Spiking Neural P Systems With Colored Spikes

Song, Tao; Rodríguez‐Patón, Alfonso; Zheng, Pan; Zeng, Xiangxiang

doi:10.1109/tcds.2017.2785332

Cited by 119 publications

(50 citation statements)

References 57 publications

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“…This should promote further development of DNA storage, using our successful improvement of this algorithm. In addition, the constructed reliable DNA coding set can also be applied to other fields of DNA, such as DNA neural network computing model [56], DNA coding image encryption [57], DNA parallel computing model [58,59], Using the DNA storage architecture to make embedded storage materials [60], etc., which has a wide range of application value.…”

Section: Resultsmentioning

confidence: 99%

An Intelligent Optimization Algorithm for Constructing a DNA Storage Code: NOL-HHO

Yin

Cao

et al. 2020

IJMS

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The high density, large capacity, and long-term stability of DNA molecules make them an emerging storage medium that is especially suitable for the long-term storage of large datasets. The DNA sequences used in storage need to consider relevant constraints to avoid nonspecific hybridization reactions, such as the No-runlength constraint, GC-content, and the Hamming distance. In this work, a new nonlinear control parameter strategy and a random opposition-based learning strategy were used to improve the Harris hawks optimization algorithm (for the improved algorithm NOL-HHO) in order to prevent it from falling into local optima. Experimental testing was performed on 23 widely used benchmark functions, and the proposed algorithm was used to obtain better coding lower bounds for DNA storage. The results show that our algorithm can better maintain a smooth transition between exploration and exploitation and has stronger global exploration capabilities as compared with other algorithms. At the same time, the improvement of the lower bound directly affects the storage capacity and code rate, which promotes the further development of DNA storage technology.

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Section: Resultsmentioning

confidence: 99%

An Intelligent Optimization Algorithm for Constructing a DNA Storage Code: NOL-HHO

Yin

Cao

et al. 2020

IJMS

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“…Next, the segments enter the binary classifier trained by temporal segment networks [19]. The binary classifier scores the similarity between the regional candidates and the standard actions, resulting in a one-dimensional actionness [13] score waveform. The fractional waveform is sent to the RTAG network.…”

Section: Methods: Human Activities Recognition Frameworkmentioning

confidence: 99%

“…To make the classification more accurate, in this paper, we used a refined temporal actionness [13] grouping (RTAG) to improve the action classification by setting the threshold of the action score. The input was an unprocessed monitoring video of an offshore oil production platform entered in an operational classifier trained by a deep CNN network to output an operational score.…”

Section: Introductionmentioning

confidence: 99%

Human Activity Recognition and Location Based on Temporal Analysis

Ding

Gong

et al. 2018

Journal of Engineering

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Current methods of human activity recognition face many challenges, such as the need for multiple sensors, poor implementation, unreliable real-time performance, and lack of temporal location. In this research, we developed a method for recognizing and locating human activities based on temporal action recognition. For this work, we used a multilayer convolutional neural network (CNN) to extract features. In addition, we used refined actionness grouping to generate precise region proposals. Then, we classified the candidate regions by employing an activity classifier based on a structured segmented network and a cascade design for end-to-end training. Compared with previous methods of action classification, the proposed method adds the time boundary and effectively improves the detection accuracy. To test this method empirically, we conducted experiments utilizing surveillance video of an offshore oil production plant. Three activities were recognized and located in the untrimmed long video: standing, walking, and falling. The accuracy of the results proved the effectiveness and real-time performance of the proposed method, demonstrating that this approach has great potential for practical application.

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“…Similarly, the model obtained by learning the optimized features with SVM was highly powerful. The method can be applied to computational intelligence techniques, such as neural networks Song et al, 2018;Cabarle et al, 2019;Hong et al, 2019;Zhong et al, 2019;Zhou et al, 2019b;Wang et al, 2019b), evolutionary algorithms Zeng et al, 2019b), and unsupervised learning (Zeng et al, 2018b;, in future research.…”

Section: Analysis Of the Algorithmsmentioning

confidence: 99%

6mA-RicePred: A Method for Identifying DNA N6-Methyladenine Sites in the Rice Genome Based on Feature Fusion

et al. 2020

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Motivation: The biological function of N 6-methyladenine DNA (6mA) in plants is largely unknown. Rice is one of the most important crops worldwide and is a model species for molecular and genetic studies. There are few methods for 6mA site recognition in the rice genome, and an effective computational method is needed. Results: In this paper, we propose a new computational method called 6mA-Pred to identify 6mA sites in the rice genome. 6mA-Pred employs a feature fusion method to combine advantageous features from other methods and thus obtain a new feature to identify 6mA sites. This method achieved an accuracy of 87.27% in the identification of 6mA sites with 10-fold cross-validation and achieved an accuracy of 85.6% in independent test sets.

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Spiking Neural P Systems With Colored Spikes

Cited by 119 publications

References 57 publications

An Intelligent Optimization Algorithm for Constructing a DNA Storage Code: NOL-HHO

An Intelligent Optimization Algorithm for Constructing a DNA Storage Code: NOL-HHO

Human Activity Recognition and Location Based on Temporal Analysis

6mA-RicePred: A Method for Identifying DNA N6-Methyladenine Sites in the Rice Genome Based on Feature Fusion

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