Trust Learning and Estimation

2017 ◽  
pp. 317-330
Author(s):  
Gehao Lu ◽  
Joan Lu
Keyword(s):  
Decision Making ◽  
Complex Systems ◽  
Learning Process ◽  
Learning Algorithm ◽  
Estimation Algorithm ◽  
Decision Making Process ◽  
Self Organizing Map ◽  
The Core ◽  
Self Organizing

Predict uncertainty is critic in decision making process, especially for the complex systems. This chapter aims to discuss the theory involved in Self-Organizing Map (SOM) and its learning process, SOM based Trust Learning Algorithm (STL), SOM based Trust Estimation Algorithm (STL) as well as features of generated trust patterns. Several patterns are discussed within context. Both algorithms and how they are processed have been described in detail. It is found that SOM based Trust Estimation algorithm is the core algorithm that help agent make trustworthy or untrustworthy decisions.

scholarly journals A HYBRID KANSEI ENGINEERING SYSTEM USING THE SELF-ORGANIZING MAP NEURAL NETWORK

2016 ◽  
Vol 2 (1) ◽  
pp. 23-38
Author(s):  
C.S. Teh ◽  
C.P. Lim
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Product Design ◽  
The Self ◽  
Design Parameters ◽  
Decision Making Process ◽  
Engineering System ◽  
Self Organizing Map ◽  
Kansei Engineering ◽  
Self Organizing

Kansei Engineering (KE), a technology founded in Japan initially for product design, translates human feelings into design parameters. Although various intelligent approaches to objectively model human functions and the relationships with the product design decisions have been introduced in KE systems, many of the approaches are not able to incorporate human subjective feelings and preferences into the decision-making process. This paper proposes a new hybrid KE system that attempts to make the machine-based decision-making process closely resembles the real-world practice. The proposed approach assimilates human perceptive and associative abilities into the decision-making process of the computer. A number of techniques based on the Self-Organizing Map (SOM) neural network are employed in the backward KE system to reveal the underlying data structures that are involved in the decision-making process. A case study on interior design is presented to evaluate the efficacy of the proposed approach. The results obtained demonstrate the effectiveness of the proposed approach in developing an intelligent KE system which is able to combine human feelings and preferences into its decision making process.

scholarly journals Exploring Factors Affecting the Emergency Specialists’ Decision-Making in Case of Emergencies in Patients

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Abbasali Ebrahimian ◽  
Seyed-Hossein Hashemi-Amrei ◽  
Mohammadreza Monesan
Keyword(s):  
Decision Making ◽  
Emergency Department ◽  
Qualitative Content Analysis ◽  
Decision Makers ◽  
Decision Making Process ◽  
Factors Affecting ◽  
Emergency Situations ◽  
The Core ◽  
Semistructured Interviews ◽  
Emergency Decision

Introduction. Appropriate decision-making is essential in emergency situations; however, little information is available on how emergency decision-makers decide on the emergency status of the patients shifted to the emergency department of the hospital. This study aimed at explaining the factors that influence the emergency specialists’ decision-making in case of emergency conditions in patients. Methods. This study was carried out with a qualitative content analysis approach. The participants were selected based on purposive sampling by the emergency specialists. The data were collected through semistructured interviews and were analyzed using the method proposed by Graneheim and Lundman. Results. The core theme of the study was “efforts to perceive the acute health threats of the patient.” This theme was derived from the main classes, including “the identification of the acute threats based on the patient’s condition” and “the identification of the acute threats based on peripheral conditions.” Conclusions. The conditions governing the decision-making process about patients in the emergency department differ from the conditions in other health-care departments at hospitals. Emergency specialists may have several approaches to decide about the patients’ emergency conditions. Therefore, notably, the emergency specialists’ working conditions and the others’ expectations from these specialists should be considered.

scholarly journals Quantification of Hepatorenal Index for Computer-Aided Fatty Liver Classification with Self-Organizing Map and Fuzzy Stretching from Ultrasonography

