Drone hyperspectral imaging and artificial intelligence for monitoring moss and lichen in Antarctica by Juan Sandino, Johan Barthelemy, Ashray Doshi, Krystal Randall, Sharon A. Robinson, Barbara Bollard, Felipe Gonzalez

“…The results show that common indices like normalised difference vegetation index (NDVI) are inadequate for moss and lichen detection, while novel spectral indices are more effective. …”
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FUSE-Net: Multi-Scale CNN for NIR Band Prediction from RGB Using GNDVI-Guided Green Channel Enhancement by Gwanghyeong Lee, Deepak Ghimire, Donghoon Kim, Sewoon Cho, Byoungjun Kim, Sunghwan Jeong

“…To explore a more accessible alternative, we propose a Green Normalized Difference Vegetation Index (GNDVI)-guided green channel adjustment method, termed G-RGB, which enables the estimation of near-infrared (NIR) reflectance from standard RGB image inputs. …”
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Longitudinal image-based prediction of surgical intervention in infants with hydronephrosis using deep learning: Is a single ultrasound enough? by Adree Khondker, Stanley Bryan Z Hua, Jethro C C Kwong, Kunj Sheth, Daniel Alvarez, Kyla N Velaer, John Weaver, Alice Xiang, Gregory E Tasian, Armando J Lorenzo, Anna Goldenberg, Mandy Rickard, Lauren Erdman

“…There was no significant difference in developing single-visit US models using the first ultrasound vs. the latest ultrasound. …”
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SpaGAN: A spatially-aware generative adversarial network for building generalization in image maps by Zhiyong Zhou, Cheng Fu, Robert Weibel

“…It takes a representative cGAN, pix2pix, as the backbone, and modifies two modules: In the U-Net-based generator, an atrous spatial pyramid pooling (ASPP) module replaces the conventional convolutional module to extract multi-scale features of buildings of varying sizes and shapes; in the PatchGAN-based discriminator, a signed distance map (SDM) module is used to capture the fine-grained shape difference for discrimination. …”
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Unraveling trends in schistosomiasis: deep learning insights into national control programs in China by Qing Su, Cici Xi Chen Bauer, Robert Bergquist, Zhiguo Cao, Fenghua Gao, Zhijie Zhang, Yi Hu

“…METHODS We obtained village-level parasitological data from cross-sectional surveys combined with environmental data in Anhui Province, China from 1997 to 2015. A convolutional neural network (CNN) based on a hierarchical integro-difference equation (IDE) framework (i.e., CNN-IDE) was used to model spatio-temporal variations in schistosomiasis. …”
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Application of Ontology Matching Algorithm Based on Linguistic Features in English Pronunciation Quality Evaluation by Shan Zhu

“…When building the neural network model, four convolutional layers, two fully connected layers, and one softmax output layer were conceived, and dropout was used to randomly suspend the work of some neurons to avoid overfitting. …”
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MTFSR: Multitemporal and Spatial Feature Reconstruction Denoising Network for Remote Sensing Change Detection by YeKai Cui, Peng Duan, Jinjiang Li

“…With the widespread application of convolutional neural networks (CNNs) in remote sensing (RS) technologies, change detection (CD) has attracted increasing attention in environmental monitoring research. …”
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The JPEG Pleno Learning-Based Point Cloud Coding Standard: Serving Man and Machine by Andre F. R. Guarda, Nuno M. M. Rodrigues, Fernando Pereira

“…The geometry is processed directly in its original 3D form using sparse convolutional neural networks, while the color data is projected onto 2D images and encoded using the also learning-based JPEG AI standard. …”
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A superpixel based self-attention network for uterine fibroid segmentation in high intensity focused ultrasound guidance images by Shen Wen, Dong Zhang, Yuting Lei, Yan Yang

“…Paired t-tests were conducted to evaluate the significant difference in IoU and NormHD between the proposed method and the comparison methods. …”
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Noninvasive Continuous Glucose Monitoring Using Multimodal Near-Infrared, Temperature, and Pressure Signals on the Earlobe by Jongdeog Kim, Bong Kyu Kim, Mi-Ryong Park, Hyoyoung Cho, Chul Huh

