Fuzzy Logic

Author(s):  
Siddhartha Bhattacharyya ◽  
Paramartha Dutta

The field of industrial informatics has emerged as one of the key disciplines for the purpose of intelligent management and dissemination of information in today’s world. With the advent of newer technical know-how, the subject of informative intelligence has assumed increasing importance in the industrial arena, thanks to the evolution of data intensive industry. Real world data exhibit varied amount of unquantifiable uncertainty in the information content. Conventional logic is often unable to explain the associated uncertainty and imprecision therein due to the principles of finiteness of observations and quantifying propositions employed. Fuzzy sets and fuzzy logic provide a logical framework for description of the varied amount of ambiguity, uncertainty and imprecision exhibited in real world data under consideration. The resultant fuzzy inference engine and the fuzzy logic control theory supplement the power of the framework in design of robust failsafe real life systems.

2018 ◽  
Vol 64 (4) ◽  
pp. 357-366
Author(s):  
Martin Haluzík ◽  
Alena Adamíková ◽  
Milan Běhunčík ◽  
Marek Macko ◽  
Radka Štěpánová

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hervé Ghesquières ◽  
Cédric Rossi ◽  
Fanny Cherblanc ◽  
Sandra Le Guyader-Peyrou ◽  
Fontanet Bijou ◽  
...  

Abstract Background Age-adjusted lymphoma incidence rates continue to rise in France since the early 80’s, although rates have slowed since 2010 and vary across subtypes. Recent improvements in patient survival in major lymphoma subtypes at population level raise new questions about patient outcomes (i.e. quality of life, long-term sequelae). Epidemiological studies have investigated factors related to lymphoma risk, but few have addressed the extent to which socioeconomic status, social institutional context (i.e. healthcare system), social relationships, environmental context (exposures), individual behaviours (lifestyle) or genetic determinants influence lymphoma outcomes, especially in the general population. Moreover, the knowledge of the disease behaviour mainly obtained from clinical trials data is partly biased because of patient selection. Methods The REALYSA (“REal world dAta in LYmphoma and Survival in Adults”) study is a real-life multicentric cohort set up in French areas covered by population-based cancer registries to study the prognostic value of epidemiological, clinical and biological factors with a prospective 9-year follow-up. We aim to include 6000 patients over 4 to 5 years. Adult patients without lymphoma history and newly diagnosed with one of the following 7 lymphoma subtypes (diffuse large B-cell, follicular, marginal zone, mantle cell, Burkitt, Hodgkin, mature T-cell) are invited to participate during a medical consultation with their hematologist. Exclusion criteria are: having already received anti-lymphoma treatment (except pre-phase) and having a documented HIV infection. Patients are treated according to the standard practice in their center. Clinical data, including treatment received, are extracted from patients’ medical records. Patients’ risk factors exposures and other epidemiological data are obtained at baseline by filling out a questionnaire during an interview led by a clinical research assistant. Biological samples are collected at baseline and during treatment. A virtual tumor biobank is constituted for baseline tumor samples. Follow-up data, both clinical and epidemiological, are collected every 6 months in the first 3 years and every year thereafter. Discussion This cohort constitutes an innovative platform for clinical, biological, epidemiological and socio-economic research projects and provides an opportunity to improve knowledge on factors associated to outcome of lymphoma patients in real life. Trial registration 2018-A01332–53, ClinicalTrials.gov identifier: NCT03869619.


2014 ◽  
Vol 971-973 ◽  
pp. 1633-1636
Author(s):  
Hai Lin

In the last a few decodes, there has been a lot of interest on systems with large amount of data. In some scenarios, people want to take advantage of data history and predict new coming data. There have been a lot models used for prediction. In this paper, we develop a new model for data prediction. The new model is based on fuzzy inference. We do some experiments on real-world data and show that this new model is appropriate for data prediction and can produce desirable results.


2018 ◽  
Vol 13 (1-2) ◽  
pp. 37-37
Author(s):  
Đeiti Prvulović ◽  
Martina Menegoni ◽  
Božo Vujeva ◽  
Krešimir Gabaldo ◽  
Irzal Hadžibegović ◽  
...  

2016 ◽  
Vol 22 ◽  
pp. 219
Author(s):  
Roberto Salvatori ◽  
Olga Gambetti ◽  
Whitney Woodmansee ◽  
David Cox ◽  
Beloo Mirakhur ◽  
...  

2020 ◽  
Author(s):  
Jersy Cardenas ◽  
Gomez Nancy Sanchez ◽  
Sierra Poyatos Roberto Miguel ◽  
Luca Bogdana Luiza ◽  
Mostoles Naiara Modroño ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document