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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Combination of the Improved Diffraction Nonlocal Boundary Condition and Three-Dimensional Wide-Angle Parabolic Equation Decomposition Model for Predicting Radio Wave Propagation by Ruidong Wang, Guizhen Lu, Rongshu Zhang, Weizhang Xu

“…Diffraction nonlocal boundary condition (BC) is one kind of the transparent boundary condition which is used in the finite-difference (FD) parabolic equation (PE). The greatest advantage of the diffraction nonlocal boundary condition is that it can absorb the wave completely by using one layer of grid. …”
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Electrocardiograph analysis for risk assessment of heart failure with preserved ejection fraction: A deep learning model by Zheng Gao, Yuqing Yang, Zhiqiang Yang, Xinyue Zhang, Chao Liu

“…Methods and results A cohort study was conducted utilising data from Cohorts A and B. A convolutional neural network‐long short‐term memory (CNN‐LSTM) DLM was employed. …”
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Application of Deep Learning Techniques in Uranium Microparticle Fission Track Detection by ZHAO Xiong, REN Fangda, SHEN Yan

“…Uranium microparticle isotopic ratios are closely linked to uranium enrichment activities and distinctly differ from natural uranium isotopes. Through isotopic analysis of uranium microparticles, important information regarding material origins, production processes, and products can be obtained. …”
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CART-ANOVA-Based Transfer Learning Approach for Seven Distinct Tumor Classification Schemes with Generalization Capability by Shiraz Afzal, Muhammad Rauf, Shahzad Ashraf, Shahrin Bin Md Ayob, Zeeshan Ahmad Arfeen

“…<b>Background/Objectives:</b> Deep transfer learning, leveraging convolutional neural networks (CNNs), has become a pivotal tool for brain tumor detection. …”
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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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PI-ADFM: Enhancing Multimodal Remote Sensing Image Matching Through Phase-Integrated Aggregated Deep Features by Haiqing He, Shixun Yu, Yongjun Zhang, Yufeng Zhu, Ting Chen, Fuyang Zhou

“…Geometric distortions and significant nonlinear radiometric differences in multimodal remote sensing images (MRSIs) introduce substantial noise in feature extraction. …”
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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 Novel Dual-Branch Global and Local Feature Extraction Network for SAR and Optical Image Registration by Xuanran Zhao, Yan Wu, Xin Hu, Zhikang Li, Ming Li

“…However, the inherent differences between the two modalities pose a challenge to the existing deep-learning algorithms that only depend on local features. …”
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Wi-FiAG: Fine-Grained Abnormal Gait Recognition via CNN-BiGRU with Attention Mechanism from Wi-Fi CSI by Anming Dong, Jiahao Zhang, Wendong Xu, Jia Jia, Shanshan Yun, Jiguo Yu

“…This dual-feature extraction capability positions the proposed CNN-BiGRU architecture as a promising approach for enhancing classification accuracy in scenarios involving multiple gaits with subtle differences in their characteristics. Moreover, the attention mechanism is employed to selectively focus on critical spatiotemporal features for fine-grained abnormal gait detection, enhancing the model’s sensitivity to subtle anomalies. …”
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Panel defect detection algorithm based on improved Faster R-CNN by Chen Wanqin, Tang Qingshan, Huang Tao

“…It also analyzes the difference in the aspect ratio of the defect data set, sets the generation size of the aiming window, and combines the DIoU-NMS suggestion frame screening mechanism to improve the matching rate of the prior frame and the target frame. …”
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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

“…The impact of the MSDA placement on model performance was analyzed by adding it at different positions in the Neck layer, and relevant experiments were designed to verify this. …”
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Automatic identification of clinically important Aspergillus species by artificial intelligence-based image recognition: proof-of-concept study by Chi-Ching Tsang, Chenyang Zhao, Yueh Liu, Ken P. K. Lin, James Y. M. Tang, Kar-On Cheng, Franklin W. N. Chow, Weiming Yao, Ka-Fai Chan, Sharon N. L. Poon, Kelly Y. C. Wong, Lianyi Zhou, Oscar T. N. Mak, Jeremy C. Y. Lee, Suhui Zhao, Antonio H. Y. Ngan, Alan K. L. Wu, Kitty S. C. Fung, Tak-Lun Que, Jade L. L. Teng, Dirk Schnieders, Siu-Ming Yiu, Susanna K. P. Lau, Patrick C. Y. Woo

“…In this proof-of-concept study, using 2813, 2814 and 1240 images from four clinically important Aspergillus species for training, validation and testing, respectively; the performances and accuracies of automatic Aspergillus identification using colonial images by three different convolutional neural networks were evaluated. …”
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LBNet: A Lightweight Bilateral Network for Semantic Segmentation of Martian Rock by Pengfei Wei, Zezhou Sun, He Tian

“…In the deep semantic information branch, channel split convolution (CSConv) is adopted to extract features by adopting different convolution kernels on different channel, reducing the similarity between different feature maps and increasing feature maps diversity. …”
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A high-precision correction method in non-rigid 3D motion poses reconstruction by Cuihong Fan, Weina Fu, Shuai Liu

“…According to the frame difference and morphological processing, the background of image is separated and denoised. …”
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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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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

“…However, multi-angle sensor configurations contain implicit information about 3D structure, due to parallax and atmospheric path differences. Extracting that implicit information requires computationally expensive radiative transfer techniques. …”
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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

“…Correct field diagnosis of brood disease is challenging and requires years of experience to identify and differentiate various disease states according to subtle differences in larval symptomology. To explore the feasibility of an image-based AI diagnosis tool, we collaborated with apiary inspectors and researchers to generate a dataset of 2,759 honey bee larvae images from Michigan apiaries, molecularly verified through 16 S rRNA microbiome sequencing and qPCR viral screening. …”
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