Deep Learning Algorithm for Keratoconus Detection from Tomographic Maps and Corneal Biomechanics: A Diagnostic Study by Wiyada Quanchareonsap, Ngamjit Kasetsuwan, Usanee Reinprayoon, Yonrawee Piyacomn, Thitima Wungcharoen, Monthira Jermjutitham

“…The sample size was divided into the Pentacam and combined Pentacam-Corvis groups. Different convolutional neural network approaches were used to enhance the KC and subclinical KC detection performance. …”
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Progressive Cluster-Guided Knowledge Distillation for Remote Sensing Image Scene Classification by Zhaopeng Deng, Zheng Zhou, Haoran Zhao, Xiaolin Chen, Danfeng Hong, Xin Sun

“…Knowledge distillation (KD) has recently demonstrated remarkable potential in developing lightweight convolutional neural networks for remote sensing image (RSI) scene classification tasks. …”
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A Hierarchical and Self-Evolving Digital Twin (HSE-DT) Method for Multi-Faceted Battery Situation Awareness Realisation by Kai Zhao, Ying Liu, Yue Zhou, Wenlong Ming, Jianzhong Wu

“…The model integrates a Transformer–Convolutional Neural Network (Transformer-CNN) architecture to process historical and real-time data, capturing dynamic state variations with high precision. …”
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Improving computer vision for plant pathology through advanced training techniques by Jamie R. Sykes, Katherine J. Denby, Daniel W. Franks

“…Abstract Premise This study investigates advanced training techniques to improve the performance of convolutional neural networks for disease detection in cocoa, Theobroma cacao. …”
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Oilfield Production Prediction Method Based on Multi-Input CNN-LSTM With Attention Mechanism by Lihui Tang, Zhenpeng Wang, Yajun Gao, Hao Wu, Wenbo Zhang, Xiaoqing Xie

“…This model achieves prediction through two primary input paths: firstly, utilizing CNN to extract spatial dynamic features between wells to capture interwell production relationships and secondly, employing LSTM to extract temporal dynamic features of the oilfield. …”
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TWD-DepNet: a deep network enhanced by three-way decisions for EEG-based depression detection by Chunlei Shi, Shouzhu Yan, Wei He, Haiyan Wang

“…Then, a lightweight convolutional backbone (DepNet) with multi-scale convolution is designed, depthwise separable layers, and dynamic channel attention to capture rich spatiotemporal patterns efficiently. …”
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Federated Deep Learning for Scalable and Explainable Load Forecasting in Privacy-Conscious Smart Cities by Ibrahim Alzamil

“…As smart cities evolve, energy infrastructures are becoming more decentralized and dynamic due to the increased integration of renewables, electric vehicles, and consumer-driven usage behaviors. …”
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ERA-MADDPG: An Elastic Routing Algorithm Based on Multi-Agent Deep Deterministic Policy Gradient in SDN by Wanwei Huang, Hongchang Liu, Yingying Li, Linlin Ma

“…The architecture’s processing flow, including real-time data layer information collection and dynamic policy generation, enables the ERA-MADDPG algorithm to exhibit strong elasticity by quickly adjusting routing decisions in response to topology changes. …”
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Autonomous Quadrotor Path Planning Through Deep Reinforcement Learning With Monocular Depth Estimation by Mahdi Shahbazi Khojasteh, Armin Salimi-Badr

“…Autonomous navigation is a formidable challenge for autonomous aerial vehicles operating in dense or dynamic environments. This paper proposes a path-planning approach based on deep reinforcement learning for a quadrotor equipped with only a monocular camera. …”
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Research on a Crime Spatiotemporal Prediction Method Integrating Informer and ST-GCN: A Case Study of Four Crime Types in Chicago by Yuxiao Fan, Xiaofeng Hu, Jinming Hu

