Personalized Landmark Recommendation for Language-Specific Users by Open Data Mining

2018 ◽  
pp. 107-121 ◽  
Author(s):  
Siya Bao ◽  
Masao Yanagisawa ◽  
Nozomu Togawa
Keyword(s):  
Data Mining ◽  
Open Data

scholarly journals Applying Logistic Regression Data mining techniques for Ethiopian Government Agricultural Open Data Sets

2021 ◽  
Vol 10 (2) ◽  
pp. 473-488
Keyword(s):  
Data Mining ◽  
Logistic Regression ◽  
Agricultural Sector ◽  
Model Building ◽  
Open Data ◽  
Research Process ◽  
Attribute Selection ◽  
Sufficient Evidence ◽  
Vegetable Crops ◽  
Logistic Regression Algorithm

Ethiopia has a great agricultural potential because of its vast areas of fertile land, diverse climate, generally adequate rainfall, and large labor force. With its verified importance to the Ethiopian economy, there is sufficient evidence to show that the potential of the agricultural sector can be expanded considerably by attracting investors towards the sector. This study aims at applying classification techniques in developing a predictive model that can estimate yield production of vegetable crops and the correlation of crops based on their class. In the process of building a model, different steps were undertaken. Among the steps, data collection, data preprocessing and model building and validation were the major ones. Different tasks performed in each step are mentioned as follows. The data were collected Food and Agriculture Organization of the United Nations (FAO). Under preprocessing, data cleaning, discretization and attribute selection were done. The final step was model building and validation and it was performed using the selected tools and techniques. The data mining tool used in this research was Weka. In this software the logistic regression algorithm was selected since it is capable to score more accuracy. After successive experiments were done using this software, a model that can classify crop yield as high, medium and low with better accuracy to the extent of 88.6%. Experimental results show that logistic regression is a very helpful tool to depict the contribution of yield estimation and crop correlation. The reported findings are optimistic, making the proposed model a useful tool in the decision making process. Eventually, the whole research process can be a good input for further indepth research

scholarly journals Prediksi Kemenangan eSport DOTA 2 Berdasarkan Data Pertandingan

AVITEC ◽  
2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Eduardus Hardika Sandy Atmaja
Keyword(s):  
Data Mining ◽  
Naive Bayes ◽  
Open Data ◽  
Prediction Method ◽  
Naïve Bayes ◽  
Professional Communities ◽  
Accurate Analysis ◽  
Great Demand ◽  
Analysis System ◽  
General Society

DOTA 2 is one of the eSports that are in great demand both by the general society and the game professional communities. They compete with each other to develop the best strategy to defeat all enemies they faced. In order to develop the best strategy, a good and accurate analysis system is needed. Data mining can be used to solve these problems by digging valuable information from dataset using certain method. Prediction method is one of the methods in data mining that is most appropriate for finding the winning predictions for the DOTA 2 game. One method that is quite simple and can be used is Naive Bayes. The results of this study indicate that Naive Bayes can make predictions well with an accuracy of 98,804 %. The data used in this research as much as 50000 that obtained from open data. It is expected that this research can assist players in providing information for developing game strategies.

scholarly journals Data Management at the French National Audiovisual Institute (Ina)

Atlanti ◽  
2016 ◽  
Vol 26 (1) ◽  
pp. 101-108
Author(s):  
Eleonore Alquier
Keyword(s):  
Data Mining ◽  
Big Data ◽  
General Public ◽  
Open Data ◽  
Digital Technologies ◽  
Data Access ◽  
Technical Data ◽  
Production Processes ◽  
Data Policy ◽  
Online Access

The French National Audiovisual Institute has been responsible since 1974 for the preservation of the audiovisual heritage produced by national broadcasting corporation (or “Office de radio et television française”: ORTF, for French radio and television corporation). The massive digitalization of these collections in the 1990s, the native digital capture of 120 channels since 2001, the opening of a “general public” website in 2006, are some of the steps taken by the Institute to progressively take into account the digital technologies to benefit the audiovisual preservation. This proposal of presentation would provide an update on the evolution of our processing, concerning most specifically a multi-year project which aims, linked to a new big data policy, to harmonize descriptive metadata according to common thesaurus and to streamline production processes as well as to promote new uses of these contents within the Institute (partial automation of documentary processing by automatic detecting of quoted or represented entities (faces, names, …), automatic articulation of documentary and legal metadata, …), but also outside of the Institute (online access to open data, access to media by technical data mining, …).

scholarly journals Data Visualization of European Regional Operational Programmes: Unleashing the Informative Potential of Open Data for Performance Assessment

2018 ◽  
Author(s):  
Emanuele Frontoni ◽  
Roberto Palloni
Keyword(s):  
Economic Growth ◽  
Data Mining ◽  
Performance Assessment ◽  
Data Visualization ◽  
Open Data ◽  
Cohesion Policy ◽  
Investment Funds ◽  
Data Portal ◽  
European Regional ◽  
New Jobs

The implementation of the European Cohesion Policy aiming at fostering regions competitiveness, economic growth and creation of new jobs is documented over the period 2014–2020 in the publicly available Open Data Portal for the European Structural and Investment funds. On the base of this source, this paper aims at describing the process of data mining and visualization for information production on regional programmes performace in achieving effective expenditure of resouces.

Computational Model to Support the Detection of Profiles of Missing Person in Colombia.

2021 ◽  
Vol 24 (67) ◽  
pp. 121-128
Author(s):  
Gerardo Ernesto Rolong Agudelo ◽  
Carlos Enrique Montenegro Marin ◽  
Paulo Alonso Gaona-Garcia
Keyword(s):  
Data Mining ◽  
Image Analysis ◽  
Decision Rules ◽  
Open Data ◽  
Computational Tools ◽  
The World ◽  
Real World Applications ◽  
Study Case ◽  
Available Information ◽  
Made In

In the world and some countries like Colombia, the number of missing person is a phenome very worrying and growing, every year, thousands of people are reported missing all over the world, the fact that this keeps happening might indicate that there are still analyses that have not been done and tools that have not been considered in order to find patterns in the information of missing person. The present article presents a study of the way informatics and computational tools can be used to help find missing person and what patterns can be found in missing person datasets using as a study case open data about missing person in Colombia in 2017. The goal of this study is to review how computational tools like data mining and image analysis can be used to help find missing person and draw patterns in the available information about missing person. For this, first it will be review of the state of art of image analysis in real world applications was made in order to explore the possibilities when studying the photos of missing person, then a data mining process with data of missing person in Colombia was conducted to produce a set of decision rules that can explain the cause of the disappearance, as a result is generated decision rules algorithm suggest links between socioeconomic stratification, age, gender and specific locations of Colombia and the missing person reports. In conclusion, this work reviews what information about missing person is available publicly and what analysis can me made with them, showing that data mining and face recognition can be useful tools to extract patterns and identify patterns in missing person data.

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