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Kwang Baek Kim ◽  
Chang Won Kim
Keyword(s):  
Fatty Liver ◽  
Hepatic Steatosis ◽  
Learning Algorithm ◽  
Fat Content ◽  
Liver Fat ◽  
Self Organizing Map ◽  
Liver Fat Content ◽  
Computer Aided ◽  
Good Set ◽  
Self Organizing

Accurate measures of liver fat content are essential for investigating hepatic steatosis. For a noninvasive inexpensive ultrasonographic analysis, it is necessary to validate the quantitative assessment of liver fat content so that fully automated reliable computer-aided software can assist medical practitioners without any operator subjectivity. In this study, we attempt to quantify the hepatorenal index difference between the liver and the kidney with respect to the multiple severity status of hepatic steatosis. In order to do this, a series of carefully designed image processing techniques, including fuzzy stretching and edge tracking, are applied to extract regions of interest. Then, an unsupervised neural learning algorithm, the self-organizing map, is designed to establish characteristic clusters from the image, and the distribution of the hepatorenal index values with respect to the different levels of the fatty liver status is experimentally verified to estimate the differences in the distribution of the hepatorenal index. Such findings will be useful in building reliable computer-aided diagnostic software if combined with a good set of other characteristic feature sets and powerful machine learning classifiers in the future.

scholarly journals International Student Mobility

2018 ◽  
pp. 15-30
Author(s):  
Rashim Wadhwa
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
International Students ◽  
International Student ◽  
Student Mobility ◽  
Internationalization Of Higher Education ◽  
Decision Making Process ◽  
Core Element ◽  
International Student Mobility ◽  
The Core

International student mobility is the core element of the internationalization of higher education. In recent years, a significant change has been observed in the outlook of individuals which is giving a boost to this phenomenon. Within this context, the present chapter analyzed the phenomenon of international student mobility through different approaches by providing critical outlook. An attempt has been made to list the important determinants which influence the decision-making process of international students.

Decision making for highway complex scenario by improved safety field with learning process

2021 ◽  
pp. 095440702110532
Author(s):  
Can Xu ◽  
Wanzhong Zhao ◽  
Jingqiang Liu ◽  
Feng Chen
Keyword(s):  
Decision Making ◽  
Learning Process ◽  
Moving Objects ◽  
Learning Algorithm ◽  
Autonomous Vehicle ◽  
Kinematic Model ◽  
Action Sequence ◽  
Decision Algorithm ◽  
The Road

To improve the agility and efficiency of the highway decision-making system and overcome the local optimal dilemma of the existing safety field, this paper builds an improved safety field to reflect the advantage of the reachable states and the learning process is further employed to make the decision long-term optimal. Firstly, the improved safety field is prepared by the kinematic model-based prediction of surrounding vehicles and the boundary is determined elaborately to ensure real-time performance. Then, the field is constructed by three individual fields. One is the kinematic field, which is built based the safe-distance model to measure the colliding risk of both moving or no-moving objects accurately. Another is the road field that reflects the lane-marker constraint. The last is the efficiency field, which is introduced creatively to improve efficiency. Furthermore, the learning algorithm is adopted to learn the long-term optimal state-action sequence in the safety field. Finally, the simulations are conducted in Prescan platform to validate the feasibility of the improved safety field in complex scenarios. The results show that the proposed decision algorithm can always drive autonomous vehicle to the state with a long-term optimal payoff and can improve the overall performance compared to the existing pure safety field and the interaction-aware method.

