A Telecommunications Model for Managing Complexity of Voice and Data Networks and Services

2011 ◽  
pp. 202-218
Author(s):  
Bahador Ghahramani
Keyword(s):  
Data Mining ◽  
Information Transfer ◽  
Information Overload ◽  
Data Networks ◽  
Global Networks ◽  
Web Based ◽  
Data Services ◽  
Data Mining Algorithms ◽  
Continuous Quality ◽  
Mining Algorithms

The telecommunications industry (TI) is challenged by a significant increase in the complexity of information transfer due to a recent proliferation of data mining technologies, techniques and applications. As the result, TI is facing a fundamental paradigm shift, with the convergence of voice and data services as well as ever expanding technologies to its users. This technological movement towards a convergence of telephony and computer technologies, web-based networks, and wired and wireless services is creating areas of tremendous opportunities. These areas of opportunity are for continuous quality improvements and applications of the voice and data convergence mining techniques and their implementations. The TI’s implementation of the data mining algorithms reduces information overload, increases data integrity and accuracy, and effectively manages its global networks.

scholarly journals Modelling of e-Governance Framework for Mining Knowledge from Massive Grievance Redressal Data

2017 ◽  
Vol 6 (1) ◽  
pp. 32
Author(s):  
Sangeetha G ◽  
L. Manjunatha Rao
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Knowledge Discovery ◽  
Discussion Forums ◽  
Discovery Process ◽  
Web Based ◽  
Governance Framework ◽  
Data Mining Algorithms ◽  
Massive Number ◽  
Mining Algorithms

With the massive proliferation of online applications for the citizens with abundant resources, there is a tremendous hike in usage of e-governance platforms. Right from entrepreneur, players, politicians, students, or anyone who are highly depending on web-based grievance redressal networking sites, which generates loads of massive grievance data that are not only challenging but also highly impossible to understand. The prime reason behind this is grievance data is massive in size and they are highly unstructured. Because of this fact, the proposed system attempts to understand the possibility of performing knowledge discovery process from grievance Data using conventional data mining algorithms. Designed in Java considering massive number of online e-governance framework from civilian’s grievance discussion forums, the proposed system evaluates the effectiveness of performing datamining for Big data.

Modelling of E-Governance Framework for Mining Knowledge from Massive Grievance Redressal Data

2016 ◽  
Vol 6 (1) ◽  
pp. 367
Author(s):  
Sangeetha G ◽  
L. Manjunatha Rao
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Knowledge Discovery ◽  
Discussion Forums ◽  
Discovery Process ◽  
Web Based ◽  
Governance Framework ◽  
Data Mining Algorithms ◽  
Massive Number ◽  
Mining Algorithms

With the massive proliferation of online applications for the citizens with abundant resources, there is a tremendous hike in usage of e-governance platforms. Right from entrepreneur, players, politicians, students, or anyone who are highly depending on web-based grievance redressal networking sites, which generates loads of massive grievance data that are not only challenging but also highly impossible to understand. The prime reason behind this is grievance data is massive in size and they are highly unstructured. Because of this fact, the proposed system attempts to understand the possibility of performing knowledge discovery process from grievance Data using conventional data mining algorithms. Designed in Java considering massive number of online e-governance framework from civilian’s grievance discussion forums, the proposed system evaluates the effectiveness of performing datamining for Big data.

scholarly journals Modelling of E-Governance Framework for Mining Knowledge from Massive Grievance Redressal Data

2016 ◽  
Vol 6 (1) ◽  
pp. 367
Author(s):  
Sangeetha G ◽  
L. Manjunatha Rao
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Knowledge Discovery ◽  
Discussion Forums ◽  
Discovery Process ◽  
Web Based ◽  
Governance Framework ◽  
Data Mining Algorithms ◽  
Massive Number ◽  
Mining Algorithms

With the massive proliferation of online applications for the citizens with abundant resources, there is a tremendous hike in usage of e-governance platforms. Right from entrepreneur, players, politicians, students, or anyone who are highly depending on web-based grievance redressal networking sites, which generates loads of massive grievance data that are not only challenging but also highly impossible to understand. The prime reason behind this is grievance data is massive in size and they are highly unstructured. Because of this fact, the proposed system attempts to understand the possibility of performing knowledge discovery process from grievance Data using conventional data mining algorithms. Designed in Java considering massive number of online e-governance framework from civilian’s grievance discussion forums, the proposed system evaluates the effectiveness of performing datamining for Big data.

