Application Data Mining Technology in Identifying and Analyzing Decision-Making on the Road Black Spot

ICTIS 2011 ◽  
2011 ◽  
Author(s):  
Hao Wu
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Black Spot ◽  
Mining Technology ◽  
The Road ◽  
Application Data ◽  
On The Road

scholarly journals On the Road to Holistic Decision Making in Adaptive Security

2013 ◽  
Vol 3 (8) ◽  
pp. 59-64 ◽  
Author(s):  
Mahsa Emami-Taba ◽  
Mehdi Amoui ◽  
Ladan Tahvildari
Keyword(s):  
Decision Making ◽  
Adaptive Security ◽  
The Road ◽  
On The Road

scholarly journals On the Road to Holistic Decision Making in Adaptive Security

2013 ◽  
Vol 3 (8) ◽  
pp. 59-64 ◽  
Author(s):  
Mahsa Emami-Taba ◽  
Mehdi Amoui ◽  
Ladan Tahvildari
Keyword(s):  
Decision Making ◽  
Adaptive Security ◽  
The Road ◽  
On The Road

Game Theory-Based Framework for Modeling Human–Vehicle Interactions on the Road

2020 ◽  
Vol 2674 (9) ◽  
pp. 701-713
Author(s):  
Yalda Rahmati ◽  
Alireza Talebpour ◽  
Archak Mittal ◽  
James Fishelson
Keyword(s):  
Decision Making ◽  
Human Behavior ◽  
Ground Truth ◽  
Realistic Model ◽  
Shared Environment ◽  
The Road ◽  
Interactive Behavior ◽  
Interactive Decision Making ◽  
Decision Making Framework ◽  
On The Road

New application domains have faded the barriers between humans and robots, introducing a new set of complexities to robotic systems. The major impediment is the uncertainties associated with human decision making, which makes it challenging to predict human behavior. A realistic model of human behavior is thus vital to capture humans’ interactive behavior with their surroundings and provide robots with reliable estimates on what is most likely to happen. Focusing on operations of connected and automated vehicles (CAVs) in areas with a high presence of human actors (i.e., pedestrians), this study creates an interactive decision-making framework to predict pedestrians’ trajectories when walking in a shared environment with vehicles and other pedestrians. It develops a game theoretical structure to approximate the movement and directional components of pedestrian motion using the theory of Nash equilibria in non-cooperative games. It also introduces a novel payoff structure to address the inherent uncertainties in human behavior. Ground truth pedestrian trajectories are then used to calibrate the game parameters and evaluate the model’s performance in approximating the motion decisions of human agents in interaction with interfering vehicles and pedestrians. The main contribution of the study is to develop an interactive human–vehicle decision-making framework toward realizing human–vehicle coexistence by capturing the effect of pedestrian–vehicle and pedestrian–pedestrian interactions on choice of walking strategies. The derived knowledge could be used in CAV navigation algorithms to provide the vehicle with more accurate predictions of pedestrian behavior, and in turn, improve CAV motion planning in human-populated areas.

More obstacles on the road to unification

2007 ◽  
Vol 30 (1) ◽  
pp. 41-41 ◽  
Author(s):  
Eric Alden Smith
Keyword(s):  
Decision Making ◽  
Human Behavior ◽  
Real World ◽  
Institutional Constraints ◽  
Historical Contingency ◽  
Behavioral Sciences ◽  
The Road ◽  
On The Road

The synthesis proposed by Gintis is valuable but insufficient. Greater consideration must be given to epistemological diversity within the behavioral sciences, to incorporating historical contingency and institutional constraints on decision-making, and to vigorously testing deductive models of human behavior in real-world contexts.

The Safety Monitoring of Colliery Equipments Based on Data Mining Technology

2011 ◽  
Vol 128-129 ◽  
pp. 731-734
Author(s):  
Shu Fang Zhao ◽  
Li Chao Chen
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Essential Role ◽  
Scientific Information ◽  
Safety Monitoring ◽  
Monitoring Data ◽  
Mining Technology ◽  
Use Of Data ◽  
Stochastic Data

Data mining is the process of abstracting unaware, potential and useful information and knowledge from plentiful, incomplete, noisy, fuzzy and stochastic data. The reliability of colliery equipments takes an essential role in the safety of production. Not only since their continuance of operation, had the accumulation of historical error data of colliery equipments resulted in a mass of surplus data, but also because their lacks of helpful information, which as a result makes colliery managers as well as equipment operators hard to make decisions. Seeing that, we introduced ways here that makes use of data mining technology by processing and analyzing historical monitoring data, recognizing and extracting meaningful patterns so as to provide scientific information for decision-making on the safety of colliery operations, which would help for the forecasting of potential threatens of colliery equipments’ operation, thus, make great contributions to prevent disasters from happening.

scholarly journals Analysis of Characteristics of Tennis Singles Matches Based on 5G and Data Mining Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Ming Li ◽  
Qinsheng Li ◽  
Yuening Li ◽  
Yunkun Cui ◽  
Xiufeng Zhao ◽  
...  
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Statistical Methods ◽  
Analysis Data ◽  
Scientific Basis ◽  
Experimental Simulation ◽  
Mining Technology ◽  
Offensive Tactics ◽  
Analysis System ◽  
Scientific Decision

The level of technical and tactical decision-making in a tennis game has a very important impact on the outcome of the game. How to discover the characteristics and rules of the game from a large amount of technical and tactical data, how to overcome the shortcomings of traditional statistical methods, and how to provide a scientific basis for correct decision-making are a top priority. Based on 5G and association analysis data mining theory, we established a data mining model for tennis technical offensive tactics and association rules and conducted specific case studies. It can calculate the maximization and distribution rate of certain technologies, also distinguish between the athlete’s gain and loss rate and the spatial position on the track, and use artificial statistical methods to cause errors and subjective participation. This solution provides objective and scientific decision support for this problem and is used in the decision-making of the landing point in tennis match technology and tactics. Experimental simulation shows that the data mining technology analysis system used for regional tennis matches is more concise, efficient, and accurate than traditional movie analysis methods.

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. __________________________

Data Mining technology as a tool for supporting analytical decision making process in Health Information Management System (HIMS)

2021 ◽  
Vol 5 (2) ◽  
pp. 139-147
Author(s):  
Gbenga Femi Asere ◽  
Dung Emmanuel Botson
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Information System ◽  
Strategic Management ◽  
Information Management ◽  
Management System ◽  
Clinical Decision Making ◽  
Information Management System ◽  
Decision Making Process ◽  
Mining Technology

Wide spread use of information system in the delivery of managed healthcare system and the challenges of identifying and disseminating relevant healthcare information, complex and diverse data and knowledge forms and tasks coupled with the prevalence of legacy systems require automated approaches for effective and efficient utilization of massive amount of data to support in strategic planning and decision-making and assist the strategic management mechanisms. Despite the fact that data mining is progressively used in information systems as a technology to support analytical decision making, it is however still barely used in hospital information system to support analytical decision making process. Hence, this paper presents the usefulness of data mining technology in Hospital Information Management System (HIMS). Data mining technology offered capabilities to increase the productivity of medical personnel, analyze care outcomes, lower healthcare costs, improve healthcare quality by using fast and better clinical decision making and generally assist the strategic management mechanisms.

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