GDS-YOLOv7: A High-Performance Model for Water-Surface Obstacle Detection Using Optimized Receptive Field and Attention Mechanisms by Xu Yang, Lei Huang, Fuyang Ke, Chao Liu, Ruixue Yang, Shicheng Xie

“…Furthermore, we develop a Spatial–Channel Synergetic Attention (SCSA) mechanism to counteract feature degradation in convolutional operations, embedding this module within the Extended Effective Long-Range Aggregation Network (E-ELAN) network to enhance contextual awareness. …”
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Transcriptome-Based Analysis of the Co-Expression Network of Genes Related to Nitrogen Absorption in Rice Roots Under Nitrogen Fertilizer and Density by Runnan Wang, Qi Zhu, Haiyuan Wang, Qiangqiang Xiong

“…These findings establish a gene-network framework that reveals the molecular regulatory network of crop responses to N nutrition and planting density and provides important theoretical support for N fertilizer management, population quality optimization, and variety breeding in precision agriculture.…”
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Optimization and Benchmarking of Image Segmentation for Improved Landmark Detection in Lower Limb X-Rays and Accurate Coronal Plane Alignment of the Knee Classification by Sebastian Amador Sanchez, Ashkan Zarghami, Philippe van Overschelde, Jef Vandemeulebroucke

“…We detected eight landmarks in full lower limb X-rays and investigated methodological aspects to optimize image segmentation performance: network architecture (U-Net vs. …”
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Real time weed identification with enhanced mobilevit model for mobile devices by Xiaoyan Liu, Qingru Sui, Zhihui Chen

“…Following this, we introduce an optimized MobileViT model that incorporates the Efficient Channel Attention (ECA) module into the weed feature extraction network. …”
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Detecting sand gradation based on the two-dimensional sand particle features in sand images by Chuanyun Xu, Heng Wang, Yang Zhang, Song Sun, Gang Li

“…Images of sand particles with a single gradation are captured to extract five sand particle feature parameters, which are then used to train a mature network model. The equivalent volume of the sand particles is calculated by multiplying the equivalent projected area by the equivalent elliptical Feret short diameter, and the Feret short diameter is optimized using an optimization algorithm to enhance the volume calculation. …”
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Forecasting motion trajectories of elbow and knee joints during infant crawling based on long–short-term memory (LSTM) networks by Jieyi Mo, Qiliang Xiong, Ying Chen, Yuan Liu, Xiaoying Wu, Nong Xiao, Wensheng Hou

“…As expected, the LSTM predicting 5 output time steps has the lowest average error, while the LSTM predicting 15 time steps has the highest average error. …”
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Research on Mobile Agent Path Planning Based on Deep Reinforcement Learning by Shengwei Jin, Xizheng Zhang, Ying Hu, Ruoyuan Liu, Qing Wang, Haihua He, Junyu Liao, Lijing Zeng

“…This study proposes LSTM Bézier–Double Deep Q-Network (LB-DDQN), an advanced path-planning framework for mobile agents based on deep reinforcement learning. …”
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Computational analysis of turbulent flow characteristics in nanofluids containing 1-D and 2-D carbon nanomaterials: grid optimization and performance evaluation by Hai Tao, Mohammed Suleman Aldlemy, Raad Z. Homod, Mustafa K. A. Mohammed, Abdul Rahman Mallah, Omer A. Alawi, Shafik S. Shafik, Hussein Togun, Blanka Klimova, Hassan Alzahrani, Zaher Mundher Yaseen

“…The 3D geometry was solved under a fully developed turbulent flow of 6000 ≤ Re ≤ 16,000 using the model of k-ω SST via (ANSYS FLUENT 2022R2) software. Four numerical networks, Polyhedra, Polyhexacore, Hexacore, and Tetrahedral, were optimized. …”
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Artificial intelligence-optimized shield parameters for soft ground tunneling in urban environment: A case study of Bangkok MRT Blue Line by Sahatsawat Wainiphithapong, Chana Phutthananon, Sompote Youwai, Pitthaya Jamsawang, Phattarawan Malaisree, Ochok Duangsano, Pornkasem Jongpradist

“…This integrated framework, which combines the non-dominated sorting genetic algorithm (NSGA-II) with LSTM neural networks, is applied to MOO to identify the optimal SOPs, while accounting for their influence on S variation as a time-series over 11 timesteps, as considered in this study. …”
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Bone Age Estimation of Chinese Han Adolescents’s and Children’s Elbow Joint X-rays Based on Multiple Deep Convolutional Neural Network Models by LI Dan-yang, ZHOU Hui-ming, WAN Lei, LIU Tai-ang, LI Yuan-zhe, WANG Mao-wen, WANG Ya-hui

