Research on Fault Prediction of Power Devices in Rod Control Power Cabinets Based on BiTCN-Attention Transfer Learning Model by Zhi Chen, Liqi Ye, Yifan Jian, Meiyuan Chen, Yuan Min

“…Firstly, an IGBT fault simulation model was built to collect the life cycle state data of the module under different working conditions. Then, after pre-processing such as removing outliers, kernel principal component analysis (KPCA) was used to integrate all source domain data, obtain source domain characterization data, and train the BiTCN-attention model. …”
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A Predictive Method for Unplanned Postoperative Readmission Risk Based on Heterogeneous Data by Kai YU, Xiaofeng DONG, Zhenming YUAN, Zhaojian CUI, Weibin LUO

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A generative adversarial network with multiscale and attention mechanisms for underwater image enhancement by Liquan Zhao, Yuda Li, Tie Zhong

“…Next, we design a multi-scale dilated convolution module to capture underwater features at different scales. …”
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On the Synergy of Optimizers and Activation Functions: A CNN Benchmarking Study by Khuraman Aziz Sayın, Necla Kırcalı Gürsoy, Türkay Yolcu, Arif Gürsoy

“…Additionally, two-way ANOVA was employed to validate the significance of differences across optimizer–activation combinations. …”
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Identifying Canopy Snow in Subalpine Forests: A Comparative Study of Methods by Natasha Harvey, Sean P. Burns, Keith N. Musselman, Holly Barnard, Peter D. Blanken

“…Timelapse photography images were analyzed using thresholding analysis and used to train a Convolutional Neural Network (CNN) model to estimate canopy snow presence. …”
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Establishing an AI-based diagnostic framework for pulmonary nodules in computed tomography by Ruiting Jia, Baozhi Liu, Mohsin Ali

“…Method The proposed deep learning framework used convolutional neural networks, and the image database totaled 1,056 3D-DICOM CT images. …”
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Towards an Energy Consumption Index for Deep Learning Models: A Comparative Analysis of Architectures, GPUs, and Measurement Tools by Sergio Aquino-Brítez, Pablo García-Sánchez, Andrés Ortiz, Diego Aquino-Brítez

“…The results reveal significant differences in energy efficiency across architectures and GPUs, providing insights into the trade-offs between model performance and energy use. …”
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Enhancing synchrotron radiation micro-CT images using deep learning: an application of Noise2Inverse on bone imaging by Yoshihiro Obata, Dilworth Y. Parkinson, Daniël M. Pelt, Claire Acevedo

“…Following this, new models were trained using a larger dataset to determine differences between full dose and one-third dose simulations. …”
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PCCNN: A CNN classification model integrating EEG time-frequency features for stroke classification by Teng Wang, Fenglian Li, Jia Yang, Wenhui Jia, Fengyun Hu

“…This method accounts for both the intrinsic information content of EEG signals and the inter-class differences between hemorrhagic and ischemic stroke subjects. …”
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Expression Recognition Method Based on CBAM-DSC Network by SONG Wen bo, GAO Lu, MIAO Zhuang, LIN Ke zheng

“…The network usage depth separable convolution is combined with the traditional convolution. …”
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Cover Stories and the Counterfactual by Lisa Blackman

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Learning Dynamic Spatial-Temporal Dependence in Traffic Forecasting by Chaoyu Ren, Yuezhu Li

“…Finally, we propose a temporal fusion layer with multi-scale features to model accurate temporal semantic information from contextual environment with different window sizes, to further obtain accurate prediction results. …”
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Comparison of Doubling the Size of Image Algorithms by S. E. Vaganov, S. I. Khashin

“…For each method of upscaling to twice optimal coefficients of kernel convolutions for different down-scale to twice algorithms were found. …”
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BAM-SLDK: biologically inspired attention mechanism with spiking learnable delayed kernel synapses by Mario Chacón-Falcón, Alberto Patiño-Saucedo, Luis Camuñas-Mesa, Teresa Serrano-Gotarredona, Bernabé Linares-Barranco

“…More precisely, our main technical contributions are: (1) we add kernels to the temporal dimension to enlarge the receptive field of the convolution; (2) we time kernels activations to mimic multiple delayed times; and (3) we introduce three different pruning techniques to optimize the number of delays and parameters used. …”
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Deep hybrid architecture with stacked ensemble learning for binary classification of retinal disease by Priyadharsini C, Asnath Victy Phamila Y

“…Methods: This work experimented one hundred and forty-four different hybrid architectures amalgamating each of the eight convolutional neural architectures (VGG, EfficientNet, Inception, ResNet, NasNet, DenseNet, InceptionResNet, Xception) with seven classifiers (Logistic regression, K-Nearest Neighbours, Support Vector Classifier, Decision Tree, Bagging classifier, Random Forest, Adaptive Boosting, Light Gradient Boost and Extra tree classifier). …”
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CMENet: A Cross-Modal Enhancement Network for Tobacco Leaf Grading by Qinglin He, Xiaobing Zhang, Jianxin Hu, Zehua Sheng, Qi Li, Si-Yuan Cao, Hui-Liang Shen

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A new CNN deep learning model for computer-intelligent color matching by He Zhibin, Tan Yanmei, Li Sa

“…In practical applications, the model had an average color difference of only 0.51, 0.49, and 0.47 for the three primary colors of red, green, and blue, with small color differences and high color-matching accuracy. …”
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Automatic assessment of lower limb deformities using high-resolution X-ray images by Reyhaneh Rostamian, Masoud Shariat Panahi, Morad Karimpour, Alireza Almasi Nokiani, Ramin Jafarzadeh Khaledi, Hadi Ghattan Kashani

“…The average absolute error (difference between automatically and manually determined coordinates) for landmarks was 0.79 ± 0.57 mm on test data, and the average absolute error (difference between automatically and manually calculated angles) for angles was 0.45 ± 0.42°. …”
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SpeakerNet for Cross-lingual Text-Independent Speaker Verification by Hafsa HABIB, Huma TAUSEEF, Muhammad Abuzar FAHIEM, Saima FARHAN, Ghousia USMAN

“…Extracted features from Siamese then can be classified using difference or correlation measures. We have implemented a customized scoring scheme that utilizes Siamese’ capability of applying distance measures with the convolutional learning. …”
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BPFun: a deep learning framework for bioactive peptide function prediction using multi-label strategy by transformer-driven and sequence rich intrinsic information by Lun Zhu, Hao Sun, Sen Yang

“…Meanwhile, adopting data augmentation to solve the problem of data imbalance. We combine convolutional networks of different scales and Bi-LSTM layers to obtain high-level feature vectors of different features. …”
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