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capso-lstm  (Capso Vision Inc)

 
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    Structured Review

    Capso Vision Inc capso-lstm
    The image depicts an <t>LSTM</t> (long short-term memory) neural network architecture, illustrating the flow and transformation of data within. It shows the internal gating mechanisms—forget, input, and output gates—of an LSTM cell, how they process the input X, and generate an output Y. The LSTM layer connects to a fully connected layer that integrates the features, leading to the final output layer where the result is produced.
    Capso Lstm, supplied by Capso Vision Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/capso-lstm/product/Capso Vision Inc
    Average 90 stars, based on 1 article reviews
    capso-lstm - by Bioz Stars, 2026-05
    90/100 stars

    Images

    1) Product Images from "A cosine adaptive particle swarm optimization based long-short term memory method for urban green area prediction"

    Article Title: A cosine adaptive particle swarm optimization based long-short term memory method for urban green area prediction

    Journal: PeerJ Computer Science

    doi: 10.7717/peerj-cs.2048

    The image depicts an LSTM (long short-term memory) neural network architecture, illustrating the flow and transformation of data within. It shows the internal gating mechanisms—forget, input, and output gates—of an LSTM cell, how they process the input X, and generate an output Y. The LSTM layer connects to a fully connected layer that integrates the features, leading to the final output layer where the result is produced.
    Figure Legend Snippet: The image depicts an LSTM (long short-term memory) neural network architecture, illustrating the flow and transformation of data within. It shows the internal gating mechanisms—forget, input, and output gates—of an LSTM cell, how they process the input X, and generate an output Y. The LSTM layer connects to a fully connected layer that integrates the features, leading to the final output layer where the result is produced.

    Techniques Used: Transformation Assay, Produced

    The process begins with normalizing the input data, followed by initializing the particle velocity and position. It then calculates the particle fitness and checks if the predefined number of iterations has been reached. If not, it updates the individual and global optima using CAPSO. This loop continues until the iteration condition is met. Once completed, the process outputs the optimal parameters for the LSTM model, which are then used to predict the green area. The flow is sequential and iterative, with a decision point that loops back until the stopping criterion is satisfied.
    Figure Legend Snippet: The process begins with normalizing the input data, followed by initializing the particle velocity and position. It then calculates the particle fitness and checks if the predefined number of iterations has been reached. If not, it updates the individual and global optima using CAPSO. This loop continues until the iteration condition is met. Once completed, the process outputs the optimal parameters for the LSTM model, which are then used to predict the green area. The flow is sequential and iterative, with a decision point that loops back until the stopping criterion is satisfied.

    Techniques Used:

    Prediction results of LSTM, PSO-LSTM and CAPSO-LSTM.
    Figure Legend Snippet: Prediction results of LSTM, PSO-LSTM and CAPSO-LSTM.

    Techniques Used:

    Results of mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE).
    Figure Legend Snippet: Results of mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE).

    Techniques Used:

    The black line indicates the predicted values generated by an LSTM model, while the green line represents the actual observed values.
    Figure Legend Snippet: The black line indicates the predicted values generated by an LSTM model, while the green line represents the actual observed values.

    Techniques Used: Generated

    The black line indicates the predicted values generated by an CAPSO-LSTM model, while the purple line represents the actual observed values.
    Figure Legend Snippet: The black line indicates the predicted values generated by an CAPSO-LSTM model, while the purple line represents the actual observed values.

    Techniques Used: Generated



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    Image Search Results


    The image depicts an LSTM (long short-term memory) neural network architecture, illustrating the flow and transformation of data within. It shows the internal gating mechanisms—forget, input, and output gates—of an LSTM cell, how they process the input X, and generate an output Y. The LSTM layer connects to a fully connected layer that integrates the features, leading to the final output layer where the result is produced.

    Journal: PeerJ Computer Science

    Article Title: A cosine adaptive particle swarm optimization based long-short term memory method for urban green area prediction

    doi: 10.7717/peerj-cs.2048

    Figure Lengend Snippet: The image depicts an LSTM (long short-term memory) neural network architecture, illustrating the flow and transformation of data within. It shows the internal gating mechanisms—forget, input, and output gates—of an LSTM cell, how they process the input X, and generate an output Y. The LSTM layer connects to a fully connected layer that integrates the features, leading to the final output layer where the result is produced.

