Rapid estimation of DON content in wheat flour using close‐range hyperspectral imaging and machine learning by Dinesh Kumar Saini, Anshul Rana, Jyotirmoy Halder, Mohammad Maruf Billah, Harsimardeep S. Gill, Jinfeng Zhang, Subash Thapa, Shaukat Ali, Brent Turnipseed, Karl Glover, Maitiniyazi Maimaitijiang, Sunish K. Sehgal

“…However, the one‐dimensional convolutional neural network (1D‐CNN) achieved the highest prediction accuracies (R2P = 0.90 and = 0.96 for original and augmented datasets, respectively) compared to all tested models and demonstrated the lowest error. …”
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Assessment of Tumor Infiltrating Lymphocytes in Predicting Stereotactic Ablative Radiotherapy (SABR) Response in Unresectable Breast Cancer by Mateusz Bielecki, Khadijeh Saednia, Fang-I Lu, Shely Kagan, Danny Vesprini, Katarzyna J. Jerzak, Roberto Salgado, Raffi Karshafian, William T. Tran

“…Whole slide images (WSIs) were pre-processed, and then a pre-trained convolutional neural network model (CNN) was employed to identify the regions of interest. …”
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Estimating and mapping tailings properties of the largest iron cluster in China for resource potential and reuse: A new perspective from interpretable CNN model and proposed spectr... by Haimei Lei, Nisha Bao, Moli Yu, Yue Cao

“…Simultaneously, it minimizes the impact of moisture content and particle size variations in surface tailings. In addition, the partial least squares regression (PLSR), random forest (RF) and convolutional neural network (CNN) algorithms based on laboratory spectra were used to calibrate spectral information with associated tailing properties. …”
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A Lightweight Citrus Ripeness Detection Algorithm Based on Visual Saliency Priors and Improved RT-DETR by Yutong Huang, Xianyao Wang, Xinyao Liu, Liping Cai, Xuefei Feng, Xiaoyan Chen

“…To reduce computational overhead, we designed the E-CSPPC module, which efficiently combines cross-stage partial networks with gated and partial convolutions, combined with cascaded group attention (CGA) and inverted residual mobile block (iRMB), which minimizes model complexity and computational demand and simultaneously strengthens the model’s capacity for feature representation. …”
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A Novel Method for Traceability of Crude Oil Leakage on Offshore Platforms Based on Improved YOLOv5 Model Multi-Target Identification and Correlation Analysis by Zhenghua Wang, Shihai Zhang, Chongnian Qu, Zongyi Zhang, Feng Sun

“…In response to practical issues such as the complex structure of offshore platform scenes and the overlap of associated oil spill targets, a deformable convolution DCNv2 module is introduced to improve the model’s recognition accuracy for multi-shaped targets. …”
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Stone inscription image segmentation based on Stacked-UNets and GANs by Pan Zhang, Chao Li, Yuanhua Sun

“…Abstract To overcome the challenges posed in effectively extracting stone inscriptions characterized by highly self-similarity between the foreground and background, a character image segmentation framework is proposed that integrates Stacked-UNets and Generative Adversarial Networks (GANs). Initially, a convolutional rule tailored for self-similar feature extraction is introduced to enhance the image detail segmentation. …”
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The role of learned song in the evolution and speciation of Eastern and Spotted towhees. by Ximena León Du'Mottuchi, Nicole Creanza

“…The Eastern and Spotted towhees are recently diverged sister species that now have partially overlapping ranges with evidence of some hybridization. …”
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Optical Coherence Tomography — Angiography in Revealing Predictors of Small Choroidal Melanoma Transpupillary Thermotherapy Efficiency by E. B. Myakoshina, S. V. Saakyan, O. A. Ivanova

“…Prior to TTT — a loop-shaped, cranked-convoluted with an uneven lumen heterogeneous nature of the vasculature of the tumor with numerous bends and interlacing, located under the vessels of the retina in the central zone; the area of neovascular vasculature is 32.82 mm2, the density is 12.42 %. …”
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Correction of crop water deficit indicators based on time-lag effects for improved farmland water status assessment by Yujin Wang, Zhitao Zhang, Yinwen Chen, Shaoshuai Fan, Haiying Chen, Xuqian Bai, Ning Yang, Zijun Tang, Long Qian, Zhengxuan Mao, Siying Zhang, Junying Chen, Youzhen Xiang

