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

Bile Acids, Gut Microbiome and the Road to Fatty Liver Disease

2021 ◽  
pp. 2719-2730
Author(s):  
Phillip B. Hylemon ◽  
Lianyong Su ◽  
Po‐Cheng Zheng ◽  
Jasmohan S. Bajaj ◽  
Huiping Zhou
Keyword(s):  
Liver Disease ◽  
Fatty Liver ◽  
Bile Acids ◽  
Gut Microbiome ◽  
Fatty Liver Disease ◽  
The Road

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 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. __________________________

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