scholarly journals Social Parking: Applying the Citizens as Sensors Paradigm to Parking Guidance and Information

2019 ◽  
Vol 11 (23) ◽  
pp. 6549 ◽  
Author(s):  
Julio Barbancho ◽  
Jorge Ropero ◽  
Joaquín Luque ◽  
Alejandro Caraballo ◽  
Carlos León
Keyword(s):  
Data Mining ◽  
Sensor Networks ◽  
Smart Cities ◽  
Administrative Cost ◽  
Data Mining Techniques ◽  
Parking Lots ◽  
Parking Lot ◽  
Using Data ◽  
The City ◽  
Guidance Information

Nowadays, the problem of parking guidance information (PGI) is one of the great challenges of smart cities. Sensor networks have been traditionally used, but they sometimes constitute a high administrative cost. For this reason, this paper presents social parking, a system that is based on the citizens as sensors paradigm, where data are collected by users and are processed using data mining techniques. Moreover, an ontology is used to enable the standardization of information. This way, social parking is compatible with the FIWARE platform. A forecast algorithm was also designed and verified to estimate the number of free parking spots inside a parking lot. With this aim, we used public parking data from eight parking lots in the city of Zaragoza. Client applications allowed testing of all the functions of the parking system. These tests were carried out in three experimental parking lots in the city of Málaga.

scholarly journals Troubleshooting interactive complexity bugs in wireless sensor networks using data mining techniques

2014 ◽  
Vol 10 (2) ◽  
pp. 1-35 ◽  
Author(s):  
Mohammad Maifi Hasan Khan ◽  
Hieu Khac Le ◽  
Hossein Ahmadi ◽  
Tarek F. Abdelzaher ◽  
Jiawei Han
Keyword(s):  
Data Mining ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Data Mining Techniques ◽  
Using Data

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

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.

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