“…Time-lag cross-correlation, time-lag mutual information, grey time-lag correlation analysis, time-lag Almon, and time-lag partial least squares (PLS) were applied to calculate the time-lag parameters. …”
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A proximal policy optimization based deep reinforcement learning framework for tracking control of a flexible robotic manipulator by Joshi Kumar V, Vinodh Kumar Elumalai

“…This paper puts forward a policy feedback based deep reinforcement learning (DRL) control scheme for a partially observable system by leveraging the potentials of proximal policy optimization (PPO) algorithm and convolutional neural network (CNN). …”
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Application of Artificial Intelligence in Prosthodontics in the 21st century by Lavanya V, Keerthivasan MS, Venkatakrishnan CJ, Tamizhesai BV, Anandh V

“…In removable prosthodontics convolutional neural networks CNNs have enabled accurate classification of partially edentulous arches and prediction of facial aesthetics. …”
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Task Offloading with LLM-Enhanced Multi-Agent Reinforcement Learning in UAV-Assisted Edge Computing by Feifan Zhu, Fei Huang, Yantao Yu, Guojin Liu, Tiancong Huang

“…This framework integrates the QTRAN algorithm with a large language model (LLM) for efficient region decomposition and employs graph convolutional networks (GCNs) combined with self-attention mechanisms to adeptly manage inter-subregion relationships. …”
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Singular Perturbation of Nonlinear Systems with Regular Singularity by Domingos H. U. Marchetti, William R. P. Conti

“…A simple lemma is applied to tame convolutions that appear in the power series expansion of nonlinear equations. …”
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Resilience driven EV coordination in multiple microgrids using distributed deep reinforcement learning by Yuxin Wu, Ting Cai, Xiaoli Li

“…The proposed method applies an architecture with multi-actor, single-learner to reduce training complexity, employing a convolutional neural network to capture spatial characteristics from the CPTN, and incorporating a long short-term memory to derive temporal sequence features across multiple time steps, thereby enhancing the exploration efficiency of the action space. …”
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Autonomous Quadrotor Path Planning Through Deep Reinforcement Learning With Monocular Depth Estimation by Mahdi Shahbazi Khojasteh, Armin Salimi-Badr

“…The former module uses a convolutional encoder-decoder network to learn image depth from visual cues self-supervised, with the output serving as input for the latter module. …”
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Obstacle inversion based on the self-healing property of structured light by Shuailing Wang, Zhe Zhao, Mingjian Cheng, Jingping Xu, Yaping Yang

“…Abstract The self-healing property of structured light allows it to partially recover its original intensity distribution during propagation after a portion of its intensity has been obscured by an obstacle. …”
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From pixels to planning: scale-free active inference by Karl Friston, Karl Friston, Conor Heins, Tim Verbelen, Lancelot Da Costa, Lancelot Da Costa, Tommaso Salvatori, Dimitrije Markovic, Dimitrije Markovic, Alexander Tschantz, Magnus Koudahl, Christopher Buckley, Christopher Buckley, Thomas Parr

“…The ensuing renormalizing generative models (RGM) can be regarded as discrete homologs of deep convolutional neural networks or continuous state-space models in generalized coordinates of motion. …”
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Machine Learning to Recognise ACL Tears: A Systematic Review by Julius Michael Wolfgart, Ulf Krister Hofmann, Maximilian Praster, Marina Danalache, Filippo Migliorini, Martina Feierabend

“…Deep learning algorithms in the form of convolutional neural networks (CNNs) were most frequently used. …”
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Direction of Arrival Estimation Algorithm for Underwater Distributed Sources Based on Deep Neural Network by Yinian LIANG, Jie LI, Fangjiong CHEN, Fei JI, Hua YU

“…By leveraging the separability of temporal and angular coherence components in the partially coherent distributed source signal model and simplifying the model by segmented mean normalization, a DNN model was constructed and trained with multiple samples of different coherence coefficients, thus achieving robust DOA estimation for distributed sources with different coherence levels. …”
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Development and validation of a deep learning system for detection of small bowel pathologies in capsule endoscopy: a pilot study in a Singapore institution by Bochao Jiang, Michael Dorosan, Justin Wen Hao Leong, Marcus Eng Hock Ong, Sean Shao Wei Lam, Tiing Leong Ang

“…They serve as a decision support system, partially automating the diagnosis process by providing probability predictions for abnormalities. …”
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