Context-Aware Deep Learning Model for Yield Prediction in Potato Using Time-Series UAS Multispectral Data by Suraj A. Yadav, Xin Zhang, Nuwan K. Wijewardane, Max Feldman, Ruijun Qin, Yanbo Huang, Sathishkumar Samiappan, Wyatt Young, Francisco G. Tapia

“…The proposed feature engineering and prediction model followed a two-fold approach: first, adoption of partial least squares regression (PLSR) algorithm to extract features relevant to yield, and second, a novel context-aware attention and residual connection convolution-bidirectional gated recurrent unit bidirectional long short-term memory-network (CAR Conv1D-BiGRU-BiLSTM-Net) to exploit time-series multifeatures information to predict final yield. …”
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Automatic detection of floating instream large wood in videos using deep learning by J. Aarnink, J. Aarnink, T. Beucler, T. Beucler, M. Vuaridel, V. Ruiz-Villanueva, V. Ruiz-Villanueva

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Research on the Construction of Crossborder e-Commerce Logistics Service System Based on Machine Learning Algorithms by Jinbo Xu, Shibiao Mu

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An Improved Phase Space Reconstruction Method-Based Hybrid Model for Chaotic Traffic Flow Prediction by Yue Hou, Da Li, Di Zhang, Zhiyuan Deng

“…Traffic flow is chaotic due to nonstationary realistic factors, and revealing the internal nonlinear dynamics of chaotic data and making high-accuracy predictions is the key to traffic control and inducement. …”
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Attention Neural Network for Biomedical Word Sense Disambiguation by Chun-Xiang Zhang, Shu-Yang Pang, Xue-Yao Gao, Jia-Qi Lu, Bo Yu

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Evaluation of Flavor Type of Tobacco Blending Module: A Prediction Model Based on Near-Infrared Spectrum by Lin Wang, Yuhan Guan, Yaohua Zhang

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3D Model Classification Based on Bayesian Classifier with AdaBoost by Xue-Yao Gao, Kai-Peng Li, Chun-Xiang Zhang, Bo Yu

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Prediction of Grain Yield in Henan Province Based on Grey BP Neural Network Model by Bingjun Li, Yifan Zhang, Shuhua Zhang, Wenyan Li

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Face Detection and Segmentation Based on Improved Mask R-CNN by Kaihan Lin, Huimin Zhao, Jujian Lv, Canyao Li, Xiaoyong Liu, Rongjun Chen, Ruoyan Zhao

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On a q-Analogue of the Elzaki Transform Called Mangontarum q-Transform by Mahid M. Mangontarum

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Singular Perturbation of Nonlinear Systems with Regular Singularity by Domingos H. U. Marchetti, William R. P. Conti

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Machine learning for experimental design of ultrafast electron diffraction by Mohammad Shaaban, Sami El-Borgi, Aravind Krishnamoorthy

“…The lack of real-time data prevents in situ tuning of experimental parameters toward desirable material dynamics or avoid sample damage. We demonstrate that machine learning methods based on Convolutional Neural Networks trained on synthetic and experimental diffraction patterns can perform real-time analysis of diffraction data to resolve dynamical processes in a representative material, $${\textrm{MoTe}_{2}}$$ , and identify signs of material damage. …”
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Novel Hybrid Deep Learning Model for Forecasting FOWT Power Output by Mohammad Barooni, Deniz Velioglu Sogut, Parviz Sedigh, Masoumeh Bahrami

“…The study addresses the challenges of designing and assessing the power generation of FOWTs due to their multidisciplinary nature involving aerodynamics, hydrodynamics, structural dynamics, and control systems. A hybrid deep learning model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks is proposed to predict the performance of FOWTs accurately and more efficiently than traditional numerical models. …”
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Vessel Trajectory Prediction Method Based on the Time Series Data Fusion Model by Xinyun WU, Jiafei CHEN, Caiquan XIONG, Donghua LIU, Xiang WAN, Zexi CHEN

“…To address this issue, this study introduces a method consisting of temporal convolutional network (TCN), convolutional neural network (CNN) and convolutional long short-term memory (ConvLSTM) to predict vessel trajectories, called TCC. …”
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Evaluation of machine learning and deep learning algorithms for fire prediction in Southeast Asia by Aditya Eaturu, Krishna Prasad Vadrevu

“…Accurately predicting fire occurrences in SEA remains challenging due to its complex spatiotemporal dynamics. Improved fire predictions enable timely interventions, helping to control and mitigate fires. …”
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Full-Scale Piano Score Recognition by Xiang-Yi Zhang, Jia-Lien Hsu

“…Then, the identified dynamics symbols are removed from the original score, and the remaining score serves as the input into a Convolutional Recurrent Neural Network (CRNN) for the following steps. …”
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Universal scaling laws of absorbing phase transitions in artificial deep neural networks by Keiichi Tamai, Tsuyoshi Okubo, Truong Vinh Truong Duy, Naotake Natori, Synge Todo

“…We demonstrate that conventional artificial deep neural networks operating near the phase boundary of the signal propagation dynamics—also known as the edge of chaos—exhibit universal scaling laws of absorbing phase transitions in nonequilibrium statistical mechanics. …”
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Fusing Events and Frames with Coordinate Attention Gated Recurrent Unit for Monocular Depth Estimation by Huimei Duan, Chenggang Guo, Yuan Ou

“…Unlike the conventional ConvGRUs, our CAGRU abandons the conventional practice of using convolutional layers for all the gates and innovatively designs the coordinate attention as an attention gate and combines it with the convolutional gate. …”
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Economic Structure Analysis Based on Neural Network and Bionic Algorithm by Yanjun Dai, Lin Su

“…In deep neuroevolutionary method, the structure space of convolutional neural network is proposed to solve the search space design of neural structure search (NAS), and the GA-based deep neuroevolutionary method under the structure space of convolutional neural network is proposed to solve the problem that numerous hyperparameters and network structure parameters can produce explosive combinations when designing deep learning models. …”
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Enhancing environmental monitoring of harmful algal blooms with ConvLSTM image prediction by Sung Jae Kim, Yongbok Cho

“…These interpolated images are then used as input for a ConvLSTM (Convolutional Long Short-Term Memory) network, which integrates convolutional layers to capture spatial patterns and LSTM units to model temporal dependencies. …”
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