scholarly journals Artificial Intelligence-Based Methods for Decision Support to Avoid Collisions at Sea

Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2360
Author(s):  
Mostefa Mohamed-Seghir ◽  
Krzysztof Kula ◽  
Abdellah Kouzou
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Energy Consumption ◽  
Collision Avoidance ◽  
Trajectory Optimization ◽  
Process Model ◽  
Human Error ◽  
Subjective Evaluation ◽  
Ship Control

Ship collisions cause major losses in terms of property, equipment, and human lives. Therefore, more investigations should be focused on this problem, which mainly results from human error during ship control. Indeed, to reduce human error and considerably improve the safe traffic of ships, an intelligent tool based on fuzzy set theory is proposed in this paper that helps navigators make fast and competent decisions in eventual collision situations. Moreover, as a result of selecting the shortest collision avoidance trajectory, our tool minimizes energy consumption. The main aim of this paper was the development of a decision-support system based on an artificial intelligence technique for safe ship trajectory determination in collision situations. The ship’s trajectory optimization is ensured by multistage decision making in collision situations in a fuzzy environment. Furthermore, the navigator’s subjective evaluation in decision making is taken into account in the process model and is included in the modified membership function of constraints. A comparative analysis of two methods, i.e., a method based on neural networks and a method based on the evolutionary algorithm, is presented. The proposed technique is a promising solution for use in real time in onboard decision-support systems. It demonstrated a high accuracy in finding the optimal collision avoidance trajectory, thus ensuring the safety of the crew, property, and equipment, while minimizing energy consumption.

scholarly journals Decision making with support of artificial intelligence

2012 ◽  
Vol 51 (No. 9) ◽  
pp. 385-388
Author(s):  
I. Rábová ◽  
V. Konečný ◽  
A. Matiášová
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Decision Process ◽  
Process Model ◽  
Decision Processes ◽  
Enterprise Information ◽  
Enterprise Information Systems ◽  
Software Modules ◽  
Competition Ability

  Development of software modules for decision support is currently a basic trend in the creation of enterprise Information Systems (IS). The IS is basically a support system of the enterprise Decision System, therefore we can regard it as a very important factor of the competition ability and enterprise prosperity. Conventional IS modules provide the enterprise managers a lot of useful information. Nevertheless, own decision process in view of difficulty, complexity or creation disability of decision process model is very often problematic. This contribution is oriented by its content to appropriate choice realization of modules for support decision processes by using of artificial intelligence methods.      

Perceived Effectiveness of Artificial Intelligence based Decision Support System in a Clinical Setting: A Systematic Literature Review (Preprint)

2020 ◽  
Author(s):  
Avishek Choudhury
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Literature Review ◽  
Decision Support System ◽  
Support System ◽  
Clinical Setting ◽  
Clinical Decision Making ◽  
Theoretical Perspective ◽  
Care Providers

UNSTRUCTURED Objective: The potential benefits of artificial intelligence based decision support system (AI-DSS) from a theoretical perspective are well documented and perceived by researchers but there is a lack of evidence showing its influence on routine clinical practice and how its perceived by care providers. Since the effectiveness of AI systems depends on data quality, implementation, and interpretation. The purpose of this literature review is to analyze the effectiveness of AI-DSS in clinical setting and understand its influence on clinician’s decision making outcome. Materials and Methods: This review protocol follows the Preferred Reporting Items for Systematic Reviews and Meta- Analyses reporting guidelines. Literature will be identified using a multi-database search strategy developed in consultation with a librarian. The proposed screening process consists of a title and abstract scan, followed by a full-text review by two reviewers to determine the eligibility of articles. Studies outlining application of AI based decision support system in a clinical setting and its impact on clinician’s decision making, will be included. A tabular synthesis of the general study details will be provided, as well as a narrative synthesis of the extracted data, organised into themes. Studies solely reporting AI accuracy an but not implemented in a clinical setting to measure its influence on clinical decision making were excluded from further review. Results: We identified 8 eligible studies that implemented AI-DSS in a clinical setting to facilitate decisions concerning prostate cancer, post traumatic stress disorder, cardiac ailment, back pain, and others. Five (62.50%) out of 8 studies reported positive outcome of AI-DSS. Conclusion: The systematic review indicated that AI-enabled decision support systems, when implemented in a clinical setting and used by clinicians might not ensure enhanced decision making. However, there are very limited studies to confirm the claim that AI based decision support system can uplift clinicians decision making abilities.