Novel Adverse Events of Iloperidone: A Disproportionality Analysis in US Food and Drug Administration Adverse Event Reporting System (FAERS) Database

2019 ◽  
Vol 14 (1) ◽  
pp. 21-26 ◽  
Author(s):  
Viswam Subeesh ◽  
Eswaran Maheswari ◽  
Hemendra Singh ◽  
Thomas Elsa Beulah ◽  
Ann Mary Swaroop
Keyword(s):  
Data Mining ◽  
Adverse Event ◽  
Adverse Events ◽  
Reporting System ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Disproportionality Analysis ◽  
Positive Signal ◽  
Data Mining Algorithms ◽  
Mining Algorithms

Background: The signal is defined as “reported information on a possible causal relationship between an adverse event and a drug, of which the relationship is unknown or incompletely documented previously”. Objective: To detect novel adverse events of iloperidone by disproportionality analysis in FDA database of Adverse Event Reporting System (FAERS) using Data Mining Algorithms (DMAs). Methodology: The US FAERS database consists of 1028 iloperidone associated Drug Event Combinations (DECs) which were reported from 2010 Q1 to 2016 Q3. We consider DECs for disproportionality analysis only if a minimum of ten reports are present in database for the given adverse event and which were not detected earlier (in clinical trials). Two data mining algorithms, namely, Reporting Odds Ratio (ROR) and Information Component (IC) were applied retrospectively in the aforementioned time period. A value of ROR-1.96SE>1 and IC- 2SD>0 were considered as the threshold for positive signal. Results: The mean age of the patients of iloperidone associated events was found to be 44years [95% CI: 36-51], nevertheless age was not mentioned in twenty-one reports. The data mining algorithms exhibited positive signal for akathisia (ROR-1.96SE=43.15, IC-2SD=2.99), dyskinesia (21.24, 3.06), peripheral oedema (6.67,1.08), priapism (425.7,9.09) and sexual dysfunction (26.6-1.5) upon analysis as those were well above the pre-set threshold. Conclusion: Iloperidone associated five potential signals were generated by data mining in the FDA AERS database. The result requires an integration of further clinical surveillance for the quantification and validation of possible risks for the adverse events reported of iloperidone.

scholarly journals Simple Correlation Between Weather and COVID-19 Pandemic Using Data Mining Algorithms

2020 ◽  
Vol 982 ◽  
pp. 012015
Author(s):  
Ari Fadli ◽  
Azis Wisnu Widhi Nugraha ◽  
Muhammad Syaiful Aliim ◽  
Acep Taryana ◽  
Yogiek Indra Kurniawan ◽  
...  
Keyword(s):  
Data Mining ◽  
Simple Correlation ◽  
Data Mining Algorithms ◽  
Using Data ◽  
Mining Algorithms

Predicting effects of selected impregnation processes on the observed bending strength of wood, with use of data mining models

BioResources ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. 4891-4904
Author(s):  
Selahattin Bardak ◽  
Timucin Bardak ◽  
Hüseyin Peker ◽  
Eser Sözen ◽  
Yildiz Çabuk
Keyword(s):  
Data Mining ◽  
Goodness Of Fit ◽  
Bending Strength ◽  
Wood Preservation ◽  
Ambient Conditions ◽  
Data Mining Algorithms ◽  
Use Of Data ◽  
The Cost ◽  
Mining Algorithms ◽  
The Relationship

Wood materials have been used in many products such as furniture, stairs, windows, and doors for centuries. There are differences in methods used to adapt wood to ambient conditions. Impregnation is a widely used method of wood preservation. In terms of efficiency, it is critical to optimize the parameters for impregnation. Data mining techniques reduce most of the cost and operational challenges with accurate prediction in the wood industry. In this study, three data-mining algorithms were applied to predict bending strength in impregnated wood materials (Pinus sylvestris L. and Millettia laurentii). Models were created from real experimental data to examine the relationship between bending strength, diffusion time, vacuum duration, and wood type, based on decision trees (DT), random forest (RF), and Gaussian process (GP) algorithms. The highest bending strength was achieved with wenge (Millettia laurentii) wood in 10 bar vacuum and the diffusion condition during 25 min. The results showed that all algorithms are suitable for predicting bending strength. The goodness of fit for the testing phase was determined as 0.994, 0.986, and 0.989 in the DT, RF, and GP algorithms, respectively. Moreover, the importance of attributes was determined in the algorithms.

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