ASRNN: A recurrent neural network with an attention model for sequence labeling

Lin, Jerry Chun‐Wei; Shao, Yinan; Djenouri, Youcef; Yun, Unil

doi:10.1016/j.knosys.2020.106548

Cited by 187 publications

(75 citation statements)

References 17 publications

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“…Equations (1) and (2) show the encryption criteria on the plaintext using the private key. The detailed mathematical model is provided below:…”

Section: (P T K) → Ctmentioning

confidence: 99%

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Enhancing Security of Health Information Using Modular Encryption Standard in Mobile Cloud Computing

et al. 2021

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“…Equations (1) and (2) show the encryption criteria on the plaintext using the private key. The detailed mathematical model is provided below:…”

Section: (P T K) → Ctmentioning

confidence: 99%

“…As computing technologies have rapidly growth [1,2], cloud computing has earned a lot of popularity in recent years through applications, services, storage, and computing over the Internet. It is commonly utilized in many domains like Medical Science, Agriculture, Business, Information Technology, and many others.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Security of Health Information Using Modular Encryption Standard in Mobile Cloud Computing

et al. 2021

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“…The experimental algorithms have been applied on a Macbook computer with Intel i5 2.7GHz Processor, implemented by Java and performed on macOS Mojave with 8 GB Ram. There are three real-world databases called chess 1 , mushroom 2 and foodmart 3 , and one article database T10I4D100K generated by IBM database generator 4 used in these experiments. "chess" and "mushroom" were provided by Roberto Bayardo from UCI datasets.…”

Section: Algorithm 2 the Dynamic Minimum Support Thresholdmentioning

confidence: 99%

“…Data mining and machine learning techniques [1][2][3] have been utilized in the last few decades in various domains and applications, to retrieve very large-scale useful and meaningful knowledge. Apriori 4 is called the fundamental algorithm of mining required patterns.…”

Section: Introductionmentioning

confidence: 99%

An evolutionary computation‐based privacy‐preserving data mining model under a multithreshold constraint

Wu¹,

Srivastava

Yun

et al. 2021

Trans Emerging Tel Tech

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Privacy-preserving data mining (PPDM) is a popular research topic in the data mining field. For individual information protection, it is vital to protect sensitive information during data mining procedures. Furthermore, it is also a serious offense to spill sensitive private knowledge. Recently, many PPDM data mining algorithms have been proposed to conceal sensitive items in a given database to disclose high-frequency items. These recent methods have already proven to be excellent in protecting confidential information and maintaining the integrity of the input database. All prior techniques, however, ignored a crucial problem in setting minimum support thresholds. If a sensitive itemset includes more items, it will cause it the become more likely to be found. Before performing mining processes, a fixed value of the minimum support threshold will be set. In this paper, a new concept of minimal support for solving this issue is proposed. In compliance with a given threshold function, the proposed approach would set a tighter threshold for an object containing several items. The results of the experiments show the performance of the traditional Greedy PPDM approach, Genetic algorithm (GA)-based PPDM approaches, and the proposed particle swarm optimization-based algorithm with the new minimal support function. The results show that the proposed method performs similarly to conventional algorithms and offers higher protection than previous methods.

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“…With the rapid development of computing power and parallel computing, numerous layers of NN are then formalized, which is known as deep-learning [15,16]. Common deep-learning architectures include recurrent neural networks (RNN) [17,18] and conventional neural networks (CNN) [19,20]. Financial data comprises timeseries information related to prices, i.e., opening, highest, lowest, and closing prices (abbreviated as OHLC prices).…”

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confidence: 99%

Portfolio management system in equity market neutral using reinforcement learning

Syu

Lin

et al. 2021

Appl Intell

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Portfolio management involves position sizing and resource allocation. Traditional and generic portfolio strategies require forecasting of future stock prices as model inputs, which is not a trivial task since those values are difficult to obtain in the real-world applications. To overcome the above limitations and provide a better solution for portfolio management, we developed a Portfolio Management System (PMS) using reinforcement learning with two neural networks (CNN and RNN). A novel reward function involving Sharpe ratios is also proposed to evaluate the performance of the developed systems. Experimental results indicate that the PMS with the Sharpe ratio reward function exhibits outstanding performance, increasing return by 39.0% and decreasing drawdown by 13.7% on average compared to the reward function of trading return. In addition, the proposed model is more suitable for the construction of a reinforcement learning portfolio, but has 1.98 times more drawdown risk than the . Among the conducted datasets, the PMS outperforms the benchmark strategies in TW50 and traditional stocks, but is inferior to a benchmark strategy in the financial dataset. The PMS is profitable, effective, and offers lower investment risk among almost all datasets. The novel reward function involving the Sharpe ratio enhances performance, and well supports resource-allocation for empirical stock trading.

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ASRNN: A recurrent neural network with an attention model for sequence labeling

Cited by 187 publications

References 17 publications

Enhancing Security of Health Information Using Modular Encryption Standard in Mobile Cloud Computing

Enhancing Security of Health Information Using Modular Encryption Standard in Mobile Cloud Computing

An evolutionary computation‐based privacy‐preserving data mining model under a multithreshold constraint

Portfolio management system in equity market neutral using reinforcement learning

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