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Convolutional Neural Networks (CNNs) are a class of deep neural networks that have proven highly effective in areas such as image and video recognition. CNNs typically include several types of layers, such as convolutional layers, activation layers, pooling layers, and fully connected layers, all of which contribute to the network’s ability to recognize patterns and features. The pooling layer, which often follows the convolutional layer, is crucial for reducing computational complexity by performing down-sampling while maintaining essential features. This layer’s role in balancing the symmetry of information across the network is vital for optimal performance. However, the choice of pooling method is often based on intuition, which can lead to less accurate or efficient results. This research compares various standard pooling methods (MAX and AVERAGE pooling) on standard datasets (MNIST, CIFAR-10, and CIFAR-100) to determine the most effective approach in preserving detail, performance, and overall computational efficiency while maintaining the symmetry necessary for robust CNN performance.

This research proposes constructing a network used for person re-identification called MGNACP (Multiple Granularity Network with Attention Mechanisms and Combination Poolings). Based on the MGN (Multiple Granularity Network) that combines global and local features and the characteristics of the MGN branch, the MGNA (Multiple Granularity Network with Attentions) is designed by adding a channel attention mechanism to each global and local branch of the MGN. The MGNA, with attention mechanisms, learns the most identifiable information about global and local features to improve the person re-identification accuracy. Based on the constructed MGNA, a single pooling used in each branch is replaced by combination pooling to form MGNACP. The combination pooling parameters are the proportions of max pooling and average pooling in combination pooling. Through experiments, suitable combination pooling parameters are found, the advantages of max pooling and average pooling are preserved and enhanced, and the disadvantages of both types of pooling are overcome, so that poolings can achieve optimal results in MGNACP and improve the person re-identification accuracy. In experiments on the Market-1501 dataset, MGNACP achieved competitive experimental results; the values of mAP and top-1 are 88.82% and 95.46%. The experimental results demonstrate that MGNACP is a competitive person re-identification network, and that the attention mechanisms and combination poolings can significantly improve the person re-identification accuracy.

Alcohol use disorder (AUD) is an important brain disease. It alters the brain structure. Recently, scholars tend to use computer vision based techniques to detect AUD. We collected 235 subjects, 114 alcoholic and 121 non-alcoholic. Among the 235 image, 100 images were used as training set, and data augmentation method was used. The rest 135 images were used as test set. Further, we chose the latest powerful technique—convolutional neural network (CNN) based on convolutional layer, rectified linear unit layer, pooling layer, fully connected layer, and softmax layer. We also compared three different pooling techniques: max pooling, average pooling, and stochastic pooling. The results showed that our method achieved a sensitivity of 96.88%, a specificity of 97.18%, and an accuracy of 97.04%. Our method was better than three state-of-the-art approaches. Besides, stochastic pooling performed better than other max pooling and average pooling. We validated CNN with five convolution layers and two fully connected layers performed the best. The GPU yielded a 149× acceleration in training and a 166× acceleration in test, compared to CPU.

Diabetic Retinopathy (DR) is one of the major causes of visual impairment and blindness across the world. It is usually found in patients who suffer from diabetes for a long period. The major focus of this work is to derive optimal representation of retinal images that further helps to improve the performance of DR recognition models. To extract optimal representation, features extracted from multiple pre-trained ConvNet models are blended using proposed multi-modal fusion module. These final representations are used to train a Deep Neural Network (DNN) used for DR identification and severity level prediction. As each ConvNet extracts different features, fusing them using 1D pooling and cross pooling leads to better representation than using features extracted from a single ConvNet. Experimental studies on benchmark Kaggle APTOS 2019 contest dataset reveals that the model trained on proposed blended feature representations is superior to the existing methods. In addition, we notice that cross average pooling based fusion of features from Xception and VGG16 is the most appropriate for DR recognition. With the proposed model, we achieve an accuracy of 97.41%, and a kappa statistic of 94.82 for DR identification and an accuracy of 81.7% and a kappa statistic of 71.1% for severity level prediction. Another interesting observation is that DNN with dropout at input layer converges more quickly when trained using blended features, compared to the same model trained using uni-modal deep features.

In light of current developments in dental care, dental professionals have increasingly used deep learning methods to get precise diagnoses of oral problems. Using intraoral X-rays in dental radiography is imperative in many dental interventions. Integrating deep learning techniques with a unique collection of intraoral X-ray images has been undertaken to enhance the accuracy of dental disease detection. In this study, we propose an alternative pooling layer, namely the Common Vector Approach Pooling technique, to address the constraints associated with average pooling in deep learning methods. The experiments are conducted on a large dataset, involving twenty different dental conditions, divided into seven categories. Our proposed approach achieved a high accuracy rate of 86.4% in identifying dental problems across the seven oral categories.

Cold-air pooling is a global phenomenon that frequently sustains low temperatures in sheltered, low-lying depressions and valleys and drives other key environmental conditions, such as soil temperature, soil moisture, vapor pressure deficit, frost frequency, and winter dynamics. Local climate patterns in areas prone to cold-air pooling are partly decoupled from regional climates and thus may be buffered from macroscale climate change. There is compelling evidence from studies across the globe that cold-air pooling impacts plant communities and species distributions, making these decoupled microclimate areas potentially important microrefugia for species under climate warming. Despite interest in the potential for cold-air pools to enable species persistence under warming, studies investigating the effects of cold-air pooling on ecosystem processes are scarce. Because local temperatures and vegetation composition are critical drivers of ecosystem processes like carbon cycling and storage, cold-air pooling may also act to preserve ecosystem functions. We review research exploring the ecological impacts of cold-air pooling with a focus on vegetation, and then present a new conceptual framework in which cold-air pooling creates feedbacks between species and ecosystem properties that generate unique hotspots for carbon accrual in some systems relative to areas more vulnerable to regional climate change impacts. Finally, we describe key steps to motivate future research investigating the potential for cold-air pools to serve as microrefugia for ecosystem functions under climate change.