DETERMINISTIC-BASED SHIP ANTI-COLLISION ROUTE OPTIMIZATION WITH WEB-BASED APPLICATION

2021 ◽  
Vol 161 (A4) ◽  
Author(s):  
R Fışkın ◽  
E Nasibov ◽  
M O Yardımcı
Keyword(s):  
Decision Making ◽  
Collision Avoidance ◽  
Traffic Engineering ◽  
Human Error ◽  
Experimental Tests ◽  
Route Optimization ◽  
Process Automation ◽  
International Regulation ◽  
Web Based ◽  
Ship Traffic

Most of the accidents are caused by human error at sea so, decision making process made by navigators should be more computerised and automated. The supported decision making can be a step forward to decrease the risk of collision. This paper, in this respect, aims to present a deterministic approach to support optimum collision avoidance trajectory. This approach involves a collision avoidance course alteration. A web-based application coded with "JavaScript" programming language on the "Processing" software platform which allows the own ship to change her course in a deterministic manner to avoid collision optimally has been introduced. Algorithm structure of the method has been formulated and organized according to the International Regulation for Preventing Collision at Sea (COLREGs). The experimental tests results have revealed that the system is practicable and feasible and considerably outperforms heuristic-based method. It is thought that the developed method can be applied in an intelligent avoidance system on board and provides contribution to ship collision avoidance process, automation of ship motion control and ship traffic engineering.

Decision Support Software

2009 ◽  
pp. 440-447
Author(s):  
John Wang ◽  
Huanyu Ouyang ◽  
Chandana Chakraborty
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Mathematical Logic ◽  
Decision Support ◽  
Support Systems ◽  
Scientific Methods ◽  
Simulation And Optimization ◽  
Business Software ◽  
The Right ◽  
Support Software

Throughout the years many have argued about different definitions for DSS; however they have all agreed that in order to succeed in the decision-making process, companies or individuals need to choose the right software that best fits their requirements and demands. The beginning of business software extends back to the early 1950s. Since the early 1970s, the decision support technologies became the most popular and they evolved most rapidly (Shim, Warkentin, Courtney, Power, Sharda, & Carlsson, 2002). With the existence of decision support systems came the creation of decision support software (DSS). Scientists and computer programmers applied analytical and scientific methods for the development of more sophisticated DSS. They used mathematical models and algorithms from such fields of study as artificial intelligence, mathematical simulation and optimization, and concepts of mathematical logic, and so forth.

The Role of Artificial Intelligence in Clinical Decision Support Systems and a Classification Framework

2020 ◽  
pp. 167-186
Author(s):  
Steven Walczak
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Making ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Classification Framework

Clinical decision support systems are meant to improve the quality of decision-making in healthcare. Artificial intelligence is the science of creating intelligent systems that solve complex problems at the level of or better than human experts. Combining artificial intelligence methods into clinical decision support will enable the utilization of large quantities of data to produce relevant decision-making information to practitioners. This article examines various artificial intelligence methodologies and shows how they may be incorporated into clinical decision-making systems. A framework for describing artificial intelligence applications in clinical decision support systems is presented.

Extension of TODIM combined with grey numbers: an integrated decision making module

2015 ◽  
Vol 5 (3) ◽  
pp. 367-391 ◽  
Author(s):  
Dilip Kumar Sen ◽  
Saurav Datta ◽  
Siba Sankar Mahapatra
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Set Theory ◽  
Support Systems ◽  
Subjective Evaluation ◽  
Subjective Data ◽  
Content Type ◽  
Application Potential ◽  
Robot Selection

Purpose – Decision making is the task of selecting the most appropriate alternative among a finite set of possible alternatives with respect to some attributes. The attributes may be subjective or objective (or combination of both), depending upon the situation; requirements may also be conflicting. In practice, most of the real-world decision-making problems are based on subjective evaluation criteria which are basically ill-defined and vague. Since subjective human judgment bears ambiguity and vagueness in the decision making; application of grey numbers set theory may be proved fruitful in this context. The paper aims to discuss these issues. Design/methodology/approach – Owing to the advantages of grey numbers set theory in tackling subjectivity in decision making; the crisp-TODIM needs to be extended by integrating with grey numbers set theory in order to facilitate decision making consisting of subjective data. Hence, the unified objective of this paper is to propose a grey-based TODIM approach in the context of decision making. Findings – Application potential of grey-TODIM has been demonstrated through a case empirical robot selection problem. Result obtained thereof, has also been compared to that of existing grey-based decision support systems available in literature. Originality/value – Application potential of grey-based decision support systems (grey-TOPSIS, grey analysis, grey-MOORA) have been highlighted in available literature resource. However, the shortcoming of these approaches is that they do not consider decision-makers’ risk attitude while decision making. TODIM method is derived from the philosophy of Cumulative Prospect Theory (CPT) which considers risk averting attitude of the decision maker in case of gain and risk seeking attitude in case of loss, while comparing dominance between two alternatives with respect to a particular criterion. Hence, this paper contributes a mathematical foundation of TODIM coupled with grey numbers set theory for logical decision making.

