scholarly journals Improved Text Mining Algorithm for Fault Detection using Combined D-Matrix

2019 ◽  
Vol 8 (4) ◽  
pp. 1376-1379
Keyword(s):  
Signs And Symptoms ◽  
Fault Analysis ◽  
Data Dependency ◽  
Dependency Matrix ◽  
Unstructured Text ◽  
Content Mining ◽  
Fault Dependency ◽  
Preprocessing Technique ◽  
Textual Content ◽  
Combined Data

Systematic diagnostic version of Fault dependency (D-matrix) mostly use for setup the fault method records and its contributing courting on the classified system-degree. It includes dependencies and association between recognizable failure approaches and signs and symptoms related to a machine. Proposed system in this paper describes an relations of domain primarily based textual content repository for construction and renovate combined data dependency matrix through mining lacks of the tuple exact unstructured text ,cumulative during the analysis incidents. Here paradigm is combined D matrix and then fault analysis through textual content mining using advance data preprocessing technique approach to pick out dependencies. Using real-existence statistics accumulated and validated in proposed method

scholarly journals Dependency Model-Based Multiple Fault Diagnosis Using Knowledge of Test Result and Fault Prior Probability

2019 ◽  
Vol 9 (2) ◽  
pp. 311 ◽  
Author(s):  
Xiaofeng Lv ◽  
Deyun Zhou ◽  
Ling Ma ◽  
Yongchuan Tang
Keyword(s):  
Fault Diagnosis ◽  
Prior Probability ◽  
Test Results ◽  
Multiple Fault ◽  
Dependency Matrix ◽  
Multiple Faults ◽  
Fault Type ◽  
Multiple Fault Diagnosis ◽  
Fault Dependency ◽  
Dependency Model

Aiming at solving the multiple fault diagnosis problem as well as the sequence of all the potential multiple faults simultaneously, a new multiple fault diagnosis method based on the dependency model method as well as the knowledge in test results and the prior probability of each fault type is proposed. Firstly, the dependency model of the system can be built and used to formulate the fault-test dependency matrix. Then, the dependency matrix is simplified according to the knowledge in the test results of the system. After that, the logic ‘OR’ operation is performed on the feature vectors of the fault status in the simplified dependency matrix to formulate the multiple fault dependency matrix. Finally, fault diagnosis is based on the multiple fault dependency matrix and the ranking of each fault type calculated basing on the prior probability of each fault status. An illustrative numerical example and a case study are presented to verify the effectiveness and superiority of the proposed method.

Research of AMU Unit Fault Testability Based on Multi-Signal Modeling and Fuzzy Decision

2013 ◽  
Vol 760-762 ◽  
pp. 1089-1094 ◽  
Author(s):  
De Xin Zhou ◽  
Ming Yu Song ◽  
Teng Da Ma
Keyword(s):  
Fault Diagnosis ◽  
System Modeling ◽  
Fuzzy Decision ◽  
Dependency Matrix ◽  
Modeling Methodology ◽  
Maintenance Time ◽  
Effective System ◽  
Diagnosis Method ◽  
Fault Dependency ◽  
Set Up

The Multi-signal Flow Graph (MFG) is a simple and effective system modeling methodology, widely used in testability analysis and fault diagnosis field. In order to shorten the maintenance time and reduce the influence of human factors, the MFG method was introduced, and it was used to set up the testability model of aircraft Audio Management Unit (AMU) and found the fault-testability dependency matrix and the fault-fault dependency matrix. Based on dependency matrix and real unit fault message, the trapezoidal fuzzy number algorithm was introduced, thus the new fault diagnosis method was generated. Finally, an example proves that the fault diagnosis algorithm can not only locate the fault more accurately, but also improve the maintenance efficiency.

