustering depends on the data features and clustering algorithm. The current data is so complex and diverse that traditional machinelearning clustering algorithms can no longer handle such large-scale and high latitude data. Therefore, this paper introduces two types of face clustering algorithms based on deep learning, face clustering algorithms based on deep subspace and face clustering algorithms based on graph convolutional networks, and introduces each algorithm and compares its advantages and shortcomings.
In this paper, we present a novel approach for finding association rules from locally frequent itemsets using rough set and boolean reasoning. The rules mined so are termed as local association rules. The efficacy of ...
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ISBN:
(纸本)9783642111631
In this paper, we present a novel approach for finding association rules from locally frequent itemsets using rough set and boolean reasoning. The rules mined so are termed as local association rules. The efficacy of the proposed approach is established through experiment over retail dataset that contains retail market basket data from an anonymous Belgian retail store.
datamining and Knowledge Discovery is an indispensable technology for business and researches in many fields such as statistics, machinelearning, patternrecognition, databases and high performance computing. In whi...
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ISBN:
(纸本)9781467356930
datamining and Knowledge Discovery is an indispensable technology for business and researches in many fields such as statistics, machinelearning, patternrecognition, databases and high performance computing. In which Privacy Preserving datamining has the potential to increase the reach and benefits of datamining technology. This allows publishing a micro data without disclosing private information. Publishing data about individuals without revealing sensitive information about them is an important problem. k-anonymity and I-Diversity has been proposed as a mechanism for protecting privacy in microdata publishing. But both the mechanisms are insufficient to protect the privacy issues like Homogeneity attack, Skewness Attack, Similarity attack and Background Knowledge Attack. A new privacy measure called "(n, f)-proximity" is proposed which is more flexible model. Here first introduction about datamining is presented, and then research challenges are given. Followed by privacy preservation measures and problems with k-anonymity and l-Diversity are discussed. The rest of the paper is organised as (n, f)-proximity model, experimental results and analysis followed by conclusion.
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