PSO-based optimized CNN for Hindi ASR

Passricha, Vishal; Aggarwal, Rajesh

doi:10.1007/s10772-019-09652-3

Cited by 32 publications

(14 citation statements)

References 27 publications

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“…It is difficult for limited teachers to take care of every student Meanwhile, in addition, it is also different in students' personalities, way of thinking, and ability to understand problems. e same teacher's teaching methods are usually relatively fixed, which causes differences in the understanding and mastery of classroom content by different students [9,10]. ese differences continue to accumulate over time, and the problems of students continue to accumulate, and the pressure of learning will be greater, which will eventually lead to learning fatigue.…”

Section: Analysis Of Traditional Teaching Methods By Questionmentioning

confidence: 99%

Intelligent Question Answering System by Deep Convolutional Neural Network in Finance and Economics Teaching

Chen

Zhong

Zhu

2022

Computational Intelligence and Neuroscience

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The question answering link in the traditional teaching method is analyzed to optimize the shortcomings and deficiencies of the existing question-and-answer (Q&A) machines and solve the problems of financial students’ difficulty in answering questions. Firstly, the difficulties and needs of students in answering questions are understood. Secondly, the traditional algorithm principle by the Q&A system is introduced and analyzed, and the problems and defects existing in the traditional Q&A system are summarized. On this basis, deep learning algorithms are introduced, the long short-term memory (LSTM) neural network and convolutional neural network (CNN) are combined, and a Q&A system by long short-term memory-convolutional neural network (LSTM-CNN) is proposed, the gated recurrent unit (GRU) attention mechanism is introduced, and the algorithm is optimized. Finally, the design experiments to determine the nearest parameters of the neural network algorithm and verify the effectiveness of the algorithm are carried out. The results show that for the LSTM-CNN, the effect is the best when dropout = 0.5. After introducing the attention mechanism optimization, the effect is the best when dropout = 0.6. The test results of the comparison between the recommended algorithm and the traditional Q&A model algorithm show that the LSTM-CNN algorithm maintains the ability of the LSTM algorithm to arrange information in chronological order. After being combined with the CNN algorithm, the language features of the sentence can be extracted more deeply, the semantic feature information can be captured more accurately from the sentence, and better performance can be maintained when processing more complex sentences. The introduction of a BANet can simultaneously obtain the past and future information so that the algorithm can more appropriately combine it with the context to retrieve the semantic features, and the effectiveness of the model has been greatly improved. The research results have played an optimizing role in improving the Q&A effect of finance and economics teaching and provided a reference for research in related fields.

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Section: Analysis Of Traditional Teaching Methods By Questionmentioning

confidence: 99%

Intelligent Question Answering System by Deep Convolutional Neural Network in Finance and Economics Teaching

Chen

Zhong

Zhu

2022

Computational Intelligence and Neuroscience

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“…More and more ENAS algorithms are proposed recently. Vast neural architectures automatically designed by these ENAS algorithms have surpassed manual designs in many tasks [34], [49]. Despite the positive results of the existing ENAS algorithms, there are still some challenges and issues which need to be addressed, such as the fair comparison issue and the efficient evaluation mentioned above.…”

Section: Algorithmmentioning

confidence: 99%

BenchENAS: A Benchmarking Platform for Evolutionary Neural Architecture Search

Xiangning¹,

Liu²,

Sun³

et al. 2021

Preprint

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Neural architecture search (NAS), which automatically designs the architectures of deep neural networks, has achieved breakthrough success over many applications in the past few years. Among different classes of NAS methods, evolutionary computation based NAS (ENAS) methods have recently gained much attention. Unfortunately, the issues of fair comparisons and efficient evaluations have hindered the development of ENAS. The current benchmark architecture datasets designed for fair comparisons only provide the datasets, not the ENAS algorithms or the platform to run the algorithms. The existing efficient evaluation methods are either not suitable for the population-based ENAS algorithm or are too complex to use. This paper develops a platform named BenchENAS to address these issues. BenchENAS aims to achieve fair comparisons by running different algorithms in the same environment and with the same settings. To achieve efficient evaluation in a common lab environment, BenchENAS designs a parallel component and a cache component with high maintainability. Furthermore, BenchENAS is easy to install and highly configurable and modular, which brings benefits in good usability and easy extensibility. The paper conducts efficient comparison experiments on eight ENAS algorithms with high GPU utilization on this platform. The experiments validate that the fair comparison issue does exist, and BenchENAS can alleviate this issue. A website has been built to promote BenchENAS at https://benchenas.com, where interested researchers can obtain the source code and document of BenchENAS for free.

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“…Another way is reducing the population dynamically. For instance, Fan et al [122] use the (µ + λ) evolution Weight inheritance [19], [36], [38], [40], [55], [56], [62], [67], [77], [78], [84], [86], [88], [105], [109], [111], [120], [121], [125], [132] Early stopping policy [21], [32], [36], [42], [43], [62], [65], [71], [79], [80], [83], [86], [86], [95], [98], [103], [108], [112], [114], [125], [145] Reduced training set [46], [84], [124], [129], [188] Reduced population [98],…”

Section: Shorten the Evaluation Timementioning

confidence: 99%

A Survey on Evolutionary Neural Architecture Search

Liu,

Sun,

Xue

et al. 2020

Preprint

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Deep Neural Networks (DNNs) have achieved great success in many applications, such as image classification, natural language processing and speech recognition. The architectures of DNNs have been proved to play a crucial role in its performance. However, designing architectures for different tasks is a difficult and time-consuming process of trial and error. Neural Architecture Search (NAS), which received great attention in recent years, can design the architecture automatically. Among different kinds of NAS methods, Evolutionary Computation (EC) based NAS methods have recently gained much attention and success. Unfortunately, there is not a comprehensive summary of the EC-based methods. This paper reviews 100+ papers of EC-based NAS methods in light of the common process. Four steps of the process have been covered in this paper including population initialization, population operators, evaluation and selection. Furthermore, current challenges and issues are also discussed to identify future research in this field.

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PSO-based optimized CNN for Hindi ASR

Cited by 32 publications

References 27 publications

Intelligent Question Answering System by Deep Convolutional Neural Network in Finance and Economics Teaching

Intelligent Question Answering System by Deep Convolutional Neural Network in Finance and Economics Teaching

BenchENAS: A Benchmarking Platform for Evolutionary Neural Architecture Search

A Survey on Evolutionary Neural Architecture Search

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