scholarly journals Sports beyond genders. Sociological analysis on the participation of trangender women in female sport competitions

2020 ◽  
Vol 3 (2) ◽  
pp. 105-113
Author(s):  
Salvatore Monaco
Keyword(s):  
Data Mining ◽  
Quantitative Analysis ◽  
Female Student ◽  
Sociological Analysis ◽  
Design Data ◽  
The Road ◽  
Female Sports ◽  
Discrimination Factors ◽  
Sporting Activities ◽  
Physiological Benefits

Although sport historically represents an important vehicle for the dissemination of values and principles, it is often an arena of discrimination, most all based on gender (Balbo, 2001). The paper is aimed at analyzing the issue of the participation of transgender women in female sports in order to identify which are the main discrimination factors. To achieve this goal, the article analyzes the online conversations on the House Bill 2706 of Arizona that proposes that transgender female student athletes should not take part in female sporting activities, as they have physiological benefits that would make unequal the sport competitions. In particular, the paper studies the contents about this issue hosted on Twitter, the popular real-time microblogging social network. The method is based on design data mining analysis, supported by the use of software for quantitative analysis of the content. The study considers Tweets published during the period between February and March 2020. Sentiment analysis of Tweets shows that the road to the complete acceptance of the female transgender universe in sport is still very long and difficult to follow.

scholarly journals Characterization of Road Condition with Data Mining Based on Measured Kinematic Vehicle Parameters

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Johannes Masino ◽  
Jakob Thumm ◽  
Guillaume Levasseur ◽  
Michael Frey ◽  
Frank Gauterin ◽  
...  
Keyword(s):  
Data Mining ◽  
Support Vector ◽  
Matlab Toolbox ◽  
Data Set ◽  
The Road ◽  
Acceleration Sensors ◽  
Road Surfaces ◽  
Road Condition ◽  
Sensor Signals

This work aims at classifying the road condition with data mining methods using simple acceleration sensors and gyroscopes installed in vehicles. Two classifiers are developed with a support vector machine (SVM) to distinguish between different types of road surfaces, such as asphalt and concrete, and obstacles, such as potholes or railway crossings. From the sensor signals, frequency-based features are extracted, evaluated automatically with MANOVA. The selected features and their meaning to predict the classes are discussed. The best features are used for designing the classifiers. Finally, the methods, which are developed and applied in this work, are implemented in a Matlab toolbox with a graphical user interface. The toolbox visualizes the classification results on maps, thus enabling manual verification of the results. The accuracy of the cross-validation of classifying obstacles yields 81.0% on average and of classifying road material 96.1% on average. The results are discussed on a comprehensive exemplary data set.

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.

scholarly journals DAMPAK GUNA LAHAN TERHADAP TINGKAT KEMAMPUAN KINERJA JALAN STUDI KASUS : JALAN AHMAD YANI DI KECAMATAN KARTASURA

2019 ◽  
Vol 14 (1) ◽  
pp. 40
Author(s):  
Taufik Setyawan ◽  
Mila Karmilah
Keyword(s):  
Land Use ◽  
Quantitative Analysis ◽  
Traffic Congestion ◽  
Road Traffic ◽  
Rapid Development ◽  
Economic Activities ◽  
Economic Relations ◽  
The Road ◽  
Road Capacity ◽  
Development Area

The use of land as a trade and service area contributes greatly to the development of urban economic structures, including in the District of Kartasura. Especially Kartasura's market activity which is always developing because it is a place to fulfill primary needs. However, the existence of this market and also the trade and service activities around it are increasingly troubling due to irregularities and disrupting transportation activities around the market. Geographically, Kartasura Subdistrict is quite close to the Surakarta City area (around 10 Km), and Surakarta City has a very rapid and dense development intensity and has a limited development area, so the development of socio-economic activities tends to move towards the Kartasura Sub-District area.Close socio-economic relations with Surakarta City made Kartasura experience rapid development in the growth of new activities along the A. Yani road. Such as education, health, trade and services, industry and office activities. With the growth of new activities along the A Yani road, traffic jams often occur at peak hours. The congestion is due to the mixture between modes of transportation, trade, industry and offices.The purpose of this study is to identify the performance of the A Yani road, to determine the effect of land use on congestion that occurs. To achieve these objectives, the analysis used is quantitative calculations. By comparing the road conditions at peak and non peak hours on the A Yani road. The method used in this study is analyzing the volume of the road (V) A Yani experiencing congestion, analyzing side barriers, speed, road capacity (C) A Yani, and the level of road service (V / C) A Yani. In addition to the quantitative analysis also conduct qualitative analysis to clarify the quantitative analysis that has been done. So that what is a problem on Jalan A Yani can also be analyzed, what are the factors that cause congestion and finally show conclusions and recommendations of problems.Keywords: Land use, Traffic Congestion

scholarly journals Implementasi Algoritma K-Mean dalam Pengelompokan Data Kecelakaan di Kabupaten Kediri

2020 ◽  
Vol 1 (2) ◽  
pp. 53-60
Author(s):  
Adimas Ketut Nalendra ◽  
M. Mujiono ◽  
Rafika Akhsani ◽  
Adiguna Sasama Wahyu Utama
Keyword(s):  
Data Mining ◽  
Information System ◽  
Programming Languages ◽  
Human Population ◽  
The Road ◽  
Cluster Group ◽  
The World ◽  
On The Road ◽  
Minor Injuries ◽  
Motorized Vehicles

