Cost-Efficient Fall Risk Assessment With Attention Augmented Vision Machine Learning on Sit-to-Stand Test Videos by Chunhua Pan, Boting Qu, Rui Miao, Xin Wang

“…Furthermore, a novel Attention-augmented Spatial-Temporal Graph Convolutional Network (AST-GCN) is developed for reliably identifying the action in each frame, enabling accurate computation of key kinematic features for fall risk prediction. …”
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Determining optimal strategies for primary prevention of cardiovascular disease: a synopsis of an evidence synthesis study by Olalekan A Uthman, Lena Al-Khudairy, Chidozie Nduka, Rachel Court, Jodie Enderby, Seun Anjorin, Hema Mistry, G J Melendez-Torres, Sian Taylor-Phillips, Aileen Clarke

“…A machine learning study developed a parallel Convolutional Neural Network algorithm with 96.4% recall and 99.1% precision for study screening. …”
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Models, systems, networks in economics, engineering, nature and society by D.V. Mirosh

“…The materials of this article present the technology of using convolutional neural networks for the diagnosis of inter-turn circuits in three-phase asynchronous motors with a short-circuited rotor, based on the use of a graphical representation of the relations of energy characteristics. …”

Enhancing Indian summer monsoon prediction: Deep learning approach for skillful long-lead forecasts of rainfall by Kalpesh R. Patil, Takeshi Doi, J.V. Ratnam, Swadhin K. Behera

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Bearing Fault Diagnosis in Induction Motors Using Low-Cost Triaxial ADXL355 Accelerometer and a Hybrid CWT-DCNN-LSTM Model by Muhammad Ahsan, Jose Rodriguez, Mohamed Abdelrahem

“…This paper presents a novel approach for bearing fault diagnosis in induction motor utilizing an improved hybrid Continuous Wavelet Transform-Deep Convolutional Neural Network-Long Short-Term Memory (CWT-DCNN-LSTM) model. …”
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Precision Enhanced Bioactivity Prediction of Tyrosine Kinase Inhibitors by Integrating Deep Learning and Molecular Fingerprints Towards Cost-Effective and Targeted Cancer Therapy by Fatma Hilal Yagin, Yasin Gormez, Cemil Colak, Abdulmohsen Algarni, Fahaid Al-Hashem, Luca Paolo Ardigò

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Semantic Segmentation of Archaeological Features on Public Lands: Case Study of Historical Cotton Terraces within the Piedmont National Wildlife Refuge, Georgia, USA by Claudine Gravel-Miguel, Grant Snitker, Jayde N. Hirniak, Katherine Peck, Alex Fetterhoff

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Learnable Resized and Laplacian-Filtered U-Net: Better Road Marking Extraction and Classification on Sparse-Point-Cloud-Derived Imagery by Miguel Luis Rivera Lagahit, Xin Liu, Haoyi Xiu, Taehoon Kim, Kyoung-Sook Kim, Masashi Matsuoka

“…While cost effective, these sensors produce sparser point clouds, leading to poor feature representation and degraded performance in deep learning techniques, such as convolutional neural networks (CNN), for tasks like road marking extraction and classification, which are essential for HD map generation. …”
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PM2.5 prediction using population-based centrality weight by Hee Joon Choi, Won Kyung Lee, So Young Sohn

“…We propose to apply a population-based centrality weight to the cost function of the forecasting model, reflecting both of residential and changes in active populations. …”
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HALF: Histogram of Angles in Linked Features for 3D Point Cloud Data Segmentation of Plants for Robust Sensing by Hidenori Takauji, Naofumi Wada, Shun’ichi Kaneko, Takanari Tanabata

“…To enhance robustness and interpretability, we extend HALF to a convolution-based mathematical framework and introduce the Sequential Competitive Segmentation Algorithm (SCSA) for phytomer-level classification. …”
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Complex-Valued CNN Nonlinear Equalization Enabled 36-Tbit/s (45×800-Gbit/s) WDM Transmission Over 3150 Km Using Silicon-Based IC-TROSA by Yuhan Gong, Xiaoshuo Jia, Ying Zhu, Kailai Liu, Ming Luo, Jin Tao, Zhixue He, Chao Li, Zichen Liu, Yan Li, Jian Wu, Chao Yang

“…The growing Internet traffic urgently needs large-capacity and cost-effective optical transmissions. To maintain system performance under low-cost conditions, the silicon-based integrated coherent transmit and receive optical sub-assembly (IC-TROSA) and the complex-valued convolutional neural network (CVCNN) algorithm provide an effective solution for high-capacity and long-distance WDM optical transmission. …”
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Speech enhancement method based on multi-domain fusion and neural architecture search by Rui ZHANG, Pengyun ZHANG, Chaoli SUN

