A simple monocular depth estimation network for balancing complexity and accuracy by Xuanxuan Liu, Shuai Tang, Mengdie Feng, Xueqi Guo, Yanru Zhang, Yan Wang

“…For the extraction of finer local features, we propose a Local Multi-dimensional Convolutional Attention (LMC) module. Meanwhile, we propose a Wavelet Attention Transformer (WAT) module to achieve pixel-level precise classification of images. …”
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Comparative Analysis of Machine Learning Algorithms for Antenna Alignments by Mohammad Al Bataineh, Dana I. Abu Abdoun, Mahmoud Al Ahmad

“…By providing a robust machine learning framework, this research contributes significantly to advancing automated alignment processes, reducing dependency on manual methods, and paving the way for future innovations in RF systems. …”
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A superpixel based self-attention network for uterine fibroid segmentation in high intensity focused ultrasound guidance images by Shen Wen, Dong Zhang, Yuting Lei, Yan Yang

“…The method is implemented based on the framework of region splitting and merging. The ultrasound guidance image is first over-segmented into superpixels, then features within the superpixels are extracted and encoded into superpixel feature matrices with the uniform size. …”
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An Interpretable Siamese Attention Res-CNN for Fingerprint Spoofing Detection by Chengsheng Yuan, Zhenyu Xu, Xinting Li, Zhili Zhou, Junhao Huang, Ping Guo

“…Most of the previous work concentrated on how to build a deep learning framework to improve the PAD performance by augmenting fingerprint samples, and little attention has been paid to the fundamental difference between live and fake fingerprints to optimize feature extractors. …”
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A review on artificial intelligence thermal fluids and the integration of energy conservation with blockchain technology by Abdullah Ayub Khan, Asif Ali Laghari, Syed Azeem Inam, Sajid Ullah, Laila Nadeem

“…By combining blockchain technology with artificial intelligence, a decentralised framework environment is introduced that offers energy conservation methods that are safe, transparent, honest, and reliable. …”
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Task-Driven Real-World Super-Resolution of Document Scans by Maciej Zyrek, Tomasz Tarasiewicz, Jakub Sadel, Aleksandra Krzywon, Michal Kawulok

“…In this study, we introduce a task-driven, multi-task learning framework for training a super-resolution network specifically optimized for optical character recognition tasks. …”
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Multi-module UNet++ for colon cancer histopathological image segmentation by Qi Liu, Zhenfeng Zhao, Yingbo Wu, Siqi Wu, Yutong He, Haibin Wang, Shenwen Wang

“…To this end, this study proposes the RPAU-Net++ model, which integrates the ResNet-50 encoder (R), the Joint Pyramid Fusion Module (P), and the Convolutional Block Attention Module (A) into the UNet++ framework, forming a multi-module-enhanced segmentation architecture. …”
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FED-GEM-CN: A federated dual-CNN architecture with contrastive cross-attention for maritime radar intrusion detection by Md. Alamgir Hossain

“…This study introduces FED-GEM-CN, a novel federated learning framework designed explicitly for maritime radar intrusion detection. …”
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Lightweight insulator target detection algorithm based on improved YOLOX by Bing Zeng, Wei Hua, Dezhi Li, Zhihao Zhou, Hao Wan, Yunmin Xie, Tangbing Li, Yucong Chen, Jianglei Li, Shenli Wang, Shixun Fu, Zihan Jin, Wenhua Zhang

“…Abstract To address the challenges of high parameter volume, insufficient detection accuracy, and high false positive rates in traditional insulator target detection algorithms, this paper proposes a lightweight detection algorithm based on an enhanced YOLOX framework. First, Depthwise Separable Convolutions (DWConv) are employed to replace traditional convolutions, simplifying the model structure and reducing parameter count. …”
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Underwater Time Delay Estimation Based on Meta-DnCNN with Frequency-Sliding Generalized Cross-Correlation by Meiqi Ji, Xuerong Cui, Juan Li, Lei Li, Bin Jiang