Objective: To describe the frequency of Chlamydia trachomatis in local community visiting a tertiary care hospital and to estimate the cost saving achieved as a result of pooling strategy. Study Design: Cross-sectional study. Place and Duration of Study: Department of Microbiology, Armed Forces Institute of Pathology, Rawalpindi Pakistan, from Jan 2022 to Jun 2022. Methodology: A pool of three urine samples was created after individual manual DNA extraction of each sample and tested by RT PCR. Any pool signaling positive was identified and all samples in that pool were retested individually to determine the positive sample. A total of 66 asymptomatic young people including males and females were tested. Results: The frequency of Chlamydia trachomatis was found to be 7.57%. About 22 pools were created resulting in a 48.0% savings in costs. Conclusion: The Pooling strategy adopted with the objective of saving test costs resulted in getting timely and reliable results in a resource limited setting. It also provided with the means to keep Sexually Transmitted Infections detection programs going on at various healthcare levels and in screening a larger number of populations.

Intelligent fault diagnosis methods based on deep learning becomes a research hotspot in the fault diagnosis field. Automatically and accurately identifying the incipient micro-fault of rotating machinery, especially for fault orientations and severity degree, is still a major challenge in the field of intelligent fault diagnosis. The traditional fault diagnosis methods rely on the manual feature extraction of engineers with prior knowledge. To effectively identify an incipient fault in rotating machinery, this paper proposes a novel method, namely improved the convolutional neural network-support vector machine (CNN-SVM) method. This method improves the traditional convolutional neural network (CNN) model structure by introducing the global average pooling technology and SVM. Firstly, the temporal and spatial multichannel raw data from multiple sensors is directly input into the improved CNN-Softmax model for the training of the CNN model. Secondly, the improved CNN are used for extracting representative features from the raw fault data. Finally, the extracted sparse representative feature vectors are input into SVM for fault classification. The proposed method is applied to the diagnosis multichannel vibration signal monitoring data of a rolling bearing. The results confirm that the proposed method is more effective than other existing intelligence diagnosis methods including SVM, K-nearest neighbor, back-propagation neural network, deep BP neural network, and traditional CNN.

With the development of advanced information and intelligence technologies, precision agriculture has become an effective solution to monitor and prevent crop pests and diseases. However, pest and disease recognition in precision agriculture applications is essentially the fine-grained image classification task, which aims to learn effective discriminative features that can identify the subtle differences among similar visual samples. It is still challenging to solve for existing standard models troubled by oversized parameters and low accuracy performance. Therefore, in this paper, we propose a feature-enhanced attention neural network (Fe-Net) to handle the fine-grained image recognition of crop pests and diseases in innovative agronomy practices. This model is established based on an improved CSP-stage backbone network, which offers massive channel-shuffled features in various dimensions and sizes. Then, a spatial feature-enhanced attention module is added to exploit the spatial interrelationship between different semantic regions. Finally, the proposed Fe-Net employs a higher-order pooling module to mine more highly representative features by computing the square root of the covariance matrix of elements. The whole architecture is efficiently trained in an end-to-end way without additional manipulation. With comparative experiments on the CropDP-181 Dataset, the proposed Fe-Net achieves Top-1 Accuracy up to 85.29% with an average recognition time of only 71 ms, outperforming other existing methods. More experimental evidence demonstrates that our approach obtains a balance between the model’s performance and parameters, which is suitable for its practical deployment in precision agriculture art applications.

Accurate liver segmentation in laparoscopic surgery is critical but remains challenging due to low contrast, occlusion, and irregular organ boundaries. While deep learning has advanced medical image segmentation, existing models often trade off between accuracy, computational efficiency, and boundary precision. We propose IMAU-Net, a hybrid architecture integrating a pre-trained InceptionV3 encoder with a novel bottleneck combining Multi-Core Pooling (MCP) and enhanced Atrous Spatial Pyramid Pooling (ASPP). The MCP module captures fine-to-medium spatial details through parallel multi-kernel pooling, while ASPP extracts multi-scale contextual information via dilated convolutions. Evaluated on the M2CAI dataset with 5-fold cross-validation, IMAU-Net achieves a mean Dice coefficient of 0.9179 ± 0.012 and IoU of 0.8483 ± 0.015. Furthermore, external validation on the independent CholecSeg8K dataset (250 test samples) demonstrates generalizability across different laparoscopic procedures, achieving a Dice coefficient of 0.8745 ± 0.0312 and AUC of 0.9542, with a performance degradation of only 4.3% despite domain shift between liver surgery and cholecystectomy. Comparative analysis with state of the art methods demonstrates superior performance, with computational efficiency suitable for real-time applications (45 FPS, 42.3 M parameters). The proposed architecture provides an optimal balance between accuracy and efficiency for intraoperative guidance systems. While evaluated on retrospective laparoscopic image datasets rather than real-time intraoperative workflows, the model demonstrates potential for integration into surgical guidance systems pending prospective validation.