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Human activity recognition algorithm based on the spatial feature for WBAN by Chi JIN, Zhijun LI, Dayang SUN, Fengye HU

“…Traditional image-based activity recognition algorithms have some problems,such as high computational cost,numerous blind spots and easy privacy leakage.To solve the problem above,the CCLA (convolution-convolutional long short-term memory-attention) activity recognition algorithm based on the acceleration and gyroscope data was proposed.The convolutional neural network was used to extract spatial features of activity data and got the hidden time series information from the convolutional long short-term memory network.Simulating human brain selecting attention mechanism,attention-encoder was constructed to extract the spatial and temporal features at a higher level.The CCLA algorithm was tested on UCI-HAPT (university of California Irvine-smartphone-based recognition of human activities and postural transitions) public data set,and realized the classification of 12 types of activity with the accuracy of 93.27%.…”
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Lightweight and efficient skeleton-based sports activity recognition with ASTM-Net. by Bin Wu, Mei Xue, Ying Jia, Ning Zhang, GuoJin Zhao, XiuPing Wang, Chunlei Zhang

“…Second, we introduce the Temporal Multi‑branch Graph convolution Module (TMGM), which employs parallel branches of channel‑reduction, dilated temporal convolutions with varied dilation rates, pooling, and pointwise convolutions to effectively model both fine‑grained and long‑range temporal dependencies. …”
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A small underwater object detection model with enhanced feature extraction and fusion by Tao Li, Yijin Gang, Sumin Li, Yizi Shang

“…Next, a variable kernel convolution (VKConv) is proposed to dynamically adjust the convolution kernel size, enabling better multi-scale feature extraction. …”
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A lightweight UAV target detection algorithm based on improved YOLOv8s model by Fubao Ma, Ran Zhang, Bowen Zhu, Xirui Yang

“…First, Cross Stage Partial Convolutional Neural Network (CNN) Transformer Fusion Net (CSP-CTFN) is proposed. …”
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QoS Routing in Telecommunications Networks by N. I. Listopad, O. A. Lavshuk

“…The results of numerical modeling of the search for the optimal path for various values of weight coefficients and cost coefficients are presented. It is shown that when choosing a path for multi-criteria optimization, it is necessary to choose the coefficients of the additive convolution as the product of the weight coefficients and the cost coefficients directly. …”
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A novel pansharpening method based on cross stage partial network and transformer by Yingxia Chen, Huiqi Liu, Faming Fang

“…Abstract In remote sensing image fusion, the conventional Convolutional Neural Networks (CNNs) extract local features of the image through layered convolution, which is limited by the receptive field and struggles to capture global features. …”
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Adversarial sample generation algorithm for vertical federated learning by Xiaolin CHEN, Daoguang ZAN, Bingchao WU, Bei GUAN, Yongji WANG

“…To adapt to the scenario characteristics of vertical federated learning (VFL) applications regarding high communication cost, fast model iteration, and decentralized data storage, a generalized adversarial sample generation algorithm named VFL-GASG was proposed.Specifically, an adversarial sample generation framework was constructed for the VFL architecture.A white-box adversarial attack in the VFL was implemented by extending the centralized machine learning adversarial sample generation algorithm with different policies such as L-BFGS, FGSM, and C＆amp;W.By introducing deep convolutional generative adversarial network (DCGAN), an adversarial sample generation algorithm named VFL-GASG was designed to address the problem of universality in the generation of adversarial perturbations.Hidden layer vectors were utilized as local prior knowledge to train the adversarial perturbation generation model, and through a series of convolution-deconvolution network layers, finely crafted adversarial perturbations were produced.Experiments show that VFL-GASG can maintain a high attack success while achieving a higher generation efficiency, robustness, and generalization ability than the baseline algorithm, and further verify the impact of relevant settings for adversarial attacks.…”
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SCL-YOLOv11: A Lightweight Object Detection Network for Low-Illumination Environments by Shulong Zhuo, Hao Bai, Lifeng Jiang, Xiaojian Zhou, Xu Duan, Yiqun Ma, Zihan Zhou

“…In response to the challenges of reduced detection accuracy and high edge-deployment costs encountered by mainstream single-stage object detection models under low-light conditions, this paper proposes a lightweight object detection network based on YOLOv11, integrates StarNet, C3k2-Star, and a lightweight detail-enhanced convolution and shared convolutional detection head(LSDECD), so called SCL-YOLOv11 herein. …”
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