May We Support Your Decision?

1996 ◽  
Vol 1 (3) ◽  
pp. 175-178 ◽  
Author(s):  
Colin Gordon
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Decision Making ◽  
Clinical Practice ◽  
Decision Support ◽  
Health Care Professionals ◽  
Medical Decision Making ◽  
Electronic Patient Record ◽  
Medical Decision ◽  
Technical Developments

Expert systems to support medical decision-making have so far achieved few successes. Current technical developments, however, may overcome some of the limitations. Although there are several theoretical currents in medical artificial intelligence, there are signs of them converging. Meanwhile, decision support systems, which set themselves more modest goals than replicating or improving on clinicians' expertise, have come into routine use in places where an adequate electronic patient record exists. They may also be finding a wider role, assisting in the implementation of clinical practice guidelines. There is, however, still much uncertainty about the kinds of decision support that doctors and other health care professionals are likely to want or accept.

scholarly journals Multi-Ship Collision Avoidance Decision-Making Based on Collision Risk Index

2020 ◽  
Vol 8 (9) ◽  
pp. 640
Author(s):  
Yingjun Hu ◽  
Anmin Zhang ◽  
Wuliu Tian ◽  
Jinfen Zhang ◽  
Zebei Hou
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Risk Analysis ◽  
Real Time ◽  
Collision Avoidance ◽  
Evaluation Method ◽  
Risk Index ◽  
Collision Risk ◽  
Ship Collision ◽  
Risk Thresholds

Most maritime accidents are caused by human errors or failures. Providing early warning and decision support to the officer on watch (OOW) is one of the primary issues to reduce such errors and failures. In this paper, a quantitative real-time multi-ship collision risk analysis and collision avoidance decision-making model is proposed. Firstly, a multi-ship real-time collision risk analysis system was established under the overall requirements of the International Code for Collision Avoidance at Sea (COLREGs) and good seamanship, based on five collision risk influencing factors. Then, the fuzzy logic method is used to calculate the collision risk and analyze these elements in real time. Finally, decisions on changing course or changing speed are made to avoid collision. The results of collision avoidance decisions made at different collision risk thresholds are compared in a series of simulations. The results reflect that the multi-ship collision avoidance decision problem can be well-resolved using the proposed multi-ship collision risk evaluation method. In particular, the model can also make correct decisions when the collision risk thresholds of ships in the same scenario are different. The model can provide a good collision risk warning and decision support for the OOW in real-time mode.

Towards an Intelligent System for the Territorial Planning

2018 ◽  
pp. 158-178
Author(s):  
AMRI Benaouda ◽  
Francisco José García-Peñalvo
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Information System ◽  
Decision Support ◽  
Forest Management ◽  
Intelligent System ◽  
Geographical Information ◽  
Territorial Planning ◽  
Ecology And Environment ◽  
Aid Decision

This chapter concerns the conceptualization of an intelligent system for the territorial planning, taking as an example the agriculture case as a tool in decision making. It is started by giving a comparison between the geographical information system (GIS) and the intelligent system (IS), demonstrating the limits of the GIS and the appeal to the artificial intelligence. Also, the chapter gives an overview of the application of decision support systems (DSSs), modeling and simulation applied in forest management, agriculture, ecology, and environment. Finally, the chapter proposes the methodology and the intelligent system proposed, setting up some indicators which help to aid decision making.

Decision Support Software

2011 ◽  
pp. 191-196
Author(s):  
John Wang ◽  
Huanyu Ouyang ◽  
Chandana Chakraborty
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Mathematical Logic ◽  
Decision Support ◽  
Support Systems ◽  
Scientific Methods ◽  
Simulation And Optimization ◽  
Business Software ◽  
The Right ◽  
Support Software

Throughout the years many have argued about different definitions for DSS; however they have all agreed that in order to succeed in the decision-making process, companies or individuals need to choose the right software that best fits their requirements and demands. The beginning of business software extends back to the early 1950s. Since the early 1970s, the decision support technologies became the most popular and they evolved most rapidly (Shim, Warkentin, Courtney, Power, Sharda, & Carlsson, 2002). With the existence of decision support systems came the creation of decision support software (DSS). Scientists and computer programmers applied analytical and scientific methods for the development of more sophisticated DSS. They used mathematical models and algorithms from such fields of study as artificial intelligence, mathematical simulation and optimization, and concepts of mathematical logic, and so forth.

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