Star-YOLO: A Lightweight Real-Time Wheat Grain Detection Model for Embedded Deployment by Zhihang Qu, Xiao Liang, Sicheng Liang, Xiumei Guo

“…A Shape-NWD loss function is designed, incorporating shape and scale information of target bounding boxes to refine regression, tackling the challenge of distinguishing overlapping wheat grains. …”
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SMART HYBRID MODELS FOR IMPROVED BREAST CANCER DETECTION by Nageswara Rao Gali, Panduranga Vital Terlapu, Yasaswini Mandavakuriti, Sai Manoj Somu, Madhavi Varanasi, Vijay Telugu, Maheswara Rao V V R

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Principles of creation of the big territorially distributed automated systems by V. A. Tretyakov, G. V. Kulikov, Yu. F. Lukyanets

“…The article defines large territorially distributed automated systems, which include systems that collect and process information from spatially spaced sensors on objects. …”
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Multi-task genomic prediction using gated residual variable selection neural networks by Yuhua Fan, Patrik Waldmann

“…Results The experimental results demonstrate that the GRVSNN model outperforms traditional tabular genomic prediction models, including Bayesian regression methods and LassoNet. Using genomic and pedigree information, GRVSNN achieves a lower mean squared error (MSE), and higher Pearson (r) and distance (dCor) correlation between predicted and true phenotypic values in the test data. …”
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Comprehensive Environmental Monitoring System for Industrial and Mining Enterprises Using Multimodal Deep Learning and CLIP Model by Shuqin Wang, Na Cheng, Yan Hu

“…The initial phase employs ResNet within the CLIP model for extracting image features, and a Transformer for encoding text features. …”
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MFD-KD: Multi-Scale Frequency-Driven Knowledge Distillation by Tao Dai, Rongliang Huang, Hang Guo, Jinbao Wang, Bo Li, Zexuan Zhu

“…Unlike traditional KD methods that primarily focus on the consistency of intermediate features in the spatial domain, we propose a novel Multi-scale Frequency-Driven Knowledge Distillation (MFD-KD) framework, which emphasizes the utilization of information in the frequency domain. Specifically, our method adopts Fast Fourier Transform (FFT) to shift intermediate feature maps of spatial domain into the corresponding frequency domain, enabling our approach to extract crucial high- and low-frequency information both inside and outside the frequency layer’s square center, while also minimizing interference from non-semantic information, such as noise. …”
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Early detection of mental health disorders using machine learning models using behavioral and voice data analysis by Sunil Kumar Sharma, Ahmed Ibrahim Alutaibi, Ahmad Raza Khan, Ghanshyam G. Tejani, Fuzail Ahmad, Seyed Jalaleddin Mousavirad

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Strategic analysis for advancing Morocco's nuclear infrastructure using PESTELE framework by Hafsa Housni, Naila Amrous, Najima Daoudi, Mohamed Jaouad Malzi

“…This research underscores the global imperative to transition to net-zero emissions and the pivotal role nuclear energy plays in addressing climate change. …”
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FFAE-UNet: An Efficient Pear Leaf Disease Segmentation Network Based on U-Shaped Architecture by Wenyu Wang, Jie Ding, Xin Shu, Wenwen Xu, Yunzhi Wu

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Deep learning-based object detection and robotic arm grasping by ZHANG Lei, ZHANG Senhui, YAN Song, YUAN Yuan

“…Secondly, to enhance the feature extraction capabilities of the grasping network, the parallel use of different-size convolutional kernels in the Inception-ResNet module was utilized to broaden the network's receptive field. …”
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FastPFM: a multi-scale ship detection algorithm for complex scenes based on SAR images by Wei Wang, Dezhi Han, Chongqing Chen, Zhongdai Wu

“…Firstly, we utilize FasterNet as the backbone network to reduce computational redundancy, enhancing feature extraction efficiency and overall computational performance. …”
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Quantifying CineECG Output for Enhancing Electrocardiography Signals Classification by MHD Jafar Mortada, Agnese Sbrollini, Ilaria Marcantoni, Erica Iammarino, Laura Burattini, Peter Van Dam

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Joint boundary-aware and multi-feature fusion for point cloud semantic segmentation by LU Jian, ZHAO Jie, GUO Huihui, LIANG Youcheng, ZHENG Yufei

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Hydrogeological modeling of the Salto-Arapey aquifer system: A tool to understand connectivity and improve management by Armando Borrero, Pablo Gamazo, Julián Ramos, Andrés Saracho, Lucas Bessone, Gonzalo Blanco, Rafael Navas, Elena Alvareda

“…The hydrological model estimated that although the greatest water inflow occurs through the surface water bodies (7.24E + 05 m3/d), there is a greater outflow (−9.48E + 05 m3/d). Therefore, there is net outflow through rivers (−2.24E + 05 m3/d) while the only net inflow is through the diffuse recharge (3.58E + 05 m3/d). …”
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Research on intelligent segmentation method of coal body CT image fracture based on CBAM-UNet by Shuang Song, Yilun Xue, Suinan He, Xiang Ji, Xinshuang Cao, Guoying Liu, Juntao Chen, Hongjiao Chen

“…Therefore, this paper proposes CBAM-Unet (Convolutional Block Attention Module-Unet), an improved network model for coal body fracture extraction based on U-Net. The CBAM-Unet model leverages the U-Net's symmetric structure and residual connections, enabling complete fracture structure segmentation in complex coal body. …”
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SAM2Former: Segment Anything Model 2 Assisting UNet-Like Transformer for Remote Sensing Image Semantic Segmentation by Xuewen Li, Xiaomin Tian, Zihong Wang, Feng Zhang, Yanting Zhang, Na Yang, Chuanzhao Tian

“…Secondly, we devise a decoder based on global-local transformer module (GLTM) to effectively extract global context information and local detail information, improving the segmentation ability of edge texture. …”
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Trellis-Coded Modulation-Enabled Probabilistic Shaping With Simplified Viterbi Decoder for Bandwidth-Limited IMDD Systems by Han Cui, Zhongliang Sun, Xiaoqian Huang, Du Tang, Fei Xie, Yaojun Qiao

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Adaptive Feedback-Driven Segmentation for Continuous Multi-Label Human Activity Recognition by Nasreddine Belbekri, Wenguang Wang

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Multi-scale aware dual path network for face detection in resource-constrained edge computing environment by Qi QI, Yingxin MA, Jingyu WANG, Haifeng SUN, Jianxin LIAO

“…Aiming at the problem that face detectors with complex deep neural structures are difficult to deploy in the resource-constrained edge computing environment,to reduce the resource consumption while maintain the accuracy in complex scenes such as multi-scale face changes,occlusion,blur,and illumination,SDPN(multi-scale aware dual path network) for face detection was proposed.The Face-ResNet (face residual neural network) was improved,and a dual path shallow feature extractor was used to understand the multi-scale information of the image through parallel branches.Then the deep and shallow feature fusion module,a combination of the underlying image information and the high-level semantic feature,was used in conjunction with the multi-scale awareness training strategy to supervise the multi-branch learning discriminating features.The experimental results show that SDPN can extract more diversified features,which effectively improve the accuracy and robustness of face detection while maintaining the efficiency of the model and low inference delay.…”
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Perceptions of environmental sustainability amongst mineworkers by F. Ince

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