Diabetes is a chronic disease characterized by hyperglycemia. Early screening of diabetes patients will help reduce the incidence of diabetes. Accurately modeling the early symptoms of diabetes to reduce the incidence...
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machinelearning is coming everywhere, and currently in every field, it is contributing in terms of different applications. machinelearning is a type of artificial intelligence (AI), which enables a program to learn,...
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Diabetes is a chronic disease characterized by hyperglycemia. Early screening of diabetes patients will help reduce the incidence of diabetes. Accurately modeling the early symptoms of diabetes to reduce the incidence...
Diabetes is a chronic disease characterized by hyperglycemia. Early screening of diabetes patients will help reduce the incidence of diabetes. Accurately modeling the early symptoms of diabetes to reduce the incidence of diabetes has become an urgent problem. therefore, it is a valuable research direction to mine and study the information of diabetes patients, predict the occurrence of noninfectious diseases, and assist doctors to make correct diagnoses. the precision of different datamining techniques has been compared in this research, employing machinelearning techniques to predict the incidence of diabetes. the University of California, Irvine’s open-source dataset has been used as the main database for experiment. Different machinelearning classifiers such as random forest, neural network and support vector machine have been employed in this experiment. this research gives a technology review of machinelearning algorithms that can be used for diabetes prediction through classification. the experimental results indicate that random forest performs better than other machinelearning methods.
In the present paper, I will focus on propositional knowledge, proof theory, and the deductive transmission of information in datamining. the application of proof theory to machinelearning-based metadata extraction ...
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Easy propagation and access to information on the web has the potential to become a serious issue when it comes to disinformation. the term "fake news" describes the intentional propagation of news withthe ...
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Easy propagation and access to information on the web has the potential to become a serious issue when it comes to disinformation. the term "fake news" describes the intentional propagation of news withthe intention to mislead and harm the public and has gained more attention recently. this paper proposes a style-based machinelearning (ML) approach, which relies on the textual information from news, such as manually extracted lexical features e.g. part of speech counts, and evaluates the performance of several ML algorithms. We identified a subset of the best performing linguistic features, using information-based metrics, which tend to agree withthe literature. We also, combined Named Entity recognition (NER) functionality withthe Frequent pattern (FP) Growth association rule algorithm to gain a deeper perspective of the named entities used in the two classes. Both methods reinforce the claim that fake and real news have limited differences in content, setting limitations to style-based methods. Results showed that convolutional neural networks resulted in the best accuracy, outperforming the rest of the algorithms.
the conflict between computational overhead and detection accuracy affects nearly every Automated Accident Detection (AAD) system. Although the accuracy of detection and classification approaches has recently improved...
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Design patterns commonly pre-owned in Object-Software oriented Development. It provides Information helping to overcome recurrent design challenges (problems). recognition of patterns can be described as identificatio...
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ISBN:
(纸本)9781728151977;9781728151960
Design patterns commonly pre-owned in Object-Software oriented Development. It provides Information helping to overcome recurrent design challenges (problems). recognition of patterns can be described as identification and representation of information based on knowledge obtained from statistical data derived from patterns. Detection of design patterns provides the advantages of an extensive investigation of object-oriented software applications. When using various pattern detection techniques depending on static research issues to identify patterns it detects similar pattern structures. For this, we seek to identify patterns in design of Software by using classification-based methods and information metrics. the JHotDraw, QuickUML, and Junit of object-oriented software are used to detect software problems in the patterns of designing a Software. the datasets are manual creations which are designed utilizing software criteria for classifying learners. It is carried out in two steps, namely the development of a statistics-oriented dataset and the identification of trends in software trends in software design. Tests are carried out by Usage of 3 open source computer programs to test the process, then the tests are examined.
Hypergraph data appear and are hidden in many places in the modern age. they are data structure that can be used to model many real data examples since their structures contain information about higher order relations...
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ISBN:
(纸本)9781643681351;9781643681344
Hypergraph data appear and are hidden in many places in the modern age. they are data structure that can be used to model many real data examples since their structures contain information about higher order relations among data points. One of the main contributions of our paper is to introduce a new topological structure to hypergraph data which bears a resemblance to a usual metric space structure. Using this new topological space structure of hypergraph data, we propose several approaches to study community detection problem, detecting persistent features arising from homological structure of hypergraph data. Also based on the topological space structure of hypergraph data introduced in our paper, we introduce a modified nearest neighbors methods which is a generalization of the classical nearest neighbors methods from machinelearning. Our modified nearest neighbors methods have an advantage of being very flexible and applicable even for discrete structures as in hypergraphs. We then apply our modified nearest neighbors methods to study sign prediction problem in hypegraph data constructed using our method.
Design patterns commonly pre-owned in Object-Software oriented Development. It provides Information helping to overcome recurrent design challenges (problems). recognition of patterns can be described as identificatio...
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ISBN:
(数字)9781728151977
ISBN:
(纸本)9781728151984
Design patterns commonly pre-owned in Object-Software oriented Development. It provides Information helping to overcome recurrent design challenges (problems). recognition of patterns can be described as identification and representation of information based on knowledge obtained from statistical data derived from patterns. Detection of design patterns provides the advantages of an extensive investigation of object-oriented software applications. When using various pattern detection techniques depending on static research issues to identify patterns it detects similar pattern structures. For this, we seek to identify patterns in design of Software by using classification-based methods and information metrics. the JHotDraw, QuickUML, and Junit of object-oriented software are used to detect software problems in the patterns of designing a Software. the datasets are manual creations which are designed utilizing software criteria for classifying learners. It is carried out in two steps, namely the development of a statistics-oriented dataset and the identification of trends in software trends in software design. Tests are carried out by Usage of 3 open source computer programs to test the process, then the tests are examined.
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 discrimina...
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