A Machine Learning Free Energy Functional for the 1D Reference Interaction Site Model: Towards Prediction of Solvation Free Energy for All Solvent Systems by Jonathan G. M. Conn, Abdullah Ahmad, David S. Palmer

“…In this work, we show that a single machine learning free energy functional for RISM can accurately model solvation thermodynamics in multiple solvents. A convolutional neural network is trained on solvation free energy density functions calculated by RISM for small organic molecules in approximately 100 different solvent systems. …”
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PolyNet: A self-attention based CNN model for classifying the colon polyp from colonoscopy image by Khaled Eabne Delowar, Mohammed Borhan Uddin, Md Khaliluzzaman, Riadul Islam Rabbi, Md Jakir Hossen, M. Moazzam Hossen

“…This study provides a sensitivity analysis to demonstrate how slight modifications in the network's architecture can impact the balance between accuracy and performance. We examined different CNN architectures and developed a good convolutional neural network (CNN) model for correctly predicting colon polyps using the Kvasir dataset. …”
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Image Classification Model Based on Contrastive Learning With Dynamic Adaptive Loss by Quandeng Gou, Jingxuan Zhou, Zi Li, Fangrui Zhang, Yuheng Ren

“…Most existing mainstream image classification models use the Convolutional Neural Network (CNN), the Transformer, or a combination of both as the backbone. …”
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ANF-Net: A Refined Segmentation Network for Road Scenes with Multiple Noises and Various Morphologies of Cracks by Xiao Hu, Qihao Chen, Xiuguo Liu, Gang Deng, Cheng Chi, Bin Wang

“…On the other hand, a constrained multi-morphological convolution structure is constructed by imposing learnable continuous constraints on the deformation offsets of convolutional kernels, allowing the network to adaptively fit different crack shapes. …”
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Hand Gesture Recognition From Wrist-Worn Camera for Human–Machine Interaction by Hong-Quan Nguyen, Trung-Hieu Le, Trung-Kien Tran, Hoang-Nhat Tran, Thanh-Hai Tran, Thi-Lan Le, Hai Vu, Cuong Pham, Thanh Phuong Nguyen, Huu Thanh Nguyen

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A Deep Learning–Based Multimodal F10.7 Prediction with Mamba by Dong Zhao, Fengping Dou, Xueshang Feng, Xin Huang, Sai Ma, Long Xu

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Future variation and uncertainty source decomposition in deep learning bias-corrected CMIP6 global extreme precipitation historical simulation by Xiaohua Xiang, Yongxuan Li, Xiaoling Wu, Zhu Liu, Lei Wu, Biqiong Wu, Chuanxin Jin, Zhiqiang Zeng

“…In addition, this study endeavors to separate and quantify three different components of uncertainty (model uncertainty, scenario uncertainty, and internal variability) associated with ETCCDI extreme precipitation indices and evaluate the impact of bias correction on uncertainty variation. …”
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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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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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Field-grown tomato yield estimation using point cloud segmentation with 3D shaping and RGB pictures from a field robot and digital single lens reflex cameras by B. Ambrus, G. Teschner, A.J. Kovács, M. Neményi, L. Helyes, Z. Pék, S. Takács, T. Alahmad, A. Nyéki

“…By comparing the measured and estimated yield, the average difference for DSLR camera images was more favorable at 3.42 kg.…”
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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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