Introduction to deep learning methods for multi‐species predictions by Yuqing Hu, Sara Si‐Moussi, Wilfried Thuiller

“…Specifically, we introduced four distinct deep learning models that use site × species community data but differ in their internal structure or on the input environmental data structure: (1) a multi‐layer perceptron (MLP) model for tabular data (e.g. in‐situ/raster climate or soil data), (2) a convolutional neural network (CNN) and (3) a vision transformer (ViT) models tailored for image data (e.g. aerial ortho‐photographs, satellite imagery), and a multimodal model that integrates both tabular and image data. …”
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Transferring Learned ECG Representations for Deep Neural Network Classification of Atrial Fibrillation with Photoplethysmography by Jayroop Ramesh, Zahra Solatidehkordi, Raafat Aburukba, Assim Sagahyroon, Fadi Aloul

“…However, the scarcity of large-scale public PPG datasets acquired from wearable devices hinders the development of intelligent automatic AF detection algorithms unaffected by motion artifacts, saturated ambient noise, inter- and intra-subject differences, or limited training data. In this work, we present a deep learning framework that leverages convolutional layers with a bidirectional long short-term memory (CNN-BiLSTM) network and an attention mechanism for effectively classifying raw AF rhythms from normal sinus rhythms (NSR). …”
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A study on land use change simulation based on PLUS model and the U-net structure: A case study of Jilin Province by Jiafu Liu, Xiangli Kong, Yue Zhu, Baihao Zhang

“…These results demonstrate that both models possess reliable simulation performance. 2) The two methods exhibit significant differences in predictive performance. The U-Net model, which utilizes convolutional neural networks to extract multi-scale spatial features and addresses the class imbalance issue with the OHEM-Dice composite function, significantly enhances the prediction accuracy of nonlinear dynamics. …”
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Expression Dynamics and Genetic Compensation of Cell Cycle Paralogues in <i>Saccharomyces cerevisiae</i> by Gabriele Schreiber, Facundo Rueda, Florian Renner, Asya Fatima Polat, Philipp Lorenz, Edda Klipp

“…In order to classify cells into specific cell cycle phases, we developed a convolutional neural network (CNN). We find that the expression levels of some cell-cycle related paralogues differ in their correlation, with <i>CLN1</i> and <i>CLN2</i> showing strong correlation and <i>CLB3</i> and <i>CLB4</i> showing weakest correlation. …”
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Short communication: Nanoscale heterogeneity of U and Pb in baddeleyite from atom probe tomography – ²³⁸U series alpha recoil effects and U atom clustering by S. Denyszyn, D. W. Davis, D. Fougerouse

“…Synthetic <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M10" display="inline" overflow="scroll" dspmath="mathml"><mrow class="chem"><msup><mi/><mn mathvariant="normal">206</mn></msup><mi mathvariant="normal">Pb</mi><msup><mo>/</mo><mn mathvariant="normal">238</mn></msup><mi mathvariant="normal">U</mi></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="57pt" height="15pt" class="svg-formula" dspmath="mathimg" md5hash="976d5de522f5330a2ea037e20b88a23d"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="gchron-6-607-2024-ie00002.svg" width="57pt" height="15pt" src="gchron-6-607-2024-ie00002.png"/></svg:svg></span></span> profiles were determined from the convolution of the observed U profile with the redistribution functions for different alpha recoil distances. …”
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Genome data based deep learning identified new genes predicting pharmacological treatment response of attention deficit hyperactivity disorder by Yilu Zhao, Zhao Fu, Eric J. Barnett, Ning Wang, Kangfuxi Zhang, Xuping Gao, Xiangyu Zheng, Junbin Tian, Hui Zhang, XueTong Ding, Shaoxian Li, Shuyu Li, Qingjiu Cao, Suhua Chang, Yufeng Wang, Stephen V. Faraone, Li Yang

“…Then, DL models were constructed to predict percentage changes in symptom scores using genetic variants selected based on four different genome-wide P thresholds (E-02, E-03, E-04, E-05) as inputs. …”
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EFINet: Efficient Feature Interaction Network for Real-Time RGB-D Semantic Segmentation by Zhe Yang, Baozhong Mu, Mingxun Wang, Xin Wang, Jie Xu, Baolu Yang, Cheng Yang, Hong Li, Rongqi Lv

“…Currently, although convolutional neural network (CNN) methods are less accurate than Transformer-based methods, they offer stronger real-time performance under the same computational load. …”
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The Study of Roadside Visual Perception in Internet of Vehicles Based on Improved YOLOv5 and CombineSORT by LI Xiaohui, YANG Jie, XIA Qin

“…In this process, the loss is calculated based on the differences in length, width, and diagonal between the detection and ground-truth boxes, and batch normalization (BN) layer sparsification is applied for convolutional channel filtering. …”
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Lightweight Apple Leaf Disease Detection Algorithm Based on Improved YOLOv8 by LUO Youlu, PAN Yonghao, XIA Shunxing, TAO Youzhi

“…SPD-Conv was introduced to replace the original convolutional layers to retain fine-grained information and reduce model parameters and computational costs, thereby improving the accuracy of disease detection. …”
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A rapid, low-cost deep learning system to classify strawberry disease based on cloud service by Guo-feng YANG, Yong YANG, Zi-kang HE, Xin-yu ZHANG, Yong HE

