Fuzzy clustering and dynamic tables for knowledge discovery and decision-making: Analysis of the reproductive performance of the marine copepod Cyclopina sp.

Hernández-Julio, Yamid Fabián; Prieto-Guevara, Martha; Bernal, Wilson Nieto

doi:10.1016/j.aquaculture.2020.735183

Cited by 5 publications

(5 citation statements)

References 57 publications

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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%

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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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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%

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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“…The Sugeno method consists of 2 types [23], namely: a. Zero-Order Sugeno Fuzzy Model [24]. The general form of the Zero-Order Sugeno fuzzy model is :…”

Section: Implication Function Appmentioning

confidence: 99%

“…Mean Absolute Percentage Error (MAPE) is a measure of forecasting accuracy of a forecasting method [23]. The trick is to calculate the difference between the output obtained and the actual data, then divide it by the actual data.…”

Section: Mean Absolute Percentage Error (Mape)mentioning

confidence: 99%

Design of red chili commodity pricing using the BPMN approach and Sugeno's fuzzy inference system

Marfuah¹,

Arkeman²,

Machfud³

et al. 2022

j. sist. manaj. ind.

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Red chili is Indonesia's leading commodity. Red chili is a raw material for various food products, cosmetics, pharmaceuticals and others. Fluctuations in the availability of red chili commodity supply affect the price of red chili commodity products. Pricing can occur because of supply and demand. Uncertain conditions also influence pricing due to fluctuations in raw material prices, ultimately affecting the price of carrageenan products. This condition makes price determination very difficult. Therefore, this study aims to analyze and design a pricing mechanism and determine the optimal margin in the red chili commodity marketing system. This study uses a systems analysis and design approach. Input-process-output (IPO) diagrams describe system requirements. Industrial business processes are described by the Business Process Model and Notation (BPMN) ver. 16.0. Meanwhile, to determine the optimal margin, Sugeno's fuzzy inference system approach is used by simulating the model in 3 margin scenarios: pessimistic, moderate, and optimistic. The simulation results were tested using the MAPE test, in which the results were compared between fuzzy price results and markup prices using markup values of 20%, 25%, and 30%. The analysis results show that the price is determined by demand and supply. The price obtained from the formulation of the Sugeno fuzzy model shows an optimal margin of Rp. 16,600.

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“…In the ANFIS algorithm, the abilities of ANN with fuzzy logic systems are combined. ANFIS converts the logical statements into mathematical correlations [21]. The relation between input-output pairs in a data set would determine a group of rules produced and best membership functions in ANFIS modeling [22].…”

Section: Introductionmentioning

confidence: 99%

Development of Machine Learning Models to Evaluate the Toughness of OPH Alloys

et al. 2021

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Oxide Precipitation-Hardened (OPH) alloys are a new generation of Oxide Dispersion-Strengthened (ODS) alloys recently developed by the authors. The mechanical properties of this group of alloys are significantly influenced by the chemical composition and appropriate heat treatment (HT). The main steps in producing OPH alloys consist of mechanical alloying (MA) and consolidation, followed by hot rolling. Toughness was obtained from standard tensile test results for different variants of OPH alloy to understand their mechanical properties. Three machine learning techniques were developed using experimental data to simulate different outcomes. The effectivity of the impact of each parameter on the toughness of OPH alloys is discussed. By using the experimental results performed by the authors, the composition of OPH alloys (Al, Mo, Fe, Cr, Ta, Y, and O), HT conditions, and mechanical alloying (MA) were used to train the models as inputs and toughness was set as the output. The results demonstrated that all three models are suitable for predicting the toughness of OPH alloys, and the models fulfilled all the desired requirements. However, several criteria validated the fact that the adaptive neuro-fuzzy inference systems (ANFIS) model results in better conditions and has a better ability to simulate. The mean square error (MSE) for artificial neural networks (ANN), ANFIS, and support vector regression (SVR) models was 459.22, 0.0418, and 651.68 respectively. After performing the sensitivity analysis (SA) an optimized ANFIS model was achieved with a MSE value of 0.003 and demonstrated that HT temperature is the most significant of these parameters, and this acts as a critical rule in training the data sets.

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Fuzzy clustering and dynamic tables for knowledge discovery and decision-making: Analysis of the reproductive performance of the marine copepod Cyclopina sp.

Cited by 5 publications

References 57 publications

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

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

Design of red chili commodity pricing using the BPMN approach and Sugeno's fuzzy inference system

Development of Machine Learning Models to Evaluate the Toughness of OPH Alloys

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