Abstract The increasing human population in the world with the need for mobilization of motorized vehicles both 2 wheels and 4 wheels is no longer a secondary need but has become a primary need. With the increasing population of vehicles on the road becoming its own problem that is often the occurrence of both single and successive accidents that resulted in many victims both minor injuries, severe to death. Kediri is one of the cities with high accident rates. Although in 2018 this number has decreased but in 2017 there were 1,258. This resulted in the need for an information system to dig deeper about it. The k-mean algorithm is an algorithm used to group the same data and put it into a Cluster group to dig up information. The information system was developed using PHP and MYSql programming languages. The results of clustering are of 3 types namely accident rarely, accident-prone and very accident-prone. The most common incidents in the Pare Subdistrict with the cluster being very accident-prone. Throughout 2017 pare sub-districts there were 133 accident cases. Keywords: K-Means, Data mining.,accident, PHP, clustering. __________________________ Abstrak Semakin meningkatnya populasi manusia di dunia dengan kebutuhan mobilisasi kendaraan bermontor baik roda 2 maupun roda 4 bukan lagi menjadi kebutuhan sekunder tetapi sudah menjadi kebutuhan primer. Dengan semakin meningkatnya populasi kendaraan di jalan raya menjadi maslah sendiri yakni sering terjadinya kecelakaan baik tunggal maupun beruntun yang mengakibatkan banyak korban baik luka ringan, berat sampai meninggal dunia. Kediri adalah salah satu kota yang masih tinggi angka kecelakaan. Meski di tahun 2018 ini mengalami angka penurunan akan tetapi di tahun 2017 tercatat 1.258. Hal ini mengakibatkan perlu adanya suatu system informasi untuk menggali lebih dalam mengenai hal tersebut. Algoritma k-mean adalah algoritma yang digunakan untuk mengelompokkan data yang sama dan dimaksukkan ke kelompok Cluster untuk menggali informasi. Pada system infprmasi dikembangkan menggunakan Bahasa pemograman PHP dan MYSql. Hasil dari clustering terdapat 3 jenis yaitu jarang terjadi kecelakaan, rawan kecekalaan dan sangat rawan kecelakaan. Kecataman dengan kejadian terbanyak terjadi di kecamatan Pare dengan cluster sangat rawan kecelakaan. Sepanjang tahun 2017 kecamatan pare terjadi kasus kecelakaan sebanyak 133 kasus. Kata Kunci: K-Means, Kecelakaan, Data mining, PHP, Clustering. __________________________

AI-aided Data Mining in Gut Microbiome: The Road to Precision Medicine

2021 ◽  
Author(s):  
Xiaoqing Jiang ◽  
Congmin Xu ◽  
Qian Guo ◽  
Huaiqiu Zhu
Keyword(s):  
Data Mining ◽  
Precision Medicine ◽  
Gut Microbiome ◽  
The Road

Multiple Criteria Optimization in Data Mining

2011 ◽  
pp. 1386-1389 ◽  
Author(s):  
Gang Kou ◽  
Yi Peng ◽  
Yong Shi
Keyword(s):  
Data Mining ◽  
Financial Management ◽  
Large Scale ◽  
Optimization Problems ◽  
Aircraft Design ◽  
Multiple Criteria ◽  
Design Data ◽  
Multiple Criteria Optimization ◽  
Database Technology ◽  
Classification Prediction

Multiple criteria optimization seeks to simultaneously optimize two or more objective functions under a set of constraints. It has a great variety of applications, ranging from financial management, energy planning, sustainable development, to aircraft design. Data mining is aim at extracting hidden and useful knowledge from large databases. Major contributors of data mining include machine learning, statistics, pattern recognition, algorithms, and database technology (Fayyad, Piatetsky-Shapiro, & Smyth, 1996). In recent years, the multiple criteria optimization research community has actively involved in the field of data mining (See, for example: Yu 1985; Bhattacharyya 2000; Francisci & Collard, 2003; Kou, Liu, Peng, Shi, Wise, & Xu, 2003; Freitas 2004; Shi, Peng, Kou, & Chen, 2005; Kou, Peng, Shi, Wise, & Xu, 2005; Kou, Peng, Shi, & Chen, 2006; Shi, Peng, Kou, & Chen, 2007). Many data mining tasks, such as classification, prediction, clustering, and model selection, can be formulated as multi-criteria optimization problems. Depending upon the nature of problems and the characteristics of datasets, different multi-criteria models can be built. Utilizing methodologies and approaches from mathematical programming, multiple criteria optimization is able to provide effective solutions to large-scale data mining problems. An additional advantage of multi-criteria programming is that it assumes no deterministic relationships between variables (Hand & Henley, 1997).

The Road to Digitally-Driven Mental Health Services

2022 ◽  
pp. 42-71
Author(s):  
Artemisa Rocha Dores ◽  
Andreia Geraldo ◽  
Helena Martins
Keyword(s):  
Mental Health ◽  
Data Mining ◽  
Technological Development ◽  
Data Mining Technique ◽  
Web Based ◽  
Positive Approach ◽  
Primordial Prevention ◽  
The Road ◽  
Computer Based ◽  
Theoretical Foundations

Intervention in mental health urges new solutions that merge solid theoretical foundations and new possibilities provided by technological development. This chapter is structured around results from a data mining technique using VOSViewer, which organized the field into five clusters of published literature: (1) most affected populations, (2) mental illness/disorders and their impact, (3) the expansion of remote interventions, (4) ICT potential to overcome limitations and (5) a positive approach to ICTs in mental health care. Solutions and recommendations are presented to overcome the issues identified, including how future interventions should consider old and new issues as the ones raised by the COVID-19 pandemic. Computer-based or web-based interventions are hereby presented as part of the revolution towards digital mental health or e-mental health. This approach has the potential to deconfine interventions, releasing them from the traditional settings and reaching new populations. It also reinforces the path already started, from the secondary to the primary and primordial prevention, towards the modification of the psychopathological trajectories.

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