Intuitionistic high-order fuzzy time series forecasting method based on pi-sigma artificial neural networks trained by artificial bee colony

Eğrioğlu, Erol; Yolcu, Ufuk; Baş, Eren

doi:10.1007/s41066-018-00143-5

Cited by 58 publications

(23 citation statements)

References 70 publications

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“…The research work [39] is an amendment in the traditional fuzzy inference system wherein overall computational model performs type-1, interval type-2, and general type-2 fuzzy computation by adopting genetic algorithm as an optimization technique. A contemporary intuitionistic fuzzy time series model is created for prediction purpose [40]. In the proposed model, the intuitionistic fuzzy cmeans technique is used for fuzzification and pi-sigma neural network is utilized for defining fuzzy relations.…”

Section: Related Workmentioning

confidence: 99%

COVID-19 prediction using AI analytics for South Korea

Sinha

Rathi

2021

Appl Intell

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The severe spread of the COVID-19 pandemic has created a situation of public health emergency and global awareness. In our research, we analyzed the demographical factors affecting the global pandemic spread along with the features that lead to death due to the infection. Modeling results stipulate that the mortality rate increase as the age increase and it is found that most of the death cases belong to the age group 60-80. Cluster-based analysis of age groups is also conducted to analyze the maximum targeted age-groups. An association between positive COVID-19 cases and deceased cases are also presented, with the impact on male and female death cases due to corona. Additionally, we have also presented an artificial intelligence-based statistical approach to predict the survival chances of corona infected people in South Korea with the analysis of the impact on the exploratory factors, including age-groups, gender, temporal evolution, etc. To analyze the coronavirus cases, we applied machine learning with hyperparameters tuning and deep learning models with an autoencoder-based approach for estimating the influence of the disparate features on the spread of the disease and predict the survival possibilities of the quarantined patients in isolation. The model calibrated in the study is based on positive corona infection cases and presents the analysis over different aspects that proven to be impactful to analyze the temporal trends in the current situation along with the exploration of deceased cases due to coronavirus. Analysis delineates key points in the outbreak spreading, indicating that the models driven by machine intelligence and deep learning can be effective in providing a quantitative view of the epidemical outbreak.

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Section: Related Workmentioning

confidence: 99%

COVID-19 prediction using AI analytics for South Korea

Sinha

Rathi

2021

Appl Intell

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“…1 Additionally, many relevant articles considering convolutional neural networks, long short-term memory, generative adversarial networks, etc., for trading, are accessible here. Furthermore, fuzzy inference systems may emerge in the near future [ 13 , 33 ].…”

Section: Introductionmentioning

confidence: 99%

Two robust long short-term memory frameworks for trading stocks

2021

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This paper aims to find a superior strategy for the daily trading on a portfolio of stocks for which traditional trading strategies perform poorly due to the low frequency of new information. The experimental work is divided into a set of traditional trading strategies and a set of long short-term memory networks. The networks incorporate general and specific trading patterns, where the former takes into account the universal decision factors for trading across many stocks, while the latter takes into account stock-specific decision factors. Our research shows that both long short-term memory networks, regardless of whether they are based on universal or stock-specific decision factors, significantly outperform traditional trading strategies. Interestingly, however, on average neither has the edge compared to the other, thus remaining ambivalent as to whether universality or specificality is to be preferred when it comes to designing long short-term memory networks for optimal trading.

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“…A granularity of granular computing always includes 3 parts: granules, granule features and granule structure, where the granule features are a group of common attributes shared by all the granules, and the granule structure describes the structural relationship among all the granules. Granular computing has now been widely applied in image processing [3], machine learning [4], complex problem solving [5], pattern recognition [6], intelligent control [7], artificial neural network [8], knowledge acquisition, linguistic dynamic systems [9], and so on.…”

Section: Introductionmentioning

confidence: 99%

A Granular Computing Based Classification Method From Algebraic Granule Structure

et al. 2021

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Classification, as one of the main task of machine learning, corresponds to the core work of granular computing, namely granulation. Most of granular computing models and related classification methods are uniquely classifying by granule features, but not considering granule structure, especially in information area with widespread application of algebraic structure. In this paper, we propose a granular computing based classification method from algebraic granule structure. First of all, to pre-process the original data in the algebraic granule structure area, we formulate the algebraic structure based granularity with granule structure of an algebraic binary operator. Then, we propose a novel granular computing based classification method as well as related classifying algorithm with congruence partitioning granules and homomorphicly projecting granule structure. Finally, compared with tolerance neighborhood model and quotient space model, we prove that the proposed classification method is much more effective and robust while classifying the algebraic structure based granularity. The proposed granular computing based classification method provides an approach for classifying algebraic structure based granularity, and combines granular computing theory and classification theory of machine learning.

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Intuitionistic high-order fuzzy time series forecasting method based on pi-sigma artificial neural networks trained by artificial bee colony

Cited by 58 publications

References 70 publications

COVID-19 prediction using AI analytics for South Korea

COVID-19 prediction using AI analytics for South Korea

Two robust long short-term memory frameworks for trading stocks

A Granular Computing Based Classification Method From Algebraic Granule Structure

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