Discovering Spatio-Textual Association Rules in Document Images

2011 ◽  
pp. 176-197
Author(s):  
Donato Malerba ◽  
Margherita Berardi ◽  
Michelangelo Ceci
Keyword(s):  
Association Rules ◽  
Spatial Information ◽  
Inductive Logic ◽  
Logical Structure ◽  
Document Image ◽  
Programming Approach ◽  
Analysis Process ◽  
Preprocessing Technique ◽  
Double Entry ◽  
Textual Content

This chapter introduces a data mining method for the discovery of association rules from images of scanned paper documents. It argues that a document image is a multi-modal unit of analysis whose semantics is deduced from a combination of both the textual content and the layout structure and the logical structure. Therefore, it proposes a method where both the spatial information derived from a complex document image analysis process (layout analysis), and the information extracted from the logical structure of the document (document image classification and understanding) and the textual information extracted by means of an OCR, are simultaneously considered to generate interesting patterns. The proposed method is based on an inductive logic programming approach, which is argued to be the most appropriate to analyze data available in more than one modality. It contributes to show a possible evolution of the unimodal knowledge discovery scheme, according to which different types of data describing the units of analysis are dealt with through the application of some preprocessing technique that transform them into a single double entry tabular data.

scholarly journals A Rule Based Stemmer

2019 ◽  
Vol 9 (1) ◽  
pp. 2026-2029
Keyword(s):  
Sentiment Analysis ◽  
Web Mining ◽  
Web Content ◽  
Rule Based ◽  
Web Content Mining ◽  
Digital World ◽  
Stop Word ◽  
Enormous Amount ◽  
Content Mining ◽  
Preprocessing Technique

The present digital world generates enormous amount of data instantaneously. The need to effectively mine knowledge seems to be the need of the hour. Sentiment Analysis, a part of web content mining which is a subpart of web mining has gained momentum in the field of research. It analyses the opinion of variety of people all over the world. Sentiment Analysis encompasses preprocessing, feature selection, classification and sentiment prediction. Preprocessing is an important process and it deals with many techniques. Stop word removal, punctuation removal, conversion of numbers to number names are some of the basic techniques. Stemming is yet another important preprocessing technique that reduces the different words form to its root. There are basically three types of stemmers namely truncating, statistical and hybrid. The aim of this paper is to propose a rule based stemmer that is a truncating stemmer. It deals with rules for truncation and replacement. The data given as input passes through a series of rules. If the condition specified gets satisfied then the associated rule gets executed otherwise the input is checked with the next rule and the process continues further. The result of execution is stemmed words. The performance of the proposed rule based stemmer is compared with the existing stemmers under the same rule based category namely Porter and Lancaster. Various metrics have been used for evaluation. The observations reveal the fact that the proposed stemmer out performs the Porter and Lancaster stemmers in terms of correctly stemmed words factor and shows a good average conflation factor and lesser over stemming and under stemming errors.

scholarly journals Sentiment Analysis for Product Recommendation System Using Enhanced Stochastic Learning Algorithm

2019 ◽  
pp. 228-234
Author(s):  
S. Gayathri ◽  
K. Thyagarajan
Keyword(s):  
Sentiment Analysis ◽  
Recommendation System ◽  
Learning Algorithm ◽  
Detection Methods ◽  
Research Subjects ◽  
Content Mining ◽  
Commercial Enterprise ◽  
On Line ◽  
Textual Content

E-Commerce has been known as a rapidly growing commercial enterprise, and even though on line purchasing has no longer accompanied those identical boom patterns within the beyond, it's miles now being diagnosed for its capability. Sentiment evaluation is one of the current research subjects in the subject of textual content mining. Opinions and sentiments mining from natural language are very difficult task. Sentiment analysis is the best solution. This gives important information for decision making in various domains. Various sentiment detection methods are available which affect the quality of result. In this project we are finding the sentiments of people related to the services of E-shopping websites. The sentiments include reviews, ratings and emoticons. The main goal is to recommend the products to users which are posted in E-shopping website and analyzing which one is the best. For this we use hybrid learning algorithm which analyze various feedbacks related to the services. Text mining algorithm is used to find scores of each word. Then sentiments are classified as negative, positive and neutral. It has been observed that the pre-processing of the data is greatly affecting the quality of detected sentiments. Finally analysis takes place based on classification. To find out fake review in the website can be analyzed. This device will discover fake critiques made via posting fake remarks about a product via figuring out the MAC deal with in conjunction with assessment posting styles. User will login to the device using his consumer identification and password and could view various merchandise and will give assessment approximately the product. To discover the evaluation is fake or authentic, system will find out the MAC address of the consumer if the machine observes fake assessment send by way of the identical MAC Address many a times it'll inform the admin to do away with that overview from the device. This gadget uses information mining technique. This machine allows the user to find out accurate overview of the product.

