Deep Fuzzy Neural Networks for Biomarker Selection for Accurate Cancer Detection

Mudiyanselage, Thosini Bamunu; Xiao, Xueli; Zhang, Yanqing; Pan, Yi

doi:10.1109/tfuzz.2019.2958295

Cited by 36 publications

(15 citation statements)

References 16 publications

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“…Also, fuzzy logic systems have been used in many applications, such as wireless network routing [25]. We will introduce fuzzy deep learning into our learning and prediction models in the future [26] since many news items are fuzzy in terms of their positive or negative impacts. 6.…”

Section: Future Researchmentioning

confidence: 99%

A novel ensemble deep learning model for stock prediction based on stock prices and news

Pan

2021

Int J Data Sci Anal

Self Cite

105

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In recent years, machine learning and deep learning have become popular methods for financial data analysis, including financial textual data, numerical data, and graphical data. One of the most popular and complex deep learning in finance topics is future stock prediction. The difficulty that causes the future stock forecast is that there are too many different factors that affect the amplitude and frequency of the rise and fall of stocks at the same time. Some of the company-specific factors that can affect the share price like news releases on earnings and profits, future estimated earnings, the announcement of dividends, introduction of a new product or a product recall, secure a new large contract, employee layoffs, a major change of management, anticipated takeover or merger, and accounting errors or scandals. Furthermore, these factors are only company factors, and other factors affect the future trend of stocks, such as industry performance, investor sentiment, and economic factors. This paper proposes a novel deep learning approach to predict future stock movement. The model employs a blending ensemble learning method to combine two recurrent neural networks, followed by a fully connected neural network. In our research, we use the S&P 500 Index as our test case. Our experiments show that our blending ensemble deep learning model outperforms the best existing prediction model substantially using the same dataset, reducing the mean-squared error from 438.94 to 186.32, a 57.55% reduction, increasing precision rate by 40%, recall by 50%, F1-score by 44.78%, and movement direction accuracy by 33.34%, respectively. The purpose of this work is to explain our design philosophy and show that ensemble deep learning technologies can truly predict future stock price trends more effectively and can better assist investors in making the right investment decision than other traditional methods.

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Section: Future Researchmentioning

confidence: 99%

A novel ensemble deep learning model for stock prediction based on stock prices and news

Pan

2021

Int J Data Sci Anal

Self Cite

105

View full text Add to dashboard Cite

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“…Therefore, the FDNN can be considered as an ensemble of several ANFISs. Mudiyanselage et al [29] constructed a FDNN to detect cancers. The FDNN was a deep ANFIS with seven layers.…”

Section: Past Workmentioning

confidence: 99%

A Hybrid Big Data Analytics and Industry 4.0 Approach for Projecting The Cycle Time Range

Chen¹,

Wang²

2021

Preprint

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To enhance the effectiveness of projecting the cycle time range of a job in a factory, a hybrid big data analytics and Industry 4.0 (BD-I4) approach is proposed in this study. As a joint application of big data analytics and Industry 4.0, the BD-I4 approach is distinct from existing methods in this field. In the BD-I4 approach, first, each expert constructs a fuzzy deep neural network (FDNN) to project the cycle time range of a job, which is an application of big data analytics (i.e., deep learning). Subsequently, fuzzy weighted intersection (FWI) is applied to aggregate the cycle time ranges projected by experts to consider their unequal authority levels, which is an application of Industry 4.0 (i.e., artificial intelligence). After applying the BD-I4 approach to a real case, the experimental results showed that the proposed methodology improved the projection precision by up to 72%. This result implied that instead of relying on a single expert, seeking the collaboration among multiple experts may be more effective and efficient.

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“…Combination of fuzzy logic-based ensemble with multi neural networks belongs to one of useful application of fuzzy neural networks. Moreover, there exist numerous applications with the advantage of FNNs: force control of robot manipulators [14], biomedical computing [15], road lane prediction [16], and so on [17]- [20]. Therefore, FNNs have been received a lot of attentions during last decades.…”

Section: Introductionmentioning

confidence: 99%

Affine Memory Control for Synchronization of Delayed Fuzzy Neural Networks

et al. 2021

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This paper deals with the synchronization of fuzzy neural networks (FNNs) with time-varying delays. FNNs are more complicated form of neural networks incorporated with fuzzy logics, which provide more powerful performances. Especially, the problem of delayed FNNs's synchronization is of importance in the existence of the network communication. For the synchronization of FNNs with time-varying delays, a novel form of control structure is proposed employing affinely transformed membership functions with memory element. In accordance with affine memory control, appropriate Lyapunov-Krasovskii functional is chosen to design control gain, guaranteeing stability of the systems with delays. Exploiting the more general type of control attributed by affine transformation and memory-type, a novel criterion is derived in forms of linear matrix inequalities (LMIs). As a results, the effectiveness of the proposed control is shown through numerical examples by comparisons with others.

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Deep Fuzzy Neural Networks for Biomarker Selection for Accurate Cancer Detection

Cited by 36 publications

References 16 publications

A novel ensemble deep learning model for stock prediction based on stock prices and news

A novel ensemble deep learning model for stock prediction based on stock prices and news

A Hybrid Big Data Analytics and Industry 4.0 Approach for Projecting The Cycle Time Range

Affine Memory Control for Synchronization of Delayed Fuzzy Neural Networks

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