“…The DenseNet121 model with Scheme 3 achieves optimal performance. Using segmentation networks, especially that trained with annotation areas encompassing the full elbow joint including the distal humerus, proximal radius, and proximal ulna, can improve the accuracy of bone age estimation based on elbow joint X-ray images.…”
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Development of an Artificial Neural Network Model to Predict the Tensile Strength of Friction Stir Welding of Dissimilar Materials Using Cryogenic Processes by Mingoo Cho, Jinsu Gim, Ji Hoon Kim, Sungwook Kang

“…The objective of this study was to develop an artificial neural network (ANN) model for predicting the tensile strength of friction stir welding (FSW) joints between dissimilar materials, with a particular focus on aluminum and copper, using cryogenic processes. …”
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Regional differences in wintering habitat selection strategies of Siberian Crane (Leucogeranus leucogeranus) and ecological network construction of key wintering areas by Jianying Wang, Mingqin Shao

“…Although improvements in the YRD were less marked, the optimization scheme still improved the security of the crane migration network. …”
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MicrocrackAttentionNext: Advancing Microcrack Detection in Wave Field Analysis Using Deep Neural Networks Through Feature Visualization by Fatahlla Moreh, Yusuf Hasan, Bilal Zahid Hussain, Mohammad Ammar, Frank Wuttke, Sven Tomforde

“…Microcrack detection using deep neural networks (DNNs) through an automated pipeline using wave fields interacting with the damaged areas is highly sought after. …”
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A Dual Filter Based on Radial Basis Function Neural Networks and Kalman Filters with Application to Numerical Wave Prediction Models by Athanasios Donas, Ioannis Kordatos, Alex Alexandridis, George Galanis, Ioannis Th. Famelis

“…The produced filter is self-adaptive, identifying optimal Radial Basis Function network configurations through an automated process involving various network parameters tuning. …”
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Machine learning framework to estimate ridership loss in public transport during external crises: case study of bus network in Stockholm by Mahsa Movaghar, Erik Jenelius, David Hunter

“…Abstract Recent technologies for recording and storing data, as well as advancements in data processing techniques, have opened up novel possibilities for urban planners to design a more optimal public transport network. This study aims to initially develop a robust framework for making an insightful understanding of already recorded and available data sets using machine learning approaches. …”
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Performance Analysis of Parallel Free-Space Optical/Radio Frequency Transmissions in Satellite–Aerial–Ground Integrated Network with Power Allocation by Xin Li, Yongjun Li, Shanghong Zhao, Xinkang Song, Jianjia Li

“…Satellite–aerial–ground integrated networks (SAGINs) combined with hybrid free-space optical (FSO) and radio frequency (RF) transmissions have shown great potential in improving service throughput and reliability. …”
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Coordinated Multimodal Transportation Infrastructure Planning in Megacities: An Agent-AI-Discrete Network Design Problem Approach Based on Chat-GPT by Jin Guo, Chenyang Wang, Linxiu Wang

“…The results demonstrate that AA-DNDP can effectively assess the rationality of existing transportation infrastructure planning and identify theoretically optimal configurations based on AUT and PUF. The case study further confirms that AA-DNDP enhances the evaluation of network efficiency and facilitates coordinated multimodal infrastructure planning, offering a more practical and data-informed approach to improving transportation systems in megacities.…”
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A novel cluster head selection using Hybrid Artificial Bee Colony and Firefly Algorithm for network lifetime and stability in WSNs by J. Sengathir, A. Rajesh, Gaurav Dhiman, S. Vimal, C.A. Yogaraja, Wattana Viriyasitavat

“…The results of the HMABCFA, on an average are confirmed to enhance the network lifetime by 23.21%, energy stability by 19.84% and reduce network latency by 22.88%, compared to the benchmarked approaches.…”
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3D convolutional neural network based on spatial-spectral feature pictures learning for decoding motor imagery EEG signal by Xiaoguang Li, Xiaoguang Li, Yaqi Chu, Xuejian Wu

“…The results show an average decoding accuracy rate of 86.69%, surpassing other advanced networks. …”
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A CNN-Based Approach for Classical Music Recognition and Style Emotion Classification by Yawen Shi

“…The experiment results show average title recognition accuracy is 0.98, average style recognition accuracy is 0.89 and average emotion recognition accuracy is 0.93. …”
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