scholarly journals Sentiment Analysis for Product Recommendation System Using Hybrid Algorithm

2019 ◽  
pp. 278-287
Author(s):  
R. Umamaheswari ◽  
G. Kanimozhi
Keyword(s):  
Sentiment Analysis ◽  
Recommendation System ◽  
Learning Algorithm ◽  
Detection Methods ◽  
Research Subjects ◽  
Content Mining ◽  
Commercial Enterprise ◽  
On Line ◽  
Textual Content

E-Commerce has been known as a rapidly growing commercial enterprise, and even though on line purchasing has no longer accompanied those identical boom patterns within the beyond, it's miles now being diagnosed for its capability. Sentiment evaluation is one of the current research subjects in the subject of textual content mining. Opinions and sentiments mining from natural language are very difficult task. Sentiment analysis is the best solution. This gives important information for decision making in various domains. Various sentiment detection methods are available which affect the quality of result. In this paper, finding the sentiments of people related to the services of E-shopping websites. The sentiments include reviews, ratings and emoticons. The main goal is to recommend the products to users which are posted in E-shopping website and analyzing which one is the best and use hybrid learning algorithm which analyze various feedbacks related to the services. Text mining algorithm is used to find scores of each word. Then sentiments are classified as negative, positive and neutral. It has been observed that the pre-processing of the data is greatly affecting the quality of detected sentiments. Finally analysis takes place based on classification. To find out fake review in the website can be analyzed. This device will discover fake critiques made via posting fake remarks about a product via figuring out the MAC deal with in conjunction with assessment posting styles. User will login to the device using his consumer identification and password and could view various merchandise and will give assessment approximately the product. To discover the evaluation is fake or authentic, system will find out the MAC address of the consumer if the machine observes fake assessment send by way of the identical MAC Address many a times it'll inform the admin to do away with that overview from the device. This gadget uses information mining technique. This machine allows the user to find out accurate overview of the product.

“Where is Dementia?” A Systematic Literature Review Exploring Neuroanatomical Aspects of Dementia

2017 ◽  
Vol 2 (15) ◽  
pp. 9-23 ◽  
Author(s):  
Chorong Oh ◽  
Leonard LaPointe
Keyword(s):  
English Language ◽  
Signs And Symptoms ◽  
Behavioral Symptoms ◽  
Journal Articles ◽  
People With Dementia ◽  
Different Types ◽  
Precise Diagnosis ◽  
Physical Changes ◽  
The Brain ◽  
Language Literature

Dementia is a condition caused by and associated with separate physical changes in the brain. The signs and symptoms of dementia are very similar across the diverse types, and it is difficult to diagnose the category by behavioral symptoms alone. Diagnostic criteria have relied on a constellation of signs and symptoms, but it is critical to understand the neuroanatomical differences among the dementias for a more precise diagnosis and subsequent management. With this regard, this review aims to explore the neuroanatomical aspects of dementia to better understand the nature of distinctive subtypes, signs, and symptoms. This is a review of English language literature published from 1996 to the present day of peer-reviewed academic and medical journal articles that report on older people with dementia. This review examines typical neuroanatomical aspects of dementia and reinforces the importance of a thorough understanding of the neuroanatomical characteristics of the different types of dementia and the differential diagnosis of them.

Sign in / Sign up

Export Citation Format

Share Document