P-68 LIVGUARD, A DEEP NEURAL NETWORK FOR CIRRHOSIS DETECTION IN LIVER ULTRASOUND (USD) IMAGES by DIEGO ARUFE, Pablo Gomez del Campo, Ezequiel Demirdjian, Carlos Galmarini

“…Further work is required to validate this algorithmic framework in prospective cohorts of patients in additional clinical trials and/or real-world datasets.…”
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Multi-User Activity Recognition Using Plot Images Based on Ambiental Sensors by Anca Roxana Alexan, Alexandru Iulian Alexan, Stefan Oniga

“…In the second stage, the generated data are provided to a sequential convolutional neural network, which predicts the 16 activities developed by two users. …”
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Research on Interval Probability Prediction and Optimization of Vegetation Productivity in Hetao Irrigation District Based on Improved TCLA Model by Jie Ren, Delong Tian, Hexiang Zheng, Guoshuai Wang, Zekun Li

“…We propose a multimodal regression prediction model utilizing the TCLA framework—comprising the Transient Trigonometric Harris Hawks Optimizer (TTHHO), Convolutional Neural Networks (CNN), Least Squares Support Vector Machine (LSSVM), and Adaptive Bandwidth Kernel Density Estimation (ABKDE)—with the Hetao Irrigation District, a vast irrigation basin in China, serving as the study area. …”
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A hybrid CNN-LSTM model with adaptive instance normalization for one shot singing voice conversion by Assila Yousuf, David Solomon George

“…In the proposed singing voice conversion technique, an encoder decoder framework was implemented using a hybrid model of convolutional neural network (CNN) accompanied by long short term memory (LSTM). …”
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RT-DETR-Smoke: A Real-Time Transformer for Forest Smoke Detection by Zhong Wang, Lanfang Lei, Tong Li, Xian Zu, Peibei Shi

“…To tackle these issues, we propose RT-DETR-Smoke, a specialized real-time transformer-based smoke-detection framework. First, we designed a high-efficiency hybrid encoder that combines convolutional and Transformer features, thus reducing computational cost while preserving crucial smoke details. …”
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A New Hybrid Wavelet Transform-Deep Learning for Smart Resilient Inverters in Microgrids Against Cyberattacks by Chou-Mo Yang, Pei-Min Huang, Chun-Lien Su, Mahmoud Elsisi

“…This paper proposes a new hybrid deep learning framework that integrates Long Short-Term Memory (LSTM) networks with the Gaussian Continuous Wavelet Transform (GCWT), a signal processing technique excelling at time-frequency feature extraction from power signals to detect severe FDIA in smart inverters, which are designed to significantly alter operational data and destabilize the grid. …”
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LeafDNet: Transforming Leaf Disease Diagnosis Through Deep Transfer Learning by Tofayet Sultan, Mohammad Sayem Chowdhury, Nusrat Jahan, M. F. Mridha, Sultan Alfarhood, Mejdl Safran, Dunren Che

“…These results emphasize the potential of this advanced deep learning framework as a scalable, efficient, and highly accurate solution for early plant disease detection, providing substantial benefits for plant health management and supporting sustainable agricultural practices.…”
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IMViT: Adjacency Matrix-Based Lightweight Plain Vision Transformer by Qihao Chen, Yunfeng Yan, Xianbo Wang, Jishen Peng

“…While extensive experiments prove its outstanding ability for large models, transformers with small sizes are not comparable with convolutional neural networks in various downstream tasks due to its lack of inductive bias which can benefit image understanding. …”
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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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