Generating Fuzzy Linguistic Summaries for Menstrual Cycles

Sosnowski, Łukasz; Penza, Tomasz

doi:10.15439/2020f202

Cited by 6 publications

(8 citation statements)

References 15 publications

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“…The competition attracted 151 teams from over 30 countries. The highest number of participants had IP addresses from Poland (52), India (19), Russia (15), China (7), and the United States (7). Over 700 correctly formatted solutions were submitted.…”

Section: Competition Resultsmentioning

confidence: 99%

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Network Device Workload Prediction: A Data Mining Challenge at Knowledge Pit

Janusz

Przyborowski

Biczyk³

et al. 2020

Proceedings of the 2020 Federated Conference on Computer Science and Information Systems

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We describe the 7th edition of the international data mining competition held at Knowledge Pit in association with the FedCSIS conference series. The goal was to predict workloadrelated characteristics of monitored network devices. We analyze solutions uploaded by the most successful participants. We investigate prediction errors which had the greatest influence on their results. We also present our own baseline solution which turned out to be the most reliable in the final evaluation.

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Section: Competition Resultsmentioning

confidence: 99%

“…Similar window-based summaries are widely used in data analytics, e.g. to improve representation [7] or decrease data footprint [1]. Fig.…”

Section: A Data Preparationmentioning

confidence: 99%

Network Device Workload Prediction: A Data Mining Challenge at Knowledge Pit

Janusz

Przyborowski

Biczyk³

et al. 2020

Proceedings of the 2020 Federated Conference on Computer Science and Information Systems

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“…It is definitely an approach to consider at least as an additional source of information for informed choice in the election situation. Note that linguistic summaries are being applied more and more often recently [18,19] because of their easy understandability by laymen.…”

Section: E a Linguistic Fuzzy Modelling Interface For Decision Supportmentioning

confidence: 99%

The voter's guide to the galaxy—a multiple-criteria fuzzy decision-support tool for voters and a fresh take on election survey methods

Panzeri¹,

Stoklasová²,

Stoklasa³

2022

Annals of Computer Science and Information Systems

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This paper suggests a multiple-criteria decisionsupport tool for voters, that compares the attitudes of the voters with the declared attitudes of the political parties in several sets of relevant issues. The model intends to identify parties that seem to provide the best fit with the voter attitude-wise. The data input methodology uses discrete 5-point Likert-type scales. We investigate the effect of the inclusion of weights of different sets of issues, of the numerical anchors of the values of the Likert-type scales and also of the potential presence of extremity/leniency effect on the suggestion of the "most compatible" political party suggestion. We also propose a simple fuzzyrule based evaluation tool to identify serious incompatibilities or desired compatibilities in the attitudes of the voter and the party to the relevant issues. This tool introduces (un)acceptability thresholds for the differences in attitudes between the parties and the respondents and provides lists of parties to vote for or to avoid voting for accompanied by the strengths of these suggestions. The tool is shown to have several desirable features including lower sensitivity to small differences in the attitudes, respondents' ability to express their preferences and also preventing the compensation of unacceptable differences in some categories of important issues by high compatibility in the other categories.

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“…The concern of this paper is related to what has been mentioned in the paradigmatic change in medical treatment, in particular in the context of infertility [4] which has become a civilization disease. According to statistics every fifth couple, trying to conceive (TTC), has a problem to achieve pregnancy in the first 12 months of efforts, and this tendency is increasing [5].…”

Section: Introductionmentioning

confidence: 99%

“…This paper is in continuation of a series of papers [4], [6]- [8] that attempted to establish a paltform, known as OvuFriend 1.0 1 , for helping women in determining the possibility of conceiving and understanding the hidden risk of getting related health problems based on their data input. The platform of OvuFriend 1.0 is provided as a mobile app where an user can put the data related to her physical and mental states during a specific menstrual cycle, and the underlying algorithm of the app helps to get an analysis of the possibility of conceiving or not conceiving.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Personalized Healthcare Analysis for Womens’ Menstrual Health Disorders

Sosnowski¹,

Żuławińska²,

Dutta³

et al. 2022

Annals of Computer Science and Information Systems

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The paper presents an AI-based model which depending on the input of a woman for a finite number of menstrual cycles helps in determining the possible ovulation dates as well as possibility of some health risks e.g., Premenstrual Syndrome, Luteal Phase Defect etc. The architecture of the model consists of three layers, namely analyzing and detecting the features from a single cycle, analyzing cycle level concepts based on the analyzed features, and analyzing the user's health risks based on the cycle level concepts accumulated over a finitely many cycles.

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Generating Fuzzy Linguistic Summaries for Menstrual Cycles

Cited by 6 publications

References 15 publications

Network Device Workload Prediction: A Data Mining Challenge at Knowledge Pit

Network Device Workload Prediction: A Data Mining Challenge at Knowledge Pit

The voter's guide to the galaxy—a multiple-criteria fuzzy decision-support tool for voters and a fresh take on election survey methods

Artificial Intelligence in Personalized Healthcare Analysis for Womens’ Menstrual Health Disorders

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