Theoretical Study of Oscillator Neurons in Recurrent Neural Networks

Yi, Zhang; Zhang, Yi; Амари, Шун-ичи

doi:10.1109/tnnls.2018.2793911

Cited by 45 publications

(8 citation statements)

References 29 publications

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“…These methods require the specification of rules, which is unsuitable for ROP detection because of inadequate understanding of ROP symptomatology. Deep neural networks (DNNs) [ [13] , [14] , [15] ], a branch of artificial intelligence, have lately yielded impressive results, such as AlphaGO [ 16 ]. It avoids the aforementioned “feature engineering” by automatically learning latent features from big data, which is a large set of labeled data consisting of images and corresponding labels.…”

Section: Introductionmentioning

confidence: 99%

Automated retinopathy of prematurity screening using deep neural networks

et al. 2018

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Background Retinopathy of prematurity (ROP) is the leading cause of childhood blindness worldwide. Automated ROP detection system is urgent and it appears to be a safe, reliable, and cost-effective complement to human experts. Methods An automated ROP detection system called DeepROP was developed by using Deep Neural Networks (DNNs). ROP detection was divided into ROP identification and grading tasks. Two specific DNN models, i.e., Id-Net and Gr-Net, were designed for identification and grading tasks, respectively. To develop the DNNs, large-scale datasets of retinal fundus images were constructed by labeling the images of ROP screenings by clinical ophthalmologists. Findings On the test dataset, the Id-Net achieved a sensitivity of 96.62%(95%CI, 92.29%–98.89%) and a specificity of 99.32% (95%CI, 96.29%–9.98%) for ROP identification while the Gr-Net attained sensitivity and specificity values of 88.46% (95%CI, 96.29%–99.98%) and 92.31% (95%CI, 81.46%–97.86%), respectively, on the ROP grading task. On another 552 cases, the developed DNNs outperformed some human experts. In a clinical setting, the sensitivity and specificity values of DeepROP for ROP identification were 84.91% (95%CI, 76.65%–91.12%) and 96.90% (95%CI, 95.49%–97.96%), respectively, whereas the corresponding measures for ROP grading were 93.33%(95%CI, 68.05%–99.83%) and 73.63%(95%CI, 68.05%–99.83%), respectively. Interpretation We constructed large-scale ROP datasets with adequate clinical labels and proposed novel DNN models. The DNN models can directly learn ROP features from big data. The developed DeepROP is potential to be an efficient and effective system for automated ROP screening. Fund National Natural Science Foundation of China under Grant 61432012 and U1435213.

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Section: Introductionmentioning

confidence: 99%

Automated retinopathy of prematurity screening using deep neural networks

et al. 2018

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“…Dai Y. H. also expanded the narrative theory of literary therapy ritual in his doctoral thesis. Fry, Puop, and Ye Shuxian have a common understanding of the common language of narrative and can use plain words to express surging psychological activities (Zhang et al, 2018 ). In the discussion of literature and literary therapy for the disease, Zhang M. L. thinks that literary therapy is a kind of medical means and original function (Wang et al, 2020 ).…”

Section: Research Status Of Literary Therapymentioning

confidence: 99%

Correlation Analysis Between Japanese Literature and Psychotherapy Based on Diagnostic Equation Algorithm

Shen

Jiang

2022

Front. Psychol.

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Literary therapy theory provides a new field for contemporary literature research and is of great significance for maintaining the physical and mental health of modern people. The quantitative evaluation of psychotherapy effects in Japanese healing literature is a hot research topic at present. In this study, a text convolutional neural network (Text-CNN) was selected to extract psychological therapy features with different levels of granularity by using multiple convolutional kernels of different sizes. Bidirectional threshold regression neural network (BiGRU) can characterize the relationship between literature research and the psychotherapy effect. On the basis of the CNN-BilSTM model, a parallel hybrid network integrated with the attention mechanism was constructed to analyze the correlation between literature and psychotherapy. Through experimental verification, the model in this study further improves the accuracy of correlation classification and has strong adaptability.

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“…In light of the human brain, the RNN incorporates the procedure of artificial training and testing algorithm. Typically, the input layer, hidden layer and output layer are the structure of RNN (Chen et al 2006; Selva Santhose Kumar and Girirajkumar, 2015; Zhang et al 2018). At each time step with the beginnings updated, the time t must be discretized and it is noted (Fei and Lu, 2018).…”

Section: Economical Management Of Smart Grid Based On Hybrid Techniquementioning

confidence: 99%

Optimum utilization of grid connected hybrid renewable energy sources using hybrid algorithm

Muthusamy¹,

Meenakumari²

2020

Transactions of the Institute of Measurement and Control

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An optimal utilization of smart grid connected hybrid renewable energy sources is proposed in this paper. The hybrid technique is the combination of recurrent neural network and adaptive whale optimization algorithm plus tabu search, called AWOTS. The main objective is the RES optimum operation for decreasing the electricity production cost by hourly day-ahead and real time scheduling. Here, the load demands are predicted using AWOTS to develop the correct control signals based on power difference between source and load side. Adaptive whale optimization algorithm searching behaviour is adjusted by tabu search. The proposed technique is executed in the MATLAB/Simulink working platform. To test the performance of the proposed method, the load demand for the 24-hour time period is demonstrated. By then the power generated in the sources, such as photovoltaic, wind turbine, micro turbine and battery by the proposed technique, is analyzed and compared with existing techniques, such as genetic algorithm, particle swarm optimization and whale optimization algorithm. Furthermore, the state of charge of the battery for the 24-hour period is compared with existing techniques. Likewise, the cost of the system is compared and error in the sources also compared. The comparison results affirm that the proposed technique has less computational time (35.001703) than the existing techniques. Moreover, the proposed method is cost-effective power production of smart grid and effective utilization of renewable energy sources without wasting the available energy.

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Theoretical Study of Oscillator Neurons in Recurrent Neural Networks

Cited by 45 publications

References 29 publications

Automated retinopathy of prematurity screening using deep neural networks

Automated retinopathy of prematurity screening using deep neural networks

Correlation Analysis Between Japanese Literature and Psychotherapy Based on Diagnostic Equation Algorithm

Optimum utilization of grid connected hybrid renewable energy sources using hybrid algorithm

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