“…Then, the grayscale image corresponding to the generated FS-GCC matrix is used, and the multi-level noise features are extracted by the multi-layer convolution of denoising convolutional neural network (DnCNN), effectively suppressing the noise and improving the estimation accuracy. …”
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Ensemble semi-supervised learning in facial expression recognition by Purnawansyah Purnawansyah, Adam Adnan, Herdianti Darwis, Aji Prasetya Wibawa, Triyanna Widyaningtyas, Haviluddin Haviluddin

“…This study aims to enhance the robustness and effectiveness of FER systems by integrating multiple machine learning techniques within a semi-supervised learning framework. The primary objective is to develop a more effective ensemble model that combines Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Support Vector Classifier (SVC), and Random Forest classifiers, utilizing both labeled and unlabeled data. …”
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DEPANet: A Differentiable Edge-Guided Pyramid Aggregation Network for Strip Steel Surface Defect Segmentation by Yange Sun, Siyu Geng, Chengyi Zheng, Chenglong Xu, Huaping Guo, Yan Feng

“…DEPANet adopts an end-to-end encoder-decoder framework, where the encoder consisting of three key components: a backbone network, a Differentiable Edge Feature Pyramid network (DEFP), and Edge-aware Feature Aggregation Modules (EFAMs). …”
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Transfer Learning-Based Accurate Detection of Shrub Crown Boundaries Using UAS Imagery by Jiawei Li, Huihui Zhang, David Barnard

“…While traditional image processing techniques often struggle with overlapping canopies, deep learning methods, such as convolutional neural networks (CNNs), offer promising solutions for precise segmentation. …”
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Real-Time Lightweight Morphological Detection for Chinese Mitten Crab Origin Tracing by Xiaofei Ma, Nannan Shen, Yanhui He, Zhuo Fang, Hongyan Zhang, Yun Wang, Jinrong Duan

“…To address this challenge, this study proposes a two-stage origin traceability framework that integrates a lightweight object detector and a high-precision classifier. …”
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A Secure IIoT Environment That Integrates AI-Driven Real-Time Short-Term Active and Reactive Load Forecasting with Anomaly Detection: A Real-World Application by Md. Ibne Joha, Md Minhazur Rahman, Md Shahriar Nazim, Yeong Min Jang

“…This study proposes a secure IIoT framework that simultaneously predicts both active and reactive loads while also incorporating anomaly detection. …”
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Severity classification and disposition prediction using ensemble learning for home-based patient management with adequate decision making by Amjad El Khatib, Chaima Ben Abdallah, Ahmed Nait Sidi Moh

“…Additionally, the combination of severity classification and disposition prediction contributed to improved model performance, demonstrating the advantage of integrating both tasks in a unified framework. Comparative analysis of different models provided valuable insights into performance tradeoffs, reinforcing the potential of ensemble learning for home-based patient assessment.…”
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PDSDC: Progressive Spatiotemporal Difference Capture Network for Remote Sensing Change Detection by YeKai Cui, Peng Duan, Jinjiang Li

“…To address these issues, this article proposes a progressive spatiotemporal difference capture network. This framework effectively mitigates information degradation and multiscale tradeoffs in feature fusion through a multipath optimization mechanism. …”
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Integration of Deep Learning and Machine Learning Techniques for Advancing the Detection of Plant Diseases by Abdul Karim, Touqeer Ahmed Jumani, Muhammad Amir Raza, Shadi Khan Baloch, Nouman Qadeer Soomro, Muhammad Masud, Muhammad Salman Saeed, Mouaaz Nahas, Aamir Ali Patoli

“…Extreme Gradient Boosting (XGBoost) and Convolutional Neural Network (CNN) algorithm were employed to detect diseases precisely. …”
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EnGCI: enhancing GPCR-compound interaction prediction via large molecular models and KAN network by Weihao Liu, Xiaoli Li, Bo Hang, Pu Wang

“…The EnGCI model offers a robust and efficient framework that enhances GCI predictive capabilities and has the potential to significantly contribute to GPCR drug discovery.…”
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Deep Learning-Based Prediction of Pitch Response for Floating Offshore Wind Turbines by Ruifeng Chen, Ke Zhang, Min Luo, Ye An, Lixiang Guo

“…This model integrates convolutional neural networks (CNNs) and gated recurrent units (GRUs), effectively extracting the coupling relationships among various input features and capturing the temporal dependencies to enhance predictive accuracy. …”
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