The Identification of Poverty Alleviation Targets Based on the Multiple Hybrid Decision-Making Algorithms

Fu, Ming; Wang, Lifang; Zhou, Jian

doi:10.1109/access.2020.3022807

Cited by 8 publications

(5 citation statements)

References 24 publications

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“…Gambar 7: penentuan rule base Gambar 7 menunjukkan rule yang telah dibuat berdasarkan beberapa kondisi IF-THEN. Pada penelitian [31] menunjukkan bahwa rule yang digunakan berjumlah empat jenis dengan pengelompokan sebanyak dua jenis dari masingmasing rule teersebut. Berdasrkan rule yang telah dibuat akan menghasilkan kesimpulan seperti pada gambar diatas.…”

Section: B Hasilunclassified

Perankingan Dosen Berbasis Aktifitas Forum Moodle Menggunakan Metode Fuzzy Tsukamoto

Putra

Sukadarmika

Sastra

2023

JTE

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The online learning process currently utilizes a Learning Management System, one of which is Moodle. Moodle provides a forum menu where the forum is a digital forum for asking questions and answering and sharing between lecturers and students. Lecturer and student activity can be seen from the forum menu. For this reason, research was conducted on the ranking of lecturer activities in the moodle forum. The ranking determination uses the Fuzzy Tsukamoto decision making system. Fuzzy Tsukamoto has step efficiency in determining rules and processing data that has a taboo range and produces crips output. The processing steps carried out are determining the variables, giving weights, creating a fuzzy set of each variable, then the fuzzification process and creating a rule base and finally, defuzzification, namely the calculation of negligence and output. Based on the rule and the predicate alpha value, the appropriate value is obtained to rank the quality of lecturers based on their activities in the moodle forum. Keywords — database, forum, fuzzy, moodle, tsukamoto

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Section: B Hasilunclassified

Perankingan Dosen Berbasis Aktifitas Forum Moodle Menggunakan Metode Fuzzy Tsukamoto

Putra

Sukadarmika

Sastra

2023

JTE

View full text Add to dashboard Cite

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“…mathematically. e emergency decision-making is an important branch of collaborative decision-making problems, and they have many similarities [34], while there are great differences in complexity between them. e main difference is that the emergency decision-making problem has strict restrictions on the time, and the information acquired by experts is limited; even worse, it is always difficult for experts to evaluate alternatives with single values, and they often hesitate among multiple values.…”

Section: E Collaborative Decision-making Problemmentioning

confidence: 99%

A Closed-Loop Method for Multiperiod Intelligent Information Processing with Cost Constraints under the Fuzzy Environment

Wang

Cao

et al. 2022

Computational Intelligence and Neuroscience

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From trivial matters in life to major scientific projects related to the fate of mankind, decision-making is everywhere. Whether high-quality decisions can be made often directly affects the development of affairs, especially when sudden disasters occur. As the basis of decision-making, data are crucial. The continuously probabilistic linguistic set, a data structure of the fuzzy mathematics, is selected in the paper to collect original data after careful comparisons, because this data structure can fully consider the hesitation of decision-makers and the fuzziness of complex problems. Although all alternatives are costly, the costs of different alternatives still vary greatly; obviously, the low-cost alternative is better than others when the same predetermined goal can be achieved, which is one of the research objectives and characteristics of this paper. Different from other researchers who only take the cost as one of the decision-making indicators, the algorithm proposed in the paper pays much more attention on the cost reduction. When dealing with an emergency, it is often difficult to solve the problem by taking measures only once; usually, multiple rounds of measures are needed. Each round of decision-making has both connections and differences, and the multiround decision-making model is proposed and built in the paper. Different from traditional linear structures, the model mainly adopts the closed-loop structure, which divides the whole process into multiple sub-decision-making points, the severities measured at the current time point will be compared with the values estimated at the latter time point, and then, the differences will be input into the system, the corresponding automatic adjustment modules will be activated immediately according to the values. The accuracy of the system can be verified and adjusted in time by the closed-loop control module. Finally, several experiments are carried out and the results show that the algorithm proposed in the paper is more effective and the cost is lower.

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“…Although the accuracy of each comparison result obtained by such a method is relatively high from the local perspective, however, as mentioned above, only two alternatives are compared at a time, and the data may be inconsistent from the whole perspective [24]. For this reason, we propose the definition of the consistency degree which is shown as follows:…”

Section: E Consistency Optimization Modelmentioning

confidence: 99%

High-Discrimination Comparison Algorithm for the Comprehensive Evaluation of Innovation Ability in Colleges and Universities under Uncertain Information

Wang

Cao

et al. 2022

Computational Intelligence and Neuroscience

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The innovation ability of students is one of the most important objectives that need to be cultivated in colleges and universities. The comprehensive evaluation of innovation ability discussed in the study can be divided into two stages: the first stage can be called preliminary evaluation and its main target is to identify students with innovative potential; in the second stage, the target objects found in the previous stage will be evaluated quantitatively and ranked. However, it is always difficult to quantitatively evaluate the innovation ability by using traditional algorithms. Based on the above analysis, the study proposes an algorithm to quantitatively evaluate the innovation ability with the help of management thought and fuzzy mathematics. Data are the basis of evaluation, and the accuracy of the data directly determines the quality of the algorithm; the data structure of the incompletely probabilistic fuzzy set is proposed in the study; the data structure can fully consider the fuzziness of the problem and the hesitation in the decision-making process; it can save the original detailed data to the maximum extent. Certainly, certain information may be lost or only the value range can be determined; there are usually some unknowns in the evaluation data, and the consistency optimization model is proposed for solving the problem. Usually, there are certain contradictions among the evaluation data; the definition of the consistency degree is proposed in the study; the consistency can be verified in time after all the unknowns are obtained, and the automatic adjustment module will be activated immediately if the value of the consistency degree exceeds the warning threshold. Finally, after verifying the data consistency, the solution can be obtained by solving the optimization model. Several experiments have been carried out to verify the effectiveness and high-discrimination ability of the algorithm proposed in the study; meanwhile, the superiority of the algorithm is further verified through comparisons with other outstanding algorithms.

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The Identification of Poverty Alleviation Targets Based on the Multiple Hybrid Decision-Making Algorithms

Cited by 8 publications

References 24 publications

Perankingan Dosen Berbasis Aktifitas Forum Moodle Menggunakan Metode Fuzzy Tsukamoto

Perankingan Dosen Berbasis Aktifitas Forum Moodle Menggunakan Metode Fuzzy Tsukamoto

A Closed-Loop Method for Multiperiod Intelligent Information Processing with Cost Constraints under the Fuzzy Environment

High-Discrimination Comparison Algorithm for the Comprehensive Evaluation of Innovation Ability in Colleges and Universities under Uncertain Information

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