Energy efficient fuzzy-based DASH adaptation algorithm

Mowafi, Moad; Taqieddin, Eyad; Al-Dahoud, Hiba

doi:10.1016/j.dcan.2020.04.006

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…First, we will explore the application of type-2 fuzzy set theory 58 to the HUPM algorithm 59 and compare it with the type-1 fuzzy set mining algorithm. This comparison aims to identify more valuable fuzzy high-utility itemsets in temporal pattern mining 1 , 8 , 9 , 60 . Second, we will investigate the interpretability of FHUPM and enhance its informativeness for various application scenarios, such as social network distribution 51 , recommendation in mobile app development 4 , anomaly detection 7 , and flow prediction 6 .…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

“…Extracting valuable information 8 , 9 and knowledge from large datasets 10 – 12 requires the use of various approaches such as statistics, machine learning, artificial intelligence, and database technology to find patterns, associations 13 – 16 , and rules in diverse areas 17 – 19 . Zadeh 20 utilized association rule mining methods to explore potential drugs or drug combinations associated with newly diagnosed diabetes.…”

Section: Introductionmentioning

confidence: 99%

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Improved adaptive-phase fuzzy high utility pattern mining algorithm based on tree-list structure for intelligent decision systems

Chen,

Liu,

Zhang

et al. 2024

Sci Rep

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With the rapid development of AI and big data mining technologies, computerized medical decision-making has become increasingly prominent. The aim of high-utility pattern mining (HUPM) is to discover meaningful patterns in medical databases that contribute to maximizing the utility from the perspective of diagnosis. However, HUPM pays less attention to the interpretability and explainability of these patterns in medical decision-making scenarios. This paper proposes a novel algorithm called the Improved fuzzy high-utility pattern mining (IF-HUPM) to address this problem. First, the paper applies a fuzzy preprocessing method to divide the fuzzy intervals of a medical quantitative data set, which enhances the fuzziness and interpretability of the data. Next, in the process of IF-HUPM, both fuzzy tree and list structures are employed to calculate fuzzy high-utility values. By combining the characteristics of the one-stage and two-stage algorithms of HUPM, an adaptive-phase Fuzzy HUPM hybrid frame is proposed. The experimental results demonstrate that the proposed IF-HUPM algorithm enhances both accuracy and efficiency and the mining process requires less time and space on average.

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