Advisory Systems to Support Decision Making

Beemer, Brandon A.; Gregg, Dawn G.

doi:10.1007/978-3-540-48713-5_24

Cited by 20 publications

(16 citation statements)

References 29 publications

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“…In addition to, activation function, learning rate, mo-mentum and pruning. Probably, K -Nearest-Neighbor Classifiers is regarded as the most popular NN algorithm, which is demonstrated in this paper [24].…”

Section: Classificationmentioning

confidence: 98%

A Legal Advisory System based on K-Nearest Neighbor (K-NN) neural network for Iraqi Law

Sakran¹

2019

IJATCSE

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In general, the procedures in many courts around the world, in-cluding Iraqi courts, need effort and time to be applied, which may influence the people daily routine negatively. Recently, the governments in some countries seek to reform these procedures with cooperation of existing security sector. Additionally, devel-oping in the information technology, facilities using artificial intelligent mechanism in the field of legal. This paper presents a Legal Advisory Decision Support System (LADSS) that has the ability to analyze the legal problems depending on knowledge base and taking the proper decision to the user, which could be (Judge, lawyer, client). Where the proposed system aims to smooth applying the procedures in courts, eliminate biases, min-imize the required time & effort and achieve justice. It takes the legal case as an input, and then it provides the court with the hearing scenario for each given legal case. This paper proposed a decision support system for IRAQI Law, the system was tested by many real legal cases and the results were promising..

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Section: Classificationmentioning

confidence: 98%

A Legal Advisory System based on K-Nearest Neighbor (K-NN) neural network for Iraqi Law

Sakran¹

2019

IJATCSE

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“…With CBR (Kim, 2004), users are gradually and iteratively guided towards a solution from a range of options (previous cases). Beemer and Gregg (2008) have argued that the case-based approach is more effective than the traditional approach to expert systems development based upon rules elicited from experts. In part, this seems to be due to the fact that users seem to find descriptions of the actual application of successful strategies and methods (i.e.…”

Section: Adaptation Mitigation and Resiliencementioning

confidence: 99%

Construction and Use of a ‘Green Growth’ Tourism Decision Support System: A Multi-Model Approach

McGrath

Lipman

2016

AJIS

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For design, development, implementation and use of an information system (IS) to constitute a valid research activity, the system should support the solution of a non-trivial and important problem and it should be original, drawing on existing theories and knowledge. The design of one such system is described in this paper: specifically, a decision support system (DSS) designed to support the development of 'Green Growth' (GG) strategies for Travelism (Travel & Tourism) destinations. A sound GG strategy is important: first, because tourism is a major contributor to the global economy -particularly for developing and island states; second because it represents some 5% of greenhouse gas (GHG) emissions and these are increasing faster than the global norm; and third because the environment is an essential element of destination attractiveness. Thus, the problem domain is certainly non-trivial and important. It is further argued that the design of the DSS artefact described is original and novel in the sense that: i) it supports the entire GG strategy development process (which is actually cyclical); ii) it allows for the sharing of data, functionality and knowledge between different DSS applications and different strategy development exercises in a seamless, integrated manner; and iii) it will be deployed in a global community based program in 2016. System design draws heavily on previous IS, information management and software engineering research; particularly with regard to use of abstraction and interfaces in support of component sharing and reuse.

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“…The current implementation of the methodology is semiautomated. It will be however fully automated in future developments to build a business intelligence expert system with multiple advisory features (Beemer & Gregg, 2008).…”

Section: Introductionmentioning

confidence: 99%

A business intelligence approach using web search tools and online data reduction techniques to examine the value of product-enabled services

Tanev

Liotta

Kleismantas

2015

Expert Systems with Applications

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Advisory Systems to Support Decision Making

Cited by 20 publications

References 29 publications

A Legal Advisory System based on K-Nearest Neighbor (K-NN) neural network for Iraqi Law

A Legal Advisory System based on K-Nearest Neighbor (K-NN) neural network for Iraqi Law

Construction and Use of a ‘Green Growth’ Tourism Decision Support System: A Multi-Model Approach

A business intelligence approach using web search tools and online data reduction techniques to examine the value of product-enabled services

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