scholarly journals A FUNCTIONAL ROAD CLASSIFICATION WITH DATA MINING TECHNIQUES

Transport ◽  
2014 ◽  
Vol 29 (4) ◽  
pp. 419-430 ◽  
Author(s):  
Antonino D’Andrea ◽  
Claudio Cappadona ◽  
Gianluca La Rosa ◽  
Orazio Pellegrino
Keyword(s):  
Data Mining ◽  
Positive Impact ◽  
Geographical Location ◽  
Specific Class ◽  
Data Mining Techniques ◽  
The Road ◽  
The Individual ◽  
New Variables ◽  
Gis System

The current international road standards, in order to give organization and safety, promote the classification of roads according to their technical and functional characteristics beyond their administrative membership, but the procedures are yet strongly based on the expertise’s judgment. In fact, although this activity has a great importance for the consequences that produces in terms of responsibility and allocation of economic resources, it is solely based on the quantification of some variables without specifying methods or analytical procedures. In this paper, after an instrumental survey of the road environment, we applied data mining techniques that consider the ‘vagueness’ of the analysed scenario. The type of algorithms used, therefore, permits to quantify a degree of membership (among 0 and 1) of a road to the groupings provided and to prepare any corrective action in order to direct the final result towards a specific class with greater precision. In addition, this method is very flexible and willing to contain new variables or observations at different times with great easiness. Moreover, the geographical location of the individual observations, as it was done also in this research, can be transferred to a GIS system, with a positive impact on maintenance programs.

Recognizing Job Apathy Patterns of Iraqi Higher Education Employees Using Data Mining Techniques

2019 ◽  
Vol 54 (4) ◽  
Author(s):  
Mustafa S. Abd ◽  
Suhad Faisal Behadili
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Psychiatric Patients ◽  
Human Life ◽  
A Priori ◽  
Psychological Research ◽  
Attribute Selection ◽  
Specific Class ◽  
Data Mining Techniques ◽  
Using Data

Psychological research centers help indirectly contact professionals from the fields of human life, job environment, family life, and psychological infrastructure for psychiatric patients. This research aims to detect job apathy patterns from the behavior of employee groups in the University of Baghdad and the Iraqi Ministry of Higher Education and Scientific Research. This investigation presents an approach using data mining techniques to acquire new knowledge and differs from statistical studies in terms of supporting the researchers’ evolving needs. These techniques manipulate redundant or irrelevant attributes to discover interesting patterns. The principal issue identifies several important and affective questions taken from a questionnaire, and the psychiatric researchers recommend these questions. Useless questions are pruned using the attribute selection method. Moreover, pieces of information gained through these questions are measured according to a specific class and ranked accordingly. Association and a priori algorithms are used to detect the most influential and interrelated questions in the questionnaire. Consequently, the decisive parameters that may lead to job apathy are determined.

scholarly journals Relationships of Hydrological Seasons in Rivers and Groundwaters in Selected Catchments in Poland

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 250
Author(s):  
Przemysław Tomalski ◽  
Edmund Tomaszewski ◽  
Dariusz Wrzesiński ◽  
Leszek Sobkowiak
Keyword(s):  
River Flow ◽  
Geographical Location ◽  
Base Flow ◽  
Water Shortages ◽  
Groundwater Levels ◽  
Average Value ◽  
Geographical Regions ◽  
New Variables ◽  
Mixed Types

The study applied the method of hydrological season identification in a time series of river total and base flows and in groundwater levels. The analysis covered a series of daily measurements from the period 2008–2017 in nine catchments located in different geographical regions of Poland. The basis of the classification of hydrological seasons, previously applied for river discharges only, was the transformation of the original variables into a series reflecting three statistical features estimated for single-name days of a year from a multiyear: average value, variation coefficient, and autocorrelation coefficient. New variables were standardized and after hierarchical clustering, every day of a year had a defined type, valorizing three features which refer to quantity, variability, and the stochastic nature of total and base river flow as well as groundwater stage. Finally, sequences of days were grouped into basic (homogenous) seasons of different types and transitional seasons including mixed types of days. Analysis indicated determinants of types, length, and frequency of identified hydrological seasons especially related to river regime, hydrogeological and hydrometeorological conditions as well as physiographical background were directly influenced by geographical location. Analysis of the co-occurrence of the same types of hydrological seasons allowed, in some catchments, periods of synchronic alimentation (groundwater and base flow, mainly in the cold half-year) and water shortages (all three components, mainly in the warm half-year) to be identified.

