Multi-criteria analysis in Artemia farming site selection for sustainable desert ecosystems planning and management (case study: Siahkouh Playa, Iran)

Keshtkar, A.R.; Oros, Z.; Mohammadkhan, Shirin; Eagdari, Soheil; Paktinat, Hadi

doi:10.1007/s12665-016-5998-2

Cited by 9 publications

(11 citation statements)

References 52 publications

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“…Each approach suffered from different issue like: in mathematical approach (i.e. normalization [15,16], criteria weight [17], and distance measurement [18,19]). On the other hand, the human approach suffered from the main challenge (i.e.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: A new extension of fuzzy decision by opinion score method based on Fermatean fuzzy: A benchmarking COVID-19 machine learning methods

Salih

Al-qaysi

Shuwandy

et al. 2022

IFS

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To date, for the purpose of solving the complex problems in the area of expert system, Multi criteria decision making is the best technique to offer the suitable solution. In the academic literature, the MCDM methods suffered from many challenges. The most important challenges are uncertainty and vagueness. One of the latest MCDM method, called the fuzzy decision by opinion score method (FDOSM). However, there are still some vagueness issues around these methods (mention some of them). According to the advantage of the Fermatean fuzzy set in solving these issues, in this research extends FDOSM into Fermatean-FDOSM so as to effectively benchmark the real-life problem. In this study, we present our methodology in two phases. The first phase presents the mathematical model of Fermatean-FDOSM which is composed of three stages of FDOSM. The second phase applied the new extension to benchmark the COVID-19 machine learning methods. The finding of Fermatean-FDOSM after comparing the result with the basic FDSOM and TOPSIS, is more logical and undergoing a systematic ranking. In the validation process, objective validation is applied to validate the final result of Fermatean-FDOSM. The result of Fermatean-FDOSM is valid, and more logical and in line with decision makers’ opinions.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: A new extension of fuzzy decision by opinion score method based on Fermatean fuzzy: A benchmarking COVID-19 machine learning methods

Salih

Al-qaysi

Shuwandy

et al. 2022

IFS

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“…Table 1 contains a comprehensive list of all 27 studies considered for inclusion. Fishery (20) [28]; [29]; [30] ; [31]; [32]; [33]; [34]; [35]; [36]; [37]; [38]; [39]; [40]; [41]; [42]; [43]; [13]; [44]; [45]; [46]; Aquaculture (13) [47] ; [48]; [49]; [6]; [50]; [51]; [52]; [53]; [54]; [55]; [56]; [57]; [58]; Fig. 1 The steps of the review Fig.…”

Section: B Selection Processmentioning

confidence: 99%

“…Disease management [48] ; [59]; [45] Scheduling and planning [51]; [40]; [60]; [33]; [52]; [61]; [55]; [56]; [22]; [44]; [62]; Harvest regulations [47]; [31]; [44] Sustainability [63]; [34]; [36]; [19]; [38]; [56]; [44]; [64] Catch optimization [28]; [29]; [6]; [65]; [39]; [54]; [42]; [13];…”

Section: Associated Dss Topic Documentmentioning

confidence: 99%

“…In addition, interest in the DSS combination method with spatial data has begun to develop, and research is now evolving toward an integrated Spatial Decision Support System. As demonstrated by Keshtkar [52], developing decision support systems for fisheries is a challenging undertaking, and it is challenging to apply theory to real-world situations to resolve all the elements that impact decisions that can be associated. Referring to the findings of this study, most fisheries DSS take into account just a small number of criteria, as indicated in Table 2.…”

Section: ) Critics Of the Dss For Fisheriesmentioning

confidence: 99%

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Decision Support System in Fisheries Industry: Current State and Future Agenda

Panudju¹,

Rahardja²,

Nurilmala³

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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Decision Support Systems (DSS) are systems that assist decision-makers and aim to synthesize domain and technical knowledge and package it so non-scientists can use and comprehend it. This study aims to compile initial empirical studies that can objectively reject or confirm the central hypothesis. The materials were retrieved after applying the filtered query across all sources. All search engine providers use five query strings. In each example, five findings were collected, sorted, and compared to one another, and 152 papers were generated. Seventy-six documents were discovered after applying the inclusion and exclusion criteria. Each of the 70 papers was independently examined and analyzed. The method of study followed a specific procedure explicitly developed to minimize the risk of researcher bias. It is primarily concerned with whether fisheries have decision-making systems and what empirical outcomes these systems produce, particularly in real-time analysis. The result shows a dearth of research on intelligent DSS, which accounts for less than 3% of all DSS types discussed in the article. This study offers academics and professionals an overview of the implementation of DSS. These new contributions imply the form of several different new contributions to further research. Furthermore, the possibility of identifying research gaps increases once merged with geoinformation technology or spatial information. We introduced a new model that combines spatial logistics techniques with GIS-based tracing and tracking. The envisioned logistics ensure spatial and logistical traceability in the process of fish products.

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“…The Analytic Hierarchy Process (AHP) approach [11][12][13][14] is a method that falls into the second category which considers human preferences in computations. Each method has its own set of problems, such as issues regarding normalization [15,16], criteria weight [17], and distance measurement [18,19] in the mathematical approach. The human approach, on the other hand, faces the most significant difficulty (the inconsistency ratio [20][21][22][23]).…”

Section: Introductionmentioning

confidence: 99%

A New Development of FDOSM Based on a 2-Tuple Fuzzy Environment: Evaluation and Benchmark of Network Protocols as a Case Study

et al. 2022

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Multicriteria decision-making (MCDM) is one of the most common methods used to select the best alternative from a set of available alternatives. Many methods in MCDM are presented in the academic literature, with the latest being the Fuzzy Decision by Opinion Score Method (FDOSM). The FDOSM can solve many challenges that are present in other MCDM methods. However, several problems still exist in the FDOSM and its extensions, such as uncertainty. One of the most significant problems in the use of the FDOSM is the loss of information during the conversion of a decision matrix into an opinion decision matrix. In this paper, the authors expanded the FDOSM into the 2-tuple-FDOSM to solve this problem. The methodology behind the development of the 2-tuple-FDOSM was presented. Within the methodology, definitions of the 2-tuple linguistic fuzzy method, which was used to solve the loss-of-information problem that is present in the FDSOM method, are presented. A network case study was used in the application of the 2-tuple-FDOSM. The final results show that the 2-tuple-FDOSM can be used to address the problem of loss of information. Finally, a comparison between the basic FDOSM, TOPSIS, and 2-tuple-FDOSM was presented.

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Multi-criteria analysis in Artemia farming site selection for sustainable desert ecosystems planning and management (case study: Siahkouh Playa, Iran)

Cited by 9 publications

References 52 publications

RETRACTED: A new extension of fuzzy decision by opinion score method based on Fermatean fuzzy: A benchmarking COVID-19 machine learning methods

RETRACTED: A new extension of fuzzy decision by opinion score method based on Fermatean fuzzy: A benchmarking COVID-19 machine learning methods

Decision Support System in Fisheries Industry: Current State and Future Agenda

A New Development of FDOSM Based on a 2-Tuple Fuzzy Environment: Evaluation and Benchmark of Network Protocols as a Case Study

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