IndraLab

Statements

CALM1 binds cnn. 45 / 45
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"Dastbaravardeh et al. xref proposed a lightweight action recognition framework using CNN with Channel Attention Mechanisms (CNN-CAM) and AE to detect human actions in low-resolution and low-size videos."
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"Subsequently, Partial Least Squares (PLS) regression, Convolutional Neural Network (CNN), CNN combined with channel attention mechanism (CNN-CAM), CNN combined with spatial attention mechanism (CNN-SAM), and CNN combined with Convolutional Block Attention Mechanism (CNN-CBAM) models were developed and evaluated for their suitability and stability in predicting wood moisture content."
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"Zhang et al. [ xref ] used 3D densely connected convolutional neural network (CAM-CNN) to extract brain MRI multilevel features for classification of Alzheimer's disease and mild cognitive impairment, densely connected difference at different unit levels; 3D dense unit introduces attention mechanism to generate attention maps and sum transformed MRI hierarchical data into more compact high-level; model has high classification prediction accuracy, and classification performance is at the highest level."
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"We first proposed the densely connected convolution neural network with connection-wise attention mechanism (short for ‘CAM-CNN’) to learn the multi-level features of MR brain images."
33588019
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"We proposed a 3D connection-wise-attention-model-based densely connected convolution neural network (CAM-CNN) to learn the multi-level features of brain MR images."
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"The proposed CAM-CNN method presented better performance than the popular networks such as ResNet and DenseNet."
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"The proposed CAM-CNN model achieved the highest accuracy of 97.35%."
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"The area under curve (AUC) of the four networks were all higher than 90%, where AUC of CAM-CNN almost attained 100%."
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"DenseNet and CAM-CNN performed well with accuracy of 87.53% and 87.82% respectively, and the AUC of the two models have exceeded 90%."
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"The proposed CAM-CNN method again showed the highest accuracy of 78.79% for cMCI vs. ncMCI classification, with AUC of 86.79%."
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"The proposed CAM-CNN method presented a good classification performance with accuracy of 97.35%, 87.82%, and 78.79% to distinguish AD vs. NC, cMCI vs. NC, and cMCI vs. ncMCI respectively, where it dem[MISSING/INVALID CREDENTIALS: limited to 200 char for Elsevier]"
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"Khosravi (2024) proposed a hybrid cascade attention CNN (CAM-CNN) for early AD severity diagnosis on MRI scans, achieving a 3–5% boost over plain CNN baselines xref ."
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"Different with traditional methods based on handcrafted features, we proposed a densely connected convolutional network with connection-wise attention mechanism (CAM-CNN) that facilitated detection an[MISSING/INVALID CREDENTIALS: limited to 200 char for Elsevier]"
33588019
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"This paper proposed an improved densely connected network with connection-wise attention mechanism named CAM-CNN."
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"With the proposed method, we obtained the accuracy of 78.79% for cMCI vs. ncMCI, 87.82% for cMCI vs. NC which have shown improvement on the performance compared with some popular network models and in[MISSING/INVALID CREDENTIALS: limited to 200 char for Elsevier]"
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"Similarly, Khosravi, M., Parsaei, H. and Rezaee, K xref . proposed a Cascade Attention Model-CNN (CAM-CNN), a deep learning-based approach for early Alzheimer’s disease classification using MRI scans."
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"The CAM-CNN framework enhances early detection and subtype classification, aiding in patient care and targeted therapies."
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"Therefore, this paper proposes an advanced RUL prediction method that leverages a dual-attention mechanism, combining CNN with the channel attention mechanism (CNN-CAM) and GRU with the self-attention mechanism (GRU-SAM)."
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"According to the data in xref , it can be seen that the CNN-CAM model proposed in this paper achieves the highest interpolation performance compared to the baseline model."
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"CNN-CAM can assign different weights to channels and extract features from spatial dimensions."
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"The features output from CNN-CAM were entered as input into the network combined with GRU-SAM for training and weight allocation again."
39860867
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"Initially, spatial features were extracted using CNN-CAM, while temporal features were subsequently extracted using GRU-SAM, enabling the fusion of spatio-temporal features to enhance feature extraction capabilities."
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"The results show that the proposed CNN-CAM model has a LOS recall of 92.29%, NLOS recall of 87.71%, accuracy of 90.00%, and F1-score of 90.22%."