Classification of Intrusion Detection Using Data Mining Techniques

2018 ◽  
pp. 753-764 ◽  
Author(s):  
Roma Sahani ◽  
Shatabdinalini ◽  
Chinmayee Rout ◽  
J. Chandrakanta Badajena ◽  
Ajay Kumar Jena ◽  
...  
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Data Mining Techniques ◽  
Using Data

Classification of Product Rating Using Data Mining Techniques

2017 ◽  
pp. 27-36 ◽  
Author(s):  
Pinku Deb Nath ◽  
Sowvik Kanti Das ◽  
Fabiha Nazmi Islam ◽  
Kifayat Tahmid ◽  
Raufir Ahmed Shanto ◽  
...  
Keyword(s):  
Data Mining ◽  
Data Mining Techniques ◽  
Using Data ◽  
Product Rating

scholarly journals Application of Data Mining Techniques for Medical Data Classification: A Review

2018 ◽  
Vol 150 ◽  
pp. 06003 ◽  
Author(s):  
Saima Anwar Lashari ◽  
Rosziati Ibrahim ◽  
Norhalina Senan ◽  
N. S. A. M. Taujuddin
Keyword(s):  
Data Mining ◽  
Data Classification ◽  
Past Research ◽  
Medical Data ◽  
Extensive Literature ◽  
Literature Analysis ◽  
Data Mining Techniques ◽  
Medical Data Classification ◽  
Using Data

This paper investigates the existing practices and prospects of medical data classification based on data mining techniques. It highlights major advanced classification approaches used to enhance classification accuracy. Past research has provided literature on medical data classification using data mining techniques. From extensive literature analysis, it is found that data mining techniques are very effective for the task of classification. This paper analysed comparatively the current advancement in the classification of medical data. The findings of the study showed that the existing classification of medical data can be improved further. Nonetheless, there should be more research to ascertain and lessen the ambiguities for classification to gain better precision.

scholarly journals Classification of BOF Slag by Data Mining Techniques According to Chemical Composition

2020 ◽  
Vol 12 (8) ◽  
pp. 3301
Author(s):  
Sara M. Andrés-Vizán ◽  
Joaquín M. Villanueva-Balsera ◽  
J. Valeriano Álvarez-Cabal ◽  
Gemma M. Martínez-Huerta
Keyword(s):  
Data Mining ◽  
Environmental Impact ◽  
Basic Oxygen Furnace ◽  
Added Value ◽  
Data Mining Techniques ◽  
Different Types ◽  
Potential Applications ◽  
Bof Slag ◽  
Slag Valorization

In the process of converting pig iron into steel, some co-products are generated—among which, basic oxygen furnace (BOF) slag is highlighted due to the great amount generated (about 126 kg of BOF slag per ton of steel grade). Great efforts have been made throughout the years toward finding an application to minimize the environmental impact and to increase sustainability while generating added value. Finding BOF slag valorization is difficult due to its heterogeneity, strength, and overall swallowing, which prevents its use in civil engineering projects. This work is focused on trying to resolve the heterogeneity issue. If many different types of steel are manufactured, then different types of slag could also be generated, and for each type of BOF slag, there is an adequate valorization option. Not all of the slag can be valorized, but it can be a tool for reducing the amount that must go to landfill and to minimize the environmental impact. An analysis by means of data mining techniques allows a classification of BOF slag to be obtained, and each one of these types has a better adjustment to certain valorization alternatives. In the plant used as an example of the application of these studies, eight different slag clusters were obtained, which were then linked to their different potential applications with the aim of increasing the amount valorized.

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