“…Traditional crime prevention models experience difficulties handling dynamic crime hotspots due to data lags and poor spatiotemporal resolution. …”
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Relationship extraction between entities with long distance dependencies and noise based on semantic and syntactic features by Lei Wang, Fei Wu, Xiaoqing Liu, Jilong Cao, Mingwei Ma, Zhaoyang Qu

“…Additionally, a residual shrinking network is incorporated to dynamically remove noise from the syntactic graph, further strengthening the model’s noise resistance. …”
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Linear and Non-Linear Methods to Discriminate Cortical Parcels Based on Neurodynamics: Insights from sEEG Recordings by Karolina Armonaite, Livio Conti, Luigi Laura, Michele Primavera, Franca Tecchio

“…For this study, we used a linear Power Spectral Density (PSD) estimate and three non-linear measures: the Higuchi fractal dimension (HFD), a one-dimensional convolutional neural network (1D-CNN), and a one-shot learning model. …”
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Lightweight Spatial–Spectral Shift Module With Multihead MambaOut for Hyperspectral Image Classification by Yi Liu, Yanjun Zhang, Yu Guo, Yunchao Li

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An Object Detection Model based on Augmented Reality for Iraqi Archaeology by suha Dh. Athab, Abdulamir Abdullah Karim

“…A new custom class called “AnchorBoxes”, dynamically generates predefined anchor boxes for each feature map. …”
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FRPNet: A Lightweight Multi-Altitude Field Rice Panicle Detection and Counting Network Based on Unmanned Aerial Vehicle Images by Yuheng Guo, Wei Zhan, Zhiliang Zhang, Yu Zhang, Hongshen Guo

“…The architecture integrates three core innovations: a CSP-ScConv backbone with self-calibrating convolutions for efficient multi-scale feature extraction; a Feature Pyramid Shared Convolution (FPSC) module that replaces pooling with multi-branch dilated convolutions to preserve fine-grained spatial information; and a Dynamic Bidirectional Feature Pyramid Network (DynamicBiFPN) employing input-adaptive kernels to optimize cross-scale feature fusion. …”
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A Novel Multi-Objective Fuzzy Deep Learning Framework for Predictive Maintenance in Industrial Internet of Things by Jiangang Feng, Jicheng Kan

“…This combined framework is designed to maximize equipment uptime and optimize resource allocation by dynamically adapting to varying maintenance demands and operating conditions. …”
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Flexible hybrid edge computing IoT architecture for low-cost bird songs detection system by Francisco A. Delgado-Rajó, Carlos M. Travieso-Gonzalez

“…Nodes dynamically select communication technologies based on availability, sending data to an IoT analytics platform. …”
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Forecasting Green Energy Production in Latin American Countries and Canada via Temporal Fusion Transformer by Muhammad Shoaib Saleem, Javed Rashid, Sajjad Ahmad, Ali M. Al‐Shaery, Saad Althobaiti, Muhammad Faheem

“…From the preceding results, it is clear that the proposed TFT model can identify dynamic energy patterns that will contribute towards achieving sustainable development goals by the end of 2040.…”
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Advances to IoT security using a GRU-CNN deep learning model trained on SUCMO algorithm by Amit Sagu, Nasib Singh Gill, Preeti Gulia, Noha Alduaiji, Piyush Kumar Shukla, Mohd Asif Shah

“…This paper proposes a hybrid deep learning model that combines Convolutional Neural Network (CNN) and Gated Recurrent Units (GRUs) to classify the IoT security threats. …”
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Non-intrusive load monitoring based on time-enhanced multidimensional feature visualization by Tie Chen, Yimin Yuan, Jiaqi Gao, Shinan Guo, Pingping Yang

“…By adding a time axis to the V–I trajectory, it integrates the rate of change in voltage and current, power factor, and third harmonic to form a three-dimensional spatiotemporal color V–I trajectory, addressing the gap in dynamic characteristics. The ECA-ResNet34 network model is used for load identification, avoiding the problems of network degradation and training difficulties caused by the excessive depth of traditional convolutional neural networks (CNN), and achieving efficient monitoring of household loads. …”
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