Monitoring Moso bamboo (Phyllostachys pubescens) forests damage caused by Pantana phyllostachysae Chao considering phenological differences between on-year and off-year using UAV h... by Anqi He, Zhanghua Xu, Yifan Li, Bin Li, Xuying Huang, Huafeng Zhang, Xiaoyu Guo, Zenglu Li

“…The on-year and off-year phenomenon is a distinctive phenological characteristic of Moso bamboo, reflecting variations in nutrient dynamics and endogenous hormonal rhythms during the transition from bamboo shoot to the culm. …”
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A multimodal deep learning architecture for predicting interstitial glucose for effective type 2 diabetes management by Muhammad Salman Haleem, Daphne Katsarou, Eleni I. Georga, George E. Dafoulas, Alexandra Bargiota, Laura Lopez-Perez, Miguel Rujas, Giuseppe Fico, Leandro Pecchia, Dimitrios Fotiadis, Gatekeeper Consortium

“…The CGM time series were processed using a stacked Convolutional Neural Network (CNN) and a Bidirectional Long Short-Term Memory (BiLSTM) network followed by an attention mechanism. …”
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Federated Learning and EEL-Levy Optimization in CPS ShieldNet Fusion: A New Paradigm for Cyber–Physical Security by Nalini Manogaran, Yamini Bhavani Shankar, Malarvizhi Nandagopal, Hui-Kai Su, Wen-Kai Kuo, Sanmugasundaram Ravichandran, Koteeswaran Seerangan

“…We still foresee limitations to scalability, data privacy, and handling the dynamic nature of CPS environments in existing approaches. …”
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Metering Automation System 3.0 Base Version Based on Machine Learning by Sheng Li, Leping Zhang, Hang Dai, Lukun Zeng, Yuan Ai, Shuang Qi, Yuanzhai Cui

“…The depthwise separable convolutional neural network (DSCNN) minimizes parameter overhead while capturing spatial correlations across distributed grid nodes, followed by convolutional block attention modules (CBAM) that dynamically recalibrate channel and spatial features to amplify discriminative patterns. …”
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Attention-enhanced hybrid CNN–LSTM network with self-adaptive CBAM for COVID-19 diagnosis by Fatin Nabilah Shaari, Aimi Salihah Abdul Nasir, Wan Azani Mustafa, Wan Aireene Wan Ahmed, Abdul Syafiq Abdull Sukor

“…However, baseline Convolutional Neural Network (CNN) commonly faced obstacles to fully capture the temporal dependencies present in sequential medical imaging data, limiting their diagnostic performance. …”
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Efficient Robot Localization Through Deep Learning-Based Natural Fiduciary Pattern Recognition by Ramón Alberto Mena-Almonte, Ekaitz Zulueta, Ismael Etxeberria-Agiriano, Unai Fernandez-Gamiz

“…These images are processed by a convolutional neural network (CNN), designed to detect the most distinctive features of the environment. …”
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GCN-Based Issues Classification in Software Repository by Bader Alshemaimri, Nafla Alrumayyan, Reem Alqadi

“…Graph Convolutional Network (GCN) have demon- strated significant potential in various fields, particu- larly in classification tasks. …”
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A hybrid deep learning framework for global irradiance prediction using fuzzy C-Means, CNN-WNN, and Informer models by Walid Mchara, Lazhar Manai, Mohamed Abdellatif Khalfa, Monia Raissi, Wissem Dimassi, Salah Hannachi

“…CNNs then extract high-level spatial features from each cluster, while WNNs decode multi-resolution irradiance dynamics, capturing both abrupt fluctuations and gradual trends. …”
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Potential for Evaluation of Interwell Connectivity under the Effect of Intraformational Bed in Reservoirs Utilizing Machine Learning Methods by Jinzi Liu