Organizational Data Mining (ODM)

2011 ◽  
pp. 1-8
Author(s):  
Hamid R. Nemati ◽  
Christopher D. Barko
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Decision Making ◽  
Information Technology ◽  
Competitive Advantage ◽  
Current Status ◽  
Decision Making Process ◽  
Actionable Knowledge ◽  
The Core ◽  
Mining Tools

An increasing number of organizations are struggling to overcome “information paralysis” — there is so much data available that it is difficult to understand what is and is not relevant. In addition, managerial intuition and instinct are more prevalent than hard facts in driving organizational decisions. Organizational Data Mining (ODM) is defined as leveraging data mining tools and technologies to enhance the decision-making process by transforming data into valuable and actionable knowledge to gain a competitive advantage (Nemati & Barko, 2001). The fundamentals of ODM can be categorized into three fields: Artificial Intelligence (AI), Information Technology (IT), and Organizational Theory (OT), with OT being the core differentiator between ODM and data mining. We take a brief look at the current status of ODM research and how a sample of organizations is benefiting. Next we examine the evolution of ODM and conclude our chapter by contemplating its challenging yet opportunistic future.

An Empirical Study of E-Participation Levels in Jordan

2016 ◽  
Vol 7 (1) ◽  
pp. 63-79 ◽  
Author(s):  
Emad Abu-Shanab ◽  
Raya Al-Dalou'
Keyword(s):  
Decision Making ◽  
Empirical Study ◽  
The Political ◽  
Decision Making Process ◽  
The Core ◽  
Political Dimensions ◽  
The Relationship

The relationship between citizens and governments is the core of e-government. E-participation is one of the political dimensions of e-government which focuses on informing, consulting, involving, collaborating, and empowering citizens to take part of the decision making process. This study adopted a framework for the five levels of e-participation and tried to test such model empirically using 400 responses from Jordanians. The study tried to measure Jordanian perceptions towards e-participation initiatives and practices in Jordan, and to measure the achievements on each level as perceived and reported by subjects. Results indicated that the highest perceived level was e-involving, and the lowest was e-consulting. Also, the CFA results indicated a distorted distribution of items between the major levels. Results of other issues explored are discussed further in this study.

Simulating the Foreign Policy Decision-Making Process in the Undergraduate Classroom

2009 ◽  
Vol 42 (02) ◽  
pp. 401-407 ◽  
Author(s):  
Julie A. Loggins
Keyword(s):  
Decision Making ◽  
Foreign Policy ◽  
Learning Process ◽  
Real World ◽  
Policy Decision ◽  
Decision Making Process ◽  
Multiple Factors ◽  
Political Events ◽  
Policy Decision Making ◽  
Concrete World

A simulation of the foreign policy decision-making process, as described in this article, can assist an instructor in linking students' abstract understanding of complex political events, circumstances, and decision making to the real-world interplay of the multiple factors involved in decision making. It is this type of active learning that helps bring a student's abstract understanding into the concrete world. Instead of being passive learners relying on an instructor's knowledge, students are active participants in the learning process.

A PROBABILISTIC SELF-ORGANIZING MAP FOR BINARY DATA TOPOGRAPHIC CLUSTERING

2008 ◽  
Vol 07 (04) ◽  
pp. 363-383 ◽  
Author(s):  
MUSTAPHA LEBBAH ◽  
YOUNÈS BENNANI ◽  
NICOLETA ROGOVSCHI
Keyword(s):  
Binary Data ◽  
Learning Algorithm ◽  
Data Sets ◽  
Self Organizing Map ◽  
Data Set ◽  
Binary Coding ◽  
Public Data ◽  
Multivariate Binary Data ◽  
Self Organizing

This paper introduces a probabilistic self-organizing map for topographic clustering, analysis and visualization of multivariate binary data or categorical data using binary coding. We propose a probabilistic formalism dedicated to binary data in which cells are represented by a Bernoulli distribution. Each cell is characterized by a prototype with the same binary coding as used in the data space and the probability of being different from this prototype. The learning algorithm, Bernoulli on self-organizing map, that we propose is an application of the EM standard algorithm. We illustrate the power of this method with six data sets taken from a public data set repository. The results show a good quality of the topological ordering and homogenous clustering.

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