    Article Snippet: The Cosine Adaptive Particle Swarm Optimization (CAPSO)-LSTM is used in urban green area prediction, and experimental results demonstrate that the proposed CAPSO-LSTM can accurately predict the area of urban green spaces, providing significant assistance in urban construction planning.

    Techniques: Transformation Assay, Produced

    The process begins with normalizing the input data, followed by initializing the particle velocity and position. It then calculates the particle fitness and checks if the predefined number of iterations has been reached. If not, it updates the individual and global optima using CAPSO. This loop continues until the iteration condition is met. Once completed, the process outputs the optimal parameters for the LSTM model, which are then used to predict the green area. The flow is sequential and iterative, with a decision point that loops back until the stopping criterion is satisfied.

    Journal: PeerJ Computer Science

    Article Title: A cosine adaptive particle swarm optimization based long-short term memory method for urban green area prediction

    doi: 10.7717/peerj-cs.2048

    Figure Lengend Snippet: The process begins with normalizing the input data, followed by initializing the particle velocity and position. It then calculates the particle fitness and checks if the predefined number of iterations has been reached. If not, it updates the individual and global optima using CAPSO. This loop continues until the iteration condition is met. Once completed, the process outputs the optimal parameters for the LSTM model, which are then used to predict the green area. The flow is sequential and iterative, with a decision point that loops back until the stopping criterion is satisfied.

    Article Snippet: The Cosine Adaptive Particle Swarm Optimization (CAPSO)-LSTM is used in urban green area prediction, and experimental results demonstrate that the proposed CAPSO-LSTM can accurately predict the area of urban green spaces, providing significant assistance in urban construction planning.

    Techniques:

    Prediction results of LSTM, PSO-LSTM and CAPSO-LSTM.

    Journal: PeerJ Computer Science

    Article Title: A cosine adaptive particle swarm optimization based long-short term memory method for urban green area prediction

    doi: 10.7717/peerj-cs.2048

    Figure Lengend Snippet: Prediction results of LSTM, PSO-LSTM and CAPSO-LSTM.

    Article Snippet: The Cosine Adaptive Particle Swarm Optimization (CAPSO)-LSTM is used in urban green area prediction, and experimental results demonstrate that the proposed CAPSO-LSTM can accurately predict the area of urban green spaces, providing significant assistance in urban construction planning.

    Techniques:

    Results of mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE).

    Journal: PeerJ Computer Science

    Article Title: A cosine adaptive particle swarm optimization based long-short term memory method for urban green area prediction

    doi: 10.7717/peerj-cs.2048

    Figure Lengend Snippet: Results of mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE).

    Article Snippet: The Cosine Adaptive Particle Swarm Optimization (CAPSO)-LSTM is used in urban green area prediction, and experimental results demonstrate that the proposed CAPSO-LSTM can accurately predict the area of urban green spaces, providing significant assistance in urban construction planning.

    Techniques:

    The black line indicates the predicted values generated by an LSTM model, while the green line represents the actual observed values.

    Journal: PeerJ Computer Science

    Article Title: A cosine adaptive particle swarm optimization based long-short term memory method for urban green area prediction

    doi: 10.7717/peerj-cs.2048

    Figure Lengend Snippet: The black line indicates the predicted values generated by an LSTM model, while the green line represents the actual observed values.

    Article Snippet: The Cosine Adaptive Particle Swarm Optimization (CAPSO)-LSTM is used in urban green area prediction, and experimental results demonstrate that the proposed CAPSO-LSTM can accurately predict the area of urban green spaces, providing significant assistance in urban construction planning.

    Techniques: Generated

    The black line indicates the predicted values generated by an CAPSO-LSTM model, while the purple line represents the actual observed values.

    Journal: PeerJ Computer Science

    Article Title: A cosine adaptive particle swarm optimization based long-short term memory method for urban green area prediction

    doi: 10.7717/peerj-cs.2048

    Figure Lengend Snippet: The black line indicates the predicted values generated by an CAPSO-LSTM model, while the purple line represents the actual observed values.

    Article Snippet: The Cosine Adaptive Particle Swarm Optimization (CAPSO)-LSTM is used in urban green area prediction, and experimental results demonstrate that the proposed CAPSO-LSTM can accurately predict the area of urban green spaces, providing significant assistance in urban construction planning.

    Techniques: Generated