“…Continuous glucose data capturing pre- and postprandial variations were analyzed using a 1D convolutional neural network (Conv1D). For three subjects under stable physiological conditions, the model achieved 97.0% Clarke error grid (CEG) A-Zone accuracy and a mean absolute relative difference (MARD) of 5.2%. …”
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Rep-MobileViT: Texture and Color Classification of Solid Wood Floors Based on a Re-Parameterized CNN-Transformer Hybrid Model by Anning Duanmu, Sheng Xue, Zhenye Li, Yajun Zhang, Chao Ni

“…Specifically, the RepAIRB module is introduced, incorporating an asymmetric convolutional block (ACB) and a re-parameterized structure within the inverted residual block (IRB) module to enhance the network’s receptive field without increasing computational costs. …”
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Multiple-Feature Construction for Image Segmentation Based on Genetic Programming by David Herrera-Sánchez, José-Antonio Fuentes-Tomás, Héctor-Gabriel Acosta-Mesa, Efrén Mezura-Montes, José-Luis Morales-Reyes

“…Within the medical field, computer vision has an important role in different tasks, such as health anomaly detection, diagnosis, treatment, and monitoring medical conditions. …”
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Nonlinear Spring-Mass-Damper Modeling and Parameter Estimation of Train Frontal Crash Using CLGAN Model by Shaodi Dong, Zhao Tang, Xiaosong Yang, Michelle Wu, Jianjun Zhang, Tao Zhu, Shoune Xiao

“…Due to the complexity of a train crash, it is a challenging process to describe and estimate mathematically. Although different mathematical models have been developed, it is still difficult to balance the complexity of models and the accuracy of estimation. …”
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Improving the Accuracy in Classification of Blood Pressure from Photoplethysmography Using Continuous Wavelet Transform and Deep Learning by Jiaze Wu, Hao Liang, Changsong Ding, Xindi Huang, Jianhua Huang, Qinghua Peng

“…The PPG signals were transformed into 224 ∗ 224 ∗ 3-pixel scalogram via different CWTs and segment units. All of them are fed into different convolutional neural networks (CNN) for training and validation. …”
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Rice Disease Detection: TLI-YOLO Innovative Approach for Enhanced Detection and Mobile Compatibility by Zhuqi Li, Wangyu Wu, Bingcai Wei, Hao Li, Jingbo Zhan, Songtao Deng, Jian Wang

“…Third, this study is the first to introduce the iRMB attention mechanism, which effectively integrates Inverted Residual Blocks and Transformers, and introduces deep separable convolution to maintain the spatial integrity of features, thus improving the efficiency of computational resources on mobile platforms. …”
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Enhanced prediction of heat transfer in jet impingement cooling using an artificial intelligence: A case study by Mehmet Berkant Özel, Ufuk Durmaz, Muhammed Ali Nur Öz, Ahmet Ümit Tepe, Cemil Öz, Ünal Uysal, Orhan Yalçinkaya, Ali Cemal Beni̇m, Norah Alomayrah, M.S. Al-Buriahi

“…The aim of the present study is to explain the effect of the impinging jet configuration of elongated jet holes on the heat transfer distribution with a deep Convolutional Neural Network (CNN). CNNs preserve spatial relationships through convolutional operations—unlike traditional Artificial Neural Networks (ANNs), which require flattening of input data—allowing for higher accuracy with smaller datasets and faster training due to fewer trainable parameters. …”
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MambaPose: A Human Pose Estimation Based on Gated Feedforward Network and Mamba by Jianqiang Zhang, Jing Hou, Qiusheng He, Zhengwei Yuan, Hao Xue

“…The direct use of convolutional downsampling reduces selectivity for different stages of information flow. …”
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MHAGuideNet: a 3D pre-trained guidance model for Alzheimer’s Disease diagnosis using 2D multi-planar sMRI images by Yuanbi Nie, Qiushi Cui, Wenyuan Li, Yang Lü, Tianqing Deng

“…The model utilizes a pre-trained 3D convolutional neural network to direct the feature extraction of multi-planar 2D slices, specifically targeting the detection of features like structural atrophy. …”
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Building consistency in explanations: Harmonizing CNN attributions for satellite-based land cover classification by Timo T. Stomberg, Lennart A. Reißner, Martin G. Schultz, Ribana Roscher

“…However, a key challenge is that different attribution methods often produce different outcomes undermining trust in their results. …”
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Species-independent analysis and identification of emotional animal vocalizations by Stavros Ntalampiras

“…Abstract Animal vocalizations can differ depending on the context in which they are produced and serve as an instant indicator of an animal’s emotional state. …”
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