2015 International Conference on Information and Digital Technologies 2015
DOI: 10.1109/dt.2015.7222996
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Individual prediction of the hypertensive patient condition based on computational intelligence

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Cited by 8 publications
(5 citation statements)
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“…In addition, we can use such methods for segmentation of images [20], improvement of blurred images [21], processing of x-rays [22] and low contrast [23,24]. Papers [25][26][27][28][29][30] present methods for modeling complex dependences based on computational intelligence [25], associative rules [26], negative selection [27], neural-fuzzy networks [28], agent technologies [29], stochastic search [30]. The methods proposed in [25][26][27][28][29][30] make it possible to process data presented in various formats efficiently: usual samples of multidimensional data [25,[28][29][30], transaction databases [26], samples containing missing values [26,27].…”
Section: Literature Review and Problem Statementmentioning
confidence: 99%
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“…In addition, we can use such methods for segmentation of images [20], improvement of blurred images [21], processing of x-rays [22] and low contrast [23,24]. Papers [25][26][27][28][29][30] present methods for modeling complex dependences based on computational intelligence [25], associative rules [26], negative selection [27], neural-fuzzy networks [28], agent technologies [29], stochastic search [30]. The methods proposed in [25][26][27][28][29][30] make it possible to process data presented in various formats efficiently: usual samples of multidimensional data [25,[28][29][30], transaction databases [26], samples containing missing values [26,27].…”
Section: Literature Review and Problem Statementmentioning
confidence: 99%
“…Papers [25][26][27][28][29][30] present methods for modeling complex dependences based on computational intelligence [25], associative rules [26], negative selection [27], neural-fuzzy networks [28], agent technologies [29], stochastic search [30]. The methods proposed in [25][26][27][28][29][30] make it possible to process data presented in various formats efficiently: usual samples of multidimensional data [25,[28][29][30], transaction databases [26], samples containing missing values [26,27]. However, the methods proposed in papers [25][26][27][28][29][30] do not allow solving the problems associated with processing data presented in the form of time series effectively.…”
Section: Literature Review and Problem Statementmentioning
confidence: 99%
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“…Shevchuk, V. P. Metrologiya intellektual'nyh izmeritel'nyh sistem [Text]: monografiya / V. P. Shevchuk, V. I. Kaplya [5][6][7], Big Data [8], Artificial Intelligence [9], Internet of Things [10]. For instance, the synergy of SDN and Neural Networks technologies can be expedient in the context of parallel computing systems' resources allocation [11,12].…”
Section: Introductionmentioning
confidence: 99%