An Electronic Component Recognition Algorithm Based on Deep Learning with a Faster SqueezeNet

Xu, Yuanyuan; Yang, Genke; Luo, Jiliang; He, Jianan

doi:10.1155/2020/2940286

Cited by 26 publications

(14 citation statements)

References 27 publications

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“…Additionally, CNN could be trained end to end for the selection and extraction of feature images and, finally, could be utilized for predicting or classifying the image (Muhammad et al 2021). Since the number of parameters for VGGNet and AlexNet is increasing, the SqueezeNet network method was projected to have a low number of parameters when maintaining accuracy (Xu et al 2020).…”

Section: Network Architecturementioning

confidence: 99%

RETRACTED ARTICLE: Cognitive computing-based COVID-19 detection on Internet of things-enabled edge computing environment

Lydia

Anupama

Beno³

et al. 2021

Soft Comput

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Section: Network Architecturementioning

confidence: 99%

RETRACTED ARTICLE: Cognitive computing-based COVID-19 detection on Internet of things-enabled edge computing environment

Lydia

Anupama

Beno³

et al. 2021

Soft Comput

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“…. , l − 1, and H l concatenates multiple inputs [16]. Without extremely enhancing the number of network variables, the efficiency of the network has improved from initial phases; simultaneously, some two-layer network is directly connected data.…”

Section: Feature Extraction: Optimal Faster Squeezenet Modelmentioning

confidence: 99%

“…x t+1 i,j = x t i,j + x t k,j − x t i,j × FL × rand (0, 1), (16) where FL (FL ∈ [0, 2]) denotes the scrounger who will follow the producer in searching for food. The BSA approach develops an FF for attaining enhanced classifier efficiency.…”

mentioning

confidence: 99%

Modeling of Hyperparameter Tuned Deep Learning Model for Automated Image Captioning

et al. 2022

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Image processing remains a hot research topic among research communities due to its applicability in several areas. An important application of image processing is the automatic image captioning technique, which intends to generate a proper description of an image in a natural language automated. Image captioning is a recently developed hot research topic, and it started to receive significant attention in the field of computer vision and natural language processing (NLP). Since image captioning is considered a challenging task, the recently developed deep learning (DL) models have attained significant performance with increased complexity and computational cost. Keeping these issues in mind, in this paper, a novel hyperparameter tuned DL for automated image captioning (HPTDL-AIC) technique is proposed. The HPTDL-AIC technique encompasses two major parts, namely encoder and decoder. The encoder part utilizes Faster SqueezNet with the RMSProp model to generate an effective depiction of the input image via insertion into a predefined length vector. At the same time, the decoder unit employs a bird swarm algorithm (BSA) with long short-term memory (LSTM) model to concentrate on the generation of description sentences. The design of RMSProp and BSA for the hyperparameter tuning process of the Faster SqueezeNet and LSTM models for image captioning shows the novelty of the work, which helps to accomplish enhanced image captioning performance. The experimental validation of the HPTDL-AIC technique is carried out against two benchmark datasets, and the extensive comparative study pointed out the improved performance of the HPTDL-AIC technique over recent approaches.

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“…Among the deep learning algorithms, the bidirectional gated recurrent network (BiGRU) is a type of the bidirectional recurrent neural network model that can fully express the relationship between the current output of a sequence and previous information [17]. However, the characteristic dimension of the amino acid time series is too high, and the BiGRU is directly used to process the amino acid sequence parameters, which results in low efficiency.…”

Section: Introductionmentioning

confidence: 99%

A Method of Amino Acid Terahertz Spectrum Recognition Based on the Convolutional Neural Network and Bidirectional Gated Recurrent Network Model

Luo

et al. 2021

Scientific Programming

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In order to improve the accuracy of amino acid identification, a model based on the convolutional neural network (CNN) and bidirectional gated recurrent network (BiGRU) is proposed for terahertz spectrum identification of amino acids. First, we use the CNN to extract the feature information of the terahertz spectrum; then, we use the BiGRU to process the feature vector of the amino acid time-domain spectrum, describe the time series dynamic change information, and finally achieve amino acid identification through the fully connected network. Experiments are carried out on the terahertz spectra of various amino acids. The experimental results show that the CNN-BiGRU model proposed in this study can effectively realize the terahertz spectrum identification of amino acids and will provide a new and effective analysis method for the identification of amino acids by terahertz spectroscopy technology.

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An Electronic Component Recognition Algorithm Based on Deep Learning with a Faster SqueezeNet

Cited by 26 publications

References 27 publications

RETRACTED ARTICLE: Cognitive computing-based COVID-19 detection on Internet of things-enabled edge computing environment

RETRACTED ARTICLE: Cognitive computing-based COVID-19 detection on Internet of things-enabled edge computing environment

Modeling of Hyperparameter Tuned Deep Learning Model for Automated Image Captioning

A Method of Amino Acid Terahertz Spectrum Recognition Based on the Convolutional Neural Network and Bidirectional Gated Recurrent Network Model

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