“…In this paper, Back Propagation (BP) and Convolutional Neural Networks (CNNs) are used to train the dynamic data with the influence of interlayer control connectivity in the reservoir layer as the training model. …”
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Exploiting compressed sensing and polynomial-based progressive secret image sharing for visually secure image selection encryption with authentication by Zhihua Gan, Shiping Song, Lin Zhou, Daojun Han, Jiangyu Fu, Xiuli Chai

“…Firstly, a selective encryption based on multi-task convolutional neural network (SE-MTCNN) is presented to distinguish and encrypt the sensitive and non-sensitive information of plain images. …”
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A lightweight hybrid model for accurate ammonia prediction in pig houses by Jacqueline Musabimana, Qiuju Xie, Hong Zhou, Ping Zheng, Honggui Liu, Tiemin Ma, Jiming Liu

“…The model replaces feedforward networks with separable convolutional layers to capture local and spatial dependencies more efficiently, as well as reduce computational complexity. …”
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Neural network-based forecasting and uncertainty analysis of new power generation capacity of electric energy by Xingyu Dou, Zehan Cui

“…MSCNN improves feature extraction with dynamic scale selection and deep residual modules. …”
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A method for feature division of Soccer Foul actions based on salience image semantics. by Jianming Wang, Lifeng Li

“…DLSPM combines the improved DeepPlaBV 3+architecture for salient region detection, Graph Convolutional Networks (GCN) for feature extraction and Deep Neural Network (DNN) for classification. …”
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Short-Term Target Maneuvering Trajectory Prediction Using DTW–CNN–LSTM by Haifeng Guo, Jinyi Yang, Xianyong Jing, Peng Zhang

“…Considering the characteristics of high noise, dynamic complexity, and variable data lengths inherent in short-range air combat scenarios, we employ dynamic time warping (DTW) to assess the similarity of 3D trajectory data. …”
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Divide-and-conquer routing for learning heterogeneous individualized capsules. by Hailei Yuan, Qiang Ren

“…Capsule Networks (CapsNets) have demonstrated an enhanced ability to capture spatial relationships and preserve hierarchical feature representations compared to Convolutional Neural Networks (CNNs). However, the dynamic routing mechanism in CapsNets introduces substantial computational costs and limits scalability. …”
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Synthesizing field plot and airborne remote sensing data to enhance national forest inventory mapping in the boreal forest of Interior Alaska by Pratima Khatri-Chhetri, Hans-Erik Andersen, Bruce Cook, Sean M. Hendryx, Liz van Wagtendonk, Van R. Kane

“…To achieve this goal, we compared the performance of two advanced modeling approaches, the convolutional neural network (CNN) and the XGBoost model. …”
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Forecasting Short- and Long-Term Wind Speed in Limpopo Province Using Machine Learning and Extreme Value Theory by Kgothatso Makubyane, Daniel Maposa

“…This study investigates wind speed prediction using advanced machine learning techniques, comparing the performance of Vanilla long short-term memory (LSTM) and convolutional neural network (CNN) models, alongside the application of extreme value theory (EVT) using the r-largest order generalised extreme value distribution (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>G</mi><mi>E</mi><mi>V</mi><msub><mi>D</mi><mi>r</mi></msub></mrow></semantics></math></inline-formula>). …”
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Preventive Attendance Record using Photo from Mobile Phone and Printed Paper using CNN by Bradika Almandin Almandin Wisesa, Vivin Mahat Putri, Evvin Faristasari, Sirlus Andreanto Jasman Duli

“…This study introduces a digital attendance system that combines facial recognition with liveness detection powered by Convolutional Neural Networks (CNN). Liveness verification is achieved by analyzing subtle movements and responses to ambient lighting. …”
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Advancing atomic electron tomography with neural networks by Juhyeok Lee, Yongsoo Yang

“…Recent progress has integrated deep learning, especially convolutional neural networks, into AET workflows to improve reconstruction fidelity. …”
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Analyzing the learning behavior patterns of business english learners using deep learning technology by Xiaohui Zeng

“…First, it applies a hybrid deep learning approach, integrating Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), to model both static and temporal aspects of learning behaviors. …”
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