“…Compared with popular Convolutional Neural Networks (CNN) and five other methods, our network achieves better disease classification effect. …”
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3D cloud masking across a broad swath using multi-angle polarimetry and deep learning by S. R. Foley, S. R. Foley, K. D. Knobelspiesse, A. M. Sayer, A. M. Sayer, M. Gao, M. Gao, J. Hays, J. Hoffman

“…A relatively simple convolutional neural network shows nearly identical performance to the much more complicated U-Net architecture. …”
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Brain tau PET-based identification and characterization of subpopulations in patients with Alzheimer’s disease using deep learning-derived saliency maps by Yanxiao Li, Xiuying Wang, Qi Ge, Manuel B Graeber, Shaozhen Yan, Jian Li, Shuyu Li, Wenjian Gu, Shuo Hu, Tammie L. S. Benzinger, Jie Lu, Yun Zhou

“…A three dimensional-convolutional neural network model was employed for AD detection using standardized uptake value ratio (SUVR) images. …”
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Image-based honey bee larval viral and bacterial diagnosis using machine learning by Duan C. Copeland, Brendon M. Mott, Oliver L. Kortenkamp, Robert J. Erickson, Nathan O. Allen, Kirk E. Anderson

“…We leveraged transfer learning techniques, fine-tuning deep convolutional neural networks (ResNet-50v2, ResNet-101v2, InceptionResNet-v2) pre-trained on ImageNet to discriminate between EFB and viral infections. …”
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Non-Contact Oxygen Saturation Estimation Using Deep Learning Ensemble Models and Bayesian Optimization by Andrés Escobedo-Gordillo, Jorge Brieva, Ernesto Moya-Albor

“…We applied the Eulerian Video Magnification technique using the Hermite Transform (EVM-HT) as a feature detector to feed a Three-Dimensional Convolutional Neural Network (3D-CNN). Additionally, parameters and hyperparameter Bayesian optimization and an ensemble technique over the dataset magnified were applied. …”
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Application of a deep learning algorithm for the diagnosis of HCC by Philip Leung Ho Yu, Keith Wan-Hang Chiu, Jianliang Lu, Gilbert C.S. Lui, Jian Zhou, Ho-Ming Cheng, Xianhua Mao, Juan Wu, Xin-Ping Shen, King Ming Kwok, Wai Kuen Kan, Y.C. Ho, Hung Tat Chan, Peng Xiao, Lung-Yi Mak, Vivien W.M. Tsui, Cynthia Hui, Pui Mei Lam, Zijie Deng, Jiaqi Guo, Li Ni, Jinhua Huang, Sarah Yu, Chengzhi Peng, Wai Keung Li, Man-Fung Yuen, Wai-Kay Seto

“…We conducted sensitivity analyses of different subgroups, deep learning explainability evaluation, and misclassification analysis. …”
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AI-based body composition analysis of CT data has the potential to predict disease course in patients with multiple myeloma by Franz Wegner, Malte Maria Sieren, Hanna Grasshoff, Lennart Berkel, Christoph Rowold, Marcel Philipp Röttgerding, Soleiman Khalil, Sam Mogadas, Felix Nensa, René Hosch, Gabriela Riemekasten, Anna Franziska Hamm, Nikolas von Bubnoff, Jörg Barkhausen, Roman Kloeckner, Cyrus Khandanpour, Theo Leitner

“…The BCA algorithm, powered by a convolutional neural network, quantified tissue compartments and bone density based on routine CT scans. …”
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Intelligent Forecasting for Solar Flares Using Magnetograms from SDO/SHARP, SDO/HMI, and ASO-S/FMG by Xuebao Li, Hongwei Ye, Yanfang Zheng, Ting Li, Jiaben Lin, Shunhuang Zhang, Pengchao Yan, Yongshang Lv, Noraisyah Mohamed Shah, Xuefeng Li, Xiaotian Wang, Yingbo Liu, Rui Wang, Jinfang Wei, Changtian Xiang, Honglei Jin

“…We compare the performance of four models for flare forecasting, including a convolutional neural network (CNN), CNN-BiLSTM, vision transformer (ViT), and MViT. …”
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Surface water mapping from remote sensing in Egypt’s dry season using an improved U-Net model with multi-scale information and attention mechanism by Yong Li, Xiuhui Liu, Vagner Ferreira, Heiko Balzter, Huiyu Zhou, Ying Ge, Meiyun Lai, Simin Chu, Han Ding, Zhenrong Gu

“…During dry seasons, Egyptian water bodies exhibit unique challenges for remote sensing detection due to their significant spectral differences, complex morphological patterns, and numerous small streams. …”
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Artificial Intelligence in the Surgery-First Approach: Harnessing Deep Learning for Enhanced Condylar Reshaping Analysis: A Retrospective Study by Umberto Committeri, Gabriele Monarchi, Massimiliano Gilli, Angela Rosa Caso, Federica Sacchi, Vincenzo Abbate, Stefania Troise, Giuseppe Consorti, Francesco Giovacchini, Valeria Mitro, Paolo Balercia, Antonio Tullio

“…The SFA and SLA exhibited similar rates of condylar surface remodeling, with minor differences in resorption and formation across dentoskeletal classifications. …”
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Evaluation of sports teaching quality in universities based on fuzzy decision support system by Kunjian Han, Jian Wan

“…The proposed model intakes different factors, such as training patterns, sessions, time, associated with the teaching sessions. …”
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