Automatic Sleep Stage Determination by Conditional Probability: Optimized Expert Knowledge-based Multi-Valued Decision Making

Computer field supports the existence of auxiliary program in medical development that is expert knowledge-based system, this system is one branch of Artifical Intellegence (AI). Expert systems are knowledge in learning about estimation or decision-making ability of an expert. Problem solving in the identification of a disease by using auxiliary program is needed a method and concept. Calculation techniques in computing systems are so important, given the level of need for information and the settlement of cases quickly.The results of the study are expert applications that assist in providing results of diagnosis of symptoms managed the system, with inference using forward chaining, and reasioning with Dempster Shafer. Dempster Shafer's method is not monotonous in solving uncertainty problems, due to the addition or subtraction of new facts. Rule changes will occur, allowing the system to do the work of an expert.Data changes will occur both to diseases, symptoms, solutions and rules, allowing the system to do the work of an expert. The results of manual calculations with the system gives results in accordance with the application of Dempster Shafer method. Management of rules in the database facilitates the search for symptoms within the system.

Download Full-text

Automatic Sleep Stage Determination by Multi-Valued Decision Making Based on Conditional Probability with Optimal Parameters

SICE Journal of Control Measurement and System Integration ◽

10.9746/jcmsi.1.423 ◽

2008 ◽

Vol 1 (6) ◽

pp. 423-428

Author(s):

Bei WANG ◽

Takenao SUGI ◽

Xingyu WANG ◽

Masatoshi NAKAMURA

Keyword(s):

Decision Making ◽

Conditional Probability ◽

Sleep Stage ◽

Optimal Parameters

Download Full-text

Guidelines for model adaptation: a study of the transferability of a general seagrass ecosystem DBN model

10.22541/au.164198621.13512002/v1 ◽

2022 ◽

Author(s):

Paula Hatum ◽

Kathryn McMahon ◽

Kerrie Mengersen ◽

Paul Wu

Keyword(s):

Decision Making ◽

Conditional Probability ◽

Expert Knowledge ◽

Growth Dynamics ◽

Dynamic Bayesian Networks ◽

Development Effort ◽

Model Adaptation ◽

Arcachon Bay ◽

Zostera Noltei ◽

Seagrass Ecosystem

Ecological models are extensively and increasingly used in support of environmental policy and decision making. Dynamic Bayesian Networks (DBN) as a tool for conservation have been demonstrated to be a valuable tool for providing a systematic and intuitive approach to integrating data and other critical information to help guide the decision-making process. However, data for a new ecosystem are often sparse. In this case, a general DBN developed for similar ecosystems could be applicable, but this may require the adaptation of key elements of the network. The research presented in this paper focused on a case study to identify and implement guidelines for model adaptation. We adapted a general DBN of a seagrass ecosystem to a new location where nodes were similar, but the conditional probability tables varied. We focused on two species of seagrass (Zostera noltei and Z. marina) located in Arcachon Bay, France. Expert knowledge was used to complement peer-reviewed literature to identify which components needed adjustment including parameterisation and quantification of the model and desired outcomes. We adopted both linguistic labels and scenario-based elicitation to elicit from experts the conditional probabilities used to quantify the DBN. Following the proposed guidelines, the model structure of the DBN was retained, but the conditional probability tables were adapted for nodes that characterised the growth dynamics in Zostera spp. population located in Arcachon Bay, as well as the seasonal variation on their reproduction. Particular attention was paid to the light variable as it is a crucial driver of growth and physiology for seagrasses. Our guidelines provide a way to adapt a general DBN to specific ecosystems to maximise model reuse and minimise re-development effort. Especially important from a transferability perspective are guidelines for ecosystems with limited data, and how simulation and prior predictive approaches can be used in these contexts.

Download Full-text

IMPLEMENTASI METODE VIKOR DALAM PENERIMAAN JURNALIS PADA KORAN WASPADA MEDAN

KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) ◽

10.30865/komik.v2i1.905 ◽

2018 ◽

Vol 2 (1) ◽

Author(s):

Soraya Rahma Hayati ◽

Mesran Mesran ◽

Taronisokhi Zebua ◽

Heri Nurdiyanto ◽

Khasanah Khasanah

Keyword(s):

Decision Making ◽

Decision Support ◽

Decision Support System ◽

Decision Support Systems ◽

Support System ◽

Support Systems ◽

Knowledge Based ◽

Computer Based ◽

Support Decision Making ◽

Made In

The reception of journalists at the Waspada Daily Medan always went through several rigorous selections before being determined to be accepted as journalists at the Waspada Medan Daily. There are several criteria that must be possessed by each participant as a condition for becoming a journalist in the Daily Alert Medan. To get the best participants, the Waspada Medan Daily needed a decision support system. Decision Support Systems (SPK) are part of computer-based information systems (including knowledge-based systems (knowledge management)) that are used to support decision making within an organization or company. Decision support systems provide a semitructured decision, where no one knows exactly how the decision should be made. In this study the authors applied the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) as the method to be applied in the decision support system application. The VIKOR method is part of the Multi-Attibut Decision Making (MADM) Concept, which requires normalization in its calculations. The expected results in this study can obtain maximum decisions.Keywords: Journalist Acceptance, Decision Support System, VIKOR

Download Full-text

The dependence of the majority voting decision-making probabilities on a multi-expert binary system experts number

Informatization and communication ◽

10.34219/2078-8320-2020-11-1-7-14 ◽

2020 ◽

pp. 7-14

Author(s):

E. D. Avedyan ◽

Le Thi Trang Linh

Keyword(s):

Decision Making ◽

Binary System ◽

Conditional Probability ◽

Majority Voting ◽

Correct Solution ◽

Conditional Probabilities ◽

Analytical Results ◽

Independent Expert ◽

Mutually Independent

The article presents the analytical results of the decision-making by the majority voting algorithm (MVA). Particular attention is paid to the case of an even number of experts. The conditional probabilities of the MVA for two hypotheses are given for an even number of experts and their properties are investigated depending on the conditional probability of decision-making by independent experts of equal qualifications and on their number. An approach to calculating the probabilities of the correct solution of the MVA with unequal values of the conditional probabilities of accepting hypotheses of each statistically mutually independent expert is proposed. The findings are illustrated by numerical and graphical calculations.

Download Full-text