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"They provide new ideas for the design of the proposed model. xref describes the details of the proposed NLOS/LOS identification method based on CNN-CAM. xref performs a series of visualizations on the CIR dataset and designs various experiments to evaluate the performance of the proposed method. xref summarizes the article and discusses the advantages and limitations of this approach and future work."
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"The SENet-CNN and CAM-CNN are obtained by replacing only the attention mechanism in CBAM-CNN."
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"It obtains better identification results using CNN-CAM compared with other ways."
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"In NLOS/LOS identification based on CNN-CAM, there are mainly three parts of the experiment."
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"Therefore, the best learning rate determined using the CNN-CAM model is 0.001."
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"The proposed CNN-CAM model is based on model B with the addition of a two-layer convolution module (Convolution + BN + ReLU + Max-pooling), which achieves an accuracy of 90.00%, a LOS recall of 92.29%, an NLOS recall of 87.71%, an F1-score of 90.22%, and a parameter count of 8764."
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"Model C and the proposed CNN-CAM model increase the number of parameters, but improve the accuracy by 2.00% and 3.60%, the LOS recall by 0.62% and 1.53%, the NLOS recall by 3.38% and 5.66%, and the F1-score by 1.77% and 3.25%, respectively, when compared to Model B. It indicates that adding the convolution module can improve the identification accuracy."
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"In summary, by analyzing the performance of the above models with different structures, it is found that the CNN-CAM model proposed in this paper ensures good fitting performance with fewer trainable parameters and the highest model accuracy, which verified the effectiveness of the CNN-CAM model proposed."
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"To verify the advancedness of the proposed CNN-CAM model, CNN-LSTM [ xref ], CNN-SVM, and Random Forest (RF) models are selected for comparison with the model proposed in this paper."
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"As shown in xref , the CNN-CAM model proposed in this paper achieves 90.00% accuracy, 92.29% LOS recall, 87.71% NLOS recall, and 90.22% F1-score."
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"In terms of accuracy, compared to CNN-LSTM, CNN-SVM, RF (single feature), and RF (multiple features), the accuracy of the CNN-CAM model is improved by 5.06%, 3.88%, 35.48%, and 2.57%, respectively."
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"In terms of LOS recall, CNN-CAM has a significant improvement effect compared to CNN-LSTM, CNN-SVM, RF (single feature), and RF (multiple features), with a minimum improvement of 6.62% and a maximum of 38.21%."
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"In terms of NLOS recall, CNN-CAM outperforms CNN-LSTM, CNN-SVM, and RF (single feature) and is slightly lower than RF (multiple features)."
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"In terms of F1-score, CNN-CAM shows significant improvement compared to CNN-LSTM, CNN-SVM, RF (single feature) and RF (multiple features)."
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"In summary, the CNN-CAM network model proposed in this paper extracts features with higher sensitivity and has obvious performance improvement effects compared to the neural network models and machine learning models in existing studies."
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"Khosravi, Parsaei, and Rezaee xref proposed a cascade attention model-CNN (CAM-CNN) methodology that integrates CNNs with an attention mechanism."
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"Experiments show that TS-CAM outperforms its CNN-CAM counterpart by 11.6% and 28.9% on ILSVRC and CUB-200-2011 datasets, respectively, improving the state-of-the-art with large margins."
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"Another study developed a densely connected convolutional neural network with a connection-wise attenuation mechanism (CAM-CNN) to predict AD diagnosis with higher accuracy using MR brain scans ( xref )."
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"Regarding the CNN-CAM mechanism for processing the serial fingerprint feature, we replaced the 1 × 8 kernel with three frequently used square kernels with sizes of 2, 3, and 5, and the results showed that our property-based 1 × 8 kernel performed the best (see xref ), mainly because such a kernel size made the network learn the shared weights based on each physicochemical property."
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"The application of CAM-CNN on MRI scans with VGG19 and ResNet101 network models produced a 98.85% accuracy outcome where ResNet101 provided better performance than VGG19."
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"The objective is to raise implementations and overcome challenges like computational complexity, overfitting, and ambiguity by employing AE and CNN-CAM."
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"Compared with LSTM-AM, CNN-CAM, and conventional TCN algorithms, the proposed method improved positioning accuracy by 76.12%, 25.06%, and 19.42%, respectively, thereby significantly enhancing the robustness and performance of UWB-based positioning systems."
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